Satellite, climatological, and theoretical inputs for modeling of the diurnal cycle of fire emissions

NASA Astrophysics Data System (ADS)

Hyer, E. J.; Reid, J. S.; Schmidt, C. C.; Giglio, L.; Prins, E.

2009-12-01

The diurnal cycle of fire activity is crucial for accurate simulation of atmospheric effects of fire emissions, especially at finer spatial and temporal scales. Estimating diurnal variability in emissions is also a critical problem for construction of emissions estimates from multiple sensors with variable coverage patterns. An optimal diurnal emissions estimate will use as much information as possible from satellite fire observations, compensate known biases in those observations, and use detailed theoretical models of the diurnal cycle to fill in missing information. As part of ongoing improvements to the Fire Location and Monitoring of Burning Emissions (FLAMBE) fire monitoring system, we evaluated several different methods of integrating observations with different temporal sampling. We used geostationary fire detections from WF_ABBA, fire detection data from MODIS, empirical diurnal cycles from TRMM, and simple theoretical diurnal curves based on surface heating. Our experiments integrated these data in different combinations to estimate the diurnal cycles of emissions for each location and time. Hourly emissions estimates derived using these methods were tested using an aerosol transport model. We present results of this comparison, and discuss the implications of our results for the broader problem of multi-sensor data fusion in fire emissions modeling.

New global fire emission estimates and evaluation of volatile organic compounds

Treesearch

C. Wiedinmyer; L. K. Emmons; S. K. Akagi; R. J. Yokelson; J. J. Orlando; J. A. Al-Saadi; A. J. Soja

2010-01-01

A daily, high-resolution, global fire emissions model has been built to estimate emissions from open burning for air quality modeling applications: The Fire INventory from NCAR (FINN version 1). The model framework uses daily fire detections from the MODIS instruments and updated emission factors, specifically for speciated non-methane organic compounds (NMOC). Global...

Top-down Estimates of Isoprene Emissions in Australia Inferred from OMI Satellite Data.

NASA Astrophysics Data System (ADS)

Greenslade, J.; Fisher, J. A.; Surl, L.; Palmer, P. I.

2017-12-01

Australia is a global hotspot for biogenic isoprene emission factors predicted by process-based models such as the Model of Emissions of Gases and Aerosols from Nature (MEGAN). It is also prone to increasingly frequent temperature extremes that can drive episodically high emissions. Estimates of biogenic isoprene emissions from Australia are poorly constrained, with the frequently used MEGAN model overestimating emissions by a factor of 4-6 in some areas. Evaluating MEGAN and other models in Australia is difficult due to sparse measurements of emissions and their ensuing chemical products. In this talk, we will describe efforts to better quantify Australian isoprene emissions using top-down estimates based on formaldehyde (HCHO) observations from the OMI satellite instrument, combined with modelled isoprene to HCHO yields obtained from the GEOS-Chem chemical transport model. The OMI-based estimates are evaluated using in situ observations from field campaigns conducted in southeast Australia. We also investigate the impact on the inferred emission of horizontal resolution used for the yield calculations, particularly in regions on the boundary between low- and high-NOx chemistry. The prevalence of fire smoke plumes roughly halves the available satellite dataset over Australia for much of the year; however, seasonal averages remain robust. Preliminary results show that the top-down isoprene emissions are lower than MEGAN estimates by up to 90% in summer. The overestimates are greatest along the eastern coast, including areas surrounding Australia's major population centres in Sydney, Melbourne, and Brisbane. The coarse horizontal resolution of the model significantly affects the emissions estimates, as many biogenic emitting regions lie along narrow coastal stretches. Our results confirm previous findings that the MEGAN biogenic emission model is poorly calibrated for the Australian environment and suggests that chemical transport models driven by MEGAN are likely to overpredict ozone and secondary organic aerosols from biogenic sources in the Australian environment. Further measurements of biogenic gases are critical to improving biogenic emissions and follow-on chemical transport modelling, in this region. We hope to quantify this overestimation and its flow-on effects in future work.

Modelled and field measurements of biogenic hydrocarbon emissions from a Canadian deciduous forest

NASA Astrophysics Data System (ADS)

Fuentes, J. D.; Wang, D.; Den Hartog, G.; Neumann, H. H.; Dann, T. F.; Puckett, K. J.

The Biogenic Emission Inventory System (BEIS) used by the United States Environmental Protection Agency (Lamb et al., 1993, Atmospheric Environment21, 1695-1705; Pierce and Waldruff, 1991, J. Air Waste Man. Ass.41, 937-941) was tested for its ability to provide realistic microclimate descriptions within a deciduous forest in Canada. The microclimate description within plant canopies is required because isoprene emission rates from plants are strongly influenced by foliage temperature and photosynthetically active radiation impinging on leaves while monoterpene emissions depend primarily on leaf temperature. Model microclimate results combined with plant emission rates and local biomass distribution were used to derive isoprene and α-pinene emissions from the deciduous forest canopy. In addition, modelled isoprene emission estimates were compared to measured emission rates at the leaf level. The current model formulation provides realistic microclimatic conditions for the forest crown where modelled and measured air and foliage temperature are within 3°C. However, the model provides inadequate microclimate characterizations in the lower canopy where estimated and measured foliage temperatures differ by as much as 10°C. This poor agreement may be partly due to improper model characterization of relative humidity and ambient temperature within the canopy. These uncertainties in estimated foliage temperature can lead to underestimates of hydrocarbon emission estimates of two-fold. Moreover, the model overestimates hydrocarbon emissions during the early part of the growing season and underestimates emissions during the middle and latter part of the growing season. These emission uncertainties arise because of the assumed constant biomass distribution of the forest and constant hydrocarbon emission rates throughout the season. The BEIS model, which is presently used in Canada to estimate inventories of hydrocarbon emissions from vegetation, underestimates emission rates by at least two-fold compared to emissions derived from field measurements. The isoprene emission algorithm proposed by Guenther et al. (1993), applied at the leaf level, provides relatively good agreement compared to measurements. Field measurements indicate that isoprene emissions change with leaf ontogeny and differ amongst tree species. Emission rates defined as function of foliage development stage and plant species need to be introduced in the hydrocarbon emission algorithms. Extensive model evaluation and more hydrocarbon emission measurement;: from different plant species are required to fully assess the appropriateness of this emission calculation approach for Canadian forests.

Directional Canopy Emissivity Estimation Based on Spectral Invariants

NASA Astrophysics Data System (ADS)

Guo, M.; Cao, B.; Ren, H.; Yongming, D.; Peng, J.; Fan, W.

2017-12-01

Land surface emissivity is a crucial parameter for estimating land surface temperature from remote sensing data and also plays an important role in the physical process of surface energy and water balance from local to global scales. To our knowledge, the emissivity varies with surface type and cover. As for the vegetation, its canopy emissivity is dependent on vegetation types, viewing zenith angle and structure that changes in different growing stages. Lots of previous studies have focused on the emissivity model, but few of them are analytic and suited to different canopy structures. In this paper, a new physical analytic model is proposed to estimate the directional emissivity of homogenous vegetation canopy based on spectral invariants. The initial model counts the directional absorption in six parts: the direct absorption of the canopy and the soil, the absorption of the canopy and soil after a single scattering and after multiple scattering within the canopy-soil system. In order to analytically estimate the emissivity, the pathways of photons absorbed in the canopy-soil system are traced using the re-collision probability in Fig.1. After sensitive analysis on the above six absorptions, the initial complicated model was further simplified as a fixed mathematic expression to estimate the directional emissivity for vegetation canopy. The model was compared with the 4SAIL model, FRA97 model, FRA02 model and DART model in Fig.2, and the results showed that the FRA02 model is significantly underestimated while the FRA97 model is a little underestimated, on basis of the new model. On the contrary, the emissivity difference between the new model with the 4SAIL model and DART model was found to be less than 0.002. In general, since the new model has the advantages of mathematic expression with accurate results and clear physical meaning, the model is promising to be extended to simulate the directional emissivity for the discrete canopy in further study.

Comparison of models used for national agricultural ammonia emission inventories in Europe: Litter-based manure systems

NASA Astrophysics Data System (ADS)

Reidy, B.; Webb, J.; Misselbrook, T. H.; Menzi, H.; Luesink, H. H.; Hutchings, N. J.; Eurich-Menden, B.; Döhler, H.; Dämmgen, U.

Six N-flow models, used to calculate national ammonia (NH 3) emissions from agriculture in different European countries, were compared using standard data sets. Scenarios for litter-based systems were run separately for beef cattle and for broilers, with three different levels of model standardisation: (a) standardized inputs to all models (FF scenario); (b) standard N excretion, but national values for emission factors (EFs) (FN scenario); (c) national values for N excretion and EFs (NN scenario). Results of the FF scenario for beef cattle produced very similar estimates of total losses of total ammoniacal-N (TAN) (±6% of the mean total), but large differences in NH 3 emissions (±24% of the mean). These differences arose from the different approaches to TAN immobilization in litter, other N losses and mineralization in the models. As a result of those differences estimates of TAN available at spreading differed by a factor of almost 3. Results of the FF scenario for broilers produced a range of estimates of total changes in TAN (±9% of the mean total), and larger differences in the estimate of NH 3 emissions (±17% of the mean). The different approaches among the models to TAN immobilization, other N losses and mineralization, produced estimates of TAN available at spreading which differed by a factor of almost 1.7. The differences in estimates of NH 3 emissions decreased as estimates of immobilization and other N losses increased. Since immobilization and denitrification depend also on the C:N ratio in manure, there would be advantages to include C flows in mass-flow models. This would also provide an integrated model for the estimation of emissions of methane, non-methane VOCs and carbon dioxide. Estimation of these would also enable an estimate of mass loss, calculation of the N and TAN concentrations in litter-based manures and further validation of model outputs.

A statistical approach to determining the uncertainty in power-law model estimates of emissions based on time-dependent chamber concentration measurements

EPA Science Inventory

The use of models for estimating emissions from products beyond the timeframe of an emissions test is a means of managing the time and expenses associated with product emissions certification. This paper presents a discussion of (1) the impact of uncertainty in test chamber emiss...

Population and Activity of On-road Vehicles in MOVES2014 ...

EPA Pesticide Factsheets

This report describes the sources and derivation for on-road vehicle population and activity information and associated adjustments as stored in the MOVES2014 default databases. Motor Vehicle Emission Simulator, the MOVES2014 model, is a set of modeling tools for estimating emissions produced by on-road (cars, trucks, motorcycles, etc.) and nonroad (backhoes, lawnmowers, etc.) mobile sources. The national default activity information in MOVES2014 provides a reasonable basis for estimating national emissions. However, the uncertainties and variability in the default data contribute to the uncertainty in the resulting emission estimates. Properly characterizing emissions from the on-road vehicle subset requires a detailed understanding of the cars and trucks that make up the vehicle fleet and their patterns of operation. The MOVES model calculates emission inventories by multiplying emission rates by the appropriate emission-related activity, applying correction (adjustment) factors as needed to simulate specific situations, and then adding up the emissions from all sources (populations) and regions. This report describes the sources and derivation for on-road vehicle population and activity information and associated adjustments as stored in the MOVES2014 default databases. Motor Vehicle Emission Simulator, the MOVES2014 model, is a set of modeling tools for estimating emissions produced by on-road (cars, trucks, motorcycles, etc.) and nonroad (backhoes, law

Community-LINE Source Model (C-LINE) to estimate roadway emissions

EPA Pesticide Factsheets

C-LINE is a web-based model that estimates emissions and dispersion of toxic air pollutants for roadways in the U.S. This reduced-form air quality model examines what-if scenarios for changes in emissions such as traffic volume fleet mix and vehicle speed.

Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK's greenhouse gas inventory for agriculture

NASA Astrophysics Data System (ADS)

Milne, Alice E.; Glendining, Margaret J.; Bellamy, Pat; Misselbrook, Tom; Gilhespy, Sarah; Rivas Casado, Monica; Hulin, Adele; van Oijen, Marcel; Whitmore, Andrew P.

2014-01-01

The UK's greenhouse gas inventory for agriculture uses a model based on the IPCC Tier 1 and Tier 2 methods to estimate the emissions of methane and nitrous oxide from agriculture. The inventory calculations are disaggregated at country level (England, Wales, Scotland and Northern Ireland). Before now, no detailed assessment of the uncertainties in the estimates of emissions had been done. We used Monte Carlo simulation to do such an analysis. We collated information on the uncertainties of each of the model inputs. The uncertainties propagate through the model and result in uncertainties in the estimated emissions. Using a sensitivity analysis, we found that in England and Scotland the uncertainty in the emission factor for emissions from N inputs (EF1) affected uncertainty the most, but that in Wales and Northern Ireland, the emission factor for N leaching and runoff (EF5) had greater influence. We showed that if the uncertainty in any one of these emission factors is reduced by 50%, the uncertainty in emissions of nitrous oxide reduces by 10%. The uncertainty in the estimate for the emissions of methane emission factors for enteric fermentation in cows and sheep most affected the uncertainty in methane emissions. When inventories are disaggregated (as that for the UK is) correlation between separate instances of each emission factor will affect the uncertainty in emissions. As more countries move towards inventory models with disaggregation, it is important that the IPCC give firm guidance on this topic.

Advancing Methods for Estimating Soil Nitrous Oxide Emissions by Incorporating Freeze-Thaw Cycles into a Tier 3 Model-Based Assessment

NASA Astrophysics Data System (ADS)

Ogle, S. M.; DelGrosso, S.; Parton, W. J.

2017-12-01

Soil nitrous oxide emissions from agricultural management are a key source of greenhouse gas emissions in many countries due to the widespread use of nitrogen fertilizers, manure amendments from livestock production, planting legumes and other practices that affect N dynamics in soils. In the United States, soil nitrous oxide emissions have ranged from 250 to 280 Tg CO2 equivalent from 1990 to 2015, with uncertainties around 20-30 percent. A Tier 3 method has been used to estimate the emissions with the DayCent ecosystem model. While the Tier 3 approach is considerably more accurate than IPCC Tier 1 methods, there is still the possibility of biases in emission estimates if there are processes and drivers that are not represented in the modeling framework. Furthermore, a key principle of IPCC guidance is that inventory compilers estimate emissions as accurately as possible. Freeze-thaw cycles and associated hot moments of nitrous oxide emissions are one of key drivers influencing emissions in colder climates, such as the cold temperate climates of the upper Midwest and New England regions of the United States. Freeze-thaw activity interacts with management practices that are increasing N availability in the plant-soil system, leading to greater nitrous oxide emissions during transition periods from winter to spring. Given the importance of this driver, the DayCent model has been revised to incorproate freeze-thaw cycles, and the results suggests that including this driver can significantly modify the emissions estimates in cold temperate climate regions. Consequently, future methodological development to improve estimation of nitrous oxide emissions from soils would benefit from incorporating freeze-thaw cycles into the modeling framework for national territories with a cold climate.

Estimating the agricultural fertilizer NH3 emission in China based on the bi-directional CMAQ model and an agro-ecosystem model

NASA Astrophysics Data System (ADS)

Wang, S.

2014-12-01

Atmospheric ammonia (NH3) plays an important role in fine particle formation. Accurate estimates of ammonia can reduce uncertainties in air quality modeling. China is one of the largest countries emitting ammonia with the majority of NH3 emissions coming from the agricultural practices, such as fertilizer applications and animal operations. The current ammonia emission estimates in China are mainly based on pre-defined emission factors. Thus, there are considerable uncertainties in estimating NH3 emissions, especially in time and space distribution. For example, fertilizer applications vary in the date of application and amount by geographical regions and crop types. In this study, the NH3 emission from the agricultural fertilizer use in China of 2011 was estimated online by an agricultural fertilizer modeling system coupling a regional air-quality model and an agro-ecosystem model, which contains three main components 1) the Environmental Policy Integrated Climate (EPIC) model, 2) the meso-scale meteorology Weather Research and Forecasting (WRF) model and 3) the CMAQ air quality model with bi-directional ammonia fluxes. The EPIC output information about daily fertilizer application and soil characteristics would be the input of the CMAQ model. In order to run EPIC model, much Chinese local information is collected and processed. For example, Crop land data are computed from the MODIS land use data at 500-m resolution and crop categories at Chinese county level; the fertilizer use rate for different fertilizer types, crops and provinces are obtained from Chinese statistic materials. The system takes into consideration many influencing factors on agriculture ammonia emission, including weather, the fertilizer application method, timing, amount, and rate for specific pastures and crops. The simulated fertilizer data is compared with the NH3 emissions and fertilizer application data from other sources. The results of CMAQ modeling are also discussed and analyzed with field measurements. The estimated agricultural fertilizer NH3 emission in this study is about 3Tg in 2011. The regions with the highest emission rates are located in the North China Plain. Monthly, the peak ammonia emissions occur in April to July.

SEASONAL NH 3 EMISSIONS FOR THE CONTINENTAL UNITED STATES: INVERSE MODEL ESTIMATION AND EVALUATION

EPA Science Inventory

An inverse modeling study has been conducted here to evaluate a prior estimate of seasonal ammonia (NH₃) emissions. The prior estimates were based on a previous inverse modeling study and two other bottom-up inventory studies. The results suggest that the prior estim...

INVERSE MODELING TO ESTIMATE NH3 EMISSION SEASONALLY AND THE SENSITIVITY TO UNCERTAINTY REPRESENTATIONS

EPA Science Inventory

Inverse modeling has been used extensively on the global scale to produce top-down estimates of emissions for chemicals such as CO and CH4. Regional scale air quality studies could also benefit from inverse modeling as a tool to evaluate current emission inventories; however, ...

Modeling nitrous oxide emission from rivers: a global assessment.

PubMed

Hu, Minpeng; Chen, Dingjiang; Dahlgren, Randy A

2016-11-01

Estimates of global riverine nitrous oxide (N 2 O) emissions contain great uncertainty. We conducted a meta-analysis incorporating 169 observations from published literature to estimate global riverine N 2 O emission rates and emission factors. Riverine N 2 O flux was significantly correlated with NH 4 , NO 3 and DIN (NH 4  + NO 3 ) concentrations, loads and yields. The emission factors EF(a) (i.e., the ratio of N 2 O emission rate and DIN load) and EF(b) (i.e., the ratio of N 2 O and DIN concentrations) values were comparable and showed negative correlations with nitrogen concentration, load and yield and water discharge, but positive correlations with the dissolved organic carbon : DIN ratio. After individually evaluating 82 potential regression models based on EF(a) or EF(b) for global, temperate zone and subtropical zone datasets, a power function of DIN yield multiplied by watershed area was determined to provide the best fit between modeled and observed riverine N 2 O emission rates (EF(a): R 2  = 0.92 for both global and climatic zone models, n = 70; EF(b): R 2  = 0.91 for global model and R 2  = 0.90 for climatic zone models, n = 70). Using recent estimates of DIN loads for 6400 rivers, models estimated global riverine N 2 O emission rates of 29.6-35.3 (mean = 32.2) Gg N 2 O-N yr -1 and emission factors of 0.16-0.19% (mean = 0.17%). Global riverine N 2 O emission rates are forecasted to increase by 35%, 25%, 18% and 3% in 2050 compared to the 2000s under the Millennium Ecosystem Assessment's Global Orchestration, Order from Strength, Technogarden, and Adapting Mosaic scenarios, respectively. Previous studies may overestimate global riverine N 2 O emission rates (300-2100 Gg N 2 O-N yr -1 ) because they ignore declining emission factor values with increasing nitrogen levels and channel size, as well as neglect differences in emission factors corresponding to different nitrogen forms. Riverine N 2 O emission estimates will be further enhanced through refining emission factor estimates, extending measurements longitudinally along entire river networks and improving estimates of global riverine nitrogen loads. © 2016 John Wiley & Sons Ltd.

Estimation of vegetative mercury emissions in China.

PubMed

Quan, Jiannong; Zhang, Xiaoshan; Shim, Shang Gyoo

2008-01-01

Vegetative mercury emissions were estimated within the framework of Biogenic Emission Inventory System (BEIS3 V3.11). In this estimation, the 19 categories of U.S. Geological Survey landcover data were incorporated to generate the vegetation-specific mercury emissions in a 81-km Lambert Conformal model grid covering the total Chinese continent. The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for forest, agriculture and water, or assumed for other land fields without available flux data. Annual simulations using the MM5 data were performed to investigate the seasonal emission variation. From the sensitivity analysis using two sets of emission factors, the vegetative mercury emissions in China domain were estimated to range from a lower limit of 79 x 10(3) kg/year to an upper limit of 177 x 10(3) kg/year. The modeled vegetative emissions were mainly generated from the eastern and southern China. Using the estimated data, it is shown that mercury emissions from vegetation are comparable to that from anthropogenic sources during summer. However, the vegetative emissions decrease greatly during winter, leaving anthropogenic sources as the major sources of emission.

Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile.

PubMed

Corvalán, Roberto M; Osses, Mauricio; Urrutia, Cristian M

2002-02-01

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.

Emissions databases for polycyclic aromatic compounds in the Canadian Athabasca oil sands region - development using current knowledge and evaluation with passive sampling and air dispersion modelling data

NASA Astrophysics Data System (ADS)

Qiu, Xin; Cheng, Irene; Yang, Fuquan; Horb, Erin; Zhang, Leiming; Harner, Tom

2018-03-01

Two speciated and spatially resolved emissions databases for polycyclic aromatic compounds (PACs) in the Athabasca oil sands region (AOSR) were developed. The first database was derived from volatile organic compound (VOC) emissions data provided by the Cumulative Environmental Management Association (CEMA) and the second database was derived from additional data collected within the Joint Canada-Alberta Oil Sands Monitoring (JOSM) program. CALPUFF modelling results for atmospheric polycyclic aromatic hydrocarbons (PAHs), alkylated PAHs, and dibenzothiophenes (DBTs), obtained using each of the emissions databases, are presented and compared with measurements from a passive air monitoring network. The JOSM-derived emissions resulted in better model-measurement agreement in the total PAH concentrations and for most PAH species concentrations compared to results using CEMA-derived emissions. At local sites near oil sands mines, the percent error of the model compared to observations decreased from 30 % using the CEMA-derived emissions to 17 % using the JOSM-derived emissions. The improvement at local sites was likely attributed to the inclusion of updated tailings pond emissions estimated from JOSM activities. In either the CEMA-derived or JOSM-derived emissions scenario, the model underestimated PAH concentrations by a factor of 3 at remote locations. Potential reasons for the disagreement include forest fire emissions, re-emissions of previously deposited PAHs, and long-range transport not considered in the model. Alkylated PAH and DBT concentrations were also significantly underestimated. The CALPUFF model is expected to predict higher concentrations because of the limited chemistry and deposition modelling. Thus the model underestimation of PACs is likely due to gaps in the emissions database for these compounds and uncertainties in the methodology for estimating the emissions. Future work is required that focuses on improving the PAC emissions estimation and speciation methodologies and reducing the uncertainties in VOC emissions which are subsequently used in PAC emissions estimation.

SEASONAL NH3 EMISSION ESTIMATES FOR THE EASTERN UNITED STATES BASED ON AMMONIUM WET CONCENTRATIONS AND AN INVERSE MODELING METHOD

EPA Science Inventory

Significant uncertainty exists in the magnitude and variability of ammonia (NH3) emissions. NH3 emissions are needed as input for air quality modeling of aerosols and deposition of nitrogen compounds. Approximately 85% of NH3 emissions are estimated to come from agricultural ...

Estimation of VOC emissions from produced-water treatment ponds in Uintah Basin oil and gas field using modeling techniques

NASA Astrophysics Data System (ADS)

Tran, H.; Mansfield, M. L.; Lyman, S. N.; O'Neil, T.; Jones, C. P.

2015-12-01

Emissions from produced-water treatment ponds are poorly characterized sources in oil and gas emission inventories that play a critical role in studying elevated winter ozone events in the Uintah Basin, Utah, U.S. Information gaps include un-quantified amounts and compositions of gases emitted from these facilities. The emitted gases are often known as volatile organic compounds (VOCs) which, beside nitrogen oxides (NOX), are major precursors for ozone formation in the near-surface layer. Field measurement campaigns using the flux-chamber technique have been performed to measure VOC emissions from a limited number of produced water ponds in the Uintah Basin of eastern Utah. Although the flux chamber provides accurate measurements at the point of sampling, it covers just a limited area of the ponds and is prone to altering environmental conditions (e.g., temperature, pressure). This fact raises the need to validate flux chamber measurements. In this study, we apply an inverse-dispersion modeling technique with evacuated canister sampling to validate the flux-chamber measurements. This modeling technique applies an initial and arbitrary emission rate to estimate pollutant concentrations at pre-defined receptors, and adjusts the emission rate until the estimated pollutant concentrations approximates measured concentrations at the receptors. The derived emission rates are then compared with flux-chamber measurements and differences are analyzed. Additionally, we investigate the applicability of the WATER9 wastewater emission model for the estimation of VOC emissions from produced-water ponds in the Uintah Basin. WATER9 estimates the emission of each gas based on properties of the gas, its concentration in the waste water, and the characteristics of the influent and treatment units. Results of VOC emission estimations using inverse-dispersion and WATER9 modeling techniques will be reported.

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway.

PubMed

Abou-Senna, Hatem; Radwan, Essam; Westerlund, Kurt; Cooper, C David

2013-07-01

The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25-90% from 2000 to 2030, with carbon dioxide (CO2 emissions growing 40-110% over the same period. On-road vehicles are a major source of CO2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NO(x)), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new US. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section "by hand" using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NO(x), PM2.5, PM10, and CO2. The results demonstrate that obtaining precise and comprehensive operating mode distributions on a second-by-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.

Ammonia emissions from an anaerobic digestion plant estimated using atmospheric measurements and dispersion modelling.

PubMed

Bell, Michael W; Tang, Y Sim; Dragosits, Ulrike; Flechard, Chris R; Ward, Paul; Braban, Christine F

2016-10-01

Anaerobic digestion (AD) is becoming increasingly implemented within organic waste treatment operations. The storage and processing of large volumes of organic wastes through AD has been identified as a significant source of ammonia (NH3) emissions, however the totality of ammonia emissions from an AD plant have not been previously quantified. The emissions from an AD plant processing food waste were estimated through integrating ambient NH3 concentration measurements, atmospheric dispersion modelling, and comparison with published emission factors (EFs). Two dispersion models (ADMS and a backwards Lagrangian stochastic (bLS) model) were applied to calculate emission estimates. The bLS model (WindTrax) was used to back-calculate a total (top-down) emission rate for the AD plant from a point of continuous NH3 measurement downwind from the plant. The back-calculated emission rates were then input to the ADMS forward dispersion model to make predictions of air NH3 concentrations around the site, and evaluated against weekly passive sampler NH3 measurements. As an alternative approach emission rates from individual sources within the plant were initially estimated by applying literature EFs to the available site parameters concerning the chemical composition of waste materials, room air concentrations, ventilation rates, etc. The individual emission rates were input to ADMS and later tuned by fitting the simulated ambient concentrations to the observed (passive sampler) concentration field, which gave an excellent match to measurements after an iterative process. The total emission from the AD plant thus estimated by a bottom-up approach was 16.8±1.8mgs(-1), which was significantly higher than the back-calculated top-down estimate (7.4±0.78mgs(-1)). The bottom-up approach offered a more realistic treatment of the source distribution within the plant area, while the complexity of the site was not ideally suited to the bLS method, thus the bottom-up method is believed to give a better estimate of emissions. The storage of solid digestate and the aerobic treatment of liquid effluents at the site were the greatest sources of NH3 emissions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluating the impacts of different measurement and model configurations on top-down estimates of UK methane emissions

NASA Astrophysics Data System (ADS)

Lunt, Mark; Rigby, Matt; Manning, Alistair; O'Doherty, Simon; Stavert, Ann; Stanley, Kieran; Young, Dickon; Pitt, Joseph; Bauguitte, Stephane; Allen, Grant; Helfter, Carole; Palmer, Paul

2017-04-01

The Greenhouse gAs Uk and Global Emissions (GAUGE) project aims to quantify the magnitude and uncertainty of key UK greenhouse gas emissions more robustly than previously achieved. Measurements of methane have been taken from a number of tall-tower and surface sites as well as mobile measurement platforms such as a research aircraft and a ferry providing regular transects off the east coast of the UK. Using the UK Met Office's atmospheric transport model, NAME, and a novel Bayesian inversion technique we present estimates of methane emissions from the UK from a number of different combinations of sites to show the robustness of the UK total emissions to network configuration. The impact on uncertainties will be discussed, focusing on the usefulness of the various measurement platforms for constraining UK emissions. We will examine the effects of observation selection and how a priori assumptions about model uncertainty can affect the emission estimates, even within a data-driven hierarchical inversion framework. Finally, we will show the impact of the resolution of the meteorology used to drive the NAME model on emissions estimates, and how to rationalise our understanding of the ability of transport models to represent reality.

A fuel-based approach to estimating motor vehicle exhaust emissions

NASA Astrophysics Data System (ADS)

Singer, Brett Craig

Motor vehicles contribute significantly to air pollution problems; accurate motor vehicle emission inventories are therefore essential to air quality planning. Current travel-based inventory models use emission factors measured from potentially biased vehicle samples and predict fleet-average emissions which are often inconsistent with on-road measurements. This thesis presents a fuel-based inventory approach which uses emission factors derived from remote sensing or tunnel-based measurements of on-road vehicles. Vehicle activity is quantified by statewide monthly fuel sales data resolved to the air basin level. Development of the fuel-based approach includes (1) a method for estimating cold start emission factors, (2) an analysis showing that fuel-normalized emission factors are consistent over a range of positive vehicle loads and that most fuel use occurs during loaded-mode driving, (3) scaling factors relating infrared hydrocarbon measurements to total exhaust volatile organic compound (VOC) concentrations, and (4) an analysis showing that economic factors should be considered when selecting on-road sampling sites. The fuel-based approach was applied to estimate carbon monoxide (CO) emissions from warmed-up vehicles in the Los Angeles area in 1991, and CO and VOC exhaust emissions for Los Angeles in 1997. The fuel-based CO estimate for 1991 was higher by a factor of 2.3 +/- 0.5 than emissions predicted by California's MVEI 7F model. Fuel-based inventory estimates for 1997 were higher than those of California's updated MVEI 7G model by factors of 2.4 +/- 0.2 for CO and 3.5 +/- 0.6 for VOC. Fuel-based estimates indicate a 20% decrease in the mass of CO emitted, despite an 8% increase in fuel use between 1991 and 1997; official inventory models predict a 50% decrease in CO mass emissions during the same period. Cold start CO and VOC emission factors derived from parking garage measurements were lower than those predicted by the MVEI 7G model. Current inventories in California appear to understate total exhaust CO and VOC emissions, while overstating the importance of cold start emissions. The fuel-based approach yields robust, independent, and accurate estimates of on-road vehicle emissions. Fuel-based estimates should be used to validate or adjust official vehicle emission inventories before society embarks on new, more costly air pollution control programs.

A methodology to estimate greenhouse gases emissions in Life Cycle Inventories of wastewater treatment plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodriguez-Garcia, G., E-mail: gonzalo.rodriguez.garcia@usc.es; Hospido, A., E-mail: almudena.hospido@usc.es; Bagley, D.M., E-mail: bagley@uwyo.edu

2012-11-15

The main objective of this paper is to present the Direct Emissions Estimation Model (DEEM), a model for the estimation of CO{sub 2} and N{sub 2}O emissions from a wastewater treatment plant (WWTP). This model is consistent with non-specific but widely used models such as AS/AD and ASM no. 1 and presents the benefits of simplicity and application over a common WWTP simulation platform, BioWin Registered-Sign , making it suitable for Life Cycle Assessment and Carbon Footprint studies. Its application in a Spanish WWTP indicates direct N{sub 2}O emissions to be 8 times larger than those associated with electricity usemore » and thus relevant for LCA. CO{sub 2} emissions can be of similar importance to electricity-associated ones provided that 20% of them are of non-biogenic origin. - Highlights: Black-Right-Pointing-Pointer A model has been developed for the estimation of GHG emissions in WWTP. Black-Right-Pointing-Pointer Model was consistent with both ASM no. 1 and AS/AD. Black-Right-Pointing-Pointer N{sub 2}O emissions are 8 times more relevant than the one associated with electricity. Black-Right-Pointing-Pointer CO{sub 2} emissions are as important as electricity if 20% of it is non-biogenic.« less

Estimating national landfill methane emissions: an application of the 2006 Intergovernmental Panel on Climate Change Waste Model in Panama.

PubMed

Weitz, Melissa; Coburn, Jeffrey B; Salinas, Edgar

2008-05-01

This paper estimates national methane emissions from solid waste disposal sites in Panama over the time period 1990-2020 using both the 2006 Intergovernmental Panel on Climate Change (IPCC) Waste Model spreadsheet and the default emissions estimate approach presented in the 1996 IPCC Good Practice Guidelines. The IPCC Waste Model has the ability to calculate emissions from a variety of solid waste disposal site types, taking into account country- or region-specific waste composition and climate information, and can be used with a limited amount of data. Countries with detailed data can also run the model with country-specific values. The paper discusses methane emissions from solid waste disposal; explains the differences between the two methodologies in terms of data needs, assumptions, and results; describes solid waste disposal circumstances in Panama; and presents the results of this analysis. It also demonstrates the Waste Model's ability to incorporate landfill gas recovery data and to make projections. The former default method methane emissions estimates are 25 Gg in 1994, and range from 23.1 Gg in 1990 to a projected 37.5 Gg in 2020. The Waste Model estimates are 26.7 Gg in 1994, ranging from 24.6 Gg in 1990 to 41.6 Gg in 2020. Emissions estimates for Panama produced by the new model were, on average, 8% higher than estimates produced by the former default methodology. The increased estimate can be attributed to the inclusion of all solid waste disposal in Panama (as opposed to only disposal in managed landfills), but the increase was offset somewhat by the different default factors and regional waste values between the 1996 and 2006 IPCC guidelines, and the use of the first-order decay model with a time delay for waste degradation in the IPCC Waste Model.

Emission of intermediate, semi and low volatile organic compounds from traffic and their impact on secondary organic aerosol concentrations over Greater Paris

NASA Astrophysics Data System (ADS)

Sartelet, K.; Zhu, S.; Moukhtar, S.; André, M.; André, J. M.; Gros, V.; Favez, O.; Brasseur, A.; Redaelli, M.

2018-05-01

Exhaust particle emissions are mostly made of black carbon and/or organic compounds, with some of these organic compounds existing in both the gas and particle phases. Although emissions of volatile organic compounds (VOC) are usually measured at the exhaust, emissions in the gas phase of lower volatility compounds (POAvapor) are not. However, these gas-phase emissions may be oxidised after emission and enhance the formation of secondary organic aerosols (SOA). They are shown here to contribute to most of the SOA formation in Central Paris. POAvapor emissions are usually estimated from primary organic aerosol emissions in the particle phase (POA). However, they could also be estimated from VOC emissions for both gasoline and diesel vehicles using previously published measurements from chamber measurements. Estimating POAvapor from VOC emissions and ageing exhaust emissions with a simple model included in the Polyphemus air-quality platform compare well to measurements of SOA formation performed in chamber experiments. Over Greater Paris, POAvapor emissions estimated using POA and VOC emissions are compared using the HEAVEN bottom-up traffic emissions model. The impact on the simulated atmospheric concentrations is then assessed using the Polyphemus/Polair3D chemistry-transport model. Estimating POAvapor emissions from VOC emissions rather than POA emissions lead to lower emissions along motorway axes (between -50% and -70%) and larger emissions in urban areas (up to between +120% and +140% in Central Paris). The impact on total organic aerosol concentrations (gas plus particle) is lower than the impact on emissions: between -8% and 25% along motorway axes and in urban areas respectively. Particle-phase organic concentrations are lower when POAvapor emissions are estimated from VOC than POA emissions, even in Central Paris where the total organic aerosol concentration is higher, because of different assumptions on the emission volatility distribution, stressing the importance of characterizing not only the emission strength, but also the emission volatility distribution.

LANDFILL GAS EMISSIONS MODEL (LANDGEM) VERSION 3.02 USER'S GUIDE

EPA Science Inventory

The Landfill Gas Emissions Model (LandGEM) is an automated estimation tool with a Microsoft Excel interface that can be used to estimate emission rates for total landfill gas, methane, carbon dioxide, nonmethane organic compounds, and individual air pollutants from municipal soli...

HEAVY DUTY DIESEL VEHICLE LOAD ESTIMATION: DEVELOPMENT OF VEHICLE ACTIVITY OPTIMIZATION ALGORITHM

EPA Science Inventory

The Heavy-Duty Vehicle Modal Emission Model (HDDV-MEM) developed by the Georgia Institute of Technology(Georgia Tech) has a capability to model link-specific second-by-second emissions using speed/accleration matrices. To estimate emissions, engine power demand calculated usin...

DEVELOPING SEASONAL AMMONIA EMISSION ESTIMATES WITH AN INVERSE MODELING TECHNIQUE

EPA Science Inventory

Significant uncertainty exists in magnitude and variability of ammonia (NH3) emissions, which are needed for air quality modeling of aerosols and deposition of nitrogen compounds. Approximately 85% of NH3 emissions are estimated to come from agricultural non-point sources. We sus...

Spatially explicit estimates of N2 O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management.

PubMed

Gerber, James S; Carlson, Kimberly M; Makowski, David; Mueller, Nathaniel D; Garcia de Cortazar-Atauri, Iñaki; Havlík, Petr; Herrero, Mario; Launay, Marie; O'Connell, Christine S; Smith, Pete; West, Paul C

2016-10-01

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N2 O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N2 O emissions at the country scale by aggregating all crops, under the assumption that N2 O emissions are linearly related to N application. However, field studies and meta-analyses indicate a nonlinear relationship, in which N2 O emissions are relatively greater at higher N application rates. Here, we apply a super-linear emissions response model to crop-specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N2 O emissions from croplands. We estimate 0.66 Tg of N2 O-N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N2 O emissions range from 20% to 40% lower throughout sub-Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N2 O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2 O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high-resolution N application data are critical to support accurate N2 O emissions estimates. © 2016 John Wiley & Sons Ltd.

Global Occurrence and Emission of Rotaviruses to Surface Waters

PubMed Central

Kiulia, Nicholas M.; Hofstra, Nynke; Vermeulen, Lucie C.; Obara, Maureen A.; Medema, Gertjan; Rose, Joan B.

2015-01-01

Group A rotaviruses (RV) are the major cause of acute gastroenteritis in infants and young children globally. Waterborne transmission of RV and the presence of RV in water sources are of major public health importance. In this paper, we present the Global Waterborne Pathogen model for RV (GloWPa-Rota model) to estimate the global distribution of RV emissions to surface water. To our knowledge, this is the first model to do so. We review the literature to estimate three RV specific variables for the model: incidence, excretion rate and removal during wastewater treatment. We estimate total global RV emissions to be 2 × 1018 viral particles/grid/year, of which 87% is produced by the urban population. Hotspot regions with high RV emissions are urban areas in densely populated parts of the world, such as Bangladesh and Nigeria, while low emissions are found in rural areas in North Russia and the Australian desert. Even for industrialized regions with high population density and without tertiary treatment, such as the UK, substantial emissions are estimated. Modeling exercises like the one presented in this paper provide unique opportunities to further study these emissions to surface water, their sources and scenarios for improved management. PMID:25984911

Estimating emissions from fires in North America for air quality modeling.

Treesearch

Christine Wiedinmyer; Brad Quayle; Chris Geron; Angie Belote; Don McKenzie; Xiaoyang Zhang; Susan O' Neill; Kristina Klos Wynne

2006-01-01

Fires contribute substantial emissions of trace gases and particles to the atmosphere. These emissions can impact air quality and even climate. We have developed a modeling framework to estimate the emissions from fires in North and parts of Central America (10-71 ˚N and 55-175 ˚W) by taking advantage of a combination of complementary satellite and...

Comparing emission rates derived from a model with a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality.

PubMed

Xu, Junshi; Wang, Jonathan; Hilker, Nathan; Fallah-Shorshani, Masoud; Saleh, Marc; Tu, Ran; Wang, An; Minet, Laura; Stogios, Christos; Evans, Greg; Hatzopoulou, Marianne

2018-06-05

This study presents a comparison of fleet averaged emission factors (EFs) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to carbon monoxide (CO), nitrogen oxides (NO x ), and elemental carbon (EC) along an urban corridor. To this end, a field campaign was conducted over one week in June 2016 on an arterial road in Toronto, Canada. Traffic data were collected using a traffic camera and a radar, while air quality was characterized using two monitoring stations: one located at ground-level and another at the rooftop of a four-storey building. A traffic simulation model was calibrated and validated and sec-by-sec speed profiles for all vehicle trajectories were extracted to model emissions. In addition, dispersion modelling was conducted to identify the extent to which differences in emissions translate to differences in near-road concentrations. Our results indicate that modelled EFs for CO and NO x are twice as high as plume-based EFs. Besides, modelled results indicate that transit bus emissions accounted for 60% and 70% of the total emissions of NO x and EC. Transit bus emission rates in g/passenger.km for NO x and EC were up to 8 and 22 times the emission rates of passenger cars. In contrast, the Toronto streetcars, which are electrically fuelled, were found to improve near-road air quality despite their negative impact on traffic speeds. Finally, we observe that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background given that the study network is located in a busy downtown area. Implications This study presents a comparison of fleet averaged emission factors (EFs) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to various pollutants. Besides, dispersion modelling was conducted to identify the extent to which differences in emissions translate to differences in near-road concentrations. We observe that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background as the study network is located in a busy downtown area.

Impact of Biogenic Emission Uncertainties on the Simulated Response of Ozone and Fine Particulate Matter to Anthropogenic Emission Reductions

PubMed Central

Hogrefe, Christian; Isukapalli, Sastry S.; Tang, Xiaogang; Georgopoulos, Panos G.; He, Shan; Zalewsky, Eric E.; Hao, Winston; Ku, Jia-Yeong; Key, Tonalee; Sistla, Gopal

2011-01-01

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1–0.25 μg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1–2% of the value of the annual PM2.5 NAAQS of 15 μg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions. PMID:21305893

Planning level assessment of greenhouse gas emissions for alternative transportation construction projects : carbon footprint estimator, phase II, volume I - GASCAP model.

DOT National Transportation Integrated Search

2014-03-01

The GASCAP model was developed to provide a software tool for analysis of the life-cycle GHG : emissions associated with the construction and maintenance of transportation projects. This phase : of development included techniques for estimating emiss...

MODELS TO ESTIMATE VOLATILE ORGANIC HAZARDOUS AIR POLLUTANT EMISSIONS FROM MUNICIPAL SEWER SYSTEMS

EPA Science Inventory

Emissions from municipal sewers are usually omitted from hazardous air pollutant (HAP) emission inventories. This omission may result from a lack of appreciation for the potential emission impact and/or from inadequate emission estimation procedures. This paper presents an analys...

Covariance specification and estimation to improve top-down Green House Gas emission estimates

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Whetstone, J. R.

2015-12-01

The National Institute of Standards and Technology (NIST) operates the North-East Corridor (NEC) project and the Indianapolis Flux Experiment (INFLUX) in order to develop measurement methods to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties in urban domains using a top down inversion method. Top down inversion updates prior knowledge using observations in a Bayesian way. One primary consideration in a Bayesian inversion framework is the covariance structure of (1) the emission prior residuals and (2) the observation residuals (i.e. the difference between observations and model predicted observations). These covariance matrices are respectively referred to as the prior covariance matrix and the model-data mismatch covariance matrix. It is known that the choice of these covariances can have large effect on estimates. The main objective of this work is to determine the impact of different covariance models on inversion estimates and their associated uncertainties in urban domains. We use a pseudo-data Bayesian inversion framework using footprints (i.e. sensitivities of tower measurements of GHGs to surface emissions) and emission priors (based on Hestia project to quantify fossil-fuel emissions) to estimate posterior emissions using different covariance schemes. The posterior emission estimates and uncertainties are compared to the hypothetical truth. We find that, if we correctly specify spatial variability and spatio-temporal variability in prior and model-data mismatch covariances respectively, then we can compute more accurate posterior estimates. We discuss few covariance models to introduce space-time interacting mismatches along with estimation of the involved parameters. We then compare several candidate prior spatial covariance models from the Matern covariance class and estimate their parameters with specified mismatches. We find that best-fitted prior covariances are not always best in recovering the truth. To achieve accuracy, we perform a sensitivity study to further tune covariance parameters. Finally, we introduce a shrinkage based sample covariance estimation technique for both prior and mismatch covariances. This technique allows us to achieve similar accuracy nonparametrically in a more efficient and automated way.

Fast Emission Estimates in China Constrained by Satellite Observations (Invited)

NASA Astrophysics Data System (ADS)

Mijling, B.; van der A, R.

2013-12-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for an emerging economy such as China, where rapid economic growth changes emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. Constraining emissions from concentration measurements is, however, computationally challenging. Within the GlobEmission project of the European Space Agency (ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China, using the CHIMERE model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission estimates result in a better agreement between observations and simulations of air pollutant concentrations, facilitating improved air quality forecasts. The EU project MarcoPolo will combine these emission estimates from space with statistical information on e.g. land use, population density and traffic to construct a new up-to-date emission inventory for China.

South American smoke coverage and flux estimations from the Fire Locating and Modeling of Burning Emissions (FLAMBE') system.

NASA Astrophysics Data System (ADS)

Reid, J. S.; Westphal, D. L.; Christopher, S. A.; Prins, E. M.; Gasso, S.; Reid, E.; Theisen, M.; Schmidt, C. C.; Hunter, J.; Eck, T.

2002-05-01

The Fire Locating and Modeling of Burning Emissions (FLAMBE') project is a joint Navy, NOAA, NASA and university project to integrate satellite products with numerical aerosol models to produce a real time fire and emissions inventory. At the center of the program is the Wildfire Automated Biomass Burning Algorithm (WF ABBA) which provides real-time fire products and the NRL Aerosol Analysis and Prediction System to model smoke transport. In this presentation we give a brief overview of the system and methods, but emphasize new estimations of smoke coverage and emission fluxes from the South American continent. Temporal and smoke patterns compare reasonably well with AERONET and MODIS aerosol optical depth products for the 2000 and 2001 fire seasons. Fluxes are computed by relating NAAPS output fields and MODIS optical depth maps with modeled wind fields. Smoke emissions and transport fluxes out of the continent can then be estimated by perturbing the modeled emissions to gain agreement with the satellite and wind products. Regional smoke emissions are also presented for grass and forest burning.

Estimation of carbon dioxide emissions per urban center link unit using data collected by the Advanced Traffic Information System in Daejeon, Korea

NASA Astrophysics Data System (ADS)

Ryu, B. Y.; Jung, H. J.; Bae, S. H.; Choi, C. U.

2013-12-01

CO2 emissions on roads in urban centers substantially affect global warming. It is important to quantify CO2 emissions in terms of the link unit in order to reduce these emissions on the roads. Therefore, in this study, we utilized real-time traffic data and attempted to develop a methodology for estimating CO2 emissions per link unit. Because of the recent development of the vehicle-to-infrastructure (V2I) communication technology, data from probe vehicles (PVs) can be collected and speed per link unit can be calculated. Among the existing emission calculation methodologies, mesoscale modeling, which is a representative modeling measurement technique, requires speed and traffic data per link unit. As it is not feasible to install fixed detectors at every link for traffic data collection, in this study, we developed a model for traffic volume estimation by utilizing the number of PVs that can be additionally collected when the PV data are collected. Multiple linear regression and an artificial neural network (ANN) were used for estimating the traffic volume. The independent variables and input data for each model are the number of PVs, travel time index (TTI), the number of lanes, and time slots. The result from the traffic volume estimate model shows that the mean absolute percentage error (MAPE) of the ANN is 18.67%, thus proving that it is more effective. The ANN-based traffic volume estimation served as the basis for the calculation of emissions per link unit. The daily average emissions for Daejeon, where this study was based, were 2210.19 ton/day. By vehicle type, passenger cars accounted for 71.28% of the total emissions. By road, Gyeryongro emitted 125.48 ton/day, accounting for 5.68% of the total emission, the highest percentage of all roads. In terms of emissions per kilometer, Hanbatdaero had the highest emission volume, with 7.26 ton/day/km on average. This study proves that real-time traffic data allow an emissions estimate in terms of the link unit. Furthermore, an analysis of CO2 emissions can support traffic management to make decisions related to the reduction of carbon emissions.

Spatial distribution of diesel transit bus emissions and urban populations: implications of coincidence and scale on exposure.

PubMed

Gouge, Brian; Ries, Francis J; Dowlatabadi, Hadi

2010-09-15

Macroscale emissions modeling approaches have been widely applied in impact assessments of mobile source emissions. However, these approaches poorly characterize the spatial distribution of emissions and have been shown to underestimate emissions of some pollutants. To quantify the implications of these limitations on exposure assessments, CO, NO(X), and HC emissions from diesel transit buses were estimated at 50 m intervals along a bus rapid transit route using a microscale emissions modeling approach. The impacted population around the route was estimated using census, pedestrian count and transit ridership data. Emissions exhibited significant spatial variability. In intervals near major intersections and bus stops, emissions were 1.6-3.0 times higher than average. The coincidence of these emission hot spots and peaks in pedestrian populations resulted in a 20-40% increase in exposure compared to estimates that assumed homogeneous spatial distributions of emissions and/or populations along the route. An additional 19-30% increase in exposure resulted from the underestimate of CO and NO(X) emissions by macroscale modeling approaches. The results of this study indicate that macroscale modeling approaches underestimate exposure due to poor characterization of the influence of vehicle activity on the spatial distribution of emissions and total emissions.

Modal emissions modeling with real traffic data

DOT National Transportation Integrated Search

1999-01-01

This report details the use of a modal emissions model to estimate the relative emissions of CO due to changes in vehicle operating characteristics on urban roadways. The Davis Institute for Transportation Studies Emissions Model (DITSEM) was selecte...

Direct and Indirect Measurements and Modeling of Methane Emissions in Indianapolis, Indiana.

PubMed

Lamb, Brian K; Cambaliza, Maria O L; Davis, Kenneth J; Edburg, Steven L; Ferrara, Thomas W; Floerchinger, Cody; Heimburger, Alexie M F; Herndon, Scott; Lauvaux, Thomas; Lavoie, Tegan; Lyon, David R; Miles, Natasha; Prasad, Kuldeep R; Richardson, Scott; Roscioli, Joseph Robert; Salmon, Olivia E; Shepson, Paul B; Stirm, Brian H; Whetstone, James

2016-08-16

This paper describes process-based estimation of CH4 emissions from sources in Indianapolis, IN and compares these with atmospheric inferences of whole city emissions. Emissions from the natural gas distribution system were estimated from measurements at metering and regulating stations and from pipeline leaks. Tracer methods and inverse plume modeling were used to estimate emissions from the major landfill and wastewater treatment plant. These direct source measurements informed the compilation of a methane emission inventory for the city equal to 29 Gg/yr (5% to 95% confidence limits, 15 to 54 Gg/yr). Emission estimates for the whole city based on an aircraft mass balance method and from inverse modeling of CH4 tower observations were 41 ± 12 Gg/yr and 81 ± 11 Gg/yr, respectively. Footprint modeling using 11 days of ethane/methane tower data indicated that landfills, wastewater treatment, wetlands, and other biological sources contribute 48% while natural gas usage and other fossil fuel sources contribute 52% of the city total. With the biogenic CH4 emissions omitted, the top-down estimates are 3.5-6.9 times the nonbiogenic city inventory. Mobile mapping of CH4 concentrations showed low level enhancement of CH4 throughout the city reflecting diffuse natural gas leakage and downstream usage as possible sources for the missing residual in the inventory.

Regional landfills methane emission inventory in Malaysia.

PubMed

Abushammala, Mohammed F M; Noor Ezlin Ahmad Basri; Basri, Hassan; Ahmed Hussein El-Shafie; Kadhum, Abdul Amir H

2011-08-01

The decomposition of municipal solid waste (MSW) in landfills under anaerobic conditions produces landfill gas (LFG) containing approximately 50-60% methane (CH(4)) and 30-40% carbon dioxide (CO(2)) by volume. CH(4) has a global warming potential 21 times greater than CO(2); thus, it poses a serious environmental problem. As landfills are the main method for waste disposal in Malaysia, the major aim of this study was to estimate the total CH(4) emissions from landfills in all Malaysian regions and states for the year 2009 using the IPCC, 1996 first-order decay (FOD) model focusing on clean development mechanism (CDM) project applications to initiate emission reductions. Furthermore, the authors attempted to assess, in quantitative terms, the amount of CH(4) that would be emitted from landfills in the period from 1981-2024 using the IPCC 2006 FOD model. The total CH(4) emission using the IPCC 1996 model was estimated to be 318.8 Gg in 2009. The Northern region had the highest CH(4) emission inventory, with 128.8 Gg, whereas the Borneo region had the lowest, with 24.2 Gg. It was estimated that Pulau Penang state produced the highest CH(4) emission, 77.6 Gg, followed by the remaining states with emission values ranging from 38.5 to 1.5 Gg. Based on the IPCC 1996 FOD model, the total Malaysian CH( 4) emission was forecast to be 397.7 Gg by 2020. The IPCC 2006 FOD model estimated a 201 Gg CH(4) emission in 2009, and estimates ranged from 98 Gg in 1981 to 263 Gg in 2024.

Impact of the Volkswagen emissions control defeat device on US public health

NASA Astrophysics Data System (ADS)

Barrett, Steven R. H.; Speth, Raymond L.; Eastham, Sebastian D.; Dedoussi, Irene C.; Ashok, Akshay; Malina, Robert; Keith, David W.

2015-11-01

The US Environmental Protection Agency (EPA) has alleged that Volkswagen Group of America (VW) violated the Clean Air Act (CAA) by developing and installing emissions control system ‘defeat devices’ (software) in model year 2009-2015 vehicles with 2.0 litre diesel engines. VW has admitted the inclusion of defeat devices. On-road emissions testing suggests that in-use NOx emissions for these vehicles are a factor of 10 to 40 above the EPA standard. In this paper we quantify the human health impacts and associated costs of the excess emissions. We propagate uncertainties throughout the analysis. A distribution function for excess emissions is estimated based on available in-use NOx emissions measurements. We then use vehicle sales data and the STEP vehicle fleet model to estimate vehicle distance traveled per year for the fleet. The excess NOx emissions are allocated on a 50 km grid using an EPA estimate of the light duty diesel vehicle NOx emissions distribution. We apply a GEOS-Chem adjoint-based rapid air pollution exposure model to produce estimates of particulate matter and ozone exposure due to the spatially resolved excess NOx emissions. A set of concentration-response functions is applied to estimate mortality and morbidity outcomes. Integrated over the sales period (2008-2015) we estimate that the excess emissions will cause 59 (95% CI: 10 to 150) early deaths in the US. When monetizing premature mortality using EPA-recommended data, we find a social cost of ˜450m over the sales period. For the current fleet, we estimate that a return to compliance for all affected vehicles by the end of 2016 will avert ˜130 early deaths and avoid ˜840m in social costs compared to a counterfactual case without recall.

Estimating nitrogen oxides emissions at city scale in China with a nightlight remote sensing model.

PubMed

Jiang, Jianhui; Zhang, Jianying; Zhang, Yangwei; Zhang, Chunlong; Tian, Guangming

2016-02-15

Increasing nitrogen oxides (NOx) emissions over the fast developing regions have been of great concern due to their critical associations with the aggravated haze and climate change. However, little geographically specific data exists for estimating spatio-temporal trends of NOx emissions. In order to quantify the spatial and temporal variations of NOx emissions, a spatially explicit approach based on the continuous satellite observations of artificial nighttime stable lights (NSLs) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) was developed to estimate NOx emissions from the largest emission source of fossil fuel combustion. The NSL based model was established with three types of data including satellite data of nighttime stable lights, geographical data of administrative boundaries, and provincial energy consumptions in China, where a significant growth of NOx emission has experienced during three policy stages corresponding to the 9th-11th)Five-Year Plan (FYP, 1995-2010). The estimated national NOx emissions increased by 8.2% per year during the study period, and the total annual NOx emissions in China estimated by the NSL-based model were approximately 4.1%-13.8% higher than the previous estimates. The spatio-temporal variations of NOx emissions at city scale were then evaluated by the Moran's I indices. The global Moran's I indices for measuring spatial agglomerations of China's NOx emission increased by 50.7% during 1995-2010. Although the inland cities have shown larger contribution to the emission growth than the more developed coastal cities since 2005, the High-High clusters of NOx emission located in Beijing-Tianjin-Hebei regions, the Yangtze River Delta, and the Pearl River Delta should still be the major focus of NOx mitigation. Our results indicate that the readily available DMSP/OLS nighttime stable lights based model could be an easily accessible and effective tool for achieving strategic decision making toward NOx reduction. Copyright © 2015 Elsevier B.V. All rights reserved.

Uncertainties of isoprene emissions in the MEGAN model estimated for a coniferous and broad-leaved mixed forest in Southern China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Situ, S.; Wang, Xuemei; Guenther, Alex B.

2014-12-01

Using local observed emission factor, meteorological data, vegetation 5 information and dynamic MODIS LAI, MEGANv2.1 was constrained to predict the isoprene emission from Dinghushan forest in the Pearl River Delta region during a field campaign in November 2008, and the uncertainties in isoprene emission estimates were quantified by the Monte Carlo approach. The results indicate that MEGAN can predict the isoprene emission reasonably during the campaign, and the mean value of isoprene emission is 2.35 mg m-2 h-1 in daytime. There are high uncertainties associated with the MEGAN inputs and calculated parameters, and the relative error can be as highmore » as -89 to 111% for a 95% confidence interval. The emission factor of broadleaf trees and the activity factor accounting for light and temperature dependence are the most important contributors to the uncertainties in isoprene emission estimated for the Dinghushan forest during the campaign. The results also emphasize the importance of accurate observed PAR and temperature to reduce the uncertainties in isoprene emission estimated by model, because the MEGAN model activity factor accounting for light and temperature dependence is highly sensitive to PAR and temperature.« less

Trend analysis of tropospheric NO2 column density over East Asia during 2000-2010: multi-satellite observations and model simulations with the updated REAS emission inventory

NASA Astrophysics Data System (ADS)

Itahashi, S.; Uno, I.; Irie, H.; Kurokawa, J.; Ohara, T.

2013-04-01

Satellite observations of the tropospheric NO2 vertical column density (VCD) are closely correlated to surface NOx emissions and can thus be used to estimate the latter. In this study, the NO2 VCDs simulated by a regional chemical transport model with data from the updated Regional Emission inventory in ASia (REAS) version 2.1 were validated by comparison with multi-satellite observations (GOME, SCIAMACHY, GOME-2, and OMI) between 2000 and 2010. Rapid growth in NO2 VCD driven by expansion of anthropogenic NOx emissions was revealed above the central eastern China region, except during the economic downturn. In contrast, slightly decreasing trends were captured above Japan. The modeled NO2 VCDs using the updated REAS emissions reasonably reproduced the annual trends observed by multi-satellites, suggesting that the NOx emissions growth rate estimated by the updated inventory is robust. On the basis of the close linear relationship of modeled NO2 VCD, observed NO2 VCD, and anthropogenic NOx emissions, the NOx emissions in 2009 and 2010 were estimated. It was estimated that the NOx emissions from anthropogenic sources in China beyond doubled between 2000 and 2010, reflecting the strong growth of anthropogenic emissions in China with the rapid recovery from the economic downturn during late 2008 and mid-2009.

REVIEW OF INDOOR EMISSION SOURCE MODELS: PART 2. PARAMETER ESTIMATION

EPA Science Inventory

This review consists of two sections. Part I provides an overview of 46 indoor emission source models. Part 2 (this paper) focuses on parameter estimation, a topic that is critical to modelers but has never been systematically discussed. A perfectly valid model may not be a usefu...

Airborne observations reveal elevational gradient in tropical forest isoprene emissions.

PubMed

Gu, Dasa; Guenther, Alex B; Shilling, John E; Yu, Haofei; Huang, Maoyi; Zhao, Chun; Yang, Qing; Martin, Scot T; Artaxo, Paulo; Kim, Saewung; Seco, Roger; Stavrakou, Trissevgeni; Longo, Karla M; Tóta, Julio; de Souza, Rodrigo Augusto Ferreira; Vega, Oscar; Liu, Ying; Shrivastava, Manish; Alves, Eliane G; Santos, Fernando C; Leng, Guoyong; Hu, Zhiyuan

2017-05-23

Isoprene dominates global non-methane volatile organic compound emissions, and impacts tropospheric chemistry by influencing oxidants and aerosols. Isoprene emission rates vary over several orders of magnitude for different plants, and characterizing this immense biological chemodiversity is a challenge for estimating isoprene emission from tropical forests. Here we present the isoprene emission estimates from aircraft eddy covariance measurements over the Amazonian forest. We report isoprene emission rates that are three times higher than satellite top-down estimates and 35% higher than model predictions. The results reveal strong correlations between observed isoprene emission rates and terrain elevations, which are confirmed by similar correlations between satellite-derived isoprene emissions and terrain elevations. We propose that the elevational gradient in the Amazonian forest isoprene emission capacity is determined by plant species distributions and can substantially explain isoprene emission variability in tropical forests, and use a model to demonstrate the resulting impacts on regional air quality.

Model for estimating enteric methane emissions from United States dairy and feedlot cattle.

PubMed

Kebreab, E; Johnson, K A; Archibeque, S L; Pape, D; Wirth, T

2008-10-01

Methane production from enteric fermentation in cattle is one of the major sources of anthropogenic greenhouse gas emission in the United States and worldwide. National estimates of methane emissions rely on mathematical models such as the one recommended by the Intergovernmental Panel for Climate Change (IPCC). Models used for prediction of methane emissions from cattle range from empirical to mechanistic with varying input requirements. Two empirical and 2 mechanistic models (COWPOLL and MOLLY) were evaluated for their prediction ability using individual cattle measurements. Model selection was based on mean square prediction error (MSPE), concordance correlation coefficient, and residuals vs. predicted values analyses. In dairy cattle, COWPOLL had the lowest root MSPE and greatest accuracy and precision of predicting methane emissions (correlation coefficient estimate = 0.75). The model simulated differences in diet more accurately than the other models, and the residuals vs. predicted value analysis showed no mean bias (P = 0.71). In feedlot cattle, MOLLY had the lowest root MSPE with almost all errors from random sources (correlation coefficient estimate = 0.69). The IPCC model also had good agreement with observed values, and no significant mean (P = 0.74) or linear bias (P = 0.11) was detected when residuals were plotted against predicted values. A fixed methane conversion factor (Ym) might be an easier alternative to diet-dependent variable Ym. Based on the results, the 2 mechanistic models were used to simulate methane emissions from representative US diets and were compared with the IPCC model. The average Ym in dairy cows was 5.63% of GE (range 3.78 to 7.43%) compared with 6.5% +/- 1% recommended by IPCC. In feedlot cattle, the average Ym was 3.88% (range 3.36 to 4.56%) compared with 3% +/- 1% recommended by IPCC. Based on our simulations, using IPCC values can result in an overestimate of about 12.5% and underestimate of emissions by about 9.8% for dairy and feedlot cattle, respectively. In addition to providing improved estimates of emissions based on diets, mechanistic models can be used to assess mitigation options such as changing source of carbohydrate or addition of fat to decrease methane, which is not possible with empirical models. We recommend national inventories use diet-specific Ym values predicted by mechanistic models to estimate methane emissions from cattle.

Assessing Potential Air Pollutant Emissions from Agricultural Feedstock Production using MOVES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eberle, Annika; Warner, Ethan; Zhang, Yi Min

Biomass feedstock production is expected to grow as demand for biofuels and bioenergy increases. The change in air pollutant emissions that may result from large-scale biomass supply has implications for local air quality and human health. We developed spatially explicit emissions inventories for corn grain and six cellulosic feedstocks through the extension of the National Renewable Energy Laboratory's Feedstock Production Emissions to Air Model (FPEAM). These inventories include emissions of seven pollutants (nitrogen oxides, ammonia, volatile organic compounds, particulate matter, sulfur oxides, and carbon monoxide) generated from biomass establishment, maintenance, harvest, transportation, and biofuel preprocessing activities. By integrating the EPA'smore » MOtor Vehicle Emissions Simulator (MOVES) into FPEAM, we created a scalable framework to execute county-level runs of the MOVES-Onroad model for representative counties (i.e., those counties with the largest amount of cellulosic feedstock production in each state) on a national scale. We used these results to estimate emissions from the on-road transportation of biomass and combined them with county-level runs of the MOVES-Nonroad model to estimate emissions from agricultural equipment. We also incorporated documented emission factors to estimate emissions from chemical application and the operation of drying equipment for feedstock processing, and used methods developed by the EPA and the California Air Resources Board to estimate fugitive dust emissions. The model developed here could be applied to custom equipment budgets and is extensible to accommodate additional feedstocks and pollutants. Future work will also extend this model to analyze spatial boundaries beyond the county-scale (e.g., regional or sub-county levels).« less

Mapping pan-Arctic CH4 emissions using an adjoint method by integrating process-based wetland and lake biogeochemical models and atmospheric CH4 concentrations

NASA Astrophysics Data System (ADS)

Tan, Z.; Zhuang, Q.; Henze, D. K.; Frankenberg, C.; Dlugokencky, E. J.; Sweeney, C.; Turner, A. J.

2015-12-01

Understanding CH4 emissions from wetlands and lakes are critical for the estimation of Arctic carbon balance under fast warming climatic conditions. To date, our knowledge about these two CH4 sources is almost solely built on the upscaling of discontinuous measurements in limited areas to the whole region. Many studies indicated that, the controls of CH4 emissions from wetlands and lakes including soil moisture, lake morphology and substrate content and quality are notoriously heterogeneous, thus the accuracy of those simple estimates could be questionable. Here we apply a high spatial resolution atmospheric inverse model (nested-grid GEOS-Chem Adjoint) over the Arctic by integrating SCIAMACHY and NOAA/ESRL CH4 measurements to constrain the CH4 emissions estimated with process-based wetland and lake biogeochemical models. Our modeling experiments using different wetland CH4 emission schemes and satellite and surface measurements show that the total amount of CH4 emitted from the Arctic wetlands is well constrained, but the spatial distribution of CH4 emissions is sensitive to priors. For CH4 emissions from lakes, our high-resolution inversion shows that the models overestimate CH4 emissions in Alaskan costal lowlands and East Siberian lowlands. Our study also indicates that the precision and coverage of measurements need to be improved to achieve more accurate high-resolution estimates.

Regional air quality impacts of increased natural gas production and use in Texas.

PubMed

Pacsi, Adam P; Alhajeri, Nawaf S; Zavala-Araiza, Daniel; Webster, Mort D; Allen, David T

2013-04-02

Natural gas use in electricity generation in Texas was estimated, for gas prices ranging from $1.89 to $7.74 per MMBTU, using an optimal power flow model. Hourly estimates of electricity generation, for individual electricity generation units, from the model were used to estimate spatially resolved hourly emissions from electricity generation. Emissions from natural gas production activities in the Barnett Shale region were also estimated, with emissions scaled up or down to match demand in electricity generation as natural gas prices changed. As natural gas use increased, emissions decreased from electricity generation and increased from natural gas production. Overall, NOx and SO2 emissions decreased, while VOC emissions increased as natural gas use increased. To assess the effects of these changes in emissions on ozone and particulate matter concentrations, spatially and temporally resolved emissions were used in a month-long photochemical modeling episode. Over the month-long photochemical modeling episode, decreases in natural gas prices typical of those experienced from 2006 to 2012 led to net regional decreases in ozone (0.2-0.7 ppb) and fine particulate matter (PM) (0.1-0.7 μg/m(3)). Changes in PM were predominantly due to changes in regional PM sulfate formation. Changes in regional PM and ozone formation are primarily due to decreases in emissions from electricity generation. Increases in emissions from increased natural gas production were offset by decreasing emissions from electricity generation for all the scenarios considered.

Atmospheric Compensation and Surface Temperature and Emissivity Retrieval with LWIR Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Pieper, Michael

Accurate estimation or retrieval of surface emissivity spectra from long-wave infrared (LWIR) or Thermal Infrared (TIR) hyperspectral imaging data acquired by airborne or space-borne sensors is necessary for many scientific and defense applications. The at-aperture radiance measured by the sensor is a function of the ground emissivity and temperature, modified by the atmosphere. Thus the emissivity retrieval process consists of two interwoven steps: atmospheric compensation (AC) to retrieve the ground radiance from the measured at-aperture radiance and temperature-emissivity separation (TES) to separate the temperature and emissivity from the ground radiance. In-scene AC (ISAC) algorithms use blackbody-like materials in the scene, which have a linear relationship between their ground radiances and at-aperture radiances determined by the atmospheric transmission and upwelling radiance. Using a clear reference channel to estimate the ground radiance, a linear fitting of the at-aperture radiance and estimated ground radiance is done to estimate the atmospheric parameters. TES algorithms for hyperspectral imaging data assume that the emissivity spectra for solids are smooth compared to the sharp features added by the atmosphere. The ground temperature and emissivity are found by finding the temperature that provides the smoothest emissivity estimate. In this thesis we develop models to investigate the sensitivity of AC and TES to the basic assumptions enabling their performance. ISAC assumes that there are perfect blackbody pixels in a scene and that there is a clear channel, which is never the case. The developed ISAC model explains how the quality of blackbody-like pixels affect the shape of atmospheric estimates and the clear channel assumption affects their magnitude. Emissivity spectra for solids usually have some roughness. The TES model identifies four sources of error: the smoothing error of the emissivity spectrum, the emissivity error from using the incorrect temperature, and the errors caused by sensor noise and wavelength calibration. The ways these errors interact determines the overall TES performance. Since the AC and TES processes are interwoven, any errors in AC are transferred to TES and the final temperature and emissivity estimates. Combining the two models, shape errors caused by the blackbody assumption are transferred to the emissivity estimates, where magnitude errors from the clear channel assumption are compensated by TES temperature induced emissivity errors. The ability for the temperature induced error to compensate for such atmospheric errors makes it difficult to determine the correct atmospheric parameters for a scene. With these models we are able to determine the expected quality of estimated emissivity spectra based on the quality of blackbody-like materials on the ground, the emissivity of the materials being searched for, and the properties of the sensor. The quality of material emissivity spectra is a key factor in determining detection performance for a material in a scene.

The challenge of modelling nitrogen management at the field scale: simulation and sensitivity analysis of N2O fluxes across nine experimental sites using DailyDayCent

NASA Astrophysics Data System (ADS)

Fitton, N.; Datta, A.; Hastings, A.; Kuhnert, M.; Topp, C. F. E.; Cloy, J. M.; Rees, R. M.; Cardenas, L. M.; Williams, J. R.; Smith, K.; Chadwick, D.; Smith, P.

2014-09-01

The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N2O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N2O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N2O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions.

Seasonal and Interannual Variations in BC Emissions From Open Biomass Burning in Southern Africa From 1998 to 2005

NASA Astrophysics Data System (ADS)

Ito, A.; Akimoto, H.

2006-12-01

We estimate the emissions of black carbon (BC) from open vegetation fires in southern hemisphere Africa from 1998 to 2005 using satellite information in conjunction with a biogeochemical model. Monthly burned areas at a 0.5-degree resolution are estimated from the Visible and Infrared Scanner (VIRS) fire count product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data set associated with the MODIS tree cover imagery in grasslands and woodlands. The monthly fuel load distribution is derived from a 0.5- degree terrestrial carbon cycle model in conjunction with satellite data. The monthly maps of combustion factor and emission factor are estimated using empirical models that predict the effects of fuel conditions on these factors in grasslands and woodlands. Our annual averaged BC emitted per unit area burned is 0.17 g BC m-2 which is consistent with the product of fuel consumption and emission factor typically measured in southern Africa. The BC emissions from open vegetation burning in southern Africa ranged from 0.26 Tg BC yr-1 for 2002 to 0.42 Tg BC yr-1 for 1998. The peak in BC emissions is identical to that from previous top-down estimate using the Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) data. The sum of monthly emissions during the burning season in 2000 is in good agreement between our estimate (0.38 Tg) and previous estimate constrained by numerical model and measurements (0.47 Tg).

Revised spatially distributed global livestock emissions

NASA Astrophysics Data System (ADS)

Asrar, G.; Wolf, J.; West, T. O.

2015-12-01

Livestock play an important role in agricultural carbon cycling through consumption of biomass and emissions of methane. Quantification and spatial distribution of methane and carbon dioxide produced by livestock is needed to develop bottom-up estimates for carbon monitoring. These estimates serve as stand-alone international emissions estimates, as input to global emissions modeling, and as comparisons or constraints to flux estimates from atmospheric inversion models. Recent results for the US suggest that the 2006 IPCC default coefficients may underestimate livestock methane emissions. In this project, revised coefficients were calculated for cattle and swine in all global regions, based on reported changes in body mass, quality and quantity of feed, milk production, and management of living animals and manure for these regions. New estimates of livestock methane and carbon dioxide emissions were calculated using the revised coefficients and global livestock population data. Spatial distribution of population data and associated fluxes was conducted using the MODIS Land Cover Type 5, version 5.1 (i.e. MCD12Q1 data product), and a previously published downscaling algorithm for reconciling inventory and satellite-based land cover data at 0.05 degree resolution. Preliminary results for 2013 indicate greater emissions than those calculated using the IPCC 2006 coefficients. Global total enteric fermentation methane increased by 6%, while manure management methane increased by 38%, with variation among species and regions resulting in improved spatial distributions of livestock emissions. These new estimates of total livestock methane are comparable to other recently reported studies for the entire US and the State of California. These new regional/global estimates will improve the ability to reconcile top-down and bottom-up estimates of methane production as well as provide updated global estimates for use in development and evaluation of Earth system models.

Carbon footprint estimator, phase II : volume I - GASCAP model.

DOT National Transportation Integrated Search

2014-03-01

The GASCAP model was developed to provide a software tool for analysis of the life-cycle GHG : emissions associated with the construction and maintenance of transportation projects. This phase : of development included techniques for estimating emiss...

Effects of improved spatial and temporal modeling of on-road vehicle emissions.

PubMed

Lindhjem, Christian E; Pollack, Alison K; DenBleyker, Allison; Shaw, Stephanie L

2012-04-01

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NOx and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.

Contribution of milk production to global greenhouse gas emissions. An estimation based on typical farms.

PubMed

Hagemann, Martin; Ndambi, Asaah; Hemme, Torsten; Latacz-Lohmann, Uwe

2012-02-01

Studies on the contribution of milk production to global greenhouse gas (GHG) emissions are rare (FAO 2010) and often based on crude data which do not appropriately reflect the heterogeneity of farming systems. This article estimates GHG emissions from milk production in different dairy regions of the world based on a harmonised farm data and assesses the contribution of milk production to global GHG emissions. The methodology comprises three elements: (1) the International Farm Comparison Network (IFCN) concept of typical farms and the related globally standardised dairy model farms representing 45 dairy regions in 38 countries; (2) a partial life cycle assessment model for estimating GHG emissions of the typical dairy farms; and (3) standard regression analysis to estimate GHG emissions from milk production in countries for which no typical farms are available in the IFCN database. Across the 117 typical farms in the 38 countries analysed, the average emission rate is 1.50 kg CO(2) equivalents (CO(2)-eq.)/kg milk. The contribution of milk production to the global anthropogenic emissions is estimated at 1.3 Gt CO(2)-eq./year, accounting for 2.65% of total global anthropogenic emissions (49 Gt; IPCC, Synthesis Report for Policy Maker, Valencia, Spain, 2007). We emphasise that our estimates of the contribution of milk production to global GHG emissions are subject to uncertainty. Part of the uncertainty stems from the choice of the appropriate methods for estimating emissions at the level of the individual animal.

Development and application of a mechanistic model to estimate emission of nitrous oxide from UK agriculture

NASA Astrophysics Data System (ADS)

Brown, L.; Syed, B.; Jarvis, S. C.; Sneath, R. W.; Phillips, V. R.; Goulding, K. W. T.; Li, C.

A mechanistic model of N 2O emission from agricultural soil (DeNitrification-DeComposition—DNDC) was modified for application to the UK, and was used as the basis of an inventory of N 2O emission from UK agriculture in 1990. UK-specific input data were added to DNDC's database and the ability to simulate daily C and N inputs from grazing animals and applied animal waste was added to the model. The UK version of the model, UK-DNDC, simulated emissions from 18 different crop types on the 3 areally dominant soils in each county. Validation of the model at the field scale showed that predictions matched observations well. Emission factors for the inventory were calculated from estimates of N 2O emission from UK-DNDC, in order to maintain direct comparability with the IPCC approach. These, along with activity data, were included in a transparent spreadsheet format. Using UK-DNDC, the estimate of N 2O-N emission from UK current agricultural practice in 1990 was 50.9 Gg. This total comprised 31.7 Gg from the soil sector, 5.9 Gg from animals and 13.2 Gg from the indirect sector. The range of this estimate (using the range of soil organic C for each soil used) was 30.5-62.5 Gg N. Estimates of emissions in each sector were compared to those calculated using the IPCC default methodology. Emissions from the soil and indirect sectors were smaller with the UK-DNDC approach than with the IPCC methodology, while emissions from the animal sector were larger. The model runs suggested a relatively large emission from agricultural land that was not attributable to current agricultural practices (33.8 Gg in total, 27.4 Gg from the soil sector). This 'background' component is partly the result of historical agricultural land use. It is not normally included in inventories of emission, but would increase the total emission of N 2O-N from agricultural land in 1990 to 78.3 Gg.

Highly-resolved Modeling of Emissions and Concentrations of Carbon Monoxide, Carbon Dioxide, Nitrogen Oxides, and Fine Particulate Matter in Salt Lake City, Utah

NASA Astrophysics Data System (ADS)

Mendoza, D. L.; Lin, J. C.; Mitchell, L.; Ehleringer, J. R.

2014-12-01

Accurate, high-resolution data on air pollutant emissions and concentrations are needed to understand human exposures and for both policy and pollutant management purposes. An important step in this process is also quantification of uncertainties. We present a spatially explicit and highly resolved emissions inventory for Salt Lake County, Utah, and trace gas concentration estimates for carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx) and fine particles (PM2.5) within Salt Lake City. We assess the validity of this approach by comparing measured concentrations against simulated values derived from combining the emissions inventory with an atmospheric model. The emissions inventory for the criteria pollutants was constructed using the 2011 National Emissions Inventory (NEI). The spatial and temporal allocation methods from the Emission Modeling Clearinghouse data set are used to downscale the NEI data from annual to hourly scales and from county-level to 500 m x 500 m resolution. Onroad mobile source emissions were estimated by combining a bottom-up emissions calculation approach for large roadway links with a top-down spatial allocation approach for other roadways. Vehicle activity data for road links were derived from automatic traffic responder data. The emissions inventory for CO2 was obtained from the Hestia emissions data product at an hourly, building, facility, and road link resolution. The AERMOD and CALPUFF dispersion models were used to transport emissions and estimate air pollutant concentrations at an hourly temporal and 500 m x 500 m spatial resolution. Modeled results were compared against measurements from a mobile lab equipped with trace gas measurement equipment traveling on pre-determined routes in the Salt Lake City area. The comparison between both approaches to concentration estimation highlights spatial locations and hours of high variability/uncertainty. Results presented here will inform understanding of variability and uncertainty in emissions and concentrations to better inform future policy. This work will also facilitate the development of a systematic approach to incorporate measurement data and models to better inform estimates of pollutant concentrations that determine the extent to which urban populations are exposed to adverse air quality.

Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis.

PubMed

Hukkerikar, Amol Shivajirao; Kalakul, Sawitree; Sarup, Bent; Young, Douglas M; Sin, Gürkan; Gani, Rafiqul

2012-11-26

The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals. For parameter estimation, large data sets of experimentally measured property values of a wide range of chemicals (hydrocarbons, oxygenated chemicals, nitrogenated chemicals, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22 environment-related properties, which include the fathead minnow 96-h LC(50), Daphnia magna 48-h LC(50), oral rat LD(50), aqueous solubility, bioconcentration factor, permissible exposure limit (OSHA-TWA), photochemical oxidation potential, global warming potential, ozone depletion potential, acidification potential, emission to urban air (carcinogenic and noncarcinogenic), emission to continental rural air (carcinogenic and noncarcinogenic), emission to continental fresh water (carcinogenic and noncarcinogenic), emission to continental seawater (carcinogenic and noncarcinogenic), emission to continental natural soil (carcinogenic and noncarcinogenic), and emission to continental agricultural soil (carcinogenic and noncarcinogenic) have been modeled and analyzed. The application of the developed property models for the estimation of environment-related properties and uncertainties of the estimated property values is highlighted through an illustrative example. The developed property models provide reliable estimates of environment-related properties needed to perform process synthesis, design, and analysis of sustainable chemical processes and allow one to evaluate the effect of uncertainties of estimated property values on the calculated performance of processes giving useful insights into quality and reliability of the design of sustainable processes.

AN IMPROVED MODEL FOR ESTIMATING EMISSIONS OF VOLATILE ORGANIC COMPOUNDS FROM FORESTS IN THE EASTERN UNITED STATES (Journal)

EPA Science Inventory

Regional estimates of biogenic volatile organic compound (BVOC) emissions are important inputs for models of atmospheric chemistry and carbon budgets. Since forests are the primary emitters of BVOCs, it is important to develop reliable estimates of their areal coverage and BVOC e...

Implications of emission inventory choice for modeling fire-related pollution in the U.S.

NASA Astrophysics Data System (ADS)

Koplitz, S. N.; Nolte, C. G.; Pouliot, G.

2017-12-01

Wildland fires are a major source of fine particulate matter (PM2.5), one of the most harmful ambient pollutants for human health globally. Within the U.S., wildland fires can account for more than 30% of total annual PM2.5 emissions. In order to represent the influence of fire emissions on atmospheric composition, regional and global chemical transport models (CTMs) rely on fire emission inventories developed from estimates of burned area (i.e. fire size and location). Burned area can be estimated using a range of top-down and bottom-up approaches, including satellite-derived remote sensing and on-the-ground incident reports. While burned area estimates agree with each other reasonably well in the western U.S. (within 20-30% for most years during 2002-2014), estimates for the southern U.S. vary by more than a factor of 3. Differences in burned area estimation methods lead to significant variability in the spatial and temporal allocation of emissions across fire emission inventory platforms. In this work, we implement fire emission estimates for 2011 from three different products - the USEPA National Emission Inventory (NEI), the Fire INventory of NCAR (FINN), and the Global Fire Emission Database (GFED4s) - into the Community Multiscale Air Quality (CMAQ) model to quantify and characterize differences in simulated fire-related PM2.5 and ozone concentrations across the contiguous U.S. due solely to the emission inventory used. Preliminary results indicate that the estimated contribution to national annual average PM2.5 from wildland fire in 2011 is highest using GFED4s emissions (1.0 µg m-3) followed by NEI (0.7 µg m-3) and FINN (0.3 µg m-3), with comparisons varying significantly by region and season. Understanding the sensitivity of modeling fire-related PM2.5 and ozone in the U.S. to fire emission inventory choice will inform future efforts to assess the implications of present and future fire activity for air quality and human health at national and global scales.

A refined 2010-based VOC emission inventory and its improvement on modeling regional ozone in the Pearl River Delta Region, China.

PubMed

Yin, Shasha; Zheng, Junyu; Lu, Qing; Yuan, Zibing; Huang, Zhijiong; Zhong, Liuju; Lin, Hui

2015-05-01

Accurate and gridded VOC emission inventories are important for improving regional air quality model performance. In this study, a four-level VOC emission source categorization system was proposed. A 2010-based gridded Pearl River Delta (PRD) regional VOC emission inventory was developed with more comprehensive source coverage, latest emission factors, and updated activity data. The total anthropogenic VOC emission was estimated to be about 117.4 × 10(4)t, in which on-road mobile source shared the largest contribution, followed by industrial solvent use and industrial processes sources. Among the industrial solvent use source, furniture manufacturing and shoemaking were major VOC emission contributors. The spatial surrogates of VOC emission were updated for major VOC sources such as industrial sectors and gas stations. Subsector-based temporal characteristics were investigated and their temporal variations were characterized. The impacts of updated VOC emission estimates and spatial surrogates were evaluated by modeling O₃ concentration in the PRD region in the July and October of 2010, respectively. The results indicated that both updated emission estimates and spatial allocations can effectively reduce model bias on O₃ simulation. Further efforts should be made on the refinement of source classification, comprehensive collection of activity data, and spatial-temporal surrogates in order to reduce uncertainty in emission inventory and improve model performance. Copyright © 2015 Elsevier B.V. All rights reserved.

Advances in Support of the CMAQ Bidirectional Science Option for the Estimation of Ammonia Flux from Agricultural cropland

EPA Science Inventory

Proposed Session: Emissions Inventories, Models and processes: Last year a new CMAQ bidirectional option for the estimation of ammonia flux (emission and deposition) was released. This option essentially replaces NEI crop ammonia emissions with emissions calculated dynamically...

Estimation of biogenic volatile organic compound (BVOC) emissions from the terrestrial ecosystem in China using real-time remote sensing data

NASA Astrophysics Data System (ADS)

Li, M.; Huang, X.; Li, J.; Song, Y.

2012-04-01

Because of the high emission intensity and reactivity, biogenic volatile organic compounds (BVOCs) play a significant role in the terrestrial ecosystems, human health, secondary pollution, global climate change and the global carbon cycle. Past estimations of BVOC emissions in China were based on outdated algorithms and limited meteorological data, and there have been significant inconsistences between the land surface parameters of dynamic models and those of BVOC estimation models, leading to large inaccuracies in the estimated results. To refine BVOC emission estimations for China and to further explore the role of BVOCs in atmospheric chemical processes, we used the latest algorithms of MEGAN (Model of Emissions of Gases and Aerosols from Nature) with MM5 (the Fifth-Generation Mesoscale Model) providing highly resolved meteorological data, to estimate the biogenic emissions of isoprene (C5H8) and seven monoterpene species (C10H16) in 2006. Real-time MODIS (Moderate Resolution Imaging Spectroradiometer) data were introduced to update the land surface parameters and improve the simulation performance of MM5, and to modify the influence of leaf area index (LAI) and leaf age deviation from standard conditions. In this study, the annual BVOC emissions for the whole country totaled 12.97 Tg C, a relevant value much lower than that given in global estimations but higher than the past estimations in China. Therein, the most important individual contributor was isoprene (9.36 Tg C), followed by α-pinene (1.24 Tg C yr-1) and β-pinene (0.84 Tg C yr-1). Due to the considerable regional disparity in plant distributions and meteorological conditions across China, BVOC emissions presented significant spatial-temporal variations. Spatially, isoprene emission was concentrated in South China, which is covered by large areas of broadleaf forests and shrubs. On the other hand, Southeast China was the top-ranking contributor of monoterpenes, in which the dominant vegetation genera consist of evergreen coniferous forests (mainly Pinus massoniana). Temporally, BVOC emissions primarily occurred in July and August during periods of high temperatures, high solar radiation and dense plant cover, with daily emissions peaking at about 13:00~14:00 hours (Beijing Time, BJT) and reaching their lowest values at night. Additionally, emissions of volatile organic compounds (VOCs) of biogenic origin (14.7 Tg yr-1) were approximately one-third less than anthropogenic emissions (23.2 Tg yr-1) and showed distinct spatial distributions. We present a reasonable estimation of BVOC emissions, which provides important information for further exploration of the role of BVOCs in atmospheric processes.

A simple mathematical method to estimate ammonia emission from in-house windrowing of poultry litter.

PubMed

Ro, Kyoung S; Szogi, Ariel A; Moore, Philip A

2018-05-12

In-house windrowing between flocks is an emerging sanitary management practice to partially disinfect the built-up litter in broiler houses. However, this practice may also increase ammonia (NH 3 ) emission from the litter due to the increase in litter temperature. The objectives of this study were to develop mathematical models to estimate NH 3 emission rates from broiler houses practicing in-house windrowing between flocks. Equations to estimate mass-transfer areas form different shapes windrowed litter (triangular, rectangular, and semi-cylindrical prisms) were developed. Using these equations, the heights of windrows yielding the smallest mass-transfer area were estimated. Smaller mass-transfer area is preferred as it reduces both emission rates and heat loss. The heights yielding the minimum mass-transfer area were 0.8 and 0.5 m for triangular and rectangular windrows, respectively. Only one height (0.6 m) was theoretically possible for semi-cylindrical windrows because the base and the height were not independent. Mass-transfer areas were integrated with published process-based mathematical models to estimate the total house NH 3 emission rates during in-house windrowing of poultry litter. The NH 3 emission rate change calculated from the integrated model compared well with the observed values except for the very high NH 3 initial emission rate from mechanically disturbing the litter to form the windrows. This approach can be used to conveniently estimate broiler house NH 3 emission rates during in-house windrowing between flocks by simply measuring litter temperatures.

Estimation of radionuclide (137Cs) emission rates from a nuclear power plant accident using the Lagrangian Particle Dispersion Model (LPDM).

PubMed

Park, Soon-Ung; Lee, In-Hye; Ju, Jae-Won; Joo, Seung Jin

2016-10-01

A methodology for the estimation of the emission rate of 137 Cs by the Lagrangian Particle Dispersion Model (LPDM) with the use of monitored 137 Cs concentrations around a nuclear power plant has been developed. This method has been employed with the MM5 meteorological model in the 600 km × 600 km model domain with the horizontal grid scale of 3 km × 3 km centered at the Fukushima nuclear power plant to estimate 137 Cs emission rate for the accidental period from 00 UTC 12 March to 00 UTC 6 April 2011. The Lagrangian Particles are released continuously with the rate of one particle per minute at the first level modelled, about 15 m above the power plant site. The presently developed method was able to simulate quite reasonably the estimated 137 Cs emission rate compared with other studies, suggesting the potential usefulness of the present method for the estimation of the emission rate from the accidental power plant without detailed inventories of reactors and fuel assemblies and spent fuels. The advantage of this method is not so complicated but can be applied only based on one-time forward LPDM simulation with monitored concentrations around the power plant, in contrast to other inverse models. It was also found that continuously monitored radionuclides concentrations from possibly many sites located in all directions around the power plant are required to get accurate continuous emission rates from the accident power plant. The current methodology can also be used to verify the previous version of radionuclides emissions used among other modeling groups for the cases of intermittent or discontinuous samplings. Copyright © 2016. Published by Elsevier Ltd.

Estimates of Power Plant NOx Emissions and Lifetimes from OMI NO2 Satellite Retrievals

NASA Technical Reports Server (NTRS)

de Foy, Benjamin; Lu, Zifeng; Streets, David G.; Lamsal, Lok N.; Duncan, Bryan N.

2015-01-01

Isolated power plants with well characterized emissions serve as an ideal test case of methods to estimate emissions using satellite data. In this study we evaluate the Exponentially-Modified Gaussian (EMG) method and the box model method based on mass balance for estimating known NOx emissions from satellite retrievals made by the Ozone Monitoring Instrument (OMI). We consider 29 power plants in the USA which have large NOx plumes that do not overlap with other sources and which have emissions data from the Continuous Emission Monitoring System (CEMS). This enables us to identify constraints required by the methods, such as which wind data to use and how to calculate background values. We found that the lifetimes estimated by the methods are too short to be representative of the chemical lifetime. Instead, we introduce a separate lifetime parameter to account for the discrepancy between estimates using real data and those that theory would predict. In terms of emissions, the EMG method required averages from multiple years to give accurate results, whereas the box model method gave accurate results for individual ozone seasons.

Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Zeli; Zhuang, Qianlai; Henze, Daven K.

Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates both high-precision surface measurements and column-average SCanning Imaging Absorption spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes were integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated with six biogeochemical models. In our estimates, in 2005, global methane emissions were in the range ofmore » 496.4–511.5 Tg yr −1, and pan-Arctic methane emissions were in the range of 11.9–28.5 Tg yr −1. Methane emissions from pan-Arctic wetlands and lakes were 5.5–14.2 and 2.4–14.2 Tg yr −1, respectively. Methane emissions from Siberian wetlands and lakes are the largest and also have the largest uncertainty. Our results indicate that the uncertainty introduced by different wetland models could be much larger than the uncertainty of each inversion. We also show that assimilating satellite retrievals can reduce the uncertainty of the nested-grid inversions. The significance of lake emissions cannot be identified across the pan-Arctic by high-resolution inversions, but it is possible to identify high lake emissions from some specific regions. In contrast to global inversions, high-resolution nested-grid inversions perform better in estimating near-surface methane concentrations.« less

Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models?

DOE PAGES

Tan, Zeli; Zhuang, Qianlai; Henze, Daven K.; ...

2016-10-12

Understanding methane emissions from the Arctic, a fast-warming carbon reservoir, is important for projecting future changes in the global methane cycle. Here we optimized methane emissions from north of 60° N (pan-Arctic) regions using a nested-grid high-resolution inverse model that assimilates both high-precision surface measurements and column-average SCanning Imaging Absorption spectroMeter for Atmospheric CHartogrphY (SCIAMACHY) satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes were integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated with six biogeochemical models. In our estimates, in 2005, global methane emissions were in the range ofmore » 496.4–511.5 Tg yr −1, and pan-Arctic methane emissions were in the range of 11.9–28.5 Tg yr −1. Methane emissions from pan-Arctic wetlands and lakes were 5.5–14.2 and 2.4–14.2 Tg yr −1, respectively. Methane emissions from Siberian wetlands and lakes are the largest and also have the largest uncertainty. Our results indicate that the uncertainty introduced by different wetland models could be much larger than the uncertainty of each inversion. We also show that assimilating satellite retrievals can reduce the uncertainty of the nested-grid inversions. The significance of lake emissions cannot be identified across the pan-Arctic by high-resolution inversions, but it is possible to identify high lake emissions from some specific regions. In contrast to global inversions, high-resolution nested-grid inversions perform better in estimating near-surface methane concentrations.« less

Comparison and evaluation of anthropogenic emissions of SO2 and NOx over China

NASA Astrophysics Data System (ADS)

Li, Meng; Klimont, Zbigniew; Zhang, Qiang; Martin, Randall V.; Zheng, Bo; Heyes, Chris; Cofala, Janusz; Zhang, Yuxuan; He, Kebin

2018-03-01

Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find clues to improve emission inventories. We first compared the activity rates and emission factors used in two inventories and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1 % differences), while NOx emissions in ECLIPSE's estimates are 16 % lower than those of MIX. The FGD (flue-gas desulfurization) device penetration rate and removal efficiency, LNB (low-NOx burner) application rate and abatement efficiency in power plants, emission factors of industrial boilers and various vehicle types, and vehicle fleet need further verification. Diesel consumptions are quite uncertain in current inventories. Discrepancies at the sectorial and provincial levels are much higher than those of the national total. We then examined the impacts of different inventories on model performance by using the nested GEOS-Chem model. We finally derived top-down emissions by using the retrieved columns from the Ozone Monitoring Instrument (OMI) compared with the bottom-up estimates. High correlations were observed for SO2 between model results and OMI columns. For NOx, negative biases in bottom-up gridded emission inventories (-21 % for MIX, -39 % for ECLIPSE) were found compared to the satellite-based emissions. The emission trends from 2005 to 2010 estimated by two inventories were both consistent with satellite observations. The inventories appear to be fit for evaluation of the policies at an aggregated or national level; more work is needed in specific areas in order to improve the accuracy and robustness of outcomes at finer spatial and also technological levels. To our knowledge, this is the first work in which source comparisons detailed to technology-level parameters are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we identified potential directions for further improvement in inventory development.

Regional modeling of tropospheric NO2 vertical column density over East Asia during the period 2000-2010: comparison with multisatellite observations

NASA Astrophysics Data System (ADS)

Itahashi, S.; Uno, I.; Irie, H.; Kurokawa, J.-I.; Ohara, T.

2014-04-01

Satellite observations of the tropospheric NO2 vertical column density (VCD) are closely correlated to, and thus can be used to estimate, surface NOx emissions. In this study, the NO2 VCD simulated by a regional chemical transport model with emissions data from the updated Regional Emission inventory in ASia (REAS) version 2.1 were validated through comparison with multisatellite observations during the period 2000-2010. Rapid growth in NO2 VCD (~11% year-1) driven by the expansion of anthropogenic NOx emissions was identified above the central eastern China (CEC) region, except for the period during the economic downturn. In contrast, slightly decreasing trends (~2% year-1) were identified above Japan accompanied by a decline in anthropogenic emissions. To systematically compare the modeled NO2 VCD, we estimated sampling bias and the effect of applying the averaging kernel information, with particular focus on the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data. Using the updated REAS, the modeled NO2 VCD reasonably reproduced annual trends observed by multisatellites, suggesting that the rate of increase of NOx emissions estimated by the updated REAS inventory would be robust. Province-scale revision of emissions above CEC is needed to further refine emission inventories. Based on the close linear relationship between modeled and observed NO2 VCD and anthropogenic NOx emissions, NOx emissions in 2009 and 2010, which were not covered by the updated REAS inventory, were estimated. NOx emissions from anthropogenic sources in China in 2009 and 2010 were determined to be 26.4 and 28.5 Tg year-1, respectively, indicating that NOx emissions increased more than twofold between 2000 and 2010. This increase reflected the strong growth of anthropogenic emissions in China following the rapid recovery from the economic downturn from late 2008 until mid-2009. Our method consists of simple estimations from satellite observations and provides results that are consistent with the most recent inventory of emissions data for China.

Inverse modelling of European CH4 emissions during 2006-2012 using different inverse models and reassessed atmospheric observations

NASA Astrophysics Data System (ADS)

Bergamaschi, Peter; Karstens, Ute; Manning, Alistair J.; Saunois, Marielle; Tsuruta, Aki; Berchet, Antoine; Vermeulen, Alexander T.; Arnold, Tim; Janssens-Maenhout, Greet; Hammer, Samuel; Levin, Ingeborg; Schmidt, Martina; Ramonet, Michel; Lopez, Morgan; Lavric, Jost; Aalto, Tuula; Chen, Huilin; Feist, Dietrich G.; Gerbig, Christoph; Haszpra, László; Hermansen, Ove; Manca, Giovanni; Moncrieff, John; Meinhardt, Frank; Necki, Jaroslaw; Galkowski, Michal; O'Doherty, Simon; Paramonova, Nina; Scheeren, Hubertus A.; Steinbacher, Martin; Dlugokencky, Ed

2018-01-01

We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) Tg CH4 yr-1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr-1 (2006) to 18.8 Tg CH4 yr-1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) Tg CH4 yr-1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between -40 and 20 % at the aircraft profile sites in France, Hungary and Poland.

Emissions from ships in the northwestern United States.

PubMed

Corbett, James J

2002-03-15

Recent inventory efforts have focused on developing nonroad inventories for emissions modeling and policy insights. Characterizing these inventories geographically and explicitly treating the uncertaintiesthat result from limited emissions testing, incomplete activity and usage data, and other important input parameters currently pose the largest methodological challenges. This paper presents a commercial marine vessel (CMV) emissions inventory for Washington and Oregon using detailed statistics regarding fuel consumption, vessel movements, and cargo volumes for the Columbia and Snake River systems. The inventory estimates emissions for oxides of nitrogen (NOx), particulate matter (PM), and oxides of sulfur (SOx). This analysis estimates that annual NOx emissions from marine transportation in the Columbia and Snake River systems in Washington and Oregon equal 6900 t of NOx (as NO2) per year, 2.6 times greater than previous NO, inventories for this region. Statewide CMV NO, emissions are estimated to be 9,800 t of NOx per year. By relying on a "bottom-up" fuel consumption model that includes vessel characteristics and transit information, the river system inventory may be more accurate than previous estimates. This inventory provides modelers with bounded parametric inputs for sensitivity analysis in pollution modeling. The ability to parametrically model the uncertainty in commercial marine vessel inventories also will help policy-makers determine whether better policy decisions can be enabled through further vessel testing and improved inventory resolution.

Estimation of the emission factors of PAHs by traffic with the model of atmospheric dispersion and deposition from heavy traffic road.

PubMed

Ozaki, N; Tokumitsu, H; Kojima, K; Kindaichi, T

2007-01-01

In order to consider the total atmospheric loadings of the PAHs (polycyclic aromatic hydrocarbons) from traffic activities, the emission factors of PAHs were estimated and from the obtained emission factors and vehicle transportation statistics, total atmospheric loadings were integrated and the loadings into the water body were estimated on a regional scale. The atmospheric concentration of PAHs was measured at the roadside of a road with heavy traffic in the Hiroshima area in Japan. The samplings were conducted in summer and winter. Atmospheric particulate matters (fine particle, 0.6-7 microm; coarse particle, over 7 microm) and their PAH concentration were measured. Also, four major emission sources (gasoline and diesel vehicle emissions, tire and asphalt debris) were assumed for vehicle transportation activities, the chemical mass balance method was applied and the source partitioning at the roadside was estimated. Furthermore, the dispersion of atmospheric particles from the vehicles was modelled and the emission factors of the sources were determined by the comparison to the chemical mass balance results. Based on emission factors derived from the modelling, an atmospheric dispersion model of nationwide scale (National Institute of Advanced Industrial Science and Technology - Atmospheric Dispersion Model for Exposure and Risk assessment) was applied, and the atmospheric concentration and loading to the ground were calculated for the Hiroshima Bay watershed area.

Estimating Emissions of Ammonia and Methane from an Anaerobic Livestock Lagoon Using Micrometeorological Methods and Inverse Modeling

NASA Astrophysics Data System (ADS)

Shonkwiler, K. B.; Ham, J. M.; Williams, C.

2012-12-01

Evaluating the impact of increased carbon and nitrogen emissions on local air quality and regional bionetworks due to animal agricultural activity is of great interest to the public, political, economic and ecological welfare of areas within the scope of these practices. Globally, livestock operations account for 64% of annual anthropogenic emissions of ammonia (NH3) [1]. Concerning methane (CH4), anaerobic lagoons from commercial dairy operations contribute the second largest share of CH4 emissions from manure in the United States[1], and additionally are a local source of NH3 as well. Anaerobic lagoons are commonly used in commercial animal agriculture and as significant local sources of greenhouse gases (GHG), there is a strong need to quantify GHG emissions from these systems. In 2012 at a commercial dairy operation in Northern Colorado, USA, measurements of CH4 were made using eddy covariance (EC), while NH3 was estimated using a combination of real-time monitoring (cavity ring-down spectroscopy as well as time-integrated passive samplers). Methane emissions have been measured at this lagoon using EC since 2011, with fluxes ranging from 0.5 mg m-2 s-1 in early summer to >2 mg m-2 s-1 in late summer and early fall. Concentration data of both CH4 and NH3 were used to estimate emissions using a 2-dimensional inverse model based on solving the advection-diffusion equation[2]. In the case of the CH4-EC data, results from the inverse model were compared with the EC-derived flux estimates for enhanced parameterization of surface geometry within the lagoon environment. The model was then applied using measured NH3 concentrations to achieve emissions estimates. While NH3 fluxes from the lagoon tend to be much lower than those of CH4 by comparison, modeling emissions of NH3 from the simple geometry of a lagoon will assist in applying the model to more complex surfaces. [1] FAO, 2006. Livestock's long shadow: Environmental issues and options. Livestock, Environment, and Development Initiative. Food and Agriculture Organization of the United Nations, Rome, Italy. [2] Loubet, B., Génermont, S., Ferrara, R., Bedos, C., Decuq, C., Personne, E., Fanucci, O., Durand, B., Rana, G., Cellier, P., 2010. An inverse model to estimate ammonia emissions from fields. Eur. J. Soil Sci. 61: 793-805. Panorama of a weather station (left) utilizing micrometeorological methods to aid in estimating emissions of methane and ammonia from an anaerobic livestock lagoon (center) at a commercial dairy in Northern Colorado, USA.

Reducing errors in aircraft atmospheric inversion estimates of point-source emissions: the Aliso Canyon natural gas leak as a natural tracer experiment

NASA Astrophysics Data System (ADS)

Gourdji, S. M.; Yadav, V.; Karion, A.; Mueller, K. L.; Conley, S.; Ryerson, T.; Nehrkorn, T.; Kort, E. A.

2018-04-01

Urban greenhouse gas (GHG) flux estimation with atmospheric measurements and modeling, i.e. the ‘top-down’ approach, can potentially support GHG emission reduction policies by assessing trends in surface fluxes and detecting anomalies from bottom-up inventories. Aircraft-collected GHG observations also have the potential to help quantify point-source emissions that may not be adequately sampled by fixed surface tower-based atmospheric observing systems. Here, we estimate CH4 emissions from a known point source, the Aliso Canyon natural gas leak in Los Angeles, CA from October 2015–February 2016, using atmospheric inverse models with airborne CH4 observations from twelve flights ≈4 km downwind of the leak and surface sensitivities from a mesoscale atmospheric transport model. This leak event has been well-quantified previously using various methods by the California Air Resources Board, thereby providing high confidence in the mass-balance leak rate estimates of (Conley et al 2016), used here for comparison to inversion results. Inversions with an optimal setup are shown to provide estimates of the leak magnitude, on average, within a third of the mass balance values, with remaining errors in estimated leak rates predominantly explained by modeled wind speed errors of up to 10 m s‑1, quantified by comparing airborne meteorological observations with modeled values along the flight track. An inversion setup using scaled observational wind speed errors in the model-data mismatch covariance matrix is shown to significantly reduce the influence of transport model errors on spatial patterns and estimated leak rates from the inversions. In sum, this study takes advantage of a natural tracer release experiment (i.e. the Aliso Canyon natural gas leak) to identify effective approaches for reducing the influence of transport model error on atmospheric inversions of point-source emissions, while suggesting future potential for integrating surface tower and aircraft atmospheric GHG observations in top-down urban emission monitoring systems.

Study on highway transportation greenhouse effect external cost estimation in China

NASA Astrophysics Data System (ADS)

Chu, Chunchao; Pan, Fengming

2017-03-01

This paper focuses on estimating highway transportation greenhouse gas emission volume and greenhouse gas external cost in China. At first, composition and characteristics of greenhouse gases were analysed about highway transportation emissions. Secondly, an improved model of emission volume was presented on basis of highway transportation energy consumption, which may be calculated by virtue of main affecting factors such as the annual average operation miles of each type of the motor vehicles and the unit consumption level. the model of emission volume was constructed which considered not only the availability of energy consumption statistics of highway transportation but also the greenhouse gas emission factors of various fuel types issued by IPCC. Finally, the external cost estimation model was established about highway transportation greenhouse gas emission which combined emission volume with the unit external cost of CO2 emissions. An example was executed to confirm presented model which ranged from 2011 to 2015 Year in China. The calculated result shows that the highway transportation total emission volume and greenhouse gas external cost are growing up, but the unit turnover external cost is steadily declining. On the whole overall, the situation is still grim about highway transportation greenhouse gas emission, and the green transportation strategy should be put into effect as soon as possible.

Atmospheric aerosol source identification and estimates of source contributions to air pollution in Dundee, UK

NASA Astrophysics Data System (ADS)

Qin, Y.; Oduyemi, K.

Anthropogenic aerosol (PM 10) emission sources sampled at an air quality monitoring station in Dundee have been analysed. However, the information on local natural aerosol emission sources was unavailable. A method that combines receptor model and atmospheric dispersion model was used to identify aerosol sources and estimate source contributions to air pollution. The receptor model identified five sources. These are aged marine aerosol source with some chlorine replaced by sulphate, secondary aerosol source of ammonium sulphate, secondary aerosol source of ammonium nitrate, soil and construction dust source, and incinerator and fuel oil burning emission source. For the vehicle emission source, which has been comprehensively described in the atmospheric emission inventory but cannot be identified by the receptor model, an atmospheric dispersion model was used to estimate its contributions. In Dundee, a significant percentage, 67.5%, of the aerosol mass sampled at the study station could be attributed to the six sources named above.

The Dairy Greenhouse Gas Emission Model: Reference Manual

USDA-ARS?s Scientific Manuscript database

The Dairy Greenhouse Gas Model (DairyGHG) is a software tool for estimating the greenhouse gas emissions and carbon footprint of dairy production systems. A relatively simple process-based model is used to predict the primary greenhouse gas emissions, which include the net emission of carbon dioxide...

Implementation of the MEGAN (v2.1) biogenic emission model in the ECHAM6-HAMMOZ chemistry climate model

NASA Astrophysics Data System (ADS)

Henrot, Alexandra-Jane; Stanelle, Tanja; Schröder, Sabine; Siegenthaler, Colombe; Taraborrelli, Domenico; Schultz, Martin G.

2017-02-01

A biogenic emission scheme based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 (Guenther et al., 2012) has been integrated into the ECHAM6-HAMMOZ chemistry climate model in order to calculate the emissions from terrestrial vegetation of 32 compounds. The estimated annual global total for the reference simulation is 634 Tg C yr-1 (simulation period 2000-2012). Isoprene is the main contributor to the average emission total, accounting for 66 % (417 Tg C yr-1), followed by several monoterpenes (12 %), methanol (7 %), acetone (3.6 %), and ethene (3.6 %). Regionally, most of the high annual emissions are found to be associated with tropical regions and tropical vegetation types. In order to evaluate the implementation of the biogenic model in ECHAM-HAMMOZ, global and regional biogenic volatile organic compound (BVOC) emissions of the reference simulation were compared to previous published experiment results with MEGAN. Several sensitivity simulations were performed to study the impact of different model input and parameters related to the vegetation cover and the ECHAM6 climate. BVOC emissions obtained here are within the range of previous published estimates. The large range of emission estimates can be attributed to the use of different input data and empirical coefficients within different setups of MEGAN. The biogenic model shows a high sensitivity to the changes in plant functional type (PFT) distributions and associated emission factors for most of the compounds. The global emission impact for isoprene is about -9 %, but reaches +75 % for α-pinene when switching from global emission factor maps to PFT-specific emission factor distributions. The highest sensitivity of isoprene emissions is calculated when considering soil moisture impact, with a global decrease of 12.5 % when the soil moisture activity factor is included in the model parameterization. Nudging ECHAM6 climate towards ERA-Interim reanalysis has an impact on the biogenic emissions, slightly lowering the global total emissions and their interannual variability.

EMISSION AND SURFACE EXCHANGE PROCESS

EPA Science Inventory

This task supports the development, evaluation, and application of emission and dry deposition algorithms in air quality simulation models, such as the Models-3/Community Multiscale Air Quality (CMAQ) modeling system. Emission estimates influence greatly the accuracy of air qual...

Evaluation of FOFEM fuel loading and consumption estimates in pine-oak forests and woodlands of the Ouachita Mountains, Arkansas, USA

Treesearch

Virginia L. McDaniel; Roger W. Perry; Nancy E. Koerth; James M. Guldin

2016-01-01

Accurate fuel load and consumption predictions are important to estimate fire effects and air pollutant emissions. The FOFEM (First Order Fire Effects Model) is a commonly used model developed in the western United States to estimate fire effects such as fuel consumption, soil heating, air pollutant emissions, and tree mortality. However, the accuracy of the model in...

Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density

NASA Astrophysics Data System (ADS)

Turner, Alexander J.; Shusterman, Alexis A.; McDonald, Brian C.; Teige, Virginia; Harley, Robert A.; Cohen, Ronald C.

2016-11-01

The majority of anthropogenic CO2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO2 Observation Network (BEACO2N) in California's Bay Area, in combination with an inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km2. The model uses an hourly 1 × 1 km2 emission inventory and 1 × 1 km2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model-observing system in reducing uncertainty in CO2 emissions. We examine uncertainty in estimated CO2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO2N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.

How to estimate green house gas (GHG) emissions from an excavator by using CAT's performance chart

NASA Astrophysics Data System (ADS)

Hajji, Apif M.; Lewis, Michael P.

2017-09-01

Construction equipment activities are a major part of many infrastructure projects. This type of equipment typically releases large quantities of green house gas (GHG) emissions. GHG emissions may come from fuel consumption. Furthermore, equipment productivity affects the fuel consumption. Thus, an estimating tool based on the construction equipment productivity rate is able to accurately assess the GHG emissions resulted from the equipment activities. This paper proposes a methodology to estimate the environmental impact for a common construction activity. This paper delivers sensitivity analysis and a case study for an excavator based on trench excavation activity. The methodology delivered in this study can be applied to a stand-alone model, or a module that is integrated with other emissions estimators. The GHG emissions are highly correlated to diesel fuel use, which is approximately 10.15 kilograms (kg) of CO2 per gallon of diesel fuel. The results showed that the productivity rate model as the result from multiple regression analysis can be used as the basis for estimating GHG emissions, and also as the framework for developing emissions footprint and understanding the environmental impact from construction equipment activities introduction.

Potential of European 14CO2 observation network to estimate the fossil fuel CO2 emissions via atmospheric inversions

NASA Astrophysics Data System (ADS)

Wang, Yilong; Broquet, Grégoire; Ciais, Philippe; Chevallier, Frédéric; Vogel, Felix; Wu, Lin; Yin, Yi; Wang, Rong; Tao, Shu

2018-03-01

Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75° × 2.5° resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as posterior uncertainty, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called prior uncertainty). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic true emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and true estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

High-global warming potential F-gas emissions in California: comparison of ambient-based versus inventory-based emission estimates, and implications of refined estimates.

PubMed

Gallagher, Glenn; Zhan, Tao; Hsu, Ying-Kuang; Gupta, Pamela; Pederson, James; Croes, Bart; Blake, Donald R; Barletta, Barbara; Meinardi, Simone; Ashford, Paul; Vetter, Arnie; Saba, Sabine; Slim, Rayan; Palandre, Lionel; Clodic, Denis; Mathis, Pamela; Wagner, Mark; Forgie, Julia; Dwyer, Harry; Wolf, Katy

2014-01-21

To provide information for greenhouse gas reduction policies, the California Air Resources Board (CARB) inventories annual emissions of high-global-warming potential (GWP) fluorinated gases, the fastest growing sector of greenhouse gas (GHG) emissions globally. Baseline 2008 F-gas emissions estimates for selected chlorofluorocarbons (CFC-12), hydrochlorofluorocarbons (HCFC-22), and hydrofluorocarbons (HFC-134a) made with an inventory-based methodology were compared to emissions estimates made by ambient-based measurements. Significant discrepancies were found, with the inventory-based emissions methodology resulting in a systematic 42% under-estimation of CFC-12 emissions from older refrigeration equipment and older vehicles, and a systematic 114% overestimation of emissions for HFC-134a, a refrigerant substitute for phased-out CFCs. Initial, inventory-based estimates for all F-gas emissions had assumed that equipment is no longer in service once it reaches its average lifetime of use. Revised emission estimates using improved models for equipment age at end-of-life, inventories, and leak rates specific to California resulted in F-gas emissions estimates in closer agreement to ambient-based measurements. The discrepancies between inventory-based estimates and ambient-based measurements were reduced from -42% to -6% for CFC-12, and from +114% to +9% for HFC-134a.

Estimation of Nitrous Oxide Emissions from US Grasslands.

PubMed

Mummey; Smith; Bluhm

2000-02-01

/ Nitrous oxide (N(2)O) emissions from temperate grasslands are poorly quantified and may be an important part of the atmospheric N(2)O budget. In this study N(2)O emissions were simulated for 1052 grassland sites in the United States using the NGAS model of Parton and others (1996) coupled with an organic matter decomposition model. N(2)O flux was calculated for each site using soil and land use data obtained from the National Resource Inventory (NRI) database and weather data obtained from NASA. The estimates were regionalized based upon temperature and moisture isotherms. Annual N(2)O emissions for each region were based on the grassland area of each region and the mean estimated annual N(2)O flux from NRI grassland sites in the region. The regional fluxes ranged from 0.18 to 1.02 kg N(2)O N/ha/yr with the mean flux for all regions being 0.28 kg N(2)O N/ha/yr. Even though fluxes from the western regions were relatively low, these regions made the largest contribution to total emissions due to their large grassland area. Total US grassland N(2)O emissions were estimated to be about 67 Gg N(2)O N/yr. Emissions from the Great Plains states, which contain the largest expanse of natural grassland in the United States, were estimated to average 0.24 kg N(2)O N/ha/yr. Using the annual flux estimate for the temperate Great Plains, we estimate that temperate grasslands worldwide may potentially produce 0.27 Tg N(2)O N/yr. Even though our estimate for global temperate grassland N(2)O emissions is less than published estimates for other major temperate and tropical biomes, our results indicate that temperate grasslands are a significant part of both United States and global atmospheric N(2)O budgets. This study demonstrates the utility of models for regional N(2)O flux estimation although additional data from carefully designed field studies is needed to further validate model results.

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

Modeling vehicle fuel consumption and emissions at signalized intersection approaches : integrating field-collected data into microscopic simulation.

DOT National Transportation Integrated Search

2012-07-01

Microscopic models produce emissions and fuel consumption estimates with higher temporal resolution than other scales of : models. Most emissions and fuel consumption models were developed with data from dynamometer testing which are : sufficiently a...

Top-down Estimates of Biomass Burning Emissions of Black Carbon in the Western United States

NASA Astrophysics Data System (ADS)

Mao, Y.; Li, Q.; Randerson, J. T.; Liou, K.

2011-12-01

We apply a Bayesian linear inversion to derive top-down estimates of biomass burning emissions of black carbon (BC) in the western United States (WUS) for May-November 2006 by inverting surface BC concentrations from the IMPROVE network using the GEOS-Chem chemical transport model. Model simulations are conducted at both 2°×2.5° (globally) and 0.55°×0.66° (nested over North America) horizontal resolutions. We first improve the spatial distributions and seasonal and interannual variations of the BC emissions from the Global Fire Emissions Database (GFEDv2) using MODIS 8-day active fire counts from 2005-2007. The GFEDv2 emissions in N. America are adjusted for three zones: boreal N. America, temperate N. America, and Mexico plus Central America. The resulting emissions are then used as a priori for the inversion. The a posteriori emissions are 2-5 times higher than the a priori in California and the Rockies. Model surface BC concentrations using the a posteriori estimate provide better agreement with IMPROVE observations (~20% increase in the Taylor skill score), including improved ability to capture the observed variability especially during June-July. However, model surface BC concentrations are still biased low by ~30%. Comparisons with the Fire Locating and Modeling of Burning Emissions (FLAMBE) are included.

Top-down Estimates of Biomass Burning Emissions of Black Carbon in the Western United States

NASA Astrophysics Data System (ADS)

Mao, Y.; Li, Q.; Randerson, J. T.; CHEN, D.; Zhang, L.; Liou, K.

2012-12-01

We apply a Bayesian linear inversion to derive top-down estimates of biomass burning emissions of black carbon (BC) in the western United States (WUS) for May-November 2006 by inverting surface BC concentrations from the IMPROVE network using the GEOS-Chem chemical transport model. Model simulations are conducted at both 2°×2.5° (globally) and 0.5°×0.667° (nested over North America) horizontal resolutions. We first improve the spatial distributions and seasonal and interannual variations of the BC emissions from the Global Fire Emissions Database (GFEDv2) using MODIS 8-day active fire counts from 2005-2007. The GFEDv2 emissions in N. America are adjusted for three zones: boreal N. America, temperate N. America, and Mexico plus Central America. The resulting emissions are then used as a priori for the inversion. The a posteriori emissions are 2-5 times higher than the a priori in California and the Rockies. Model surface BC concentrations using the a posteriori estimate provide better agreement with IMPROVE observations (~50% increase in the Taylor skill score), including improved ability to capture the observed variability especially during June-September. However, model surface BC concentrations are still biased low by ~30%. Comparisons with the Fire Locating and Modeling of Burning Emissions (FLAMBE) are included.

Assessing methane emission estimation methods based on atmospheric measurements from oil and gas production using LES simulations

NASA Astrophysics Data System (ADS)

Saide, P. E.; Steinhoff, D.; Kosovic, B.; Weil, J.; Smith, N.; Blewitt, D.; Delle Monache, L.

2017-12-01

There are a wide variety of methods that have been proposed and used to estimate methane emissions from oil and gas production by using air composition and meteorology observations in conjunction with dispersion models. Although there has been some verification of these methodologies using controlled releases and concurrent atmospheric measurements, it is difficult to assess the accuracy of these methods for more realistic scenarios considering factors such as terrain, emissions from multiple components within a well pad, and time-varying emissions representative of typical operations. In this work we use a large-eddy simulation (LES) to generate controlled but realistic synthetic observations, which can be used to test multiple source term estimation methods, also known as an Observing System Simulation Experiment (OSSE). The LES is based on idealized simulations of the Weather Research & Forecasting (WRF) model at 10 m horizontal grid-spacing covering an 8 km by 7 km domain with terrain representative of a region located in the Barnett shale. Well pads are setup in the domain following a realistic distribution and emissions are prescribed every second for the components of each well pad (e.g., chemical injection pump, pneumatics, compressor, tanks, and dehydrator) using a simulator driven by oil and gas production volume, composition and realistic operational conditions. The system is setup to allow assessments under different scenarios such as normal operations, during liquids unloading events, or during other prescribed operational upset events. Methane and meteorology model output are sampled following the specifications of the emission estimation methodologies and considering typical instrument uncertainties, resulting in realistic observations (see Figure 1). We will show the evaluation of several emission estimation methods including the EPA Other Test Method 33A and estimates using the EPA AERMOD regulatory model. We will also show source estimation results from advanced methods such as variational inverse modeling, and Bayesian inference and stochastic sampling techniques. Future directions including other types of observations, other hydrocarbons being considered, and assessment of additional emission estimation methods will be discussed.

Near-road air pollutant concentrations of CO and PM 2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

NASA Astrophysics Data System (ADS)

Zhang, Kai; Batterman, Stuart

2010-05-01

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM 2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM 2.5 showed that GAM emission estimates were much higher (by 4-5 times) than the dispersion model results, and that the traffic-PM 2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM 2.5 concentrations, a likely result of underestimating PM 2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.

Fast emission estimates in China and South Africa constrained by satellite observations

NASA Astrophysics Data System (ADS)

Mijling, Bas; van der A, Ronald

2013-04-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for emerging economies such as China and South Africa, where rapid economic growth change emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. However, constraining emissions from observations of concentrations is computationally challenging. Within the GlobEmission project (part of the Data User Element programme of ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China and South Africa, using the CHIMERE chemical transport model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission inventories result in a better agreement between observations and simulations of air pollutant concentrations, facilitating improved air quality forecasts.

Model comparisons for estimating carbon emissions from North American wildland fire

Treesearch

Nancy H.F. French; William J. de Groot; Liza K. Jenkins; Brendan M. Rogers; Ernesto Alvarado; Brian Amiro; Bernardus De Jong; Scott Goetz; Elizabeth Hoy; Edward Hyer; Robert Keane; B.E. Law; Donald McKenzie; Steven G. McNulty; Roger Ottmar; Diego R. Perez-Salicrup; James Randerson; Kevin M. Robertson; Merritt Turetsky

2011-01-01

Research activities focused on estimating the direct emissions of carbon from wildland fires across North America are reviewed as part of the North American Carbon Program disturbance synthesis. A comparison of methods to estimate the loss of carbon from the terrestrial biosphere to the atmosphere from wildland fires is presented. Published studies on emissions from...

Modeling emission rates and exposures from outdoor cooking

NASA Astrophysics Data System (ADS)

Edwards, Rufus; Princevac, Marko; Weltman, Robert; Ghasemian, Masoud; Arora, Narendra K.; Bond, Tami

2017-09-01

Approximately 3 billion individuals rely on solid fuels for cooking globally. For a large portion of these - an estimated 533 million - cooking is outdoors, where emissions from cookstoves pose a health risk to both cooks and other household and village members. Models that estimate emissions rates from stoves in indoor environments that would meet WHO air quality guidelines (AQG), explicitly don't account for outdoor cooking. The objectives of this paper are to link health based exposure guidelines with emissions from outdoor cookstoves, using a Monte Carlo simulation of cooking times from Haryana India coupled with inverse Gaussian dispersion models. Mean emission rates for outdoor cooking that would result in incremental increases in personal exposure equivalent to the WHO AQG during a 24-h period were 126 ± 13 mg/min for cooking while squatting and 99 ± 10 mg/min while standing. Emission rates modeled for outdoor cooking are substantially higher than emission rates for indoor cooking to meet AQG, because the models estimate impact of emissions on personal exposure concentrations rather than microenvironment concentrations, and because the smoke disperses more readily outdoors compared to indoor environments. As a result, many more stoves including the best performing solid-fuel biomass stoves would meet AQG when cooking outdoors, but may also result in substantial localized neighborhood pollution depending on housing density. Inclusion of the neighborhood impact of pollution should be addressed more formally both in guidelines on emissions rates from stoves that would be protective of health, and also in wider health impact evaluation efforts and burden of disease estimates. Emissions guidelines should better represent the different contexts in which stoves are being used, especially because in these contexts the best performing solid fuel stoves have the potential to provide significant benefits.

Modeling methane emissions from Arctic lakes under warming conditions

NASA Astrophysics Data System (ADS)

Zhuang, Qianlai; Tan, Zeli

2014-05-01

To investigate the response of methane emissions from arctic lakes, a process-based climate-sensitive lake methane model is developed. The processes of methane production, oxidation and transport are modeled within a one-dimensional water and sediment column. Dynamics of point-source ebullition seeps are explicitly modeled. The model was calibrated and verified using observational data in the region. The model was further used to estimate the lake methane emissions from the Arctic from 2002 to 2004. We estimate that the total amount of methane emissions is 24.9 Tg CH4 yr-1, which is consistent with a recent estimation of 24±10 Tg CH4 yr-1 and two-fold of methane emissions from natural wetlands in the north of 60 oN. The methane emission rate of lakes spatially varies over high latitudes from 170.5 mg CH4 m-2 day-1 in northern Siberia to only 10.1 mg CH4 m-2 day-1 in northern Europe. A projection assuming 2-7.5oC warming and 15-25% expansion of lake coverage shows that the total amount of methane emitted from Arctic lakes will increase to 29.8-35.6 Tg CH4 yr-1.

Global assessment of the effect of climate change on ammonia emissions from seabirds

NASA Astrophysics Data System (ADS)

Riddick, Stuart N.; Dragosits, Ulrike; Blackall, Trevor D.; Tomlinson, Sam J.; Daunt, Francis; Wanless, Sarah; Hallsworth, Stephen; Braban, Christine F.; Tang, Y. Sim; Sutton, Mark A.

2018-07-01

Seabird colonies alter the biogeochemistry of nearby ecosystems, while the associated emissions of ammonia (NH3) may cause acidification and eutrophication of finely balanced biomes. To examine the possible effects of future climate change on the magnitude and distribution of seabird NH3 emissions globally, a global seabird database was used as input to the GUANO model, a dynamic mass-flow process-based model that simulates NH3 losses from seabird colonies at an hourly resolution in relation to environmental conditions. Ammonia emissions calculated by the GUANO model were in close agreement with measured NH3 emissions across a wide range of climates. For the year 2010, the total global seabird NH3 emission is estimated at 82 [37-127] Gg year-1. This is less than previously estimated using a simple temperature-dependent empirical model, mainly due to inclusion of nitrogen wash-off from colonies during precipitation events in the GUANO model. High precipitation, especially between 40° and 60° S, results in total emissions for the penguin species that are 82% smaller than previously estimated, while for species found in dry tropical areas, emissions are 83-133% larger. Application of temperature anomalies for several IPCC scenarios for 2099 in the GUANO model indicated a predicted net increase in global seabird NH3 emissions of 27% (B1 scenario) and 39% (A2 scenario), compared with the 2010 estimates. At individual colonies, the net change was the result of influences of temperature, precipitation and relative humidity change, with smaller effects of wind-speed changes. The largest increases in NH3 emissions (mean: 60% [486 to -50] increase; A2 scenario for 2099 compared with 2010) were found for colonies 40°S to 65°N, and may lead to increased plant growth and decreased biodiversity by eliminating nitrogen sensitive plant species. Only 7% of the seabird colonies assessed globally (mainly limited to the sub-polar Southern Ocean) were estimated to experience a reduction in NH3 emission (average: -18% [-50 to 0] reduction between 2010 and 2099, A2 scenario), where an increase in precipitation was found to more than offset the effect of rising temperatures.

Projections of emissions from burning of biomass foruse in studies of global climate and atmospheric chemistry

Treesearch

Darold E. Ward; Weimin Hao

1991-01-01

Emissions of trace gases and particulate matter from burning of biomass are generally factored into global climate models. Models for improving the estimates of the global annual release of emissions from biomass fires are presented. Estimates of total biomass consumed on a global basis range from 2 to 10 Pg (1 petagram = 1015 g) per year. New...

Net Carbon Emissions from Deforestation in Bolivia during 1990-2000 and 2000-2010: Results from a Carbon Bookkeeping Model.

PubMed

Andersen, Lykke E; Doyle, Anna Sophia; del Granado, Susana; Ledezma, Juan Carlos; Medinaceli, Agnes; Valdivia, Montserrat; Weinhold, Diana

2016-01-01

Accurate estimates of global carbon emissions are critical for understanding global warming. This paper estimates net carbon emissions from land use change in Bolivia during the periods 1990-2000 and 2000-2010 using a model that takes into account deforestation, forest degradation, forest regrowth, gradual carbon decomposition and accumulation, as well as heterogeneity in both above ground and below ground carbon contents at the 10 by 10 km grid level. The approach permits detailed maps of net emissions by region and type of land cover. We estimate that net CO2 emissions from land use change in Bolivia increased from about 65 million tons per year during 1990-2000 to about 93 million tons per year during 2000-2010, while CO2 emissions per capita and per unit of GDP have remained fairly stable over the sample period. If we allow for estimated biomass increases in mature forests, net CO2 emissions drop to close to zero. Finally, we find these results are robust to alternative methods of calculating emissions.

Net Carbon Emissions from Deforestation in Bolivia during 1990-2000 and 2000-2010: Results from a Carbon Bookkeeping Model

PubMed Central

Andersen, Lykke E.; Doyle, Anna Sophia; del Granado, Susana; Ledezma, Juan Carlos; Medinaceli, Agnes; Valdivia, Montserrat; Weinhold, Diana

2016-01-01

Accurate estimates of global carbon emissions are critical for understanding global warming. This paper estimates net carbon emissions from land use change in Bolivia during the periods 1990–2000 and 2000–2010 using a model that takes into account deforestation, forest degradation, forest regrowth, gradual carbon decomposition and accumulation, as well as heterogeneity in both above ground and below ground carbon contents at the 10 by 10 km grid level. The approach permits detailed maps of net emissions by region and type of land cover. We estimate that net CO2 emissions from land use change in Bolivia increased from about 65 million tons per year during 1990–2000 to about 93 million tons per year during 2000–2010, while CO2 emissions per capita and per unit of GDP have remained fairly stable over the sample period. If we allow for estimated biomass increases in mature forests, net CO2 emissions drop to close to zero. Finally, we find these results are robust to alternative methods of calculating emissions. PMID:26990865

Frequency and Angular Variations of Land Surface Microwave Emissivities: Can we Estimate SSM/T and AMSU Emissivities from SSM/I Emissivities?

NASA Technical Reports Server (NTRS)

Prigent, Catherine; Wigneron, Jean-Pierre; Rossow, William B.; Pardo-Carrion, Juan R.

1999-01-01

To retrieve temperature and humidity profiles from SSM/T and AMSU, it is important to quantify the contribution of the Earth surface emission. So far, no global estimates of the land surface emissivities are available at SSM/T and AMSU frequencies and scanning conditions. The land surface emissivities have been previously calculated for the globe from the SSM/I conical scanner between 19 and 85 GHz. To analyze the feasibility of deriving SSM/T and AMSU land surface emissivities from SSM/I emissivities, the spectral and angular variations of the emissivities are studied, with the help of ground-based measurements, models and satellite estimates. Up to 100 GHz, for snow and ice free areas, the SSM/T and AMSU emissivities can be derived with useful accuracy from the SSM/I emissivities- The emissivities can be linearly interpolated in frequency. Based on ground-based emissivity measurements of various surface types, a simple model is proposed to estimate SSM/T and AMSU emissivities for all zenith angles knowing only the emissivities for the vertical and horizontal polarizations at 53 deg zenith angle. The method is tested on the SSM/T-2 91.655 GHz channels. The mean difference between the SSM/T-2 and SSM/I-derived emissivities is less than or equal to 0.01 for all zenith angles with an r.m.s. difference of approx. = 0.02. Above 100 GHz, preliminary results are presented at 150 GHz, based on SSM/T-2 observations and are compared with the very few estimations available in the literature.

Assessment of methane emission and oxidation at Air Hitam Landfill site cover soil in wet tropical climate.

PubMed

Abushammala, Mohammed F M; Basri, Noor Ezlin Ahmad; Elfithri, Rahmah

2013-12-01

Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 gm(−2) day(−1), respectively. The total CH₄ emissions from the studied area were 53.8 kg day(−1). The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year(−1). Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.

Uncertainties in Emissions In Emissions Inputs for Near-Road Assessments

EPA Science Inventory

Emissions, travel demand, and dispersion models are all needed to obtain temporally and spatially resolved pollutant concentrations. Current methodology combines these three models in a bottom-up approach based on hourly traffic and emissions estimates, and hourly dispersion conc...

U.S. emissions of HFC-134a derived for 2008-2012 from an extensive flask-air sampling network

NASA Astrophysics Data System (ADS)

Hu, Lei; Montzka, Stephen A.; Miller, John B.; Andrews, Aryln E.; Lehman, Scott J.; Miller, Benjamin R.; Thoning, Kirk; Sweeney, Colm; Chen, Huilin; Godwin, David S.; Masarie, Kenneth; Bruhwiler, Lori; Fischer, Marc L.; Biraud, Sebastien C.; Torn, Margaret S.; Mountain, Marikate; Nehrkorn, Thomas; Eluszkiewicz, Janusz; Miller, Scot; Draxler, Roland R.; Stein, Ariel F.; Hall, Bradley D.; Elkins, James W.; Tans, Pieter P.

2015-01-01

national and regional emissions of HFC-134a are derived for 2008-2012 based on atmospheric observations from ground and aircraft sites across the U.S. and a newly developed regional inverse model. Synthetic data experiments were first conducted to optimize the model assimilation design and to assess model-data mismatch errors and prior flux error covariances computed using a maximum likelihood estimation technique. The synthetic data experiments also tested the sensitivity of derived national and regional emissions to a range of assumed prior emissions, with the goal of designing a system that was minimally reliant on the prior. We then explored the influence of additional sources of error in inversions with actual observations, such as those associated with background mole fractions and transport uncertainties. Estimated emissions of HFC-134a range from 52 to 61 Gg yr-1 for the contiguous U.S. during 2008-2012 for inversions using air transport from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model driven by the 12 km resolution meteorogical data from North American Mesoscale Forecast System (NAM12) and all tested combinations of prior emissions and background mole fractions. Estimated emissions for 2008-2010 were 20% lower when specifying alternative transport from Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Research and Forecasting (WRF) meteorology. Our estimates (for HYSPLIT-NAM12) are consistent with annual emissions reported by U.S. Environmental Protection Agency for the full study interval. The results suggest a 10-20% drop in U.S. national HFC-134a emission in 2009 coincident with a reduction in transportation-related fossil fuel CO2 emissions, perhaps related to the economic recession. All inversions show seasonal variation in national HFC-134a emissions in all years, with summer emissions greater than winter emissions by 20-50%.

Application of inverse dispersion model for estimating volatile organic compounds emitted from the offshore industrial park

NASA Astrophysics Data System (ADS)

Tsai, M.; Lee, C.; Yu, H.

2013-12-01

In the last 20 years, the Yunlin offshore industrial park has significantly contributed to the economic development of Taiwan. Its annual production value has reached almost 12 % of Taiwan's GDP in 2012. The offshore industrial park also balanced development of urban and rural in areas. However, the offshore industrial park is considered the major source of air pollution to nearby counties, especially, the emission of Volatile Organic Compounds(VOCs). Studies have found that exposures to high level of some VOCs have caused adverse health effects on both human and ecosystem. Since both health and ecological effects of air pollution have been the subject of numerous studies in recent years, it is a critical issue in estimating VOCs emissions. Nowadays emission estimation techniques are usually used emissions factors in calculation. Because the methodology considered totality of equipment activities based on statistical assumptions, it would encounter great uncertainty between these coefficients. This study attempts to estimate VOCs emission of the Yunlin Offshore Industrial Park using an inverse atmospheric dispersion model. The inverse modeling approach will be applied to the combination of dispersion modeling result which input a given one-unit concentration and observations at air quality stations in Yunlin. The American Meteorological Society-Environmental Protection Agency Regulatory Model (AERMOD) is chosen as the tool for dispersion modeling in the study. Observed concentrations of VOCs are collected by the Taiwanese Environmental Protection Administration (TW EPA). In addition, the study also analyzes meteorological data including wind speed, wind direction, pressure and temperature etc. VOCs emission estimations from the inverse atmospheric dispersion model will be compared to the official statistics released by Yunlin Offshore Industrial Park. Comparison of estimated concentration from inverse dispersion modeling and official statistical concentrations will give a better understanding about the uncertainty of regulatory methodology. The model results will be discussed with the importance of evaluating air pollution exposure in risk assessment.

Network design for quantifying urban CO 2 emissions: assessing trade-offs between precision and network density

DOE PAGES

Turner, Alexander J.; Shusterman, Alexis A.; McDonald, Brian C.; ...

2016-11-01

The majority of anthropogenic CO 2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO 2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO 2 Observation Network (BEACO 2N) in California's Bay Area, in combination with anmore » inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km 2. The model uses an hourly 1 × 1 km 2 emission inventory and 1 × 1 km 2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model–observing system in reducing uncertainty in CO 2 emissions. We examine uncertainty in estimated CO 2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO 2N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO 2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.« less

Network design for quantifying urban CO 2 emissions: assessing trade-offs between precision and network density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turner, Alexander J.; Shusterman, Alexis A.; McDonald, Brian C.

The majority of anthropogenic CO 2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO 2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO 2 Observation Network (BEACO 2N) in California's Bay Area, in combination with anmore » inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km 2. The model uses an hourly 1 × 1 km 2 emission inventory and 1 × 1 km 2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model–observing system in reducing uncertainty in CO 2 emissions. We examine uncertainty in estimated CO 2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO 2N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO 2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.« less

[Study on the quantitative estimation method for VOCs emission from petrochemical storage tanks based on tanks 4.0.9d model].

PubMed

Li, Jing; Wang, Min-Yan; Zhang, Jian; He, Wan-Qing; Nie, Lei; Shao, Xia

2013-12-01

VOCs emission from petrochemical storage tanks is one of the important emission sources in the petrochemical industry. In order to find out the VOCs emission amount of petrochemical storage tanks, Tanks 4.0.9d model is utilized to calculate the VOCs emission from different kinds of storage tanks. VOCs emissions from a horizontal tank, a vertical fixed roof tank, an internal floating roof tank and an external floating roof tank were calculated as an example. The consideration of the site meteorological information, the sealing information, the tank content information and unit conversion by using Tanks 4.0.9d model in China was also discussed. Tanks 4.0.9d model can be used to estimate VOCs emissions from petrochemical storage tanks in China as a simple and highly accurate method.

Implications of overestimated anthropogenic CO2 emissions on East Asian and global land CO2 flux inversion

NASA Astrophysics Data System (ADS)

Saeki, Tazu; Patra, Prabir K.

2017-12-01

Measurement and modelling of regional or country-level carbon dioxide (CO2) fluxes are becoming critical for verification of the greenhouse gases emission control. One of the commonly adopted approaches is inverse modelling, where CO2 fluxes (emission: positive flux, sink: negative flux) from the terrestrial ecosystems are estimated by combining atmospheric CO2 measurements with atmospheric transport models. The inverse models assume anthropogenic emissions are known, and thus the uncertainties in the emissions introduce systematic bias in estimation of the terrestrial (residual) fluxes by inverse modelling. Here we show that the CO2 sink increase, estimated by the inverse model, over East Asia (China, Japan, Korea and Mongolia), by about 0.26 PgC year-1 (1 Pg = 1012 g) during 2001-2010, is likely to be an artifact of the anthropogenic CO2 emissions increasing too quickly in China by 1.41 PgC year-1. Independent results from methane (CH4) inversion suggested about 41% lower rate of East Asian CH4 emission increase during 2002-2012. We apply a scaling factor of 0.59, based on CH4 inversion, to the rate of anthropogenic CO2 emission increase since the anthropogenic emissions of both CO2 and CH4 increase linearly in the emission inventory. We find no systematic increase in land CO2 uptake over East Asia during 1993-2010 or 2000-2009 when scaled anthropogenic CO2 emissions are used, and that there is a need of higher emission increase rate for 2010-2012 compared to those calculated by the inventory methods. High bias in anthropogenic CO2 emissions leads to stronger land sinks in global land-ocean flux partitioning in our inverse model. The corrected anthropogenic CO2 emissions also produce measurable reductions in the rate of global land CO2 sink increase post-2002, leading to a better agreement with the terrestrial biospheric model simulations that include CO2-fertilization and climate effects.

Two Surface Temperature Retrieval Methods Compared Over Agricultural Lands

NASA Technical Reports Server (NTRS)

French, Andrew N.; Schmugge, Thomas J.; Jacob, Frederic; Ogawa, Kenta; Houser, Paul R. (Technical Monitor)

2002-01-01

Accurate, spatially distributed surface temperatures are required for modeling evapotranspiration (ET) over agricultural fields under wide ranging conditions, including stressed and unstressed vegetation. Modeling approaches that use surface temperature observations, however, have the burden of estimating surface emissivities. Emissivity estimation, the subject of much recent research, is facilitated by observations in multiple thermal infrared bands. But it is nevertheless a difficult task. Using observations from a multiband thermal sensor, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), estimated surface emissivities and temperatures are retrieved in two different ways: the temperature emissivity separation approach (TES) and the normalized emissivity approach (NEM). Both rely upon empirical relationships, but the assumed relationships are different. TES relies upon a relationship between the minimum spectral emissivity and the range of observed emissivities. NEM relies upon an assumption that at least one thermal band has a pre-determined emissivity (close to 1.0). The benefits and consequences of each approach will be demonstrated for two different landscapes: one in central Oklahoma, USA and another in southern New Mexico.

Screening level risk assessment model for chemical fate and effects in the environment.

PubMed

Arnot, Jon A; Mackay, Don; Webster, Eva; Southwood, Jeanette M

2006-04-01

A screening level risk assessment model is developed and described to assess and prioritize chemicals by estimating environmental fate and transport, bioaccumulation, and exposure to humans and wildlife for a unit emission rate. The most sensitive risk endpoint is identified and a critical emission rate is then calculated as a result of that endpoint being reached. Finally, this estimated critical emission rate is compared with the estimated actual emission rate as a risk assessment factor. This "back-tracking" process avoids the use of highly uncertain emission rate data as model input. The application of the model is demonstrated in detail for three diverse chemicals and in less detail for a group of 70 chemicals drawn from the Canadian Domestic Substances List. The simple Level II and the more complex Level III fate calculations are used to "bin" substances into categories of similar probable risk. The essential role of the model is to synthesize information on chemical and environmental properties within a consistent mass balance framework to yield an overall estimate of screening level risk with respect to the defined endpoint. The approach may be useful to identify and prioritize those chemicals of commerce that are of greatest potential concern and require more comprehensive modeling and monitoring evaluations in actual regional environments and food webs.

Estimating global nitrous oxide emissions by lichens and bryophytes with a process-based productivity model

NASA Astrophysics Data System (ADS)

Porada, Philipp; Pöschl, Ulrich; Kleidon, Axel; Beer, Christian; Weber, Bettina

2017-03-01

Nitrous oxide is a strong greenhouse gas and atmospheric ozone-depleting agent which is largely emitted by soils. Recently, lichens and bryophytes have also been shown to release significant amounts of nitrous oxide. This finding relies on ecosystem-scale estimates of net primary productivity of lichens and bryophytes, which are converted to nitrous oxide emissions by empirical relationships between productivity and respiration, as well as between respiration and nitrous oxide release. Here we obtain an alternative estimate of nitrous oxide emissions which is based on a global process-based non-vascular vegetation model of lichens and bryophytes. The model quantifies photosynthesis and respiration of lichens and bryophytes directly as a function of environmental conditions, such as light and temperature. Nitrous oxide emissions are then derived from simulated respiration assuming a fixed relationship between the two fluxes. This approach yields a global estimate of 0.27 (0.19-0.35) (Tg N2O) year-1 released by lichens and bryophytes. This is lower than previous estimates but corresponds to about 50 % of the atmospheric deposition of nitrous oxide into the oceans or 25 % of the atmospheric deposition on land. Uncertainty in our simulated estimate results from large variation in emission rates due to both physiological differences between species and spatial heterogeneity of climatic conditions. To constrain our predictions, combined online gas exchange measurements of respiration and nitrous oxide emissions may be helpful.

A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0)

NASA Astrophysics Data System (ADS)

Bloom, A. Anthony; Bowman, Kevin W.; Lee, Meemong; Turner, Alexander J.; Schroeder, Ronny; Worden, John R.; Weidner, Richard; McDonald, Kyle C.; Jacob, Daniel J.

2017-06-01

Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009-2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001-2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.

(EDMUNDS, WA) WILDLAND FIRE EMISSIONS MODELING: INTEGRATING BLUESKY AND SMOKE

EPA Science Inventory

This presentation is a status update of the BlueSky emissions modeling system. BlueSky-EM has been coupled with the Sparse Matrix Operational Kernel Emissions (SMOKE) system, and is now available as a tool for estimating emissions from wildland fires

Estimation of biogenic volatile organic compound (BVOC) emissions from the terrestrial ecosystem in China using real-time remote sensing data

NASA Astrophysics Data System (ADS)

Li, M.; Huang, X.; Li, J.; Song, Y.

2012-03-01

Because of the high emission rate and reactivity, biogenic volatile organic compounds (BVOCs) play a significant role in the terrestrial ecosystems, human health, secondary pollution, global climate change and the global carbon cycle. Past estimations of BVOC emissions in China were based on outdated algorithms and coarsely resolved meteorological data, and there have been significant inconsistences between the land surface parameters of dynamic models and those of BVOC estimation models, leading to large inaccuracies in the estimated results. To refine BVOC emission estimations for China and to further explore the role of BVOCs in the atmosphere, we used the latest algorithms of MEGAN (Model of Emissions of Gases and Aerosols from Nature), with MM5 (the Fifth-Generation Mesoscale Model) providing highly resolved meteorological data, to estimate the biogenic emissions of isoprene (C5H8) and seven monoterpene species (C10H16) in 2006. Real-time MODIS (Moderate Resolution Imaging Spectroradiometer) data were introduced to update the land surface parameters and to improve the simulation performance of MM5, and to determine the influence of leaf area index (LAI) and leaf age deviation from standard conditions. In this study, the annual BVOC emissions for the whole country totaled 12.97 Tg C, a relevant value compared with past studies. Therein, the most important individual contributor was isoprene (9.36 Tg C yr-1), followed by α-pinene (1.24 Tg C yr-1) and β-pinene (0.84 Tg C yr-1). Due to the considerable regional disparity in plant distributions and meteorological conditions across China, BVOC emissions presented significant spatial and temporal variations. Spatially, isoprene emission was concentrated in South China, which is covered by large areas of broadleaf forests and shrubs. While Southeast China was the top-ranking contributor of monoterpenes, in which the dominant vegetation genera consist of evergreen coniferous forests. Temporally, BVOC emissions primarily occurred in July and August, with daily emissions peaking at about 13:00∼14:00 h (Beijing Time, BJT). In this study, we present an improved estimation of BVOC emissions, which provides important information for further exploration of the role of BVOCs in atmospheric processes.

Estimate of biogenic VOC emissions in Japan and their effects on photochemical formation of ambient ozone and secondary organic aerosol

NASA Astrophysics Data System (ADS)

Chatani, Satoru; Matsunaga, Sou N.; Nakatsuka, Seiji

2015-11-01

A new gridded database has been developed to estimate the amount of isoprene, monoterpene, and sesquiterpene emitted from all the broadleaf and coniferous trees in Japan with the Model of Emissions of Gases and Aerosols from Nature (MEGAN). This database reflects the vegetation specific to Japan more accurately than existing ones. It estimates much lower isoprene emitted from other vegetation than trees, and higher sesquiterpene emissions mainly emitted from Cryptomeria japonica, which is the most abundant plant type in Japan. Changes in biogenic emissions result in the decrease in ambient ozone and increase in organic aerosol simulated by the air quality simulation over the Tokyo Metropolitan Area in Japan. Although newly estimated biogenic emissions contribute to a better model performance on overestimated ozone and underestimated organic aerosol, they are not a single solution to solve problems associated with the air quality simulation.

MOVES (MOTOR VEHICLE EMISSION SIMULATOR) MODEL ...

EPA Pesticide Factsheets

A computer model, intended to eventually replace the MOBILE model and to incorporate the NONROAD model, that will provide the ability to estimate criteria and toxic air pollutant emission factors and emission inventories that are specific to the areas and time periods of interest, at scales ranging from local to national. Development of a new emission factor and inventory model for mobile source emissions. The model will be used by air pollution modelers within EPA, and at the State and local levels.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantitative Assessment of Cancer Risk from Exposure to Diesel Engine Emissions

EPA Science Inventory

Quantitative estimates of lung cancer risk from exposure to diesel engine emissions were developed using data from three chronic bioassays with Fischer 344 rats. uman target organ dose was estimated with the aid of a comprehensive dosimetry model. This model accounted for rat-hum...

TEMPORALLY-RESOLVED AMMONIA EMISSION INVENTORIES: CURRENT ESTIMATES, EVALUATION TOOLS, AND MEASUREMENT NEEDS

EPA Science Inventory

In this study, we evaluate the suitability of a three-dimensional chemical transport model (CTM) as a tool for assessing ammonia emission inventories, calculate the improvement in CTM performance owing to recent advances in temporally-varying ammonia emission estimates, and ident...

Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750-2015)

NASA Astrophysics Data System (ADS)

van Marle, Margreet J. E.; Kloster, Silvia; Magi, Brian I.; Marlon, Jennifer R.; Daniau, Anne-Laure; Field, Robert D.; Arneth, Almut; Forrest, Matthew; Hantson, Stijn; Kehrwald, Natalie M.; Knorr, Wolfgang; Lasslop, Gitta; Li, Fang; Mangeon, Stéphane; Yue, Chao; Kaiser, Johannes W.; van der Werf, Guido R.

2017-09-01

Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data have shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently, there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emission estimates back in time based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant, with 10-year averages varying between 1.8 and 2.3 Pg C yr-1. Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. There is substantial uncertainty in these estimates, and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58 % of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emission estimates are mostly suited for global analyses and will be used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations.

Delay correction model for estimating bus emissions at signalized intersections based on vehicle specific power distributions.

PubMed

Song, Guohua; Zhou, Xixi; Yu, Lei

2015-05-01

The intersection is one of the biggest emission points for buses and also the high exposure site for people. Several traffic performance indexes have been developed and widely used for intersection evaluations. However, few studies have focused on the relationship between these indexes and emissions at intersections. This paper intends to propose a model that relates emissions to the two commonly used measures of effectiveness (i.e. delay time and number of stops) by using bus activity data and emission data at intersections. First, with a large number of field instantaneous emission data and corresponding activity data collected by the Portable Emission Measurement System (PEMS), emission rates are derived for different vehicle specific power (VSP) bins. Then, 2002 sets of trajectory data, an equivalent of about 140,000 sets of second-by-second activity data, are obtained from Global Position Systems (GPSs)-equipped diesel buses in Beijing. The delay and the emission factors of each trajectory are estimated. Then, by using baseline emission factors for two types of intersections, e.g. the Arterial @ Arterial Intersection and the Arterial @ Collector, delay correction factors are calculated for the two types of intersections at different congestion levels. Finally, delay correction models are established for adjusting emission factors for each type of intersections and different numbers of stops. A comparative analysis between estimated and field emission factors demonstrates that the delay correction model is reliable. Copyright © 2015 Elsevier B.V. All rights reserved.

Seasonal and interannual variations in CO and BC emissions from open biomass burning in Southern Africa during 1998-2005

NASA Astrophysics Data System (ADS)

Ito, Akinori; Ito, Akihiko; Akimoto, Hajime

2007-06-01

We estimate the emissions of carbon monoxide (CO) and black carbon (BC) from open vegetation fires in the Southern Hemisphere Africa from 1998 to 2005 using satellite information in conjunction with a biogeochemical model. Monthly burned areas at a 0.5-degree resolution are estimated from the Visible InfraRed Scanner (VIRS) fire count product and the MODerate resolution Imaging Spectroradiometer (MODIS) burned area data set associated with the MODIS tree cover imagery in grasslands and woodlands. The monthly fuel load distributions are derived from a 0.5-degree terrestrial carbon cycle model in conjunction with satellite data. The monthly maps of combustion factors and emission factors are estimated using empirical models that predict the effects of fuel conditions on these factors in grasslands and woodlands. Our annually averaged effective CO and BC emissions per area burned are 27 g CO m-2 and 0.17 g BC m-2 which are consistent with the products of fuel consumption and emission factors typically measured in southern Africa. The CO and BC emissions from open vegetation burning in southern Africa range from 45 Tg CO yr-1 and 0.26 Tg BC yr-1 for 2002 to 75 Tg CO yr-1 and 0.42 Tg BC yr-1 for 1998. The monthly averaged burned areas from VIRS fire counts peak earlier than modeled CO emissions. This characteristic delay between burned areas and emissions is mainly explained by significant changes in combustion factors for woodlands in our model. Consequently, the peaks in CO and BC emissions from our bottom-up approach are identical to those from previous top-down estimates using the Measurement Of the Pollution In The Troposphere (MOPITT) and the Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) data.

Preindustrial nitrous oxide emissions from the land biosphere estimated by using a global biogeochemistry model

NASA Astrophysics Data System (ADS)

Xu, Rongting; Tian, Hanqin; Lu, Chaoqun; Pan, Shufen; Chen, Jian; Yang, Jia; Zhang, Bowen

2017-07-01

To accurately assess how increased global nitrous oxide (N2O) emission has affected the climate system requires a robust estimation of the preindustrial N2O emissions since only the difference between current and preindustrial emissions represents net drivers of anthropogenic climate change. However, large uncertainty exists in previous estimates of preindustrial N2O emissions from the land biosphere, while preindustrial N2O emissions on the finer scales, such as regional, biome, or sector scales, have not been well quantified yet. In this study, we applied a process-based Dynamic Land Ecosystem Model (DLEM) to estimate the magnitude and spatial patterns of preindustrial N2O fluxes at the biome, continental, and global level as driven by multiple environmental factors. Uncertainties associated with key parameters were also evaluated. Our study indicates that the mean of the preindustrial N2O emission was approximately 6.20 Tg N yr-1, with an uncertainty range of 4.76 to 8.13 Tg N yr-1. The estimated N2O emission varied significantly at spatial and biome levels. South America, Africa, and Southern Asia accounted for 34.12, 23.85, and 18.93 %, respectively, together contributing 76.90 % of global total emission. The tropics were identified as the major source of N2O released into the atmosphere, accounting for 64.66 % of the total emission. Our multi-scale estimates provide a robust reference for assessing the climate forcing of anthropogenic N2O emission from the land biosphere

Current and future levels of mercury atmospheric pollution on a global scale

NASA Astrophysics Data System (ADS)

Pacyna, Jozef M.; Travnikov, Oleg; De Simone, Francesco; Hedgecock, Ian M.; Sundseth, Kyrre; Pacyna, Elisabeth G.; Steenhuisen, Frits; Pirrone, Nicola; Munthe, John; Kindbom, Karin

2016-10-01

An assessment of current and future emissions, air concentrations, and atmospheric deposition of mercury worldwide is presented on the basis of results obtained during the performance of the EU GMOS (Global Mercury Observation System) project. Emission estimates for mercury were prepared with the main goal of applying them in models to assess current (2013) and future (2035) air concentrations and atmospheric deposition of this contaminant. The combustion of fossil fuels (mainly coal) for energy and heat production in power plants and in industrial and residential boilers, as well as artisanal and small-scale gold mining, is one of the major anthropogenic sources of Hg emissions to the atmosphere at present. These sources account for about 37 and 25 % of the total anthropogenic Hg emissions globally, estimated to be about 2000 t. Emissions in Asian countries, particularly in China and India, dominate the total emissions of Hg. The current estimates of mercury emissions from natural processes (primary mercury emissions and re-emissions), including mercury depletion events, were estimated to be 5207 t year-1, which represents nearly 70 % of the global mercury emission budget. Oceans are the most important sources (36 %), followed by biomass burning (9 %). A comparison of the 2035 anthropogenic emissions estimated for three different scenarios with current anthropogenic emissions indicates a reduction of these emissions in 2035 up to 85 % for the best-case scenario. Two global chemical transport models (GLEMOS and ECHMERIT) have been used for the evaluation of future mercury pollution levels considering future emission scenarios. Projections of future changes in mercury deposition on a global scale simulated by these models for three anthropogenic emissions scenarios of 2035 indicate a decrease in up to 50 % deposition in the Northern Hemisphere and up to 35 % in Southern Hemisphere for the best-case scenario. The EU GMOS project has proved to be a very important research instrument for supporting the scientific justification for the Minamata Convention and monitoring of the implementation of targets of this convention, as well as the EU Mercury Strategy. This project provided the state of the art with regard to the development of the latest emission inventories for mercury, future emission scenarios, dispersion modelling of atmospheric mercury on a global and regional scale, and source-receptor techniques for mercury emission apportionment on a global scale.

A regional mass balance model based on total ammoniacal nitrogen for estimating ammonia emissions from beef cattle in Alberta Canada

NASA Astrophysics Data System (ADS)

Chai, Lilong; Kröbel, Roland; Janzen, H. Henry; Beauchemin, Karen A.; McGinn, Sean M.; Bittman, Shabtai; Atia, Atta; Edeogu, Ike; MacDonald, Douglas; Dong, Ruilan

2014-08-01

Animal feeding operations are primary contributors of anthropogenic ammonia (NH3) emissions in North America and Europe. Mathematical modeling of NH3 volatilization from each stage of livestock manure management allows comprehensive quantitative estimates of emission sources and nutrient losses. A regionally-specific mass balance model based on total ammoniacal nitrogen (TAN) content in animal manure was developed for estimating NH3 emissions from beef farming operations in western Canada. Total N excretion in urine and feces was estimated from animal diet composition, feed dry matter intake and N utilization for beef cattle categories and production stages. Mineralization of organic N, immobilization of TAN, nitrification, and denitrification of N compounds in manure, were incorporated into the model to account for quantities of TAN at each stage of manure handling. Ammonia emission factors were specified for different animal housing (feedlots, barns), grazing, manure storage (including composting and stockpiling) and land spreading (tilled and untilled land), and were modified for temperature. The model computed NH3 emissions from all beef cattle sub-classes including cows, calves, breeding bulls, steers for slaughter, and heifers for slaughter and replacement. Estimated NH3 emissions were about 1.11 × 105 Mg NH3 in Alberta in 2006, with a mean of 18.5 kg animal-1 yr-1 (15.2 kg NH3-N animal-1 yr-1) which is 23.5% of the annual N intake of beef cattle (64.7 kg animal-1 yr-1). The percentage of N intake volatilized as NH3-N was 50% for steers and heifers for slaughter, and between 11 and 14% for all other categories. Steers and heifers for slaughter were the two largest contributors (3.5 × 104 and 3.9 × 104 Mg, respectively) at 31.5 and 32.7% of total NH3 emissions because most growing animals were finished in feedlots. Animal housing and grazing contributed roughly 63% of the total NH3 emissions (feedlots, barns and pastures contributed 54.4, 0.2 and 8.1% of total emissions, respectively.). Manure storage (composting and stockpiling) and land spreading contributed 23 and 14% of the total emissions, respectively. Parameters from this TAN-based mass balance model will be incorporated into the HOLOS model - a farm-level greenhouse gas calculator.

Using the NAME Lagrangian Particle Dispersion model, and aircraft measurements to assess the accuracy of trace gas emission inventories from the U.K.

NASA Astrophysics Data System (ADS)

O'Sullivan, D. A.; Harrison, M.; Ploson, D.; Oram, D.; Reeves, C.

2007-12-01

A top-down approach using a combination of aircraft data and atmospheric dispersion modelling has been used to estimate emissions for 24 halogenated trace gases from the United Kingdom. This has been done using data collected during AMPEP/FLUXEX, a U.K based measurement campaign which took place between April and September 2005. The primary objective relating to this work was to make direct airborne measurements of concentration enhancements within the boundary layer arising from anthropogenic pollution events, and then to use mass balance methods to determine an emission flux. This was done by analysing Whole Air Samples (WAS) collected in the boundary layer upwind and downwind of the UK at frequent intervals around the coast using the technique of gas chromatography mass spectrometry (GCMS). Emissions were then calculated using a simple box-model approach and also using NAME (Numerical Atmospheric-dispersion Modelling Environment) which is a Lagrangian particle model using 3 hourly 3D meteorology fields from the Met Office Unified Model. By using such an approach it is also possible to identify the most likely main source regions in the UK for the compounds measured. Among the trace gases studied are many which through their effects on stratospheric ozone, and their large radiative forcing have a direct impact on global climate such as CFC's 11, 12, 113 and 114, HCFC's 21, 22, 141b and 142b, HFC's 134a and 152a, methyl chloroform, methyl bromide and carbon tetrachloride. Also the emissions of some short lived gases with have direct effects on human health, such as tetrachloroethene, and trichloroethene, have been derived. The UK emissions estimates calculated from this experimental and modelling work are compared with bottom-up and other top-down emission inventories for the UK and Europe. It was found that the estimates from this study were often higher than those in bottom-up emission inventories derived from industry. In addition for a number of trace gases, for example HCFC-21 and the HFC's, there are no accurate emissions estimates available due to privacy laws which in the UK restrict the availability of the production and sales data required to construct bottom-up emission inventories. Therefore for some of the compounds included in this study, this work provides the first available estimate of UK emissions.

Greenhouse Gas Emissions Model (GEM) for Medium- and Heavy-Duty Vehicle Compliance

EPA Pesticide Factsheets

EPA’s Greenhouse Gas Emissions Model (GEM) is a free, desktop computer application that estimates the greenhouse gas (GHG) emissions and fuel efficiency performance of specific aspects of heavy-duty vehicles.

Global modelling of Cryptosporidium in surface water

NASA Astrophysics Data System (ADS)

Vermeulen, Lucie; Hofstra, Nynke

2016-04-01

Introduction Waterborne pathogens that cause diarrhoea, such as Cryptosporidium, pose a health risk all over the world. In many regions quantitative information on pathogens in surface water is unavailable. Our main objective is to model Cryptosporidium concentrations in surface waters worldwide. We present the GloWPa-Crypto model and use the model in a scenario analysis. A first exploration of global Cryptosporidium emissions to surface waters has been published by Hofstra et al. (2013). Further work has focused on modelling emissions of Cryptosporidium and Rotavirus to surface waters from human sources (Vermeulen et al 2015, Kiulia et al 2015). A global waterborne pathogen model can provide valuable insights by (1) providing quantitative information on pathogen levels in data-sparse regions, (2) identifying pathogen hotspots, (3) enabling future projections under global change scenarios and (4) supporting decision making. Material and Methods GloWPa-Crypto runs on a monthly time step and represents conditions for approximately the year 2010. The spatial resolution is a 0.5 x 0.5 degree latitude x longitude grid for the world. We use livestock maps (http://livestock.geo-wiki.org/) combined with literature estimates to calculate spatially explicit livestock Cryptosporidium emissions. For human Cryptosporidium emissions, we use UN population estimates, the WHO/UNICEF JMP sanitation country data and literature estimates of wastewater treatment. We combine our emissions model with a river routing model and data from the VIC hydrological model (http://vic.readthedocs.org/en/master/) to calculate concentrations in surface water. Cryptosporidium survival during transport depends on UV radiation and water temperature. We explore pathogen emissions and concentrations in 2050 with the new Shared Socio-economic Pathways (SSPs) 1 and 3. These scenarios describe plausible future trends in demographics, economic development and the degree of global integration. Results and Conclusions GloWPa-Crypto is the first global model that can be used to analyse dynamics in surface water pathogen concentrations worldwide. Global human Cryptosporidium emissions are estimated at 1 x 10^17 oocysts/ year for the year 2010.We estimated future emissions for SSP1 and SSP3. Preliminary results show that for SSP1human emissions are approximately halved by 2050. The SSP3 human emissions are 1.5 times higher than the 2010 emissions due to increased population growth and urbanisation. Livestock Cryptosporidium emissions are expected to increase under both SSP1 and SSP3, as meat consumption continues to rise. We conclude that population growth, urbanization, changes in sanitation systems and treatment, and changes in livestock consumption and production systems are important processes that determine future Cryptosporidium emissions to surface water. References Hofstra N, Bouwman A F, Beusen A H W and Medema G J 2013 Exploring global Cryptosporidium emissions to surface water Sci. Total Environ. 442 10-9 Kiulia N M, Hofstra N, Vermeulen L C, Obara M A, Medema G J and Rose J B 2015 Global occurrence and emission of rotaviruses to surface waters Pathogens 4 229-55 Vermeulen L C, De Kraker J, Hofstra N, Kroeze C and Medema G J 2015 Modelling the impact of sanitation, population and urbanization estimates on human emissions of Cryptosporidium to surface waters - a case study for Bangladesh and India Environ. Res. Lett. 10

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

DOE PAGES

Ray, J.; Lee, J.; Yadav, V.; ...

2015-04-29

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) andmore » fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ray, J.; Lee, J.; Yadav, V.

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) andmore » fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less

FRP MODEL - VERSION 1.0 FOR ESTIMATING STYRENE EMISSIONS FROM FIBER-REINFORCED PLASTICS FABRICATION PROCESSES

EPA Science Inventory

This software estimates styrene emissions from the manufacture of fiber-reinforced plastics/composite (FRP/C) products. In using the model, the user first chooses the appropriate process: gel coating, resin sprayup, hand layup, etc. Choosing a process will cause the 'baseline' in...

The impact of considering land intensification and updated data on biofuels land use change and emissions estimates.

DOT National Transportation Integrated Search

2017-01-01

Background: The GTAP model has been used to estimate biofuel policy induced land use changes and consequent GHG emissions for more than a decade. This paper reviews the history of the model and database modifications and improvements that have occurr...

Comparison of Landfill Methane Oxidation Measured Using Stable Isotope Analysis and CO2/CH4 Fluxes Measured by the Eddy Covariance Method

NASA Astrophysics Data System (ADS)

Xu, L.; Chanton, J.; McDermitt, D. K.; Li, J.; Green, R. B.

2015-12-01

Methane plays a critical role in the radiation balance and chemistry of the atmosphere. Globally, landfill methane emission contributes about 10-19% of the anthropogenic methane burden into the atmosphere. In the United States, 18% of annual anthropogenic methane emissions come from landfills, which represent the third largest source of anthropogenic methane emissions, behind enteric fermentation and natural gas and oil production. One uncertainty in estimating landfill methane emissions is the fraction of methane oxidized when methane produced under anaerobic conditions passes through the cover soil. We developed a simple stoichiometric model to estimate methane oxidation fraction when the anaerobic CO2 / CH4 production ratio is known, or can be estimated. The model predicts a linear relationship between CO2 emission rates and CH4 emission rates, where the slope depends on anaerobic CO2 / CH4 production ratio and the fraction of methane oxidized, and the intercept depends on non-methane-dependent oxidation processes. The model was tested using carbon dioxide emission rates (fluxes) and methane emission rates (fluxes) measured using the eddy covariance method over a one year period at the Turkey Run landfill in Georgia, USA. The CO2 / CH4 production ratio was estimated by measuring CO2 and CH4 concentrations in air sampled under anaerobic conditions deep inside the landfill. We also used a mass balance approach to independently estimate fractional oxidation based on stable isotope measurements (δ13C of methane) of gas samples taken from deep inside the landfill and just above the landfill surface. Results from the two independent methods agree well. The model will be described and methane oxidation will be discussed in relation to wind direction, location at the landfill, and age of the deposited refuse.

Using RAQMS Chemical Transport Model, Aircraft In-situ and Satellite Data to Verify Ground-based Biomass Burning Emissions from the Extreme Fire Event in Boreal Alaska 2004

NASA Astrophysics Data System (ADS)

Soja, A. J.; Pierce, R. B.; Al-Saadi, J. A.; Alvarado, E.; Sandberg, D. V.; Ottmar, R. D.; Kittaka, C.; McMillian, W. W.; Sachse, G. W.; Warner, J. X.; Szykman, J. J.

2006-12-01

Current climate change scenarios are predicted to result in increased biomass burning, particularly in boreal regions. Biomass burning events feedback to the climate system by altering albedo (affecting the energy balance) and by direct and indirect fire emissions. Additionally, fire emissions influence air quality and human health downwind of burning. Biomass burning emission estimates are difficult to quantify in near-real-time and accurate estimates are useful for large-scale chemical transport models, which could be used to warn the public of potential health risks and for climate modeling. In this talk, we describe a methodology to quantify emissions, validate those emission estimates, transport the emissions and verify the resultant CO plume 100's of kilometers from the fire events using aircraft in-situ and satellite data. First, we developed carbon consumption estimates that are specifically designed for near-real-time use in conjunction with satellite-derived fire data for regional- to global-chemical transport models. Large-scale carbon consumption estimates are derived for 10 ecozones across North America and each zone contains 3 classes of severity. The estimates range is from a low severity 3.11 t C ha-1 estimate from the Western Taiga Shield to a high severity 59.83 t C ha-1 estimate from the Boreal Cordillera. These estimates are validated using extensive supplementary ground-based Alaskan data. Then, the RAQMS chemical transport model ingests these data and transports CO from the Alaskan 2004 fires across North America, where results are compared with in-situ flight CO data measured during INTEX-A and satellite-based CO data (AIRS and MOPITT). Ground-based CO is 6 to 14 times greater than the typically modeled fire climatology. RAQMS often overestimates CO in the biomass plumes in comparison to satellite- derived CO data and we suspect this may be due to the satellite instruments low sensitivity to planetary boundary layer CO, which is prevalent in the near field plumes, and also the assumption of high-severity fires throughout the burning season. RAQMS underestimates biomass CO in comparison to in-situ CO data (146 out of 148 ascents and descents), and we suspect this may be due to RAQMS difficulty in defining narrow fire plumes due to the 1.4° x 1.4° resolution.

Estimating Biases for Regional Methane Fluxes using Co-emitted Tracers

NASA Astrophysics Data System (ADS)

Bambha, R.; Safta, C.; Michelsen, H. A.; Cui, X.; Jeong, S.; Fischer, M. L.

2017-12-01

Methane is a powerful greenhouse gas, and the development and improvement of emissions models rely on understanding the flux of methane released from anthropogenic sources relative to releases from other sources. Increasing production of shale oil and gas in the mid-latitudes and associated fugitive emissions are suspected to be a dominant contributor to the global methane increase. Landfills, sewage treatment, and other sources may be dominant sources in some parts of the U.S. Large discrepancies between emissions models present a great challenge to reconciling atmospheric measurements with inventory-based estimates for various emissions sectors. Current approaches for measuring regional emissions yield highly uncertain estimates because of the sparsity of measurement sites and the presence of multiple simultaneous sources. Satellites can provide wide spatial coverage at the expense of much lower measurement precision compared to ground-based instruments. Methods for effective assimilation of data from a variety of sources are critically needed to perform regional GHG attribution with existing measurements and to determine how to structure future measurement systems including satellites. We present a hierarchical Bayesian framework to estimate surface methane fluxes based on atmospheric concentration measurements and a Lagrangian transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport). Structural errors in the transport model are estimated with the help of co-emitted traces species with well defined decay rates. We conduct the analyses at regional scales that are based on similar geographical and meteorological conditions. For regions where data are informative, we further refine flux estimates by emissions sector and infer spatially and temporally varying biases parameterized as spectral random field representations.

Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sindelarova, K.; Granier, Claire; Bouarar, I.

The Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) together with the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields were used to create a global emission dataset of biogenic VOCs available on a monthly basis for the time period of 1980 - 2010. This dataset is called MEGAN-MACC. The model estimated mean annual total BVOC emission of 760 Tg(C) yr1 consisting of isoprene (70%), monoterpenes (11%), methanol (6%), acetone (3%), sesquiterpenes (2.5%) and other BVOC species each contributing less than 2 %. Several sensitivity model runs were performed to study the impact of different modelmore » input and model settings on isoprene estimates and resulted in differences of * 17% of the reference isoprene total. A greater impact was observed for sensitivity run applying parameterization of soil moisture deficit that led to a 50% reduction of isoprene emissions on a global scale, most significantly in specific regions of Africa, South America and Australia. MEGAN-MACC estimates are comparable to results of previous studies. More detailed comparison with other isoprene in ventories indicated significant spatial and temporal differences between the datasets especially for Australia, Southeast Asia and South America. MEGAN-MACC estimates of isoprene and*-pinene showed a reasonable agreement with surface flux measurements in the Amazon andthe model was able to capture the seasonal variation of emissions in this region.« less

Evaluating Anthropogenic Carbon Emissions in the Urban Salt Lake Valley through Inverse Modeling: Combining Long-term CO2 Observations and an Emission Inventory using a Multiple-box Atmospheric Model

NASA Astrophysics Data System (ADS)

Catharine, D.; Strong, C.; Lin, J. C.; Cherkaev, E.; Mitchell, L.; Stephens, B. B.; Ehleringer, J. R.

2016-12-01

The rising level of atmospheric carbon dioxide (CO2), driven by anthropogenic emissions, is the leading cause of enhanced radiative forcing. Increasing societal interest in reducing anthropogenic greenhouse gas emissions call for a computationally efficient method of evaluating anthropogenic CO2 source emissions, particularly if future mitigation actions are to be developed. A multiple-box atmospheric transport model was constructed in conjunction with a pre-existing fossil fuel CO2 emission inventory to estimate near-surface CO2 mole fractions and the associated anthropogenic CO2 emissions in the Salt Lake Valley (SLV) of northern Utah, a metropolitan area with a population of 1 million. A 15-year multi-site dataset of observed CO2 mole fractions is used in conjunction with the multiple-box model to develop an efficient method to constrain anthropogenic emissions through inverse modeling. Preliminary results of the multiple-box model CO2 inversion indicate that the pre-existing anthropogenic emission inventory may over-estimate CO2 emissions in the SLV. In addition, inversion results displaying a complex spatial and temporal distribution of urban emissions, including the effects of residential development and vehicular traffic will be discussed.

REAL-TIME MODELING OF MOTOR VEHICLE EMISSIONS FOR ESTIMATING HUMAN EXPOSURES NEAR ROADWAYS

EPA Science Inventory

The United States Environmental Protection Agency's (EPA) National Exposure Research Laboratory is developing a real-time model of motor vehicle emissions to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop ...

Influence of spatially dependent, modeled soil carbon emission factors on life-cycle greenhouse gas emissions of corn and cellulosic ethanol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qin, Zhangcai; Dunn, Jennifer B.; Kwon, Hoyoung

Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life-cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life-cycle GHG emissions affect biofuels’ attractiveness and eligibility under a number of renewable fuel policies in the U.S. and abroad. Modeling was used to refine the spatial resolution and depth-extent of domestic estimates of SOC change for land (cropland, cropland pasture, grasslands, and forests) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow). In most regions, conversions from cropland and cropland pasture to biofuel crops ledmore » to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. Results of SOC change were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life-cycle GHG emissions for the biofuels considered. Total LUC GHG emissions (g CO2eq MJ-1) were 2.1–9.3 for corn, -0.7 for corn stover, -3.4–12.9 for switchgrass, and -20.1–-6.2 for Miscanthus; these varied with SOC modeling assumptions applied. Extending soil depth from 30 to 100cm affected spatially-explicit SOC change and overall LUC GHG emissions; however the influence on LUC GHG emissions estimates were less significant in corn and corn stover than cellulosic feedstocks. Total life-cycle GHG emissions (g CO2eq MJ-1, 100cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18-26 for switchgrass ethanol, and -0.6–-7 for Miscanthus ethanol.« less

A Reduced Form Model for Ozone Based on Two Decades of ...

EPA Pesticide Factsheets

A Reduced Form Model (RFM) is a mathematical relationship between the inputs and outputs of an air quality model, permitting estimation of additional modeling without costly new regional-scale simulations. A 21-year Community Multiscale Air Quality (CMAQ) simulation for the continental United States provided the basis for the RFM developed in this study. Predictors included the principal component scores (PCS) of emissions and meteorological variables, while the predictand was the monthly mean of daily maximum 8-hour CMAQ ozone for the ozone season at each model grid. The PCS form an orthogonal basis for RFM inputs. A few PCS incorporate most of the variability of emissions and meteorology, thereby reducing the dimensionality of the source-receptor problem. Stochastic kriging was used to estimate the model. The RFM was used to separate the effects of emissions and meteorology on ozone concentrations. by running the RFM with emissions constant (ozone dependent on meteorology), or constant meteorology (ozone dependent on emissions). Years with ozone-conducive meteorology were identified, and meteorological variables best explaining meteorology-dependent ozone were identified. Meteorology accounted for 19% to 55% of ozone variability in the eastern US, and 39% to 92% in the western US. Temporal trends estimated for original CMAQ ozone data and emission-dependent ozone were mostly negative, but the confidence intervals for emission-dependent ozone are much

Assessing air quality and climate impacts of future ground freight choice in United States

NASA Astrophysics Data System (ADS)

Liu, L.; Bond, T. C.; Smith, S.; Lee, B.; Ouyang, Y.; Hwang, T.; Barkan, C.; Lee, S.; Daenzer, K.

2013-12-01

The demand for freight transportation has continued to increase due to the growth of domestic and international trade. Emissions from ground freight (truck and railways) account for around 7% of the greenhouse gas emissions, 4% of the primary particulate matter emission and 25% of the NOx emissions in the U.S. Freight railways are generally more fuel efficient than trucks and cause less congestion. Freight demand and emissions are affected by many factors, including economic activity, the spatial distribution of demand, freight modal choice and routing decision, and the technology used in each modal type. This work links these four critical aspects of freight emission system to project the spatial distribution of emissions and pollutant concentration from ground freight transport in the U.S. between 2010 and 2050. Macroeconomic scenarios are used to forecast economic activities. Future spatial structure of employment and commodity demand in major metropolitan areas are estimated using spatial models and a shift-share model, respectively. Freight flow concentration and congestion patterns in inter-regional transportation networks are predicted from a four-step freight demand forecasting model. An asymptotic vehicle routing model is also developed to estimate delivery ton-miles for intra-regional freight shipment in metropolitan areas. Projected freight activities are then converted into impacts on air quality and climate. CO2 emissions are determined using a simple model of freight activity and fuel efficiency, and compared with the projected CO2 emissions from the Second Generation Model. Emissions of air pollutants including PM, NOx and CO are calculated with a vehicle fleet model SPEW-Trend, which incorporates the dynamic change of technologies. Emissions are projected under three economic scenarios to represent different plausible futures. Pollutant concentrations are then estimated using tagged chemical tracers in an atmospheric model with the emissions serving as input.

Global CO emission estimates inferred from assimilation of MOPITT and IASI CO data, together with observations of O3, NO2, HNO3, and HCHO.

NASA Astrophysics Data System (ADS)

Zhang, X.; Jones, D. B. A.; Keller, M.; Jiang, Z.; Bourassa, A. E.; Degenstein, D. A.; Clerbaux, C.; Pierre-Francois, C.

2017-12-01

Atmospheric carbon monoxide (CO) emissions estimated from inverse modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties in CO emission estimates by constraining the modeled abundance of ozone (O3), nitrogen dioxide (NO2), nitric acid (HNO3), and formaldehyde (HCHO), which are constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system, we estimate CO emissions by assimilating observations of CO from the Measurement of Pollution In the Troposphere (MOPITT) and the Infrared Atmospheric Sounding Interferometer (IASI), together with observations of O3 from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and IASI, NO2 and HCHO from the Ozone Monitoring Instrument (OMI), and HNO3 from the Microwave Limb Sounder (MLS). Our experiments evaluate the inferred CO emission estimates from major anthropogenic, biomass burning and biogenic sources. Moreover, we also infer surface emissions of nitrogen oxides (NOx = NO + NO2) and isoprene. Our results reveal that this multiple species chemical data assimilation produces a chemical consistent state that effectively adjusts the CO-O3-OH coupling in the model. The O3-induced changes in OH are particularly large in the tropics. Overall, our analysis results in a better constrained tropospheric chemical state.

Top-down estimates of methane and nitrogen oxide emissions from shale gas production regions using aircraft measurements and a mesoscale Bayesian inversion system together with a flux ratio inversion technique

NASA Astrophysics Data System (ADS)

Cui, Y.; Brioude, J. F.; Angevine, W. M.; McKeen, S. A.; Henze, D. K.; Bousserez, N.; Liu, Z.; McDonald, B.; Peischl, J.; Ryerson, T. B.; Frost, G. J.; Trainer, M.

2016-12-01

Production of unconventional natural gas grew rapidly during the past ten years in the US which led to an increase in emissions of methane (CH4) and, depending on the shale region, nitrogen oxides (NOx). In terms of radiative forcing, CH4 is the second most important greenhouse gas after CO2. NOx is a precursor of ozone (O3) in the troposphere and nitrate particles, both of which are regulated by the US Clean Air Act. Emission estimates of CH4 and NOx from the shale regions are still highly uncertain. We present top-down estimates of CH4 and NOx surface fluxes from the Haynesville and Fayetteville shale production regions using aircraft data collected during the Southeast Nexus of Climate Change and Air Quality (SENEX) field campaign (June-July, 2013) and the Shale Oil and Natural Gas Nexus (SONGNEX) field campaign (March-May, 2015) within a mesoscale inversion framework. The inversion method is based on a mesoscale Bayesian inversion system using multiple transport models. EPA's 2011 National CH4 and NOx Emission Inventories are used as prior information to optimize CH4 and NOx emissions. Furthermore, the posterior CH4 emission estimates are used to constrain NOx emission estimates using a flux ratio inversion technique. Sensitivity of the posterior estimates to the use of off-diagonal terms in the error covariance matrices, the transport models, and prior estimates is discussed. Compared to the ground-based in-situ observations, the optimized CH4 and NOx inventories improve ground level CH4 and O3 concentrations calculated by the Weather Research and Forecasting mesoscale model coupled with chemistry (WRF-Chem).

Estimation of Biogenic VOC Emissions From Ecosystems in the Czech Republic

NASA Astrophysics Data System (ADS)

Zemankova, K.; Brechler, J.

2008-12-01

Volatile organic compounds (VOC) are one of the crucial elements in photochemical reactions in the atmosphere which lead to tropospheric ozone formation. While modelling concentration of low-level ozone proper information about VOC sources and sinks is necessary. VOC are emitted into the atmosphere both from anthropogenic and natural sources. It has been shown in previous studies (e.g. Simpson et al, 1995) that contribution of volatile organic compounds emitted from biogenic sources to total amount of VOC in the atmosphere can be significant. Our work focuses on estimation of VOC emissions from natural ecosystems, most importantly from forests, and its application in photochemical modelling. Preliminary results have shown that inclusion of biogenic emissions in model input data leads to improvement of resulting ozone concentration which encouraged us to work on detailed biogenic VOC emission estimation. Using grid of 1x1km CORINE Land Cover over the area of the Czech Republic, emissions from deciduous, coniferous and mixed forests were estimated aplying the algorithm of Guenther et al., 1995. According to data from Forest Management Institute each cell of model grid has been assigned a proportional composition of each of thirteen tree species which are the the main forest constituents in the Czech Republic. Aggregating data of tree species composition with land cover category emission factor of particular chemical compound (isoprene, monoterpenes) has been obtained for each cell. Annual emissions of VOC on hourly basis have been calculated for domain of the Czech Republic. Biogenic emissions of isoprene and monoterpenes were compared with the emission inventory of anthropogenic sources. The inventory is provided by Czech Hydrometeorological Institute and covers emissions from major stationary sources, area sources (including domestic heating) and mobile sources. Our results show that natural emissions are approximately half the amount of organic compounds emitted from anthropogenic sources. References: - Simpson D., Guenther A., Hewit C.N. and Steinbrecher R., 1995. Biogenic emissions in Europe. 1. estimates and uncertainties. J. Geophys. Res. 100(D11), 22875-22890. - Guenther A., Hewitt N., Erickson D., Fall R., Geron Ch., Graedel T., Harley P., Klinger L., Lerdau M., McKay W. A., Pierce T., Scholes B., Steinbrecher R., Tallamraju R., Taylor J., Zimmerman P., 1995. Global model of natural organic compound emissions. J. Geophys. Res. 100, 8873-8892.

Biomass burning emissions of reactive gases estimated from satellite data analysis and ecosystem modeling for the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Potter, Christopher; Brooks-Genovese, Vanessa; Klooster, Steven; Torregrosa, Alicia

2002-10-01

To produce a new daily record of trace gas emissions from biomass burning events for the Brazilian Legal Amazon, we have combined satellite advanced very high resolution radiometer (AVHRR) data on fire counts together for the first time with vegetation greenness imagery as inputs to an ecosystem biomass model at 8 km spatial resolution. This analysis goes beyond previous estimates for reactive gas emissions from Amazon fires, owing to a more detailed geographic distribution estimate of vegetation biomass, coupled with daily fire activity for the region (original 1 km resolution), and inclusion of fire effects in extensive areas of the Legal Amazon (defined as the Brazilian states of Acre, Amapá, Amazonas, Maranhao, Mato Grosso, Pará, Rondônia, Roraima, and Tocantins) covered by open woodland, secondary forests, savanna, and pasture vegetation. Results from our emissions model indicate that annual emissions from Amazon deforestation and biomass burning in the early 1990s total to 102 Tg yr-1 carbon monoxide (CO) and 3.5 Tg yr-1 nitrogen oxides (NOx). Peak daily burning emissions, which occurred in early September 1992, were estimated at slightly more than 3 Tg d-1for CO and 0.1 Tg d-1for NOx flux to the atmosphere. Other burning source fluxes of gases with relatively high emission factors are reported, including methane (CH4), nonmethane hydrocarbons (NMHC), and sulfur dioxide (SO2), in addition to total particulate matter (TPM). We estimate the Brazilian Amazon region to be a source of between one fifth and one third for each of these global emission fluxes to the atmosphere. The regional distribution of burning emissions appears to be highest in the Brazilian states of Maranhao and Tocantins, mainly from burning outside of moist forest areas, and in Pará and Mato Grosso, where we identify important contributions from primary forest cutting and burning. These new daily emission estimates of reactive gases from biomass burning fluxes are designed to be used as detailed spatial and temporal inputs to computer models and data analysis of tropospheric chemistry over the tropical region.

INTEGRATION OF THE BIOGENIC EMISSIONS INVENTORY SYSTEM (BEIS3) INTO THE COMMUNITY MULTISCALE AIR QUALITY MODELING SYSTEM

EPA Science Inventory

The importance of biogenic emissions for regional air quality modeling is generally recognized [Guenther et al., 2000]. Since the 1980s, biogenic emission estimates have been derived from algorithms such as the Biogenic Emissions Inventory System (BEIS) [Pierce et. al., 1998]....

Modeling Seasonality in Carbon Dioxide Emissions From Fossil Fuel Consumption

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Andres, R. J.

2004-05-01

Using United States data, a method is developed to estimate the monthly consumption of solid, liquid and gaseous fossil fuels using monthly sales data to estimate the relative monthly proportions of the total annual national fossil fuel use. These proportions are then used to estimate the total monthly carbon dioxide emissions for each state. From these data, the goal is to develop mathematical models that describe the seasonal flux in consumption for each type of fuel, as well as the total emissions for the nation. The time series models have two components. First, the general long-term yearly trend is determined with regression models for the annual totals. After removing the general trend, two alternatives are considered for modeling the seasonality. The first alternative uses the mean of the monthly proportions to predict the seasonal distribution. Because the seasonal patterns are fairly consistent in the United States, this is an effective modeling technique. Such regularity, however, may not be present with data from other nations. Therefore, as a second alternative, an ordinary least squares autoregressive model is used. This model is chosen for its ability to accurately describe dependent data and for its predictive capacity. It also has a meaningful interpretation, as each coefficient in the model quantifies the dependency for each corresponding time lag. Most importantly, it is dynamic, and able to adapt to anomalies and changing patterns. The order of the autoregressive model is chosen by the Akaike Information Criterion (AIC), which minimizes the predicted variance for all models of increasing complexity. To model the monthly fuel consumption, the annual trend is combined with the seasonal model. The models for each fuel type are then summed together to predict the total carbon dioxide emissions. The prediction error is estimated with the root mean square error (RMSE) from the actual estimated emission values. Overall, the models perform very well, with relative RMSE less than 10% for all fuel types, and under 5% for the national total emissions. Development of successful models is important to better understand and predict global environmental impacts from fossil fuel consumption.

The Boston Methane Project: Mapping Surface Emissions to Inform Atmospheric Estimation of Urban Methane Flux

NASA Astrophysics Data System (ADS)

Phillips, N.; Crosson, E.; Down, A.; Hutyra, L.; Jackson, R. B.; McKain, K.; Rella, C.; Raciti, S. M.; Wofsy, S. C.

2012-12-01

Lost and unaccounted natural gas can amount to over 6% of Massachusetts' total annual greenhouse gas inventory (expressed as equivalent CO2 tonnage). An unknown portion of this loss is due to natural gas leaks in pipeline distribution systems. The objective of the Boston Methane Project is to estimate the overall leak rate from natural gas systems in metropolitan Boston, and to compare this flux with fluxes from the other primary methane emissions sources. Companion talks at this meeting describe the atmospheric measurement and modeling framework, and chemical and isotopic tracers that can partition total atmospheric methane flux into natural gas and non-natural gas components. This talk focuses on estimation of surface emissions that inform the atmospheric modeling and partitioning. These surface emissions include over 3,300 pipeline natural gas leaks in Boston. For the state of Massachusetts as a whole, the amount of natural gas reported as lost and unaccounted for by utility companies was greater than estimated landfill emissions by an order of magnitude. Moreover, these landfill emissions were overwhelmingly located outside of metro Boston, while gas leaks are concentrated in exactly the opposite pattern, increasing from suburban Boston toward the urban core. Work is in progress to estimate spatial distribution of methane emissions from wetlands and sewer systems. We conclude with a description of how these spatial data sets will be combined and represented for application in atmospheric modeling.

Modeling regional-scale wildland fire emissions with the wildland fire emissions information system

Treesearch

Nancy H.F. French; Donald McKenzie; Tyler Erickson; Benjamin Koziol; Michael Billmire; K. Endsley; Naomi K.Y. Scheinerman; Liza Jenkins; Mary E. Miller; Roger Ottmar; Susan Prichard

2014-01-01

As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent...

On the Specification of Smoke Injection Heights for Aerosol Forecasting

NASA Astrophysics Data System (ADS)

da Silva, A.; Schaefer, C.; Randles, C. A.

2014-12-01

The proper forecasting of biomass burning (BB) aerosols in global or regional transport models requires not only the specification of emission rates with sufficient temporal resolution but also the injection layers of such emissions. While current near realtime biomass burning inventories such as GFAS, QFED, FINN, GBBEP and FLAMBE provide such emission rates, it is left for each modeling system to come up with its own scheme for distributing these emissions in the vertical. A number of operational aerosol forecasting models deposits BB emissions in the near surface model layers, relying on the model's parameterization of turbulent and convective transport to determine the vertical mass distribution of BB aerosols. Despite their simplicity such schemes have been relatively successful reproducing the vertical structure of BB aerosols, except for those large fires that produce enough buoyancy to puncture the PBL and deposit the smoke at higher layers. Plume Rise models such as the so-called 'Freitas model', parameterize this sub-grid buoyancy effect, but require the specification of fire size and heat fluxes, none of which is readily available in near real-time from current remotely-sensed products. In this talk we will introduce a bayesian algorithm for estimating file size and heat fluxes from MODIS brightness temperatures. For small to moderate fires the Freitas model driven by these heat flux estimates produces plume tops that are highly correlated with the GEOS-5 model estimate of PBL height. Comparison to MINX plume height estimates from MISR indicates moderate skill of this scheme predicting the injection height of large fires. As an alternative, we make use of OMPS UV aerosol index data in combination with estimates of Overshooting Convective Tops (from MODIS and Geo-stationary satellites) to detect PyCu events and specify the BB emission vertical mass distribution in such cases. We will present a discussion of case studies during the SEAC4RS field campaign in August-September 2013.

Description and History of the MOBILE Highway Vehicle Emission Factor Model

EPA Pesticide Factsheets

MOBILE is an EPA model for estimating pollution from highway vehicles. It has been superseded by the Motor Vehicle Emission Simulator (MOVES). MOBILE calculates emissions of hydrocarbons (HC), oxides of nitrogen (NOx) and carbon monoxide (CO).

Carbon loss and greenhouse gas emission from extreme fire events occurred in Sardinia, Italy

NASA Astrophysics Data System (ADS)

Bacciu, V. M.; Salis, M.; Pellizzaro, G.; Arca, B.; Duce, P.; Spano, D.

2011-12-01

It is widely recognized that biomass burning is a significant driver of CO2 cycling and a source of greenhouse gases, aerosol particles, and other chemically reactive atmospheric gases. The large amounts of carbon that fires release into the atmosphere could approach levels of anthropogenic carbon emissions, especially in years of extreme fire activity. CO2 emissions from 2007 forest fires in Greece were in the range of 4.5 Mt, representing about the 4% of the total annual CO2 emissions of that country (http://effis.jrc.it/). Barbosa et al. (2006) reported a similar percentage of fire emissions to total emissions of CO2 in Portugal during the extreme fire seasons of 2003 and 2005. Currently, inventory methods for biomass burning emission use the equation first proposed by Seiler and Crutzen (1980), taking into account the area burned, the amount of biomass burned, and the emission factors associated with each specific chemical species. However, several errors and uncertainties can affect the emission assessment, due to the estimate consistency of the various parameters involved in the equation, including flaming and smoldering combustion periods, appropriate fuel load evaluations and gaseous emission factors for different fuel fractions and fire types. In this context, model approaching can contribute to better appraise fuel consumption and the resultant emissions. In addition, more comprehensive and accurate data inputs would be of valuable help for predicting and quantifying the source and the composition of fire emissions. The purpose of this work is to explore the impacts of extreme fire events occurred in Sardinia Island (Italy) using an integrated approach combining modelling fire emissions, field observations and remotely-sensed data. In order to achieve realistic fire emission estimates, we used the FOFEM model, due to the necessity to use a consistent modeling methodology across source categories, the input required, and its ability to estimate flaming and smoldering emissions. FOFEM input fuel load data were surveyed to represent those combusted, and fuel availability was obtained from supervised classification of remotely-sensed images. Data relative to fire perimeters, fire weather data, and fire behaviour were gathered by the Sardinian Forestry Corps (CFVA). Consumptions and emissions for each fuel types were estimated through FOFEM. Finally, all the data were assembled into a Geographical Information System (GIS) to facilitate manipulation and display of the data. The results showed the crucial role of appropriate fuel, fire, and weather data and maps to attain reasonable simulations of fuel consumption and smoke emissions. Carbon emission estimates are sensitive to pre-fire fuel loads, so the method used to establish initial fuel conditions is crucial. The FOFEM outputs and the derived smoke emission maps are useful for several applications including emissions inventories, air quality management plans, and emission source models coupled with dispersion models and decision support systems.

An evaluation of the uncertainties in biomass burning emissions

NASA Astrophysics Data System (ADS)

Yano, A.; Garcia Menendez, F.; Hu, Y.; Odman, M.

2012-12-01

The contribution of biomass burning emissions to the atmospheric loads of gases and aerosols can lead to major air quality problems and have significant climate impacts. Whether from wildfires, natural or human-induced, or controlled burns, biomass burning emissions are an important source of air pollutants regionally in certain parts of the world as well as globally. There are two common ways of estimating biomass burning emissions: by using either ground-based information or satellite observations. When there is sufficient local information about the burn area, the types of fuels and their consumption amounts, and the progression of the fire, ground-based estimation is preferred. For controlled burns a.k.a. prescribed burns and wildfires in places where land management is practiced to a certain extent there is typically sufficient ground-based information for emissions estimation. However, for remote regions where no ground-based information is available on the size, intensity, or the spread of the fire, estimates based on satellite observations are preferred. For example, burn location, size and timing information can be obtained from satellite retrievals of thermal anomalies and fuel loading information can be obtained from satellite products of vegetation cover. In both cases, reasonable emission estimates for a variety of pollutants can be obtained by using emission factors (mass of pollutant released per unit mass of fuel consumed) derived from field or laboratory studies. Here, emissions from a controlled burn and a wildfire are estimated using both ground-based information and satellite observations. The controlled burn was conducted on 17 November 2009 near Santa Barbara, California over 80 ha of land covered with chaparral. An aircraft tracked the smoke plume and measured CO2, light scattering, as well as meteorological parameters during the burn (Akagi et al., 2011). The wildfire is from the summer of 2008 when tens of thousands hectares of wild land burned in Northern California causing unprecedented damage. NASA Aircraft commissioned for the ARCTAS campaign at the time flew over the fires and collected data detailing composition of gases and aerosols in the fire plumes (Singh et al., 2012). We model the fires using a newly developed system consisting of a plume rise and dispersion model specifically designed for wild-land fire plumes (Daysmoke; Achtemeier et al., 2011) coupled with a regional-scale chemistry-transport model (CMAQ). Wind fields generated by a weather prediction model (WRF) are adjusted locally to match the aircraft measurements of wind speed and direction. The fires are simulated using both ground-based and satellite-based estimates of emissions. Predicted concentrations of gases and aerosols are compared to corresponding aircraft measurements. Satellite retrievals of aerosol optical depth are also used in evaluating model predictions. The new modeling system along with the wind adjustments reduces several of the uncertainties inherent to regional-scale modeling of plume transport. This allows for a more reliable analysis of the uncertainties related to emissions. Uncertainties in the magnitudes and timings of emissions, and in plume injection heights with respect to boundary layer heights are investigated. Uncertainties associated with ground-based and satellite-based emissions estimation methods are compared to each other.

Optimizing Fukushima Emissions Through Pattern Matching and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Simpson, M. D.; Philip, C. S.; Baskett, R.

2017-12-01

Hazardous conditions during the Fukushima Daiichi nuclear power plant (NPP) accident hindered direct observations of the emissions of radioactive materials into the atmosphere. A wide range of emissions are estimated from bottom-up studies using reactor inventories and top-down approaches based on inverse modeling. We present a new inverse modeling estimate of cesium-137 emitted from the Fukushima NPP. Our estimate considers weather uncertainty through a large ensemble of Weather Research and Forecasting model simulations and uses the FLEXPART atmospheric dispersion model to transport and deposit cesium. The simulations are constrained by observations of the spatial distribution of cumulative cesium deposited on the surface of Japan through April 2, 2012. Multiple spatial metrics are used to quantify differences between observed and simulated deposition patterns. In order to match the observed pattern, we use a multi-objective genetic algorithm to optimize the time-varying emissions. We find that large differences with published bottom-up estimates are required to explain the observations. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Towards national-scale greenhouse gas emissions evaluation with robust uncertainty estimates

NASA Astrophysics Data System (ADS)

Rigby, Matthew; Swallow, Ben; Lunt, Mark; Manning, Alistair; Ganesan, Anita; Stavert, Ann; Stanley, Kieran; O'Doherty, Simon

2016-04-01

Through the Deriving Emissions related to Climate Change (DECC) network and the Greenhouse gAs Uk and Global Emissions (GAUGE) programme, the UK's greenhouse gases are now monitored by instruments mounted on telecommunications towers and churches, on a ferry that performs regular transects of the North Sea, on-board a research aircraft and from space. When combined with information from high-resolution chemical transport models such as the Met Office Numerical Atmospheric dispersion Modelling Environment (NAME), these measurements are allowing us to evaluate emissions more accurately than has previously been possible. However, it has long been appreciated that current methods for quantifying fluxes using atmospheric data suffer from uncertainties, primarily relating to the chemical transport model, that have been largely ignored to date. Here, we use novel model reduction techniques for quantifying the influence of a set of potential systematic model errors on the outcome of a national-scale inversion. This new technique has been incorporated into a hierarchical Bayesian framework, which can be shown to reduce the influence of subjective choices on the outcome of inverse modelling studies. Using estimates of the UK's methane emissions derived from DECC and GAUGE tall-tower measurements as a case study, we will show that such model systematic errors have the potential to significantly increase the uncertainty on national-scale emissions estimates. Therefore, we conclude that these factors must be incorporated in national emissions evaluation efforts, if they are to be credible.

Mitigating Satellite-Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF-Chem Model Over the Northern sub-Saharan African Region

NASA Astrophysics Data System (ADS)

Wang, Jun; Yue, Yun; Wang, Yi; Ichoku, Charles; Ellison, Luke; Zeng, Jing

2018-01-01

Largely used in several independent estimates of fire emissions, fire products based on MODIS sensors aboard the Terra and Aqua polar-orbiting satellites have a number of inherent limitations, including (a) inability to detect fires below clouds, (b) significant decrease of detection sensitivity at the edge of scan where pixel sizes are much larger than at nadir, and (c) gaps between adjacent swaths in tropical regions. To remedy these limitations, an empirical method is developed here and applied to correct fire emission estimates based on MODIS pixel level fire radiative power measurements and emission coefficients from the Fire Energetics and Emissions Research (FEER) biomass burning emission inventory. The analysis was performed for January 2010 over the northern sub-Saharan African region. Simulations from WRF-Chem model using original and adjusted emissions are compared with the aerosol optical depth (AOD) products from MODIS and AERONET as well as aerosol vertical profile from CALIOP data. The comparison confirmed an 30-50% improvement in the model simulation performance (in terms of correlation, bias, and spatial pattern of AOD with respect to observations) by the adjusted emissions that not only increases the original emission amount by a factor of two but also results in the spatially continuous estimates of instantaneous fire emissions at daily time scales. Such improvement cannot be achieved by simply scaling the original emission across the study domain. Even with this improvement, a factor of two underestimations still exists in the modeled AOD, which is within the current global fire emissions uncertainty envelope.

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors.

PubMed

Albanito, Fabrizio; Lebender, Ulrike; Cornulier, Thomas; Sapkota, Tek B; Brentrup, Frank; Stirling, Clare; Hillier, Jon

2017-03-10

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N 2 O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N 2 O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N 2 O-N emissions to estimate tropic-specific annual N 2 O emission factors (N 2 O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N 2 O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N 2 O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central &South America. Our annual N 2 O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N 2 O-EFs as a function of applied N. Our findings highlight that in reporting annual N 2 O emissions and estimating N 2 O-EFs, particular attention should be paid in modelling the effect of study length on response of N 2 O.

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors

PubMed Central

Albanito, Fabrizio; Lebender, Ulrike; Cornulier, Thomas; Sapkota, Tek B.; Brentrup, Frank; Stirling, Clare; Hillier, Jon

2017-01-01

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N2O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N2O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N2O-N emissions to estimate tropic-specific annual N2O emission factors (N2O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N2O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N2O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central & South America. Our annual N2O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N2O-EFs as a function of applied N. Our findings highlight that in reporting annual N2O emissions and estimating N2O-EFs, particular attention should be paid in modelling the effect of study length on response of N2O. PMID:28281637

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems - A Review And Modelling Of Emission Factors

NASA Astrophysics Data System (ADS)

Albanito, Fabrizio; Lebender, Ulrike; Cornulier, Thomas; Sapkota, Tek B.; Brentrup, Frank; Stirling, Clare; Hillier, Jon

2017-03-01

There has been much debate about the uncertainties associated with the estimation of direct and indirect agricultural nitrous oxide (N2O) emissions in developing countries and in particular from tropical regions. In this study, we report an up-to-date review of the information published in peer-review journals on direct N2O emissions from agricultural systems in tropical and sub-tropical regions. We statistically analyze net-N2O-N emissions to estimate tropic-specific annual N2O emission factors (N2O-EFs) using a Generalized Additive Mixed Model (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear continuous functions. Overall the mean N2O-EF was 1.2% for the tropics and sub-tropics, thus within the uncertainty range of IPCC-EF. On a regional basis, mean N2O-EFs were 1.4% for Africa, 1.1%, for Asia, 0.9% for Australia and 1.3% for Central & South America. Our annual N2O-EFs, estimated for a range of fertiliser rates using the available data, do not support recent studies hypothesising non-linear increase N2O-EFs as a function of applied N. Our findings highlight that in reporting annual N2O emissions and estimating N2O-EFs, particular attention should be paid in modelling the effect of study length on response of N2O.

Variability and uncertainty in life cycle assessment models for greenhouse gas emissions from Canadian oil sands production.

PubMed

Brandt, Adam R

2012-01-17

Because of interest in greenhouse gas (GHG) emissions from transportation fuels production, a number of recent life cycle assessment (LCA) studies have calculated GHG emissions from oil sands extraction, upgrading, and refining pathways. The results from these studies vary considerably. This paper reviews factors affecting energy consumption and GHG emissions from oil sands extraction. It then uses publicly available data to analyze the assumptions made in the LCA models to better understand the causes of variability in emissions estimates. It is found that the variation in oil sands GHG estimates is due to a variety of causes. In approximate order of importance, these are scope of modeling and choice of projects analyzed (e.g., specific projects vs industry averages); differences in assumed energy intensities of extraction and upgrading; differences in the fuel mix assumptions; treatment of secondary noncombustion emissions sources, such as venting, flaring, and fugitive emissions; and treatment of ecological emissions sources, such as land-use change-associated emissions. The GHGenius model is recommended as the LCA model that is most congruent with reported industry average data. GHGenius also has the most comprehensive system boundaries. Last, remaining uncertainties and future research needs are discussed.

The Impact of Advanced Greenhouse Gas Measurement Science on Policy Goals and Research Strategies

NASA Astrophysics Data System (ADS)

Abrahams, L.; Clavin, C.; McKittrick, A.

2016-12-01

In support of the Paris agreement, accurate characterizations of U.S. greenhouse gas (GHG) emissions estimates have been area of increased scientific focus. Over the last several years, the scientific community has placed significant emphasis on understanding, quantifying, and reconciling measurement and modeling methods that characterize methane emissions from petroleum and natural gas sources. This work has prompted national policy discussions and led to the improvement of regional and national methane emissions estimates. Research campaigns focusing on reconciling atmospheric measurements ("top-down") and process-based emissions estimates ("bottom-up") have sought to identify where measurement technology advances could inform policy objectives. A clear next step is development and deployment of advanced detection capabilities that could aid U.S. emissions mitigation and verification goals. The breadth of policy-relevant outcomes associated with advances in GHG measurement science are demonstrated by recent improvements in the petroleum and natural gas sector emission estimates in the EPA Greenhouse Gas Inventory, ambitious efforts to apply inverse modeling results to inform or validate national GHG inventory, and outcomes from federal GHG measurement science technology development programs. In this work, we explore the variety of policy-relevant outcomes impacted by advances in GHG measurement science, with an emphasis on improving GHG inventory estimates, identifying emissions mitigation strategies, and informing technology development requirements.

Public health impacts of excess NOx emissions from Volkswagen diesel passenger vehicles in Germany

NASA Astrophysics Data System (ADS)

Chossière, Guillaume P.; Malina, Robert; Ashok, Akshay; Dedoussi, Irene C.; Eastham, Sebastian D.; Speth, Raymond L.; Barrett, Steven R. H.

2017-03-01

In September 2015, the Volkswagen Group (VW) admitted the use of ‘defeat devices’ designed to lower emissions measured during VW vehicle testing for regulatory purposes. Globally, 11 million cars sold between 2008 and 2015 are affected, including about 2.6 million in Germany. On-road emissions tests have yielded mean on-road NOx emissions for these cars of 0.85 g km-1, over four times the applicable European limit of 0.18 g km-1. This study estimates the human health impacts and costs associated with excess emissions from VW cars driven in Germany. A distribution of on-road emissions factors is derived from existing measurements and combined with sales data and a vehicle fleet model to estimate total excess NOx emissions. These emissions are distributed on a 25 by 28 km grid covering Europe, using the German Federal Environmental Protection Agency’s (UBA) estimate of the spatial distribution of NOx emissions from passenger cars in Germany. We use the GEOS-Chem chemistry-transport model to predict the corresponding increase in population exposure to fine particulate matter and ozone in the European Union, Switzerland, and Norway, and a set of concentration-response functions to estimate mortality outcomes in terms of early deaths and of life-years lost. Integrated over the sales period (2008-2015), we estimate median mortality impacts from VW excess emissions in Germany to be 1200 premature deaths in Europe, corresponding to 13 000 life-years lost and 1.9 billion EUR in costs associated with life-years lost. Approximately 60% of mortality costs occur outside Germany. For the current fleet, we estimate that if on-road emissions for all affected VW vehicles in Germany are reduced to the applicable European emission standard by the end of 2017, this would avert 29 000 life-years lost and 4.1 billion 2015 EUR in health costs (median estimates) relative to a counterfactual case with no recall.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gu, Dasa; Guenther, Alex B.; Shilling, John E.

Terrestrial vegetation emits vast quantities of volatile organic compounds (VOCs) to he atmosphere1-3, which influence oxidants and aerosols leading to complex feedbacks on air quality and climate4-6. Isoprene dominates global non-methane VOC emissions with tropical regions contributing ~80% of global isoprene emissions2. Isoprene emission rates vary over several orders of magnitude for different plant species, and characterizing this immense biological chemodiversity is a challenge for estimating isoprene emission from tropical forests. Here we present the isoprene emission estimates from aircraft direct eddy covariance measurements over the pristine Amazon forest. We report isoprene emission rates that are 3 times higher thanmore » satellite top-down estimates and 35% higher than model predictions based on satellite land cover and vegetation specific emission factors (EFs). The results reveal strong correlations between observed isoprene emission rates and terrain elevations which are confirmed by similar correlations between satellite-derived isoprene emissions and terrain elevations. We propose that the elevational gradient in the Amazonian forest isoprene emission capacity is determined by plant species distributions and can explain a substantial degree of isoprene emission variability in tropical forests. Finally, we apply this approach over the central Amazon and use a model to demonstrate the impacts on regional air quality.« less

Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain).

PubMed

Quaassdorff, Christina; Borge, Rafael; Pérez, Javier; Lumbreras, Julio; de la Paz, David; de Andrés, Juan Manuel

2016-10-01

This paper presents the evaluation of emissions from vehicle operations in a domain of 300m×300m covering a complex urban roundabout with high traffic density in Madrid. Micro-level simulation was successfully applied to estimate the emissions on a scale of meters. Two programs were used: i) VISSIM to simulate the traffic on the square and to compute velocity-time profiles; and ii) VERSIT+micro through ENVIVER that uses VISSIM outputs to compute the related emissions at vehicle level. Data collection was achieved by a measurement campaign obtaining empirical data of vehicle flows and traffic intensities. Twelve simulations of different traffic situations (scenarios) were conducted, representing different hours from several days in a week and the corresponding NOX and PM10 emissions were estimated. The results show a general reduction on average speeds for higher intensities due to braking-acceleration patterns that contribute to increase the average emission factor and, therefore, the total emissions in the domain, especially on weekdays. The emissions are clearly related to traffic volume, although maximum emission scenario does not correspond to the highest traffic intensity due to congestion and variations in fleet composition throughout the day. These results evidence the potential that local measures aimed at alleviating congestion may have in urban areas to reduce emissions. In general, scenario-averaged emission factors estimated with the VISSIM-VERSIT+micro modelling system fitted well those from the average-speed model COPERT, used as a preliminary validation of the results. The largest deviations between these two models occur in those scenarios with more congestion. The design and resolution of the microscale modelling system allow to reflect the impact of actual traffic conditions on driving patterns and related emissions, making it useful for the design of mitigation measures for specific traffic hot-spots. Copyright © 2016 Elsevier B.V. All rights reserved.

Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NOx and CO2 and their impacts

NASA Astrophysics Data System (ADS)

Brioude, J.; Angevine, W. M.; Ahmadov, R.; Kim, S.-W.; Evan, S.; McKeen, S. A.; Hsie, E.-Y.; Frost, G. J.; Neuman, J. A.; Pollack, I. B.; Peischl, J.; Ryerson, T. B.; Holloway, J.; Brown, S. S.; Nowak, J. B.; Roberts, J. M.; Wofsy, S. C.; Santoni, G. W.; Oda, T.; Trainer, M.

2013-04-01

We present top-down estimates of anthropogenic CO, NOx and CO2 surface fluxes at mesoscale using a Lagrangian model in combination with three different WRF model configurations, driven by data from aircraft flights during the CALNEX campaign in southern California in May-June 2010. The US EPA National Emission Inventory 2005 (NEI 2005) was the prior in the CO and NOx inversion calculations. The flux ratio inversion method, based on linear relationships between chemical species, was used to calculate the CO2 inventory without prior knowledge of CO2 surface fluxes. The inversion was applied to each flight to estimate the variability of single-flight-based flux estimates. In Los Angeles (LA) County, the uncertainties on CO and NOx fluxes were 10% and 15%, respectively. Compared with NEI 2005, the CO posterior emissions were lower by 43% in LA County and by 37% in the South Coast Air Basin (SoCAB). NOx posterior emissions were lower by 32% in LA County and by 27% in the SoCAB. NOx posterior emissions were 40% lower on weekends relative to weekdays. The CO2 posterior estimates were 183 Tg yr-1 in SoCAB. A flight during ITCT (Intercontinental Transport and Chemical Transformation) in 2002 was used to estimate emissions in the LA Basin in 2002. From 2002 to 2010, the CO and NOx posterior emissions decreased by 41% and 37%, respectively, in agreement with previous studies. Over the same time period, CO2 emissions increased by 10% in LA County but decreased by 4% in the SoCAB, a statistically insignificant change. Overall, the posterior estimates were in good agreement with the California Air Resources Board (CARB) inventory, with differences of 15% or less. However, the posterior spatial distribution in the basin was significantly different from CARB for NOx emissions. WRF-Chem mesoscale chemical-transport model simulations allowed an evaluation of differences in chemistry using different inventory assumptions, including NEI 2005, a gridded CARB inventory and the posterior inventories derived in this study. The biases in WRF-Chem ozone were reduced and correlations were increased using the posterior from this study compared with simulations with the two bottom-up inventories, suggesting that improving the spatial distribution of ozone precursor surface emissions is also important in mesoscale chemistry simulations.

Global anthropogenic methane emissions 2005-2030: technical mitigation potentials and costs

NASA Astrophysics Data System (ADS)

Höglund-Isaksson, L.

2012-10-01

This paper presents estimates of current and future global anthropogenic methane emissions, their technical mitigation potential and associated costs for the period 2005 to 2030. The analysis uses the GAINS model framework to estimate emissions, mitigation potentials and costs for all major sources of anthropogenic methane for 83 countries/regions, which are aggregated to produce global estimates. Global emissions are estimated at 323 Mt methane in 2005, with an expected increase to 414 Mt methane in 2030. The technical mitigation potential is estimated at 195 Mt methane in 2030, whereof about 80 percent is found attainable at a marginal cost less than 20 Euro t-1 CO2eq when using a social planner cost perspective. With a private investor cost perspective, the corresponding fraction is only 30 percent. Major uncertainty sources in emission estimates are identified and discussed.

Implications of near-term coal power plant retirement for SO2 and NOX and life cycle GHG emissions.

PubMed

Venkatesh, Aranya; Jaramillo, Paulina; Griffin, W Michael; Matthews, H Scott

2012-09-18

Regulations monitoring SO(2), NO(X), mercury, and other metal emissions in the U.S. will likely result in coal plant retirement in the near-term. Life cycle assessment studies have previously estimated the environmental benefits of displacing coal with natural gas for electricity generation, by comparing systems that consist of individual natural gas and coal power plants. However, such system comparisons may not be appropriate to analyze impacts of coal plant retirement in existing power fleets. To meet this limitation, simplified economic dispatch models for PJM, MISO, and ERCOT regions are developed in this study to examine changes in regional power plant dispatch that occur when coal power plants are retired. These models estimate the order in which existing power plants are dispatched to meet electricity demand based on short-run marginal costs, with cheaper plants being dispatched first. Five scenarios of coal plant retirement are considered: retiring top CO(2) emitters, top NO(X) emitters, top SO(2) emitters, small and inefficient plants, and old and inefficient plants. Changes in fuel use, life cycle greenhouse gas emissions (including uncertainty), and SO(2) and NO(X) emissions are estimated. Life cycle GHG emissions were found to decrease by less than 4% in almost all scenarios modeled. In addition, changes in marginal damage costs due to SO(2), and NO(X) emissions are estimated using the county level marginal damage costs reported in the Air Pollution Emissions Experiments and Policy (APEEP) model, which are a proxy for measuring regional impacts of SO(2) and NO(X) emissions. Results suggest that location specific parameters should be considered within environmental policy frameworks targeting coal plant retirement, to account for regional variability in the benefits of reducing the impact of SO(2) and NO(X) emissions.

Understanding Methane Emission from Natural Gas Activities Using Inverse Modeling Techniques

NASA Astrophysics Data System (ADS)

Abdioskouei, M.; Carmichael, G. R.

2015-12-01

Natural gas (NG) has been promoted as a bridge fuel that can smooth the transition from fossil fuels to zero carbon energy sources by having lower carbon dioxide emission and lower global warming impacts in comparison to other fossil fuels. However, the uncertainty around the estimations of methane emissions from NG systems can lead to underestimation of climate and environmental impacts of using NG as a replacement for coal. Accurate estimates of methane emissions from NG operations is crucial for evaluation of environmental impacts of NG extraction and at larger scale, adoption of NG as transitional fuel. However there is a great inconsistency within the current estimates. Forward simulation of methane from oil and gas operation sites for the US is carried out based on NEI-2011 using the WRF-Chem model. Simulated values are compared against measurements of observations from different platforms such as airborne (FRAPPÉ field campaign) and ground-based measurements (NOAA Earth System Research Laboratory). A novel inverse modeling technique is used in this work to improve the model fit to the observation values and to constrain methane emission from oil and gas extraction sites.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenberg, Jim; Penuelas, J.; Guenther, Alex B.

To survey landscape-scale fluxes of biogenic gases, a100-meterTeflon tube was attached to a tethered balloon as a sampling inlet for a fast response Proton Transfer Reaction Mass Spectrometer (PTRMS). Along with meteorological instruments deployed on the tethered balloon and at 3-mand outputs from a regional weather model, these observations were used to estimate landscape scale biogenic volatile organic compound fluxes with two micrometeorological techniques: mixed layer variance and surface layer gradients. This highly mobile sampling system was deployed at four field sites near Barcelona to estimate landscape-scale BVOC emission factors in a relatively short period (3 weeks). The two micrometeorologicalmore » techniques agreed within the uncertainty of the flux measurements at all four sites even though the locations had considerable heterogeneity in species distribution and complex terrain. The observed fluxes were significantly different than emissions predicted with an emission model using site-specific emission factors and land-cover characteristics. Considering the wide range in reported BVOC emission factors of VOCs for individual vegetation species (more than an order of magnitude), this flux estimation technique is useful for constraining BVOC emission factors used as model inputs.« less

Verifying the UK agricultural N2O emission inventory with tall tower measurements

NASA Astrophysics Data System (ADS)

Carnell, E. J.; Meneguz, E.; Skiba, U. M.; Misselbrook, T. H.; Cardenas, L. M.; Arnold, T.; Manning, A.; Dragosits, U.

2016-12-01

Nitrous oxide (N2O) is a key greenhouse gas (GHG), with a global warming potential 300 times greater than that of CO2. N2O is emitted from a variety of sources, predominantly from agriculture. Annual UK emission estimates are reported, to comply with government commitments under the United Nations Framework Convention on Climate Change (UNFCCC). The UK N2O inventory follows internationally agreed protocols and emission estimates are derived by applying emission factors to estimates of (anthropogenic) emission sources. This approach is useful for comparing anthropogenic emissions from different countries, but does not capture regional differences and inter-annual variability associated with environmental factors (such as climate and soils) and agricultural management. In recent years, the UK inventory approach has been refined to include regional information into its emissions estimates, in an attempt to reduce uncertainty. This study attempts to assess the difference between current published inventory methodology (default IPCC methodology) and an alternative approach, which incorporates the latest thinking, using data from recent work. For 2013, emission estimates made using the alternative approach were 30 % lower than those made using default IPCC methodology, due to the use of lower emission factors suggested by recent projects (Defra projects: AC0116, AC0213 and MinNO). The 2013 emissions estimates were disaggregated on a monthly basis using agricultural management (e.g. sowing dates), climate data and soil properties. The temporally disaggregated emission maps were used as input to the Met Office atmospheric dispersion model NAME, for comparison with measured N2O concentrations, at three observation stations (Tacolneston, E. England; Ridge Hill, W. England; Mace Head, W. Ireland) in the UK DECC network (Deriving Emissions linked to Climate Change). The Mace Head site, situated on the west coast of Ireland, was used to establish baseline concentrations. The trends in the modelled data were found to correspond with the observational data trends, with concentration peaks coinciding with periods of land spreading of manures and fertiliser application. The model run using the default IPCC methodology was found to correspond with the observed data more closely than the alternative approach.

Verifying the UK N_{2}O emission inventory with tall tower measurements

NASA Astrophysics Data System (ADS)

Carnell, Ed; Meneguz, Elena; Skiba, Ute; Misselbrook, Tom; Cardenas, Laura; Arnold, Tim; Manning, Alistair; Dragosits, Ulli

2016-04-01

Nitrous oxide (N2O) is a key greenhouse gas (GHG), with a global warming potential ˜300 times greater than that of CO2. N2O is emitted from a variety of sources, predominantly from agriculture. Annual UK emission estimates are reported, to comply with government commitments under the United Nations Framework Convention on Climate Change (UNFCCC). The UK N2O inventory follows internationally agreed protocols and emission estimates are derived by applying emission factors to estimates of (anthropogenic) emission sources. This approach is useful for comparing anthropogenic emissions from different countries, but does not capture regional differences and inter-annual variability associated with environmental factors (such as climate and soils) and agricultural management. In recent years, the UK inventory approach has been refined to include regional information into its emissions estimates (e.g. agricultural management data), in an attempt to reduce uncertainty. This study attempts to assess the difference between current published inventory methodology (default IPCC methodology) and a revised approach, which incorporates the latest thinking, using data from recent work. For 2013, emission estimates made using the revised approach were 30 % lower than those made using default IPCC methodology, due to the use of lower emission factors suggested by recent projects (www.ghgplatform.org.uk, Defra projects: AC0116, AC0213 and MinNO). The 2013 emissions estimates were disaggregated on a monthly basis using agricultural management (e.g. sowing dates), climate data and soil properties. The temporally disaggregated emission maps were used as input to the Met Office atmospheric dispersion model NAME, for comparison with measured N2O concentrations, at three observation stations (Tacolneston, E England; Ridge Hill, W England; Mace Head, W Ireland) in the UK DECC network (Deriving Emissions linked to Climate Change). The Mace Head site, situated on the west coast of Ireland, was used to establish baseline concentrations. The trends in the modelled data were found to fit with the observational data trends, with concentration peaks coinciding with periods of fertiliser application and land spreading of manures. The model run using the 'experimental' approach was found to give a closer agreement with the observed data.

Historical emissions critical for mapping decarbonization pathways

NASA Astrophysics Data System (ADS)

Majkut, J.; Kopp, R. E.; Sarmiento, J. L.; Oppenheimer, M.

2016-12-01

Policymakers have set a goal of limiting temperature increase from human influence on the climate. This motivates the identification of decarbonization pathways to stabilize atmospheric concentrations of CO2. In this context, the future behavior of CO2 sources and sinks define the CO2 emissions necessary to meet warming thresholds with specified probabilities. We adopt a simple model of the atmosphere-land-ocean carbon balance to reflect uncertainty in how natural CO2 sinks will respond to increasing atmospheric CO2 and temperature. Bayesian inversion is used to estimate the probability distributions of selected parameters of the carbon model. Prior probability distributions are chosen to reflect the behavior of CMIP5 models. We then update these prior distributions by running historical simulations of the global carbon cycle and inverting with observationally-based inventories and fluxes of anthropogenic carbon in the ocean and atmosphere. The result is a best-estimate of historical CO2 sources and sinks and a model of how CO2 sources and sinks will vary in the future under various emissions scenarios, with uncertainty. By linking the carbon model to a simple climate model, we calculate emissions pathways and carbon budgets consistent with meeting specific temperature thresholds and identify key factors that contribute to remaining uncertainty. In particular, we show how the assumed history of CO2 emissions from land use change (LUC) critically impacts estimates of the strength of the land CO2 sink via CO2 fertilization. Different estimates of historical LUC emissions taken from the literature lead to significantly different parameterizations of the carbon system. High historical CO2 emissions from LUC lead to a more robust CO2 fertilization effect, significantly lower future atmospheric CO2 concentrations, and an increased amount of CO2 that can be emitted to satisfy temperature stabilization targets. Thus, in our model, historical LUC emissions have a significant impact on allowable carbon budgets under temperture targets.

Assessment of fire emission inventories during the South American Biomass Burning Analysis (SAMBBA) experiment

NASA Astrophysics Data System (ADS)

Pereira, Gabriel; Siqueira, Ricardo; Rosário, Nilton E.; Longo, Karla L.; Freitas, Saulo R.; Cardozo, Francielle S.; Kaiser, Johannes W.; Wooster, Martin J.

2016-06-01

Fires associated with land use and land cover changes release large amounts of aerosols and trace gases into the atmosphere. Although several inventories of biomass burning emissions cover Brazil, there are still considerable uncertainties and differences among them. While most fire emission inventories utilize the parameters of burned area, vegetation fuel load, emission factors, and other parameters to estimate the biomass burned and its associated emissions, several more recent inventories apply an alternative method based on fire radiative power (FRP) observations to estimate the amount of biomass burned and the corresponding emissions of trace gases and aerosols. The Brazilian Biomass Burning Emission Model (3BEM) and the Fire Inventory from NCAR (FINN) are examples of the first, while the Brazilian Biomass Burning Emission Model with FRP assimilation (3BEM_FRP) and the Global Fire Assimilation System (GFAS) are examples of the latter. These four biomass burning emission inventories were used during the South American Biomass Burning Analysis (SAMBBA) field campaign. This paper analyzes and inter-compared them, focusing on eight regions in Brazil and the time period of 1 September-31 October 2012. Aerosol optical thickness (AOT550 nm) derived from measurements made by the Moderate Resolution Imaging Spectroradiometer (MODIS) operating on board the Terra and Aqua satellites is also applied to assess the inventories' consistency. The daily area-averaged pyrogenic carbon monoxide (CO) emission estimates exhibit significant linear correlations (r, p > 0.05 level, Student t test) between 3BEM and FINN and between 3BEM_ FRP and GFAS, with values of 0.86 and 0.85, respectively. These results indicate that emission estimates in this region derived via similar methods tend to agree with one other. However, they differ more from the estimates derived via the alternative approach. The evaluation of MODIS AOT550 nm indicates that model simulation driven by 3BEM and FINN typically underestimate the smoke particle loading in the eastern region of Amazon forest, while 3BEM_FRP estimations to the area tend to overestimate fire emissions. The daily regional CO emission fluxes from 3BEM and FINN have linear correlation coefficients of 0.75-0.92, with typically 20-30 % higher emission fluxes in FINN. The daily regional CO emission fluxes from 3BEM_FRP and GFAS show linear correlation coefficients between 0.82 and 0.90, with a particularly strong correlation near the arc of deforestation in the Amazon rainforest. In this region, GFAS has a tendency to present higher CO emissions than 3BEM_FRP, while 3BEM_FRP yields more emissions in the area of soybean expansion east of the Amazon forest. Atmospheric aerosol optical thickness is simulated by using the emission inventories with two operational atmospheric chemistry transport models: the IFS from Monitoring Atmospheric Composition and Climate (MACC) and the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modelling System (CCATT-BRAMS). Evaluation against MODIS observations shows a good representation of the general patterns of the AOT550 nm time series. However, the aerosol emissions from fires with particularly high biomass consumption still lead to an underestimation of the atmospheric aerosol load in both models.

40 CFR 98.237 - Records that must be retained.

Code of Federal Regulations, 2013 CFR

2013-07-01

... emissions computer model runs used for engineering estimation of emissions. (e) The records required under § 98.3(g)(2)(i) shall include an explanation of how company records, engineering estimation, or best...

40 CFR 98.237 - Records that must be retained.

Code of Federal Regulations, 2014 CFR

2014-07-01

... emissions computer model runs used for engineering estimation of emissions. (e) The records required under § 98.3(g)(2)(i) shall include an explanation of how company records, engineering estimation, or best...

40 CFR 98.237 - Records that must be retained.

Code of Federal Regulations, 2012 CFR

2012-07-01

... emissions computer model runs used for engineering estimation of emissions. (e) The records required under § 98.3(g)(2)(i) shall include an explanation of how company records, engineering estimation, or best...

Comparison of different modeling approaches to better evaluate greenhouse gas emissions from whole wastewater treatment plants.

PubMed

Corominas, Lluís; Flores-Alsina, Xavier; Snip, Laura; Vanrolleghem, Peter A

2012-11-01

New tools are being developed to estimate greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). There is a trend to move from empirical factors to simple comprehensive and more complex process-based models. Thus, the main objective of this study is to demonstrate the importance of using process-based dynamic models to better evaluate GHG emissions. This is tackled by defining a virtual case study based on the whole plant Benchmark Simulation Model Platform No. 2 (BSM2) and estimating GHG emissions using two approaches: (1) a combination of simple comprehensive models based on empirical assumptions and (2) a more sophisticated approach, which describes the mechanistic production of nitrous oxide (N(2) O) in the biological reactor (ASMN) and the generation of carbon dioxide (CO(2) ) and methane (CH(4) ) from the Anaerobic Digestion Model 1 (ADM1). Models already presented in literature are used, but modifications compared to the previously published ASMN model have been made. Also model interfaces between the ASMN and the ADM1 models have been developed. The results show that the use of the different approaches leads to significant differences in the N(2) O emissions (a factor of 3) but not in the CH(4) emissions (about 4%). Estimations of GHG emissions are also compared for steady-state and dynamic simulations. Averaged values for GHG emissions obtained with steady-state and dynamic simulations are rather similar. However, when looking at the dynamics of N(2) O emissions, large variability (3-6 ton CO(2) e day(-1) ) is observed due to changes in the influent wastewater C/N ratio and temperature which would not be captured by a steady-state analysis (4.4 ton CO(2) e day(-1) ). Finally, this study also shows the effect of changing the anaerobic digestion volume on the total GHG emissions. Decreasing the anaerobic digester volume resulted in a slight reduction in CH(4) emissions (about 5%), but significantly decreased N(2) O emissions in the water line (by 14%). Copyright © 2012 Wiley Periodicals, Inc.

Estimation of Anthropogenic Heat Emissions in Delhi, India and Their Role in Urban Heat Island Effect

NASA Astrophysics Data System (ADS)

Bhati, S.; Mohan, M.

2016-12-01

Energy consumption in the urban environment impacts the urban surface energy budget and leads to the emission of anthropogenic sensible heat into the atmosphere. Anthropogenic heat (AH) can vary both in time and space, and are not readily measured. In present study, anthropogenic heat emissions have been estimated using an inventory approach for Delhi. The main sources that have been considered are electricity consumption, vehicular emissions, fuel consumption in domestic sector and waste heat from power plants. Total estimated anthropogenic heat is apportioned gridwise (2 km2) and incorporated in the WRF (version 3.5) model coupled with single-layer Urban canopy model (UCM) to assess the impact of these emissions on urban heat island effect in Delhi. Vehicular emissions have been found to be highest contributor to anthropogenic heat emissions (47%) followed by electricity consumption (28%), domestic fuel consumption (16%) and waste heat from power plants (9%). Highest annual average anthropogenic heat flux was estimated to be 25.2 Wm-2. High flux zones are observed in east Delhi and densely occupied and commercial zones of Sitaram Bazar and Connaught Place. Inclusion of anthropogenic heat emissions in the model improves model performance for near surface temperature as well as urban heat island intensities. Maximum simulated night-time UHI improves from 5.95°C (without AH) to 6.24°C (with AH) against observed value of 6.68°C, thereby indicating positive contribution of anthropogenic heat emissions along with urban canopy towards UHI effect in Delhi. Similarly, spatial distribution and UHI hotspots are found to be comparatively closer to corresponding observed distribution and hotspots with anthropogenic heat emissions being added to the WRF model. Overall, relatively improved model performance is indicative of the impact of anthropogenic heat emissions in local urban meteorology and urban heat island effect in Delhi. Hence, rising population and change in land use-cover and associated anthropogenic activities call for strategic mitigation measures in the city to prevent further strengthening of heat island effect.

Estimate carbon emissions from degraded permafrost with InSAR and a soil thermal model

NASA Astrophysics Data System (ADS)

Zhou, Z.; Liu, L.

2016-12-01

Climate warming, tundra fire over past decades has caused degradation in permafrost widely and quickly. Recent studies indicate that an increase in degradation could switch permafrost from a carbon sink to a source, with the potential of creating a positive feedback to anthropogenic climate warming. Unfortunately, Soil Organic Carbon (SOC) emissions from degraded permafrost unquantified, and limit our ability to understand SOC losses in arctic environments. This work will investigate recent 10 years of data already collected at the Anaktuvuk River fire (both ground and remote sensed), and will employ a soil thermal model to estimate SOC emission in this region. The model converts the increases in Active Layer Thickness (ALT), as measured by InSAR, to changes in Organic Layer Thickness (OLT), and SOC. ALOS-1/2 L-band SAR dataset will be used to produce the ATL changes over the study area. Soil prosperities (e.g. temperature at different depth, bulk density) will be used in the soil thermal model to estimate OLT changes and SOC losses. Ground measurement will validate the InSAR results and the soil thermal model. A final estimation of SOC emission will be produced in Anaktuvuk River region.

Simulation of nitrous oxide emissions at field scale using the SPACSYS model

PubMed Central

Wu, L.; Rees, R.M.; Tarsitano, D.; Zhang, Xubo; Jones, S.K.; Whitmore, A.P.

2015-01-01

Nitrous oxide emitted to the atmosphere via the soil processes of nitrification and denitrification plays an important role in the greenhouse gas balance of the atmosphere and is involved in the destruction of stratospheric ozone. These processes are controlled by biological, physical and chemical factors such as growth and activity of microbes, nitrogen availability, soil temperature and water availability. A comprehensive understanding of these processes embodied in an appropriate model can help develop agricultural mitigation strategies to reduce greenhouse gas emissions, and help with estimating emissions at landscape and regional scales. A detailed module to describe the denitrification and nitrification processes and nitrogenous gas emissions was incorporated into the SPACSYS model to replace an earlier module that used a simplified first-order equation to estimate denitrification and was unable to distinguish the emissions of individual nitrogenous gases. A dataset derived from a Scottish grassland experiment in silage production was used to validate soil moisture in the top 10 cm soil, cut biomass, nitrogen offtake and N2O emissions. The comparison between the simulated and observed data suggested that the new module can provide a good representation of these processes and improve prediction of N2O emissions. The model provides an opportunity to estimate gaseous N emissions under a wide range of management scenarios in agriculture, and synthesises our understanding of the interaction and regulation of the processes. PMID:26026411

Speed Profiles for Improvement of Maritime Emission Estimation.

PubMed

Yau, Pui Shan; Lee, Shun-Cheng; Ho, Kin Fai

2012-12-01

Maritime emissions play an important role in anthropogenic emissions, particularly for cities with busy ports such as Hong Kong. Ship emissions are strongly dependent on vessel speed, and thus accurate vessel speed is essential for maritime emission studies. In this study, we determined minute-by-minute high-resolution speed profiles of container ships on four major routes in Hong Kong waters using Automatic Identification System (AIS). The activity-based ship emissions of NO(x), CO, HC, CO(2), SO(2), and PM(10) were estimated using derived vessel speed profiles, and results were compared with those using the speed limits of control zones. Estimation using speed limits resulted in up to twofold overestimation of ship emissions. Compared with emissions estimated using the speed limits of control zones, emissions estimated using vessel speed profiles could provide results with up to 88% higher accuracy. Uncertainty analysis and sensitivity analysis of the model demonstrated the significance of improvement of vessel speed resolution. From spatial analysis, it is revealed that SO(2) and PM(10) emissions during maneuvering within 1 nautical mile from port were the highest. They contributed 7%-22% of SO(2) emissions and 8%-17% of PM(10) emissions of the entire voyage in Hong Kong.

Sensitivity of terrestrial N2O emission to atmospheric nitrogen deposition

NASA Astrophysics Data System (ADS)

Ito, A.; Sudo, K.; Nishina, K.; Ishijima, K.; Inatomi, M. I.

2015-12-01

Terrestrial N2O emission is generated from several nitrogen sources including biological fixation, agricultural fertilizer, and atmospheric deposition. There remain large uncertainties how much N2O is produced from atmospheric deposition. This is a crosscutting issue between global warming and atmospheric pollution. In this study, we assessed the sensitivity of global terrestrial N2O emission to atmospheric deposition, using a process-based model VISIT. In the model, N2O emission is estimated separately for nitrification and denitrfication with the NGAS parameterization. The global simulations were conducted from 1901 to 2014 at spatial resolution of 0.5 degree. Atmospheric deposition of ammonium, NOy, and organic nitrogen simulated by the atmospheric chemistry model CHASER from the pre-industrial time to the present was used. Annual total nitrogen deposition was estimated to increase from 27 Tg N in 1901 to 77 Tg N in 2014. The total N2O emission was also estimated to increase in the period, but it was largely attributable to the increased emission from croplands. We need further investigations for the N2O emission from natural soils, which may be nitrogen-limited.

An observationally constrained estimate of global dust aerosol optical depth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ridley, David A.; Heald, Colette L.; Kok, Jasper F.

Here, the role of mineral dust in climate and ecosystems has been largely quantified using global climate and chemistry model simulations of dust emission, transport, and deposition. However, differences between these model simulations are substantial, with estimates of global dust aerosol optical depth (AOD) that vary by over a factor of 5. Here we develop an observationally based estimate of the global dust AOD, using multiple satellite platforms, in situ AOD observations and four state-of-the-science global models over 2004–2008. We estimate that the global dust AOD at 550 nm is 0.030 ± 0.005 (1σ), higher than the AeroCom model medianmore » (0.023) and substantially narrowing the uncertainty. The methodology used provides regional, seasonal dust AOD and the associated statistical uncertainty for key dust regions around the globe with which model dust schemes can be evaluated. Exploring the regional and seasonal differences in dust AOD between our observationally based estimate and the four models in this study, we find that emissions in Africa are often overrepresented at the expense of Asian and Middle Eastern emissions and that dust removal appears to be too rapid in most models.« less

An observationally constrained estimate of global dust aerosol optical depth

DOE PAGES

Ridley, David A.; Heald, Colette L.; Kok, Jasper F.; ...

2016-12-06

Here, the role of mineral dust in climate and ecosystems has been largely quantified using global climate and chemistry model simulations of dust emission, transport, and deposition. However, differences between these model simulations are substantial, with estimates of global dust aerosol optical depth (AOD) that vary by over a factor of 5. Here we develop an observationally based estimate of the global dust AOD, using multiple satellite platforms, in situ AOD observations and four state-of-the-science global models over 2004–2008. We estimate that the global dust AOD at 550 nm is 0.030 ± 0.005 (1σ), higher than the AeroCom model medianmore » (0.023) and substantially narrowing the uncertainty. The methodology used provides regional, seasonal dust AOD and the associated statistical uncertainty for key dust regions around the globe with which model dust schemes can be evaluated. Exploring the regional and seasonal differences in dust AOD between our observationally based estimate and the four models in this study, we find that emissions in Africa are often overrepresented at the expense of Asian and Middle Eastern emissions and that dust removal appears to be too rapid in most models.« less

Historical and future land use effects on N2O and NO emissions using an ensemble modeling approach: Costa Rica's Caribbean lowlands as an example

USGS Publications Warehouse

Reiners, William A.; Liu, S.; Gerow, K.G.; Keller, M.; Schimel, D.S.

2002-01-01

[1] The humid tropical zone is a major source area for N2O and NO emissions to the atmosphere. Local emission rates vary widely with local conditions, particularly land use practices which swiftly change with expanding settlement and changing market conditions. The combination of wide variation in emission rates and rapidly changing land use make regional estimation and future prediction of biogenic trace gas emission particularly difficult. This study estimates contemporary, historical, and future N2O and NO emissions from 0.5 million ha of northeastern Costa Rica, a well-documented region in the wet tropics undergoing rapid agricultural development. Estimates were derived by linking spatially distributed environmental data with an ecosystem simulation model in an ensemble estimation approach that incorporates the variance and covariance of spatially distributed driving variables. Results include measures of variance for regional emissions. The formation and aging of pastures from forest provided most of the past temporal change in N2O and NO flux in this region; future changes will be controlled by the degree of nitrogen fertilizer application and extent of intensively managed croplands.

Historical and future land use effects on N2O and NO emissions using an ensemble modeling approach: Costa Rica's Caribbean lowlands as an example

NASA Astrophysics Data System (ADS)

Reiners, W. A.; Liu, S.; Gerow, K. G.; Keller, M.; Schimel, D. S.

2002-12-01

The humid tropical zone is a major source area for N2O and NO emissions to the atmosphere. Local emission rates vary widely with local conditions, particularly land use practices which swiftly change with expanding settlement and changing market conditions. The combination of wide variation in emission rates and rapidly changing land use make regional estimation and future prediction of biogenic trace gas emission particularly difficult. This study estimates contemporary, historical, and future N2O and NO emissions from 0.5 million ha of northeastern Costa Rica, a well-documented region in the wet tropics undergoing rapid agricultural development. Estimates were derived by linking spatially distributed environmental data with an ecosystem simulation model in an ensemble estimation approach that incorporates the variance and covariance of spatially distributed driving variables. Results include measures of variance for regional emissions. The formation and aging of pastures from forest provided most of the past temporal change in N2O and NO flux in this region; future changes will be controlled by the degree of nitrogen fertilizer application and extent of intensively managed croplands.

Assessing Concentrations and Health Impacts of Air Quality Management Strategies: Framework for Rapid Emissions Scenario and Health impact ESTimation (FRESH-EST)

PubMed Central

Milando, Chad W.; Martenies, Sheena E.; Batterman, Stuart A.

2017-01-01

In air quality management, reducing emissions from pollutant sources often forms the primary response to attaining air quality standards and guidelines. Despite the broad success of air quality management in the US, challenges remain. As examples: allocating emissions reductions among multiple sources is complex and can require many rounds of negotiation; health impacts associated with emissions, the ultimate driver for the standards, are not explicitly assessed; and long dispersion model run-times, which result from the increasing size and complexity of model inputs, limit the number of scenarios that can be evaluated, thus increasing the likelihood of missing an optimal strategy. A new modeling framework, called the "Framework for Rapid Emissions Scenario and Health impact ESTimation" (FRESH-EST), is presented to respond to these challenges. FRESH-EST estimates concentrations and health impacts of alternative emissions scenarios at the urban scale, providing efficient computations from emissions to health impacts at the Census block or other desired spatial scale. In addition, FRESH-EST can optimize emission reductions to meet specified environmental and health constraints, and a convenient user interface and graphical displays are provided to facilitate scenario evaluation. The new framework is demonstrated in an SO2 non-attainment area in southeast Michigan with two optimization strategies: the first minimizes emission reductions needed to achieve a target concentration; the second minimizes concentrations while holding constant the cumulative emissions across local sources (e.g., an emissions floor). The optimized strategies match outcomes in the proposed SO2 State Implementation Plan without the proposed stack parameter modifications or shutdowns. In addition, the lower health impacts estimated for these strategies suggest the potential for FRESH-EST to identify pollution control alternatives for air quality management planning. PMID:27318620

Global sulfur emissions from 1850 to 2000.

PubMed

Stern, David I

2005-01-01

The ASL database provides continuous time-series of sulfur emissions for most countries in the World from 1850 to 1990, but academic and official estimates for the 1990s either do not cover all years or countries. This paper develops continuous time series of sulfur emissions by country for the period 1850-2000 with a particular focus on developments in the 1990s. Global estimates for 1996-2000 are the first that are based on actual observed data. Raw estimates are obtained in two ways. For countries and years with existing published data I compile and integrate that data. Previously published data covers the majority of emissions and almost all countries have published emissions for at least 1995. For the remaining countries and for missing years for countries with some published data, I interpolate or extrapolate estimates using either an econometric emissions frontier model, an environmental Kuznets curve model, or a simple extrapolation, depending on the availability of data. Finally, I discuss the main movements in global and regional emissions in the 1990s and earlier decades and compare the results to other studies. Global emissions peaked in 1989 and declined rapidly thereafter. The locus of emissions shifted towards East and South Asia, but even this region peaked in 1996. My estimates for the 1990s show a much more rapid decline than other global studies, reflecting the view that technological progress in reducing sulfur based pollution has been rapid and is beginning to diffuse worldwide.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity.

PubMed

Morais, Sérgio Alberto; Delerue-Matos, Cristina; Gabarrell, Xavier

2013-03-15

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach. Copyright © 2013 Elsevier B.V. All rights reserved.

Carbon footprint estimator, phase II : volume II - technical appendices.

DOT National Transportation Integrated Search

2014-03-01

The GASCAP model was developed to provide a software tool for analysis of the life-cycle GHG : emissions associated with the construction and maintenance of transportation projects. This phase : of development included techniques for estimating emiss...

The Study of Biogenetic Organic Compound Emissions and Ozone in a Subtropical Bamboo Forest

NASA Astrophysics Data System (ADS)

Bai, Jianhui; Guenther, Alex; Turnipseed, Andrew; Duhl, Tiffany; Duhl, Nanhao; van der A, Ronald; Yu, Shuquan; Wang, Bin

2016-08-01

Emissions of Biogenic Volatile Organic compounds (BVOCs), Photosynthetically Active Radiation (PAR), and meteorological parameters were measured in some ecosystems in China. A Relaxed Eddy Accumulation system and an enclosure technique were used to measure BVOC emissions. Obvious diurnal and seasonal variations of BVOC emissions were found. Empirical models of BVOC emissions were developed, the estimated BVOC emissions were in agreement with observations. BVOC emissions in growing seasons in the Inner Mongolia grassland, Chnagbai Mountain temperate forest, LinAn subtropical bamboo forest were estimated. The emission factors of these ecosystems were calculated.

Simulating smoke transport from wildland fires with a regional-scale air quality model: sensitivity to spatiotemporal allocation of fire emissions.

PubMed

Garcia-Menendez, Fernando; Hu, Yongtao; Odman, Mehmet T

2014-09-15

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions. Copyright © 2014 Elsevier B.V. All rights reserved.

Estimation of refueling emissions based on theoretical model and effects of E10 fuel on refueling and evaporative emissions from gasoline cars.

PubMed

Yamada, Hiroyuki; Inomata, Satoshi; Tanimoto, Hiroshi; Hata, Hiroo; Tonokura, Kenichi

2018-05-01

The effects of Reid vapor pressure (RVP) on refueling emissions and the effects of ethanol 10% (E10) fuel on refueling and evaporative emissions were observed using six cars and seven fuels. The results indicated that refueling emissions can be reproduced by a simple theoretical model in which fuel vapor in the empty space in the tank is pushed out by the refueling process. In this model, the vapor pressures of fuels can be estimated by the Clausius-Clapeyron equation as a function of temperature. We also evaluated E10 fuel in terms of refueling and evaporative emissions, excluding the effect of contamination of ethanol in the canister. E10 fuel had no effect on the refueling emissions in cases without onboard refueling vapor recovery. E10 showed increased permeation emissions in evaporative emissions because of the high permeability of ethanol. And with E10 fuel, breakthrough emissions appeared earlier but broke through slower than normal fuel. Finally, canisters could store more fuel vapor with E10 fuel. Copyright © 2017 Elsevier B.V. All rights reserved.

On the Impact of Granularity of Space-Based Urban CO2 Emissions in Urban Atmospheric Inversions: A Case Study for Indianapolis, IN

NASA Technical Reports Server (NTRS)

Oda, Tomohiro; Lauvaux, Thomas; Lu, Dengsheng; Rao, Preeti; Miles, Natasha L.; Richardson, Scott J.; Gurney, Kevin R.

2017-01-01

Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as a priori emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1x1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1x1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 3030 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation.

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

NASA Technical Reports Server (NTRS)

Rasool, Quazi Z.; Zhang, Rui; Lash, Benjamin; Cohan, Daniel S.; Cooter, Ellen J.; Bash, Jesse O.; Lamsal, Lok N.

2016-01-01

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We also demonstrate that differences between the modeled and observed meteorology can be used to predict uncertainties associated with atmospheric transport and dispersion modeling which can help improve the skill of an inversion at urban scales.

Effect of climate change and CO2 inhibition on isoprene emissions in Europe calculated using the ALARO-0 regional climate model

NASA Astrophysics Data System (ADS)

Bauwens, Maite; Müller, Jean-François; Stavrakou, Trisevgeni; De Cruz, Lesley; Van Schaeybroeck, Bert; Termonia, Piet; De Troch, Rozemien; Berckmans, Julie; Hamdi, Rafiq

2017-04-01

Isoprene is the dominant biogenic hydrocarbon emitted in the atmosphere, with global annual emissions estimated at ca. 400-600 Tg (Guenther et al. 2006). It plays a key role in the atmospheric composition because of its influence on tropospheric ozone formation in polluted environments and its contribution to particulate matter. Its emissions depend on the type and abundance of plants, and are modulated by meteorological parameters. Climate changes therefore affect the spatiotemporal and interannual variation of these emissions. In this study we estimate the isoprene fluxes emitted by vegetation in past and future climate over the European (EURO-CORDEX) domain using the MEGAN-MOHYCAN model (Müller et al. 2008, Stavrakou et al. 2014).We first calculate isoprene emissions over 1979-2012 based on the ECMWF ERA-Interim reanalysis data, we compare with available isoprene flux measurements, and we investigate the sensitivity to solar radiation changes observed at European stations. The interannual variability and emission trends on regional and country level are derived and discussed. Next, we perform simulations using the output of the ALARO-0 regional climate model (Giot et al., 2015) forced by the RCP2.6, RCP4.5 and RCP8.5 scenarios over 2071-2099, and compare with the historical emissions over 1976-2005 derived by the same model. Furthermore, we incorporate the inhibition of isoprene emissions to the enhanced CO2 levels of the climate projections through two different parameterizations. The future climate scenarios result in higher isoprene emissions over the European domain increased by 6%, 33% and 82% for the RCP2.6, RCP4.5 and RCP8.5 scenario respectively. However, the CO2 inhibition effect results in an overall decrease of isoprene emissions relative to the standard future simulation, even though this decrease is strongly sensitive to the parameterization used. The different CO2 inhibition simulations in this study show that future isoprene emission are between 11% lower and 26% higher than the present isoprene emissions over Europe. Giot, O. et al.: Validation of the ALARO-0 model within the EURO-CORDEX framework, Geosci. Model Dev. Discuss., 8, 8387-8409, 2015. Guenther, A. et al.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181-3210, 2006. Müller, J.-F. et al.: Global isoprene emissions estimated using MEGAN, ECMWF analyses and a detailed canopy environmental model, Atmos. Chem. Phys., 8, 1329-1341, 2008 Stavrakou, T. et al.: Isoprene emissions over Asia 1979-2012 : impact of climate and land use changes, Atmos. Chem. Phys., 14, 4587-4605, 2014.

Development of the Vista Methane Emissions Inventory for Southern California: A GIS-Based Approach for Mapping Methane Emissions

NASA Astrophysics Data System (ADS)

Carranza, V.; Frausto-Vicencio, I.; Rafiq, T.; Verhulst, K. R.; Hopkins, F. M.; Rao, P.; Duren, R. M.; Miller, C. E.

2016-12-01

Atmospheric methane (CH4) is the second most prevalent anthropogenic greenhouse gas. Improved estimates of CH4 emissions from cities is essential for carbon cycle science and climate mitigation efforts. Development of spatially-resolved carbon emissions data sets may offer significant advances in understanding and managing carbon emissions from cities. Urban CH4 emissions in particular require spatially resolved emission maps to help resolve uncertainties in the CH4 budget. This study presents a Geographic Information System (GIS)-based approach to mapping CH4 emissions using locations of infrastructure known to handle and emit methane. We constrain the spatial distribution of sources to the facility level for the major CH4 emitting sources in the South Coast Air Basin. GIS spatial modeling was combined with publicly available datasets to determine the distribution of potential CH4 sources. The datasets were processed and validated to ensure accuracy in the location of individual sources. This information was then used to develop the Vista emissions prior, which is a one-year long, spatially-resolved CH4 emissions estimate. Methane emissions were calculated and spatially allocated to produce 1 km x 1 km gridded CH4 emission map spanning the Los Angeles Basin. In future work, the Vista CH4 emissions prior will be compared with existing, coarser-resolution emissions estimates and will be evaluated in inverse modeling studies using atmospheric observations. The Vista CH4 emissions inventory presents the first detailed spatial maps of CH4 sources and emissions estimates in the Los Angeles Basin and is a critical step towards sectoral attribution of CH4 emissions at local to regional scales.

Modeling crop residue burning experiments to evaluate smoke emissions and plume transport.

PubMed

Zhou, Luxi; Baker, Kirk R; Napelenok, Sergey L; Pouliot, George; Elleman, Robert; O'Neill, Susan M; Urbanski, Shawn P; Wong, David C

2018-06-15

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment in the Pacific Northwest focused on cropland residue burning was used to evaluate model performance in capturing surface and aloft impacts from the burning events. The Community Multiscale Air Quality (CMAQ) model was used to simulate multiple crop residue burns with 2 km grid spacing using field-specific information and also more general assumptions traditionally used to support National Emission Inventory based assessments. Field study specific information, which includes area burned, fuel consumption, and combustion completeness, resulted in increased biomass consumption by 123 tons (60% increase) on average compared to consumption estimated with default methods in the National Emission Inventory (NEI) process. Buoyancy heat flux, a key parameter for model predicted fire plume rise, estimated from fuel loading obtained from field measurements can be 30% to 200% more than when estimated using default field information. The increased buoyancy heat flux resulted in higher plume rise by 30% to 80%. This evaluation indicates that the regulatory air quality modeling system can replicate intensity and transport (horizontal and vertical) features for crop residue burning in this region when region-specific information is used to inform emissions and plume rise calculations. Further, previous vertical emissions allocation treatment of putting all cropland residue burning in the surface layer does not compare well with measured plume structure and these types of burns should be modeled more similarly to prescribed fires such that plume rise is based on an estimate of buoyancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Airborne observations reveal elevational gradient in tropical forest isoprene emissions

DOE PAGES

Gu, Dasa; Guenther, Alex B.; Shilling, John E.; ...

2017-05-23

Terrestrial vegetation emits vast quantities of volatile organic compounds (VOCs) to he atmosphere1-3, which influence oxidants and aerosols leading to complex feedbacks on air quality and climate4-6. Isoprene dominates global non-methane VOC emissions with tropical regions contributing ~80% of global isoprene emissions2. Isoprene emission rates vary over several orders of magnitude for different plant species, and characterizing this immense biological chemodiversity is a challenge for estimating isoprene emission from tropical forests. Here we present the isoprene emission estimates from aircraft direct eddy covariance measurements over the pristine Amazon forest. We report isoprene emission rates that are 3 times higher thanmore » satellite top-down estimates and 35% higher than model predictions based on satellite land cover and vegetation specific emission factors (EFs). The results reveal strong correlations between observed isoprene emission rates and terrain elevations which are confirmed by similar correlations between satellite-derived isoprene emissions and terrain elevations. We propose that the elevational gradient in the Amazonian forest isoprene emission capacity is determined by plant species distributions and can explain a substantial degree of isoprene emission variability in tropical forests. Finally, we apply this approach over the central Amazon and use a model to demonstrate the impacts on regional air quality.« less

Top-down Estimate of Methane Emissions from Natural Gas Production in Northeastern Pennsylvania Using Aircraft and Tower Observations

NASA Astrophysics Data System (ADS)

Barkley, Z.; Lauvaux, T.; Davis, K. J.; Deng, A.; Miles, N. L.; Richardson, S.; Martins, D. K.; Cao, Y.; Sweeney, C.; McKain, K.; Schwietzke, S.; Smith, M. L.; Kort, E. A.

2016-12-01

Leaks in natural gas infrastructure release CH4, a potent greenhouse gas, into the atmosphere. The estimated emission rate associated with the production and transportation of natural gas is uncertain, hindering our understanding of the energy's greenhouse footprint. This study presents two applications of inverse methodology for estimating regional emission rates from natural gas production and gathering facilities in northeastern Pennsylvania. First, we used the WRF-Chem mesoscale model at 3km resolution to simulate CH4 enhancements and compared them to observations obtained from a three-week flight campaign in May 2015 over the Marcellus shale region. Methane emission rates were adjusted to minimize the errors between aircraft observations and the model-simulated concentrations for each flight. Second, we present the first tower-based high resolution atmospheric inversion of CH4 emission rates from unconventional natural gas production activities. A year of continuous CH4 and calibrated δ13C isotope measurements were collected at four tower locations in northeastern Pennsylvania. The adjoint model used here combines a backward-in-time Lagrangian Particle Dispersion Model coupled with the WRF-Chem model at the same resolution. The prior for both optimization systems was compiled for major sources of CH4 within the Mid-Atlantic states, accounting for emissions from natural gas sources as well as emissions related to farming, waste management, coal, and other sources. Optimized natural gas emission rates are found to be 0.36% of total gas production, with a 2σ confidence interval between 0.27-0.45% of production. We present the results from the tower inversion over one year at 3km resolution providing additional information on spatial and temporal variability of emission rates from production and gathering facilities within the natural gas industry in comparison to flux estimates from the aircraft campaign.

Simulations of aerosol constituents and their sources of origin over Indo-Gangetic plain (IGP) to Himalayan foothills: a new perspective of GCM estimates

NASA Astrophysics Data System (ADS)

Kumar, B. D.; Verma, S.; Wang, R.; Boucher, O.

2016-12-01

In the present study, we evaluated aerosol constituents of the model using the measurements during premonsoon over Indo-Gangetic plain (IGP) to Himalayan foothills. Aerosol transport simulations were carried out in general circulation model (GCM) of Laboratoire de M ´et ´eorologie Dynamique (LMD-GCM) with three set of emissions including Indian emissions in GCM-Indemiss, global emissions in GCM coupled with aerosol interactive chemistry (GCM-INCA-I), and the global emissions with updated BC emission inventory over Asia in GCM-INCA-II. Among three models, GCM-indemiss reproduced measured single scattering albedo (SSA) at 670 nm with a relative bias of 5%. However, the estimated 30-50% of the measured aerosol optical depth (AOD) at 550 nm and 20-60% of the measured surface concentration of aerosol constituents (e.g. black carbon (BC), organic carbon (OC), and sulfate) at most of the times over the study period. Inability of model to reproduce observed AOD changes was attributed to the paucity of emissions represented in the model. Design of retrieval simulations using existing GCM-indemiss estimates was further carried out. Retrieval simulations have produced better results, which showed constituent surface concentration in the vicinity of the measurements with normalized mean bias (NMB) of <30%. Scatter analysis between surface and elevated contribution of region's emissions showed anthropogenic emissions from the IGP on anthropogenic days and the north west India (NWI) on anthropogenic with dust days influence aerosols over northern India (NI). Our analysis showed BC emissions from base inventory for the corresponding grids of source region influencing NI were lower by 200% compared to that of modified scenario. These emissions will further be implemented in an atmospheric GCM to evaluate their performance validating with measurements data.

Development of a composite line source emission model for traffic interrupted microenvironments and its application in particle number emissions at a bus station

NASA Astrophysics Data System (ADS)

Wang, Lina; Jayaratne, Rohan; Heuff, Darlene; Morawska, Lidia

A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models.

Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling

NASA Astrophysics Data System (ADS)

Bell, Michael; Flechard, Chris; Fauvel, Yannick; Häni, Christoph; Sintermann, Jörg; Jocher, Markus; Menzi, Harald; Hensen, Arjan; Neftel, Albrecht

2017-05-01

Ammonia (NH3) fluxes were estimated from a field being grazed by dairy cattle during spring by applying a backward Lagrangian stochastic model (bLS) model combined with horizontal concentration gradients measured across the field. Continuous concentration measurements at field boundaries were made by open-path miniDOAS (differential optical absorption spectroscopy) instruments while the cattle were present and for 6 subsequent days. The deposition of emitted NH3 to clean patches on the field was also simulated, allowing both net and gross emission estimates, where the dry deposition velocity (vd) was predicted by a canopy resistance (Rc) model developed from local NH3 flux and meteorological measurements. Estimated emissions peaked during grazing and decreased after the cattle had left the field, while control on emissions was observed from covariance with temperature, wind speed and humidity and wetness measurements made on the field, revealing a diurnal emission profile. Large concentration differences were observed between downwind receptors, due to spatially heterogeneous emission patterns. This was likely caused by uneven cattle distribution and a low grazing density, where hotspots of emissions would arise as the cattle grouped in certain areas, such as around the water trough. The spatial complexity was accounted for by separating the model source area into sub-sections and optimising individual source area coefficients to measured concentrations. The background concentration was the greatest source of uncertainty, and based on a sensitivity/uncertainty analysis the overall uncertainty associated with derived emission factors from this study is at least 30-40 %.Emission factors can be expressed as 6 ± 2 g NH3 cow-1 day-1, or 9 ± 3 % of excreted urine-N emitted as NH3, when deposition is not simulated and 7 ± 2 g NH3 cow-1 day-1, or 10 ± 3 % of excreted urine-N emitted as NH3, when deposition is included in the gross emission model. The results suggest that around 14 ± 4 % of emitted NH3 was deposited to patches within the field that were not affected by urine or dung.

Towards a measurement-based national verification system for GHG emissions: UK emission estimates of CO2 from the GAUGE experiment

NASA Astrophysics Data System (ADS)

Gonzi, Siegfried; Palmer, Paul; O'Doherty, Simon; Young, Dickon; Stanley, Kieran; Stavert, Ann; Grant, Aoife; Helfter, Carole; Mullinger, Neil; Nemitz, Eiko; Allen, Grant; Pitt, Joseph; Le Breton, Michael; Bösch, Hartmut; Sembhi, Harjinder; Sonderfeld, Hannah; Parker, Robert; Bauguitte, Stephane

2016-04-01

Robust quantification of emissions of greenhouse gases (GHG) is central to the success of ongoing international efforts to slow current emissions and mitigate future climate change. The Greenhouse gAs Uk and Global Emissions (GAUGE) project aims to quantify the magnitude and uncertainty of country-scale emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) using concentration measurements from a network of tall towers and mobile platforms (aircraft and ferry) distributed across the UK. The GAUGE measurement programme includes: (a) GHG measurements on a regular ferry route down the North Sea aimed at sampling UK outflow; (b) campaign deployment of the UK BAe-146 research aircraft to provide vertical profile measurements of GHG over and around the UK; (c) a high-density GHG measurement network over East Anglia that is primarily focused on the agricultural sector; and (d) regular measurements of CO2 and CH4 isotopologues used for GHG source attribution. We also use satellite observations from the Japanese Greenhouse gases Observing SATellite (GOSAT) to provide continental-scale constraints on GHG flux estimates. We present CO2 flux estimates for the UK inferred from GAUGE measurements using a nested, high-resolution (25 km) version of the GEOS-Chem global atmospheric chemistry and transport model and an ensemble Kalman filter. We will present our current best estimate for CO2 fluxes and a preliminary assessment of the efficacy of individual GAUGE data sources to spatially resolve CO2 flux estimates over the UK. We will also discuss how flux estimates inferred from the different models used within GAUGE can help to assess the role of transport model error and to determine an ensemble CO2 flux estimate for the UK.

MOVES and Related Models

EPA Pesticide Factsheets

MOVES is a state-of-the-science emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics.

INFLUENCE OF INCREASED ISOPRENE EMISSIONS ON REGIONAL OZONE MODELING

EPA Science Inventory

The role of biogenic hydrocarbons on ozone modeling has been a controversial issue since the 1970s. In recent years, changes in biogenic emission algorithms have resulted in large increases in estimated isoprene emissions. This paper describes a recent algorithm, the second gener...

The Establishment of LTO Emission Inventory of Civil Aviation Airports Based on Big Data

NASA Astrophysics Data System (ADS)

Lu, Chengwei; Liu, Hefan; Song, Danlin; Yang, Xinyue; Tan, Qinwen; Hu, Xiang; Kang, Xue

2018-03-01

An estimation model on LTO emissions of civil aviation airports was developed in this paper, LTO big data was acquired by analysing the internet with Python, while the LTO emissions was dynamically calculated based on daily LTO data, an uncertainty analysis was conducted with Monte Carlo method. Through the model, the emission of LTO in Shuangliu International Airport was calculated, and the characteristics and temporal distribution of LTO in 2015 was analysed. Results indicates that compared with the traditional methods, the model established can calculate the LTO emissions from different types of airplanes more accurately. Based on the hourly LTO information of 302 valid days, it was obtained that the total number of LTO cycles in Chengdu Shuangliu International Airport was 274,645 and the annual amount of emission of SO2, NOx, VOCs, CO, PM10 and PM2.5 was estimated, and the uncertainty of the model was around 7% to 10% varies on pollutants.

Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China

NASA Astrophysics Data System (ADS)

Saikawa, Eri; Kim, Hankyul; Zhong, Min; Avramov, Alexander; Zhao, Yu; Janssens-Maenhout, Greet; Kurokawa, Jun-ichi; Klimont, Zbigniew; Wagner, Fabian; Naik, Vaishali; Horowitz, Larry W.; Zhang, Qiang

2017-05-01

Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 µg m-3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.

Estimation of Secondary Compounds Concentrations Contributed by Biogenic VOC With Chemical Transport Model in the Central Area of Japan

NASA Astrophysics Data System (ADS)

Yamamoto, K.; Kanemaru, A.; Okumura, M.; Tohno, S.

2008-12-01

Biogenic VOC (BVOC) has comparably large contribution to generation of secondary air pollutants, such as photochemical oxidant or urban aerosol. In this study a BVOC emission inventory in the Kansai area, which is located in the central part of Japan, based on the field observation was developed. Some validations of the inventory were conducted by estimating the concentration distribution of oxidants with this developed and an existing BVOC emission inventory in Kansai area by meteorological model MM5 and atmospheric chemical transport model CMAQ. In the development of BVOC emission, the vegetation map by the Biodiversity Center of Japan which had been arranged as basic information on natural environmental preservation in a regional standard mesh (the third mesh) in 1999 was used. In this study isoprene and the mono-terpene were taken up as BVOC. Quercus crispula and Quercus serrata were selected as the source of isoprene, and Cryptomeria japonica, Chamaecyparis obtuse, Quercus phillyraeoides, Pinus densiflora, and Pinus thunbergii were selected as sources of mono-terpene. The parameter of the basic emission rate included in the model was decided by arranging the result of the observation in Kansai Research Center of Forestry and Forest Products Research Institute in each season. This emission flux from each species were calculated by G93 model by Guenther et al. and meteorological fields for the model, such as temperatures and sunlight intensities, were renewed hour by hour, therefore, this emission inventory has a high time resolution according to the season and time. In calculating meteorological fields, meteorological model MM5 Ver.3.7 was conducted in Japanese standard mesh in the selected five days of April, July, and October in 2004, and January 2005 respectively, and taking out the result of wind velocities and temperatures for substituting to the G93 model. Then atmospheric chemical transport model CMAQ Ver.4.6 with the emission inventories and meteorological fields was used for estimating secondary produced compounds concentration in the Kansai region. While the emission amount data of BVOC is also included in the EAGrid-Japan database, constructed by A. Kannari et al., another simulation with this existing BVOC emission inventory was conducted. As for other emission inventories of precursors, EAGrid-Japan was also used in both simulations. According to the result of estimation of BVOC emission, the total amount of BVOC is almost same as that of EAGrid-Japan, however, the ratio of isoprene to total BVOC emission is quite low in our estimation, due to the used vegetation map in this study, and the configuration of basic emission parameter in Autumn and Winter which is set to zero. According to the result of atmospheric chemical transport simulation with this developed BVOC inventory, oxidant concentrations are lower than observed values. This result suggests that the amount of isoprene emission strongly affected on the concentrations of oxidants, therefore, more accurate vegetation map data as a basis of BVOC emissions should be developed.

A two-dimensional photochemical model of the atmosphere. I Chlorocarbon emissions and their effect on stratospheric ozone

NASA Technical Reports Server (NTRS)

Gidel, L. T.; Crutzen, P. J.; Fishman, J.

1983-01-01

A two-dimensional photochemical model is used to examine changes to the ozone layer caused by emissions of CFCl3, CF2Cl2, CH3CCl3 and CCl4. The influence of a possible secular increase in tropospheric methane up to 2 percent per year was found to be small, although it acts to mask decreases in total ozone caused by the chlorocarbons. Increasing NO(x) emissions caused by industralization also tend to mask decreases in total ozone and may have caused total ozone to increase by about 1 percent. The model-calculated ozone decreases are estimated to be about 3 percent by 1980. This estimate is higher than estimates by similar models, although it is noted that CCl4 and CH3CCl3 emissions are included in the model in addition to CFCl3 and CF2Cl2. This is significant because the model indicates that CCl4 has dominated the ozone depletions so far, and knowledge of the historical emission rate of CCl4 to the atmosphere is incomplete. There remain sufficient significant disagreements between theoretical and observed concentrations and variabilities, particularly for odd nitrogen and ClO, to caution against assigning too much confidence in the calculated ozone depletion.

Advances in Estimating Methane Emissions from Enteric Fermentation

NASA Astrophysics Data System (ADS)

Kebreab, E.; Appuhamy, R.

2016-12-01

Methane from enteric fermentation of livestock is the largest contributor to the agricultural GHG emissions. The quantification of methane emissions from livestock on a global scale relies on prediction models because measurements require specialized equipment and may be expensive. Most countries use a fixed number (kg methane/year) or calculate as a proportion of energy intake to estimate enteric methane emissions in national inventories. However, diet composition significantly regulates enteric methane production in addition to total feed intake and thus the main target in formulating mitigation options. The two current methodologies are not able to assess mitigation options, therefore, new estimation methods are required that can take feed composition into account. The availability of information on livestock production systems has increased substantially enabling the development of more detailed methane prediction models. Limited number of process-based models have been developed that represent biological relationships in methane production, however, these require extensive inputs and specialized software that may not be easily available. Empirical models may provide a better alternative in practical situations due to less input requirements. Several models have been developed in the last 10 years but none of them work equally well across all regions of the world. The more successful models particularly in North America require three major inputs: feed (or energy) intake, fiber and fat concentration of the diet. Given the significant variability of emissions within regions, models that are able to capture regional variability of feed intake and diet composition perform the best in model evaluation with independent data. The utilization of such models may reduce uncertainties associated with prediction of methane emissions and allow a better examination and representation of policies regulating emissions from cattle.

ESTIMATE OF GLOBAL METHANE EMISSIONS FROM LANDFILLS AND OPEN DUMPS

EPA Science Inventory

The report presents an empirical model to estimate global methane (CH4) emissions from landfills and open dumps based on EPA data from landfill gas (LFG) recovery projects. The EPA CH4 estimates for 1990 range between 19 and 40 teragrams (10 to the 12th power) per year (Tg/yr), w...

Downward Atmospheric Longwave Radiation in the City of Sao Paulo

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barbaro, Eduardo W.; Oliveira, Amauri P.; Soares, Jacyra

2009-03-11

This work evaluates objectively the consistency and quality of a 9 year dataset based on 5 minute average values of downward longwave atmospheric (LW) emission, shortwave radiation, temperature and relative humidity. All these parameters were observed simultaneously and continuously from 1997 to 2006 in the IAG micrometeorological platform, located at the top of the IAG-USP building. The pyrgeometer dome emission effect was removed using neural network technique reducing the downward long wave atmospheric emission error to 3.5%. The comparison, between the monthly average values of LW emission observed in Sao Paulo and satellite estimates from SRB-NASA project, indicated a verymore » good agreement. Furthermore, this work investigates the performance of 10 empirical expressions to estimate the LW emission at the surface. The comparison between the models indicates that Brunt's one presents the better results, with smallest ''MBE,''''RMSE'' and biggest ''d'' index of agreement, therefore Brunt is the most indicated model to estimate LW emission under clear sky conditions in the city of Sao Paulo.« less

Atmospheric Ammonia Over China: Emission Estimates And Impact On Air Quality

NASA Astrophysics Data System (ADS)

Zhang, L.; Chen, Y.; Zhao, Y.; Henze, D. K.

2016-12-01

Ammonia (NH3) in the atmosphere is an important precursor of aerosols, and its deposition through wet and dry processes can cause adverse effects on ecosystems. The ammonia emissions over China are particularly large due to intensive agricultural activities, yet our current estimates of Chinese ammonia emissions and associated consequences on air quality are subject to large errors. We use the GEOS-Chem chemical transport model and its adjoint model to better quantify this issue. The TES satellite observations of ammonia concentrations and surface measurements of wet deposition fluxes are assimilated into the model to constrain the ammonia emissions over China. Optimized emissions show a strong seasonal variability with emissions in summer a factor of 3 higher than winter. This is consistent with an improved bottom-up estimate of Chinese ammonia emissions from fertilizer use by using more practical fertilizer application rates for different crop types. We further use the GEOS-Chem adjoint at 0.25x0.3125 degree resolution to examine the sources contributing to the PM2.5 air pollution over North China. We show that wintertime PM2.5 over Beijing is largely contributed by residential and industrial sources, and ammonia emissions from agriculture activities. PM2.5 concentrations over North China are particularly sensitive to emissions of ammonia and nitrogen oxides, reflecting strong formation of aerosol nitrate in the cold seasons.

[Estimation of average traffic emission factor based on synchronized incremental traffic flow and air pollutant concentration].

PubMed

Li, Run-Kui; Zhao, Tong; Li, Zhi-Peng; Ding, Wen-Jun; Cui, Xiao-Yong; Xu, Qun; Song, Xian-Feng

2014-04-01

On-road vehicle emissions have become the main source of urban air pollution and attracted broad attentions. Vehicle emission factor is a basic parameter to reflect the status of vehicle emissions, but the measured emission factor is difficult to obtain, and the simulated emission factor is not localized in China. Based on the synchronized increments of traffic flow and concentration of air pollutants in the morning rush hour period, while meteorological condition and background air pollution concentration retain relatively stable, the relationship between the increase of traffic and the increase of air pollution concentration close to a road is established. Infinite line source Gaussian dispersion model was transformed for the inversion of average vehicle emission factors. A case study was conducted on a main road in Beijing. Traffic flow, meteorological data and carbon monoxide (CO) concentration were collected to estimate average vehicle emission factors of CO. The results were compared with simulated emission factors of COPERT4 model. Results showed that the average emission factors estimated by the proposed approach and COPERT4 in August were 2.0 g x km(-1) and 1.2 g x km(-1), respectively, and in December were 5.5 g x km(-1) and 5.2 g x km(-1), respectively. The emission factors from the proposed approach and COPERT4 showed close values and similar seasonal trends. The proposed method for average emission factor estimation eliminates the disturbance of background concentrations and potentially provides real-time access to vehicle fleet emission factors.

Methane emissions from global wetlands: An assessment of the uncertainty associated with various wetland extent data sets

NASA Astrophysics Data System (ADS)

Zhang, Bowen; Tian, Hanqin; Lu, Chaoqun; Chen, Guangsheng; Pan, Shufen; Anderson, Christopher; Poulter, Benjamin

2017-09-01

A wide range of estimates on global wetland methane (CH4) fluxes has been reported during the recent two decades. This gives rise to urgent needs to clarify and identify the uncertainty sources, and conclude a reconciled estimate for global CH4 fluxes from wetlands. Most estimates by using bottom-up approach rely on wetland data sets, but these data sets show largely inconsistent in terms of both wetland extent and spatiotemporal distribution. A quantitative assessment of uncertainties associated with these discrepancies among wetland data sets has not been well investigated yet. By comparing the five widely used global wetland data sets (GISS, GLWD, Kaplan, GIEMS and SWAMPS-GLWD), it this study, we found large differences in the wetland extent, ranging from 5.3 to 10.2 million km2, as well as their spatial and temporal distributions among the five data sets. These discrepancies in wetland data sets resulted in large bias in model-estimated global wetland CH4 emissions as simulated by using the Dynamic Land Ecosystem Model (DLEM). The model simulations indicated that the mean global wetland CH4 emissions during 2000-2007 were 177.2 ± 49.7 Tg CH4 yr-1, based on the five different data sets. The tropical regions contributed the largest portion of estimated CH4 emissions from global wetlands, but also had the largest discrepancy. Among six continents, the largest uncertainty was found in South America. Thus, the improved estimates of wetland extent and CH4 emissions in the tropical regions and South America would be a critical step toward an accurate estimate of global CH4 emissions. This uncertainty analysis also reveals an important need for our scientific community to generate a global scale wetland data set with higher spatial resolution and shorter time interval, by integrating multiple sources of field and satellite data with modeling approaches, for cross-scale extrapolation.

Estimates of CO2 from fires in the United States: implications for carbon management.

PubMed

Wiedinmyer, Christine; Neff, Jason C

2007-11-01

Fires emit significant amounts of CO2 to the atmosphere. These emissions, however, are highly variable in both space and time. Additionally, CO2 emissions estimates from fires are very uncertain. The combination of high spatial and temporal variability and substantial uncertainty associated with fire CO2 emissions can be problematic to efforts to develop remote sensing, monitoring, and inverse modeling techniques to quantify carbon fluxes at the continental scale. Policy and carbon management decisions based on atmospheric sampling/modeling techniques must account for the impact of fire CO2 emissions; a task that may prove very difficult for the foreseeable future. This paper addresses the variability of CO2 emissions from fires across the US, how these emissions compare to anthropogenic emissions of CO2 and Net Primary Productivity, and the potential implications for monitoring programs and policy development. Average annual CO2 emissions from fires in the lower 48 (LOWER48) states from 2002-2006 are estimated to be 213 (+/- 50 std. dev.) Tg CO2 yr-1 and 80 (+/- 89 std. dev.) Tg CO2 yr-1 in Alaska. These estimates have significant interannual and spatial variability. Needleleaf forests in the Southeastern US and the Western US are the dominant source regions for US fire CO2 emissions. Very high emission years typically coincide with droughts, and climatic variability is a major driver of the high interannual and spatial variation in fire emissions. The amount of CO2 emitted from fires in the US is equivalent to 4-6% of anthropogenic emissions at the continental scale and, at the state-level, fire emissions of CO2 can, in some cases, exceed annual emissions of CO2 from fossil fuel usage. The CO2 released from fires, overall, is a small fraction of the estimated average annual Net Primary Productivity and, unlike fossil fuel CO2 emissions, the pulsed emissions of CO2 during fires are partially counterbalanced by uptake of CO2 by regrowing vegetation in the decades following fire. Changes in fire severity and frequency can, however, lead to net changes in atmospheric CO2 and the short-term impacts of fire emissions on monitoring, modeling, and carbon management policy are substantial.

Uncertainties of wild-land fires emission in AQMEII phase 2 case study

NASA Astrophysics Data System (ADS)

Soares, J.; Sofiev, M.; Hakkarainen, J.

2015-08-01

The paper discusses the main uncertainties of wild-land fire emission estimates used in the AQMEII-II case study. The wild-land fire emission of particulate matter for the summer fire season of 2010 in Eurasia was generated by the Integrated System for wild-land Fires (IS4FIRES). The emission calculation procedure included two steps: bottom-up emission compilation from radiative energy of individual fires observed by MODIS instrument on-board of Terra and Aqua satellites; and top-down calibration of emission factors based on the comparison between observations and modelled results. The approach inherits various uncertainties originating from imperfect information on fires, inaccuracies of the inverse problem solution, and simplifications in the fire description. These are analysed in regard to the Eurasian fires in 2010. It is concluded that the total emission is likely to be over-estimated by up to 50% with individual-fire emission accuracy likely to vary in a wide range. The first results of the new IS4FIRESv2 products and fire-resolving modelling are discussed in application to the 2010 events. It is shown that the new emission estimates have similar patterns but are lower than the IS4FIRESv1 values.

Quantification of uncertainty associated with United States high resolution fossil fuel CO2 emissions: updates, challenges and future plans

NASA Astrophysics Data System (ADS)

Gurney, K. R.; Chandrasekaran, V.; Mendoza, D. L.; Geethakumar, S.

2010-12-01

The Vulcan Project has estimated United States fossil fuel CO2 emissions at the hourly time scale and at spatial scales below the county level for the year 2002. Vulcan is built from a wide variety of observational data streams including regulated air pollutant emissions reporting, traffic monitoring, energy statistics, and US census data. In addition to these data sets, Vulcan relies on a series of modeling assumptions and constructs to interpolate in space, time and transform non-CO2 reporting into an estimate of CO2 combustion emissions. The recent version 2.0 of the Vulcan inventory has produced advances in a number of categories with particular emphasis on improved temporal structure. Onroad transportation emissions now avail of roughly 5000 automated traffic count monitors allowing for much improved diurnal and weekly time structure in our onroad transportation emissions. Though the inventory shows excellent agreement with independent national-level CO2 emissions estimates, uncertainty quantification has been a challenging task given the large number of data sources and numerous modeling assumptions. However, we have now accomplished a complete uncertainty estimate across all the Vulcan economic sectors and will present uncertainty estimates as a function of space, time, sector and fuel. We find that, like the underlying distribution of CO2 emissions themselves, the uncertainty is also strongly lognormal with high uncertainty associated with a relatively small number of locations. These locations typically are locations reliant upon coal combustion as the dominant CO2 source. We will also compare and contrast Vulcan fossil fuel CO2 emissions estimates against estimates built from DOE fuel-based surveys at the state level. We conclude that much of the difference between the Vulcan inventory and DOE statistics are not due to biased estimation but mechanistic differences in supply versus demand and combustion in space/time.

Comparisons of MOVES Light-duty Gasoline NOx Emission Rates with Real-world Measurements

NASA Astrophysics Data System (ADS)

Choi, D.; Sonntag, D.; Warila, J.

2017-12-01

Recent studies have shown differences between air quality model estimates and monitored values for nitrogen oxides. Several studies have suggested that the discrepancy between monitored and modeled values is due to an overestimation of NOx from mobile sources in EPA's emission inventory, particularly for light-duty gasoline vehicles. EPA's MOtor Vehicle Emission Simulator (MOVES) is an emission modeling system that estimates emissions for cars, trucks and other mobile sources at the national, county, and project level for criteria pollutants, greenhouse gases, and air toxics. Studies that directly measure vehicle emissions provide useful data for evaluating MOVES when the measurement conditions are properly accounted for in modeling. In this presentation, we show comparisons of MOVES2014 to thousands of real-world NOx emissions measurements from individual light-duty gasoline vehicles. The comparison studies include in-use vehicle emissions tests conducted on chassis dynamometer tests in support of Denver, Colorado's Vehicle Inspection & Maintenance Program and remote sensing data collected using road-side instruments in multiple locations and calendar years in the United States. In addition, we conduct comparisons of MOVES predictions to fleet-wide emissions measured from tunnels. We also present details on the methodology used to conduct the MOVES model runs in comparing to the independent data.

A spatial modeling framework to evaluate domestic biofuel-induced potential land use changed and emissions

USGS Publications Warehouse

Elliot, Joshua; Sharma, Bhavna; Best, Neil; Glotter, Michael; Dunn, Jennifer B.; Foster, Ian; Miguez, Fernando; Mueller, Steffen; Wang, Michael

2014-01-01

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions. Using highresolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decisionmaking, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), Miscanthus × giganteus (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively.

New methodology for modeling annual-aircraft emissions at airports

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodmansey, B.G.; Patterson, J.G.

An as-accurate-as-possible estimation of total-aircraft emissions are an essential component of any environmental-impact assessment done for proposed expansions at major airports. To determine the amount of emissions generated by aircraft using present models it is necessary to know the emission characteristics of all engines that are on all planes using the airport. However, the published data base does not cover all engine types and, therefore, a new methodology is needed to assist in estimating annual emissions from aircraft at airports. Linear regression equations relating quantity of emissions to aircraft weight using a known-fleet mix are developed in this paper. Total-annualmore » emissions for CO, NO[sub x], NMHC, SO[sub x], CO[sub 2], and N[sub 2]O are tabulated for Toronto's international airport for 1990. The regression equations are statistically significant for all emissions except for NMHC from large jets and NO[sub x] and NMHC for piston-engine aircraft. This regression model is a relatively simple, fast, and inexpensive method of obtaining an annual-emission inventory for an airport.« less

Fire and deforestation dynamics in Amazonia (1973-2014).

PubMed

van Marle, Margreet J E; Field, Robert D; van der Werf, Guido R; Estrada de Wagt, Ivan A; Houghton, Richard A; Rizzo, Luciana V; Artaxo, Paulo; Tsigaridis, Kostas

2017-01-01

Consistent long-term estimates of fire emissions are important to understand the changing role of fire in the global carbon cycle and to assess the relative importance of humans and climate in shaping fire regimes. However, there is limited information on fire emissions from before the satellite era. We show that in the Amazon region, including the Arc of Deforestation and Bolivia, visibility observations derived from weather stations could explain 61% of the variability in satellite-based estimates of bottom-up fire emissions since 1997 and 42% of the variability in satellite-based estimates of total column carbon monoxide concentrations since 2001. This enabled us to reconstruct the fire history of this region since 1973 when visibility information became available. Our estimates indicate that until 1987 relatively few fires occurred in this region and that fire emissions increased rapidly over the 1990s. We found that this pattern agreed reasonably well with forest loss data sets, indicating that although natural fires may occur here, deforestation and degradation were the main cause of fires. Compared to fire emissions estimates based on Food and Agricultural Organization's Global Forest and Resources Assessment data, our estimates were substantially lower up to the 1990s, after which they were more in line. These visibility-based fire emissions data set can help constrain dynamic global vegetation models and atmospheric models with a better representation of the complex fire regime in this region.

Estimating NOx emissions and surface concentrations at high spatial resolution using OMI

NASA Astrophysics Data System (ADS)

Goldberg, D. L.; Lamsal, L. N.; Loughner, C.; Swartz, W. H.; Saide, P. E.; Carmichael, G. R.; Henze, D. K.; Lu, Z.; Streets, D. G.

2017-12-01

In many instances, NOx emissions are not measured at the source. In these cases, remote sensing techniques are extremely useful in quantifying NOx emissions. Using an exponential modified Gaussian (EMG) fitting of oversampled Ozone Monitoring Instrument (OMI) NO2 data, we estimate NOx emissions and lifetimes in regions where these emissions are uncertain. This work also presents a new high-resolution OMI NO2 dataset derived from the NASA retrieval that can be used to estimate surface level concentrations in the eastern United States and South Korea. To better estimate vertical profile shape factors, we use high-resolution model simulations (Community Multi-scale Air Quality (CMAQ) and WRF-Chem) constrained by in situ aircraft observations to re-calculate tropospheric air mass factors and tropospheric NO2 vertical columns during summertime. The correlation between our satellite product and ground NO2 monitors in urban areas has improved dramatically: r2 = 0.60 in new product, r2 = 0.39 in operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to re-calculate vertical column data in areas with large spatial heterogeneities in NOx emissions. The methodologies developed in this work can be applied to other world regions and other satellite data sets to produce high-quality region-specific emissions estimates.

Global estimation of CO emissions using three sets of satellite data for burned area

NASA Astrophysics Data System (ADS)

Jain, Atul K.

Using three sets of satellite data for burned areas together with the tree cover imagery and a biogeochemical component of the Integrated Science Assessment Model (ISAM) the global emissions of CO and associated uncertainties are estimated for the year 2000. The available fuel load (AFL) is calculated using the ISAM biogeochemical model, which accounts for the aboveground and surface fuel removed by land clearing for croplands and pasturelands, as well as the influence on fuel load of various ecosystem processes (such as stomatal conductance, evapotranspiration, plant photosynthesis and respiration, litter production, and soil organic carbon decomposition) and important feedback mechanisms (such as climate and fertilization feedback mechanism). The ISAM estimated global total AFL in the year 2000 was about 687 Pg AFL. All forest ecosystems account for about 90% of the global total AFL. The estimated global CO emissions based on three global burned area satellite data sets (GLOBSCAR, GBA, and Global Fire Emissions Database version 2 (GFEDv2)) for the year 2000 ranges between 320 and 390 Tg CO. Emissions from open fires are highest in tropical Africa, primarily due to forest cutting and burning. The estimated overall uncertainty in global CO emission is about ±65%, with the highest uncertainty occurring in North Africa and Middle East region (±99%). The results of this study suggest that the uncertainties in the calculated emissions stem primarily from the area burned data.

A Bayesian Multivariate Receptor Model for Estimating Source Contributions to Particulate Matter Pollution using National Databases.

PubMed

Hackstadt, Amber J; Peng, Roger D

2014-11-01

Time series studies have suggested that air pollution can negatively impact health. These studies have typically focused on the total mass of fine particulate matter air pollution or the individual chemical constituents that contribute to it, and not source-specific contributions to air pollution. Source-specific contribution estimates are useful from a regulatory standpoint by allowing regulators to focus limited resources on reducing emissions from sources that are major contributors to air pollution and are also desired when estimating source-specific health effects. However, researchers often lack direct observations of the emissions at the source level. We propose a Bayesian multivariate receptor model to infer information about source contributions from ambient air pollution measurements. The proposed model incorporates information from national databases containing data on both the composition of source emissions and the amount of emissions from known sources of air pollution. The proposed model is used to perform source apportionment analyses for two distinct locations in the United States (Boston, Massachusetts and Phoenix, Arizona). Our results mirror previous source apportionment analyses that did not utilize the information from national databases and provide additional information about uncertainty that is relevant to the estimation of health effects.

Evaluation of improved land use and canopy representation in ...

EPA Pesticide Factsheets

Biogenic volatile organic compounds (BVOC) participate in reactions that can lead to secondarily formed ozone and particulate matter (PM) impacting air quality and climate. BVOC emissions are important inputs to chemical transport models applied on local to global scales but considerable uncertainty remains in the representation of canopy parameterizations and emission algorithms from different vegetation species. The Biogenic Emission Inventory System (BEIS) has been used to support both scientific and regulatory model assessments for ozone and PM. Here we describe a new version of BEIS which includes updated input vegetation data and canopy model formulation for estimating leaf temperature and vegetation data on estimated BVOC. The Biogenic Emission Landuse Database (BELD) was revised to incorporate land use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land product and 2006 National Land Cover Database (NLCD) land coverage. Vegetation species data are based on the US Forest Service (USFS) Forest Inventory and Analysis (FIA) version 5.1 for 2002–2013 and US Department of Agriculture (USDA) 2007 census of agriculture data. This update results in generally higher BVOC emissions throughout California compared with the previous version of BEIS. Baseline and updated BVOC emission estimates are used in Community Multiscale Air Quality (CMAQ) Model simulations with 4 km grid resolution and evaluated with measurements of isoprene and monoterp

Spatial and Temporal Correlates of Greenhouse Gas Diffusion from a Hydropower Reservoir in the Southern United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mosher, Jennifer; Fortner, Allison M.; Phillips, Jana Randolph

Emissions of CO 2 and CH 4 from freshwater reservoirs constitute a globally significant source of atmospheric greenhouse gases (GHGs), but knowledge gaps remain with regard to spatiotemporal drivers of emissions. We document the spatial and seasonal variation in surface diffusion of CO 2 and CH 4 from Douglas Lake, a hydropower reservoir in Tennessee, USA. Monthly estimates across 13 reservoir sites from January to November 2010 indicated that surface diffusions ranged from 236 to 18,806 mg m -2 day -1 for CO 2 and 0 to 0.95 mg m -2 day -1 for CH 4. Next, we developed statisticalmore » models using spatial and physicochemical variables to predict surface diffusions of CO 2 and CH 4. Models explained 22.7 and 20.9% of the variation in CO 2 and CH4 diffusions, respectively, and identified pH, temperature, dissolved oxygen, and Julian day as the most informative important predictors. These findings provide baseline estimates of GHG emissions from a reservoir in eastern temperate North America a region for which estimates of reservoir GHGs emissions are limited. Our statistical models effectively characterized non-linear and threshold relationships between physicochemical predictors and GHG emissions. Further refinement of such models will aid in predicting current GHG emissions in unsampled reservoirs and forecasting future GHG emissions.« less

Spatial and Temporal Correlates of Greenhouse Gas Diffusion from a Hydropower Reservoir in the Southern United States

DOE PAGES

Mosher, Jennifer; Fortner, Allison M.; Phillips, Jana Randolph; ...

2015-10-29

Emissions of CO 2 and CH 4 from freshwater reservoirs constitute a globally significant source of atmospheric greenhouse gases (GHGs), but knowledge gaps remain with regard to spatiotemporal drivers of emissions. We document the spatial and seasonal variation in surface diffusion of CO 2 and CH 4 from Douglas Lake, a hydropower reservoir in Tennessee, USA. Monthly estimates across 13 reservoir sites from January to November 2010 indicated that surface diffusions ranged from 236 to 18,806 mg m -2 day -1 for CO 2 and 0 to 0.95 mg m -2 day -1 for CH 4. Next, we developed statisticalmore » models using spatial and physicochemical variables to predict surface diffusions of CO 2 and CH 4. Models explained 22.7 and 20.9% of the variation in CO 2 and CH4 diffusions, respectively, and identified pH, temperature, dissolved oxygen, and Julian day as the most informative important predictors. These findings provide baseline estimates of GHG emissions from a reservoir in eastern temperate North America a region for which estimates of reservoir GHGs emissions are limited. Our statistical models effectively characterized non-linear and threshold relationships between physicochemical predictors and GHG emissions. Further refinement of such models will aid in predicting current GHG emissions in unsampled reservoirs and forecasting future GHG emissions.« less

A framework for modeling in-use deterioration of light-duty vehicle emissions using MOBILE6

DOT National Transportation Integrated Search

2000-09-01

The Mobile Source Emission Factor Model used to estimate the inventory of exhaust and evaporative emissions from on-road motor vehicles is currently being revised by the U.S. Environmental Protection Agency. The framework used in calculating basic ex...

Open-source LCA tool for estimating greenhouse gas emissions from crude oil production using field characteristics.

PubMed

El-Houjeiri, Hassan M; Brandt, Adam R; Duffy, James E

2013-06-04

Existing transportation fuel cycle emissions models are either general and calculate nonspecific values of greenhouse gas (GHG) emissions from crude oil production, or are not available for public review and auditing. We have developed the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) to provide open-source, transparent, rigorous GHG assessments for use in scientific assessment, regulatory processes, and analysis of GHG mitigation options by producers. OPGEE uses petroleum engineering fundamentals to model emissions from oil and gas production operations. We introduce OPGEE and explain the methods and assumptions used in its construction. We run OPGEE on a small set of fictional oil fields and explore model sensitivity to selected input parameters. Results show that upstream emissions from petroleum production operations can vary from 3 gCO2/MJ to over 30 gCO2/MJ using realistic ranges of input parameters. Significant drivers of emissions variation are steam injection rates, water handling requirements, and rates of flaring of associated gas.

Inverse modeling and mapping US air quality influences of inorganic PM2.5 precursor emissions using the adjoint of GEOS-Chem

NASA Astrophysics Data System (ADS)

Henze, D. K.; Seinfeld, J. H.; Shindell, D. T.

2009-08-01

Influences of specific sources of inorganic PM2.5 on peak and ambient aerosol concentrations in the US are evaluated using a combination of inverse modeling and sensitivity analysis. First, sulfate and nitrate aerosol measurements from the IMPROVE network are assimilated using the four-dimensional variational (4D-Var) method into the GEOS-Chem chemical transport model in order to constrain emissions estimates in four separate month-long inversions (one per season). Of the precursor emissions, these observations primarily constrain ammonia (NH3). While the net result is a decrease in estimated US~NH3 emissions relative to the original inventory, there is considerable variability in adjustments made to NH3 emissions in different locations, seasons and source sectors, such as focused decreases in the midwest during July, broad decreases throughout the US~in January, increases in eastern coastal areas in April, and an effective redistribution of emissions from natural to anthropogenic sources. Implementing these constrained emissions, the adjoint model is applied to quantify the influences of emissions on representative PM2.5 air quality metrics within the US. The resulting sensitivity maps display a wide range of spatial, sectoral and seasonal variability in the susceptibility of the air quality metrics to absolute emissions changes and the effectiveness of incremental emissions controls of specific source sectors. NH3 emissions near sources of sulfur oxides (SOx) are estimated to most influence peak inorganic PM2.5 levels in the East; thus, the most effective controls of NH3 emissions are often disjoint from locations of peak NH3 emissions. Controls of emissions from industrial sectors of SOx and NOx are estimated to be more effective than surface emissions, and changes to NH3 emissions in regions dominated by natural sources are disproportionately more effective than regions dominated by anthropogenic sources. NOx controls are most effective in northern states in October; in January, SOx controls may be counterproductive. When considering ambient inorganic PM2.5 concentrations, intercontinental influences are small, though transboundary influences within North America are significant, with SOx emissions from surface sources in Mexico contributing almost a fourth of the total influence from this sector.

Inverse modeling and mapping US air quality influences of inorganic PM2.5 precursor emissions using the adjoint of GEOS-Chem

NASA Astrophysics Data System (ADS)

Henze, D. K.; Seinfeld, J. H.; Shindell, D. T.

2008-08-01

Influences of specific sources of inorganic PM2.5 on peak and ambient aerosol concentrations in the US are evaluated using a combination of inverse modeling and sensitivity analysis. First, sulfate and nitrate aerosol measurements from the IMPROVE network are assimilated using the four-dimensional variational (4D-Var) method into the GEOS-Chem chemical transport model in order to constrain emissions estimates in four separate month-long inversions (one per season). Of the precursor emissions, these observations primarily constrain ammonia (NH3). While the net result is a decrease in estimated US NH3 emissions relative to the original inventory, there is considerable variability in adjustments made to NH3 emissions in different locations, seasons and source sectors, such as focused decreases in the midwest during July, broad decreases throughout the US~in January, increases in eastern coastal areas in April, and an effective redistribution of emissions from natural to anthropogenic sources. Implementing these constrained emissions, the adjoint model is applied to quantify the influences of emissions on representative PM2.5 air quality metrics within the US. The resulting sensitivity maps display a wide range of spatial, sectoral and seasonal variability in the susceptibility of the air quality metrics to absolute emissions changes and the effectiveness of incremental emissions controls of specific source sectors. NH3 emissions near sources of sulfur oxides (SOx) are estimated to most influence peak inorganic PM2.5 levels in the East; thus, the most effective controls of NH3 emissions are often disjoint from locations of peak NH3 emissions. Controls of emissions from industrial sectors of SOx and NOx are estimated to be more effective than surface emissions, and changes to NH3 emissions in regions dominated by natural sources are disproportionately more effective than regions dominated by anthropogenic sources. NOx controls are most effective in northern states in October; in January, SOx controls may be counterproductive. When considering ambient inorganic PM2.5 concentrations, intercontinental influences are small, though transboundary influences within North America are significant, with SOx emissions from surface sources in Mexico contributing almost a fourth of the total influence from this sector.

Modeled and monitored variation in space and time of PCB-153 concentrations in air, sediment, soil and aquatic biota on a European scale.

PubMed

Hauck, Mara; Huijbregts, Mark A J; Hollander, Anne; Hendriks, A Jan; van de Meent, Dik

2010-08-15

We evaluated various modeling options for estimating concentrations of PCB-153 in the environment and in biota across Europe, using a nested multimedia fate model coupled with a bioaccumulation model. The most detailed model set up estimates concentrations in air, soil, fresh water sediment and fresh water biota with spatially explicit environmental characteristics and spatially explicit emissions to air and water in the period 1930-2005. Model performance was evaluated with the root mean square error (RMSE(log)), based on the difference between estimated and measured concentrations. The RMSE(log) was 5.4 for air, 5.6-6.3 for sediment and biota, and 5.5 for soil in the most detailed model scenario. Generally, model estimations tended to underestimate observed values for all compartments, except air. The decline in observed concentrations was also slightly underestimated by the model for the period where measurements were available (1989-2002). Applying a generic model setup with averaged emissions and averaged environmental characteristics, the RMSE(log) increased to 21 for air and 49 for sediment. For soil the RMSE(log) decreased to 3.5. We found that including spatial variation in emissions was most relevant for all compartments, except soil, while including spatial variation in environmental characteristics was less influential. For improving predictions of concentrations in sediment and aquatic biota, including emissions to water was found to be relevant as well. Copyright 2009 Elsevier B.V. All rights reserved.

Speed Profiles for Improvement of Maritime Emission Estimation

PubMed Central

Yau, Pui Shan; Lee, Shun-Cheng; Ho, Kin Fai

2012-01-01

Abstract Maritime emissions play an important role in anthropogenic emissions, particularly for cities with busy ports such as Hong Kong. Ship emissions are strongly dependent on vessel speed, and thus accurate vessel speed is essential for maritime emission studies. In this study, we determined minute-by-minute high-resolution speed profiles of container ships on four major routes in Hong Kong waters using Automatic Identification System (AIS). The activity-based ship emissions of NOx, CO, HC, CO2, SO2, and PM10 were estimated using derived vessel speed profiles, and results were compared with those using the speed limits of control zones. Estimation using speed limits resulted in up to twofold overestimation of ship emissions. Compared with emissions estimated using the speed limits of control zones, emissions estimated using vessel speed profiles could provide results with up to 88% higher accuracy. Uncertainty analysis and sensitivity analysis of the model demonstrated the significance of improvement of vessel speed resolution. From spatial analysis, it is revealed that SO2 and PM10 emissions during maneuvering within 1 nautical mile from port were the highest. They contributed 7%–22% of SO2 emissions and 8%–17% of PM10 emissions of the entire voyage in Hong Kong. PMID:23236250

Development of Greenhouse Gas Emissions Model (GEM) for Heavy- & Medium-Duty Vehicle Compliance

EPA Science Inventory

A regulatory vehicle simulation program was designed for determining greenhouse gas (GHG) emissions and fuel consumption by estimating the performance of technologies, verifying compliance with the regulatory standards and estimating the overall benefits of the program.

Spatiotemporal variability of biogenic terpenoid emissions in Pearl River Delta, China, with high-resolution land-cover and meteorological data

NASA Astrophysics Data System (ADS)

Wang, Xuemei; Situ, Shuping; Guenther, Alex; Chen, Fei; Wu, Zhiyong; Xia, Beicheng; Wang, Tijian

2011-04-01

This study intended to provide 4-km gridded, hourly, year-long, regional estimates of terpenoid emissions in the Pearl River Delta (PRD), China. It combined Thematic Mapper images and local-survey data to characterize plant functional types, and used observed emission potential of biogenic volatile organic compounds (BVOC) from local plant species and high-resolution meteorological outputs from the MM5 model to constrain the MEGAN BVOC-emission model. The estimated annual emissions for isoprene, monoterpene and sesquiterpene are 95.55 × 106 kg C, 117.35 × 106 kg C and 9.77 × 106 kg C, respectively. The results show strong variabilities of terpenoid emissions spanning diurnal and seasonal time scales, which are mainly distributed in the remote areas (with more vegetation and less economic development) in PRD. Using MODIS PFTs data reduced terpenoid emissions by 27% in remote areas. Using MEGAN-model default emission factors led to a 24% increase in BVOC emission. The model errors of temperature and radiation in MM5 output were used to assess impacts of uncertainties in meteorological forcing on emissions: increasing (decreasing) temperature and downward shortwave radiation produces more (less) terpenoid emissions for July and January. Strong temporal variability of terpenoid emissions leads to enhanced ozone formation during midday in rural areas where the anthropogenic VOC emissions are limited.

Improved MEGAN predictions of biogenic isoprene in the contiguous United States

NASA Astrophysics Data System (ADS)

Wang, Peng; Schade, Gunnar; Estes, Mark; Ying, Qi

2017-01-01

Isoprene emitted from biogenic sources significantly contributes to ozone and secondary organic aerosol formation in the troposphere. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) has been widely used to estimate isoprene emissions from local to global scales. However, previous studies have shown that MEGAN significantly over-predicts isoprene emissions in the contiguous United States (US). In this study, ambient isoprene concentrations in the US were simulated by the Community Multiscale Air Quality (CMAQ) model (v5.0.1) using biogenic emissions estimated by MEGAN v2.10 with several different gridded isoprene emission factor (EF) fields. Best isoprene predictions were obtained with the EF field based on the Biogenic Emissions Landcover Database v4 (BELD4) from US EPA for its Biogenic Emission Inventory System (BEIS) model v3.61 (MEGAN-BEIS361). A seven-month simulation (April to October 2011) of isoprene emissions with MEGAN-BEIS361 and ambient concentrations using CMAQ shows that observed spatial and temporal variations (both diurnal and seasonal) of isoprene concentrations can be well predicted at most non-urban monitors using isoprene emission estimation from the MEGAN-BEIS361 without significant biases. The predicted monthly average vertical column density of formaldehyde (HCHO), a reactive volatile organic compound with significant contributions from isoprene oxidation, generally agree with the spatial distribution of HCHO column density derived using satellite data collected by the Ozone Monitoring Instrument (OMI), although summer month vertical column densities in the southeast US were overestimated, which suggests that isoprene emission might still be overestimated in that region. The agreement between observation and prediction may be further improved if more accurate PAR values, such as those derived from satellite-based observations, were used in modeling the biogenic emissions.

Isoprene and monoterpene emissions in south-east Australia: comparison of a multi-layer canopy model with MEGAN and with atmospheric observations

NASA Astrophysics Data System (ADS)

Emmerson, Kathryn M.; Cope, Martin E.; Galbally, Ian E.; Lee, Sunhee; Nelson, Peter F.

2018-05-01

One of the key challenges in atmospheric chemistry is to reduce the uncertainty of biogenic volatile organic compound (BVOC) emission estimates from vegetation to the atmosphere. In Australia, eucalypt trees are a primary source of biogenic emissions, but their contribution to Australian air sheds is poorly quantified. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) has performed poorly against Australian isoprene and monoterpene observations. Finding reasons for the MEGAN discrepancies and strengthening our understanding of biogenic emissions in this region is our focus. We compare MEGAN to the locally produced Australian Biogenic Canopy and Grass Emissions Model (ABCGEM), to identify the uncertainties associated with the emission estimates and the data requirements necessary to improve isoprene and monoterpene emissions estimates for the application of MEGAN in Australia. Previously unpublished, ABCGEM is applied as an online biogenic emissions inventory to model BVOCs in the air shed overlaying Sydney, Australia. The two models use the same meteorological inputs and chemical mechanism, but independent inputs of leaf area index (LAI), plant functional type (PFT) and emission factors. We find that LAI, a proxy for leaf biomass, has a small role in spatial, temporal and inter-model biogenic emission variability, particularly in urban areas for ABCGEM. After removing LAI as the source of the differences, we found large differences in the emission activity function for monoterpenes. In MEGAN monoterpenes are partially light dependent, reducing their dependence on temperature. In ABCGEM monoterpenes are not light dependent, meaning they continue to be emitted at high rates during hot summer days, and at night. When the light dependence of monoterpenes is switched off in MEGAN, night-time emissions increase by 90-100 % improving the comparison with observations, suggesting the possibility that monoterpenes emitted from Australian vegetation may not be as light dependent as vegetation globally. Targeted measurements of emissions from in situ Australian vegetation, particularly of the light dependence issue are critical to improving MEGAN for one of the world's major biogenic emitting regions.

Simulation of nitrous oxide emissions at field scale using the SPACSYS model.

PubMed

Wu, L; Rees, R M; Tarsitano, D; Zhang, Xubo; Jones, S K; Whitmore, A P

2015-10-15

Nitrous oxide emitted to the atmosphere via the soil processes of nitrification and denitrification plays an important role in the greenhouse gas balance of the atmosphere and is involved in the destruction of stratospheric ozone. These processes are controlled by biological, physical and chemical factors such as growth and activity of microbes, nitrogen availability, soil temperature and water availability. A comprehensive understanding of these processes embodied in an appropriate model can help develop agricultural mitigation strategies to reduce greenhouse gas emissions, and help with estimating emissions at landscape and regional scales. A detailed module to describe the denitrification and nitrification processes and nitrogenous gas emissions was incorporated into the SPACSYS model to replace an earlier module that used a simplified first-order equation to estimate denitrification and was unable to distinguish the emissions of individual nitrogenous gases. A dataset derived from a Scottish grassland experiment in silage production was used to validate soil moisture in the top 10 cm soil, cut biomass, nitrogen offtake and N2O emissions. The comparison between the simulated and observed data suggested that the new module can provide a good representation of these processes and improve prediction of N2O emissions. The model provides an opportunity to estimate gaseous N emissions under a wide range of management scenarios in agriculture, and synthesises our understanding of the interaction and regulation of the processes. Copyright © 2015. Published by Elsevier B.V.

Emission of hydrogen sulfide (H2S) at a waterfall in a sewer: study of main factors affecting H2S emission and modeling approaches.

PubMed

Jung, Daniel; Hatrait, Laetitia; Gouello, Julien; Ponthieux, Arnaud; Parez, Vincent; Renner, Christophe

2017-11-01

Hydrogen sulfide (H 2 S) represents one of the main odorant gases emitted from sewer networks. A mathematical model can be a fast and low-cost tool for estimating its emission. This study investigates two approaches to modeling H 2 S gas transfer at a waterfall in a discharge manhole. The first approach is based on an adaptation of oxygen models for H 2 S emission at a waterfall and the second consists of a new model. An experimental set-up and a statistical data analysis allowed the main factors affecting H 2 S emission to be studied. A new model of the emission kinetics was developed using linear regression and taking into account H 2 S liquid concentration, waterfall height and fluid velocity at the outlet pipe of a rising main. Its prediction interval was estimated by the residual standard deviation (15.6%) up to a rate of 2.3 g H 2 S·h -1 . Finally, data coming from four sampling campaigns on sewer networks were used to perform simulations and compare predictions of all developed models.

Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change.

PubMed

Plevin, Richard J; Beckman, Jayson; Golub, Alla A; Witcover, Julie; O'Hare, Michael

2015-03-03

Few of the numerous published studies of the emissions from biofuels-induced "indirect" land use change (ILUC) attempt to propagate and quantify uncertainty, and those that have done so have restricted their analysis to a portion of the modeling systems used. In this study, we pair a global, computable general equilibrium model with a model of greenhouse gas emissions from land-use change to quantify the parametric uncertainty in the paired modeling system's estimates of greenhouse gas emissions from ILUC induced by expanded production of three biofuels. We find that for the three fuel systems examined--US corn ethanol, Brazilian sugar cane ethanol, and US soybean biodiesel--95% of the results occurred within ±20 g CO2e MJ(-1) of the mean (coefficient of variation of 20-45%), with economic model parameters related to crop yield and the productivity of newly converted cropland (from forestry and pasture) contributing most of the variance in estimated ILUC emissions intensity. Although the experiments performed here allow us to characterize parametric uncertainty, changes to the model structure have the potential to shift the mean by tens of grams of CO2e per megajoule and further broaden distributions for ILUC emission intensities.

Overview of the Global Nitrous Oxide Budget: The More We Think We Know, the Less We Really Know

NASA Astrophysics Data System (ADS)

Davidson, E. A.

2016-12-01

The N2O budget is balanced in the real world, but our ability to account for past and present sources and sinks remains poor. This is true for both top-down atmospheric inversion models and bottom-up compilations of emission estimates by geographic region, economic sector, land use, and land management. Narrowing uncertainties would improve confidence in budgets and improve targeting of climate change mitigation. Estimates of the atmospheric lifetime of N2O range from 104 to 152 years, resulting in an uncertainty of nearly 5 Tg N2O-N/yr in atmospheric model inversion estimates of global sources. Top-down source estimates are also sensitive to the assumed pre-industrial, quasi-steady-state N2O concentration. However, land-use change and natural climatic variation in the centuries preceding the industrial revolution add uncertainty. While there is agreement that agricultural soils are now the largest single source of anthropogenic N2O emissions, recent estimates of direct emissions from fertilizer and manure application to soils range from 0.66 to 2.5 Tg N2O-N/yr. These discrepancies are due to differences in estimated activity data (application rates), in disaggregation of data by region and crop type, and in linear or nonlinear assumptions for estimating emission factors. Indirect N2O emissions (those occurring in downstream or downwind ecosystems receiving runoff or deposition derived from agricultural sources) have always been poorly constrained and difficult to estimate. It is unclear, for example, whether recent estimates of enhanced N2O emissions from oceans due to N inputs from land are already adequately accounted for by indirect emission estimates or are a previously underestimated source. Tropical deforestation generally results in a brief (months to years) increase in soil N2O emissions, followed by emissions from degraded lands that are lower than those of the original forest. The effect globally is probably a net reduction of soil emissions that should be included in global budgets, but that is poorly quantified and often ignored. Where land use change and management includes fire, pyrogenic emissions are important but still uncertain. N2O soil sinks are small globally, but present an interesting conundrum for our understanding of underlying processes of N2O consumption.

Impact of freeway weaving segment design on light-duty vehicle exhaust emissions.

PubMed

Li, Qing; Qiao, Fengxiang; Yu, Lei; Chen, Shuyan; Li, Tiezhu

2018-06-01

In the United States, 26% of greenhouse gas emissions is emitted from the transportation sector; these emisssions meanwhile are accompanied by enormous toxic emissions to humans, such as carbon monoxide (CO), nitrogen oxides (NO x ), and hydrocarbon (HC), approximately 2.5% and 2.44% of a total exhaust emissions for a petrol and a diesel engine, respectively. These exhaust emissions are typically subject to vehicles' intermittent operations, such as hard acceleration and hard braking. In practice, drivers are inclined to operate intermittently while driving through a weaving segment, due to complex vehicle maneuvering for weaving. As a result, the exhaust emissions within a weaving segment ought to vary from those on a basic segment. However, existing emission models usually rely on vehicle operation information, and compute a generalized emission result, regardless of road configuration. This research proposes to explore the impacts of weaving segment configuration on vehicle emissions, identify important predictors for emission estimations, and develop a nonlinear normalized emission factor (NEF) model for weaving segments. An on-board emission test was conducted on 12 subjects on State Highway 288 in Houston, Texas. Vehicles' activity information, road conditions, and real-time exhaust emissions were collected by on-board diagnosis (OBD), a smartphone-based roughness app, and a portable emission measurement system (PEMS), respectively. Five feature selection algorithms were used to identify the important predictors for the response of NEF and the modeling algorithm. The predictive power of four algorithm-based emission models was tested by 10-fold cross-validation. Results showed that emissions are also susceptible to the type and length of a weaving segment. Bagged decision tree algorithm was chosen to develop a 50-grown-tree NEF model, which provided a validation error of 0.0051. The estimated NEFs are highly correlated with the observed NEFs in the training data set as well as in the validation data set, with the R values of 0.91 and 0.90, respectively. Existing emission models usually rely on vehicle operation information to compute a generalized emission result, regardless of road configuration. In practice, while driving through a weaving segment, drivers are inclined to perform erratic maneuvers, such as hard braking and hard acceleration due to the complex weaving maneuver required. As a result, the exhaust emissions within a weaving segment vary from those on a basic segment. This research proposes to involve road configuration, in terms of the type and length of a weaving segment, in constructing an emission nonlinear model, which significantly improves emission estimations at a microscopic level.

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California.

PubMed

Ono, Duane; Kiddoo, Phill; Howard, Christopher; Davis, Guy; Richmond, Kenneth

2011-10-01

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).

Model-based estimation with boundary side information or boundary regularization [cardiac emission CT].

PubMed

Chiao, P C; Rogers, W L; Fessler, J A; Clinthorne, N H; Hero, A O

1994-01-01

The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (emission computed tomography). They have also reported difficulties with boundary estimation in low contrast and low count rate situations. Here they propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, they introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. They implement boundary regularization through formulating a penalized log-likelihood function. They also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information.

Isoprene emissions over Asia 1979-2012 : impact of climate and land use changes

NASA Astrophysics Data System (ADS)

Stavrakou, Trissevgeni; Müller, Jean-Francois; Bauwens, Maite; Guenther, Alex; De Smedt, Isabelle; Van Roozendael, Michel

2014-05-01

Due to the scarcity of observational contraints and the rapidly changing environment in East and Southeast Asia, isoprene emissions predicted by models are expected to bear substantial uncertainties. This study aims at improving upon current bottom-up estimates, and investigate the temporal evolution of isoprene fluxes in Asia over 1979-2012. For that, we use the MEGAN model and incorporate (i) changes in land use, including the rapid expansion of oil palms, (ii) meteorological variability, (iii) long-term changes in solar radiation constrained by surface network measurements, and (iv) recent experimental evidence that South Asian forests are much weaker isoprene emitters than previously assumed. These effects lead to a significant reduction of the total isoprene fluxes over the studied domain compared to the standard simulation. The bottom-up emissions are evaluated using satellite-based emission estimates derived from inverse modelling constrained by GOME-2/MetOp-A formaldehyde columns through 2007-2012. The top-down estimates support our assumptions and confirm the lower isoprene emission rate in tropical forests of Indonesia and Malaysia.

Quantifying Uncertainties in N2O Emission Due to N Fertilizer Application in Cultivated Areas

PubMed Central

Philibert, Aurore; Loyce, Chantal; Makowski, David

2012-01-01

Nitrous oxide (N2O) is a greenhouse gas with a global warming potential approximately 298 times greater than that of CO2. In 2006, the Intergovernmental Panel on Climate Change (IPCC) estimated N2O emission due to synthetic and organic nitrogen (N) fertilization at 1% of applied N. We investigated the uncertainty on this estimated value, by fitting 13 different models to a published dataset including 985 N2O measurements. These models were characterized by (i) the presence or absence of the explanatory variable “applied N”, (ii) the function relating N2O emission to applied N (exponential or linear function), (iii) fixed or random background (i.e. in the absence of N application) N2O emission and (iv) fixed or random applied N effect. We calculated ranges of uncertainty on N2O emissions from a subset of these models, and compared them with the uncertainty ranges currently used in the IPCC-Tier 1 method. The exponential models outperformed the linear models, and models including one or two random effects outperformed those including fixed effects only. The use of an exponential function rather than a linear function has an important practical consequence: the emission factor is not constant and increases as a function of applied N. Emission factors estimated using the exponential function were lower than 1% when the amount of N applied was below 160 kg N ha−1. Our uncertainty analysis shows that the uncertainty range currently used by the IPCC-Tier 1 method could be reduced. PMID:23226430

Sparse estimation of model-based diffuse thermal dust emission

NASA Astrophysics Data System (ADS)

Irfan, Melis O.; Bobin, Jérôme

2018-03-01

Component separation for the Planck High Frequency Instrument (HFI) data is primarily concerned with the estimation of thermal dust emission, which requires the separation of thermal dust from the cosmic infrared background (CIB). For that purpose, current estimation methods rely on filtering techniques to decouple thermal dust emission from CIB anisotropies, which tend to yield a smooth, low-resolution, estimation of the dust emission. In this paper, we present a new parameter estimation method, premise: Parameter Recovery Exploiting Model Informed Sparse Estimates. This method exploits the sparse nature of thermal dust emission to calculate all-sky maps of thermal dust temperature, spectral index, and optical depth at 353 GHz. premise is evaluated and validated on full-sky simulated data. We find the percentage difference between the premise results and the true values to be 2.8, 5.7, and 7.2 per cent at the 1σ level across the full sky for thermal dust temperature, spectral index, and optical depth at 353 GHz, respectively. A comparison between premise and a GNILC-like method over selected regions of our sky simulation reveals that both methods perform comparably within high signal-to-noise regions. However, outside of the Galactic plane, premise is seen to outperform the GNILC-like method with increasing success as the signal-to-noise ratio worsens.

Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NOx and CO2 and their impacts

NASA Astrophysics Data System (ADS)

Brioude, Jerome; Angevine, Wayne; Ahmadov, Ravan; Kim, Si Wan; Evan, Stephanie; McKeen, Stuart; Hsie, Eirh Yu; Frost, Greg; Neuman, Andy; Pollack, Ilana; Peischl, Jeff; Ryerson, Tom; Holloway, John; Brown, Steeve; Nowak, John; Roberts, Jim; Wofsy, Steeve; Santoni, Greg; Trainer, Michael

2013-04-01

We present top-down estimates of anthropogenic CO, NOx and CO2 surface fluxes at mesoscale using a Lagrangian model in combination with three different WRF model configurations, driven by data from aircraft flights during the CALNEX campaign in southern California in May-June 2010. The US EPA National Emission Inventory 2005 (NEI 2005) was the prior in the CO and NOx inversion calculations. The flux ratio inversion method, based on linear relationships between chemical species, was used to calculate the CO2 inventory without prior knowledge of CO2 surface fluxes. The inversion was applied to each flight to estimate the variability of single-flight-based flux estimates. In Los Angeles (LA) County, the uncertainties on CO and NOx fluxes were 10% and 15%, respectively. Compared with NEI 2005, the CO posterior emissions were lower by 43% ± 6% in LA County and by 37% ± 10% in the South Coast Air Basin (SoCAB). NOx posterior emissions were lower by 32% ± 10% in LA County and by 27% ± 15% in the SoCAB. NOx posterior emissions were 40% lower on weekends relative to weekdays. The CO2 posterior estimates were 183 ± 18 Tg yr-1 in SoCAB. A flight during ITCT in 2002 was used to estimate emissions in the LA Basin in 2002. From 2002 to 2010, the CO and NOx posterior emissions decreased by 41% and 37%, respectively, in agreement with previous studies. Over the same time period, CO2 emissions increased by 10% ± 14% in LA County but decreased by 4% ± 10% in the SoCAB, a statistically insignificant change. Overall, the posterior estimates were in good agreement with the California Air Resources Board (CARB) inventory, with differences of 15% or less. However, the posterior spatial distribution in the basin was significantly different from CARB for NOx emissions. WRF-Chem mesoscale chemical-transport model simulations allowed an evaluation of differences in chemistry using different inventory assumptions, including NEI 2005, CARB 2010 and the posterior inventories derived in this study. The biases in WRF-Chem ozone were reduced and correlations were increased using the posterior from this study compared with simulations with the two bottom-up inventories, showing that improving the spatial distribution of ozone precursor surface emissions is also important in mesoscale chemistry forecasts.

Top-down estimate of surface flux in the Los Angeles Basin using a mesoscale inverse modeling technique: assessing anthropogenic emissions of CO, NOx and CO2 and their impacts

NASA Astrophysics Data System (ADS)

Brioude, J.; Angevine, W. M.; Ahmadov, R.; Kim, S.-W.; Evan, S.; McKeen, S. A.; Hsie, E.-Y.; Frost, G. J.; Neuman, J. A.; Pollack, I. B.; Peischl, J.; Ryerson, T. B.; Holloway, J.; Brown, S. S.; Nowak, J. B.; Roberts, J. M.; Wofsy, S. C.; Santoni, G. W.; Trainer, M.

2012-12-01

We present top-down estimates of anthropogenic CO, NOx and CO2 surface fluxes at mesoscale using a Lagrangian model in combination with three different WRF model configurations, driven by data from aircraft flights during the CALNEX campaign in southern California in May-June 2010. The US EPA National Emission Inventory 2005 (NEI 2005) was the prior in the CO and NOx inversion calculations. The flux ratio inversion method, based on linear relationships between chemical species, was used to calculate the CO2 inventory without prior knowledge of CO2 surface fluxes. The inversion was applied to each flight to estimate the variability of single-flight-based flux estimates. In Los Angeles (LA) County, the uncertainties on CO and NOx fluxes were 10% and 15%, respectively. Compared with NEI 2005, the CO posterior emissions were lower by 43% ± 6% in LA County and by 37% ± 10% in the South Coast Air Basin (SoCAB). NOx posterior emissions were lower by 32% ± 10% in LA County and by 27% ± 15% in the SoCAB. NOx posterior emissions were 40% lower on weekends relative to weekdays. The CO2 posterior estimates were 183 ± 18 Tg yr-1 in SoCAB. A flight during ITCT in 2002 was used to estimate emissions in the LA Basin in 2002. From 2002 to 2010, the CO and NOx posterior emissions decreased by 41% and 37%, respectively, in agreement with previous studies. Over the same time period, CO2 emissions increased by 10% ± 14% in LA County but decreased by 4% ± 10% in the SoCAB, a statistically insignificant change. Overall, the posterior estimates were in good agreement with the California Air Resources Board (CARB) inventory, with differences of 15% or less. However, the posterior spatial distribution in the basin was significantly different from CARB for NOx emissions. WRF-Chem mesoscale chemical-transport model simulations allowed an evaluation of differences in chemistry using different inventory assumptions, including NEI 2005, CARB 2010 and the posterior inventories derived in this study. The biases in WRF-Chem ozone were reduced and correlations were increased using the posterior from this study compared with simulations with the two bottom-up inventories, showing that improving the spatial distribution of ozone precursor surface emissions is also important in mesoscale chemistry forecasts.

Improved Satellite-based Photosysnthetically Active Radiation (PAR) for Air Quality Studies

NASA Astrophysics Data System (ADS)

Pour Biazar, A.; McNider, R. T.; Cohan, D. S.; White, A.; Zhang, R.; Dornblaser, B.; Doty, K.; Wu, Y.; Estes, M. J.

2015-12-01

One of the challenges in understanding the air quality over forested regions has been the uncertainties in estimating the biogenic hydrocarbon emissions. Biogenic volatile organic compounds, BVOCs, play a critical role in atmospheric chemistry, particularly in ozone and particulate matter (PM) formation. In southeastern United States, BVOCs (mostly as isoprene) are the dominant summertime source of reactive hydrocarbon. Despite significant efforts in improving BVOC estimates, the errors in emission inventories remain a concern. Since BVOC emissions are particularly sensitive to the available photosynthetically active radiation (PAR), model errors in PAR result in large errors in emission estimates. Thus, utilization of satellite observations to estimate PAR can help in reducing emission uncertainties. Satellite-based PAR estimates rely on the technique used to derive insolation from satellite visible brightness measurements. In this study we evaluate several insolation products against surface pyranometer observations and offer a bias correction to generate a more accurate PAR product. The improved PAR product is then used in biogenic emission estimates. The improved biogenic emission estimates are compared to the emission inventories over Texas and used in air quality simulation over the period of August-September 2013 (NASA's Discover-AQ field campaign). A series of sensitivity simulations will be performed and evaluated against Discover-AQ observations to test the impact of satellite-derived PAR on air quality simulations.

A bottom up approach to on-road CO2 emissions estimates: improved spatial accuracy and applications for regional planning.

PubMed

Gately, Conor K; Hutyra, Lucy R; Wing, Ian Sue; Brondfield, Max N

2013-03-05

On-road transportation is responsible for 28% of all U.S. fossil-fuel CO2 emissions. Mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories use spatial proxies such as population and road density to downscale national or state-level data. Such procedures introduce errors where the proxy variables and actual emissions are weakly correlated, and limit analysis of the relationship between emissions and demographic trends at local scales. We develop an on-road emission inventory product for Massachusetts-based on roadway-level traffic data obtained from the Highway Performance Monitoring System (HPMS). We provide annual estimates of on-road CO2 emissions at a 1 × 1 km grid scale for the years 1980 through 2008. We compared our results with on-road emissions estimates from the Emissions Database for Global Atmospheric Research (EDGAR), with the Vulcan Product, and with estimates derived from state fuel consumption statistics reported by the Federal Highway Administration (FHWA). Our model differs from FHWA estimates by less than 8.5% on average, and is within 5% of Vulcan estimates. We found that EDGAR estimates systematically exceed FHWA by an average of 22.8%. Panel regression analysis of per-mile CO2 emissions on population density at the town scale shows a statistically significant correlation that varies systematically in sign and magnitude as population density increases. Population density has a positive correlation with per-mile CO2 emissions for densities below 2000 persons km(-2), above which increasing density correlates negatively with per-mile emissions.

Land-use change and greenhouse gas emissions from corn and cellulosic ethanol

PubMed Central

2013-01-01

Background The greenhouse gas (GHG) emissions that may accompany land-use change (LUC) from increased biofuel feedstock production are a source of debate in the discussion of drawbacks and advantages of biofuels. Estimates of LUC GHG emissions focus mainly on corn ethanol and vary widely. Increasing the understanding of LUC GHG impacts associated with both corn and cellulosic ethanol will inform the on-going debate concerning their magnitudes and sources of variability. Results In our study, we estimate LUC GHG emissions for ethanol from four feedstocks: corn, corn stover, switchgrass, and miscanthus. We use new computable general equilibrium (CGE) results for worldwide LUC. U.S. domestic carbon emission factors are from state-level modelling with a surrogate CENTURY model and U.S. Forest Service data. This paper investigates the effect of several key domestic lands carbon content modelling parameters on LUC GHG emissions. International carbon emission factors are from the Woods Hole Research Center. LUC GHG emissions are calculated from these LUCs and carbon content data with Argonne National Laboratory’s Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) model. Our results indicate that miscanthus and corn ethanol have the lowest (−10 g CO2e/MJ) and highest (7.6 g CO2e/MJ) LUC GHG emissions under base case modelling assumptions. The results for corn ethanol are lower than corresponding results from previous studies. Switchgrass ethanol base case results (2.8 g CO2e/MJ) were the most influenced by assumptions regarding converted forestlands and the fate of carbon in harvested wood products. They are greater than miscanthus LUC GHG emissions because switchgrass is a lower-yielding crop. Finally, LUC GHG emissions for corn stover are essentially negligible and insensitive to changes in model assumptions. Conclusions This research provides new insight into the influence of key carbon content modelling variables on LUC GHG emissions associated with the four bioethanol pathways we examined. Our results indicate that LUC GHG emissions may have a smaller contribution to the overall biofuel life cycle than previously thought. Additionally, they highlight the need for future advances in LUC GHG emissions estimation including improvements to CGE models and aboveground and belowground carbon content data. PMID:23575438

Enhanced representation of soil NO emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

EPA Science Inventory

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community...

Performance of the JULES land surface model for UK Biogenic VOC emissions

NASA Astrophysics Data System (ADS)

Hayman, Garry; Comyn-Platt, Edward; Vieno, Massimo; Langford, Ben

2017-04-01

Emissions of biogenic non-methane volatile organic compounds (NMVOCs) are important for air quality and tropospheric composition. Through their contribution to the production of tropospheric ozone and secondary organic aerosol (SOA), biogenic VOCs indirectly contribute to climate forcing and climate feedbacks [1]. Biogenic VOCs encompass a wide range of compounds and are produced by plants for growth, development, reproduction, defence and communication [2]. There are both biological and physico-chemical controls on emissions [3]. Only a few of the many biogenic VOCs are of wider interest and only two or three (isoprene and the monoterpenes, α- and β-pinene) are represented in chemical transport models. We use the Joint UK Land Environment Simulator (JULES), the UK community land surface model, to estimate biogenic VOC emission fluxes. JULES is a process-based model that describes the water, energy and carbon balances and includes temperature, moisture and carbon stores [4, 5]. JULES currently provides emission fluxes of the 4 largest groups of biogenic VOCs: isoprene, terpenes, methanol and acetone. The JULES isoprene scheme uses gross primary productivity (GPP), leaf internal carbon and the leaf temperature as a proxy for the electron requirement for isoprene synthesis [6]. In this study, we compare JULES biogenic VOC emission estimates of isoprene and terepenes with (a) flux measurements made at selected sites in the UK and Europe and (b) gridded estimates for the UK from the EMEP/EMEP4UK atmospheric chemical transport model [7, 8], using site-specific or EMEP4UK driving meteorological data, respectively. We compare the UK-scale emission estimates with literature estimates. We generally find good agreement in the comparisons but the estimates are sensitive to the choice of the base or reference emission potentials. References (1) Unger, 2014: Geophys. Res. Lett., 41, 8563, doi:10.1002/2014GL061616; (2) Laothawornkitkul et al., 2009: New Phytol., 183, 27, doi:10.1111/j.1469-8137.2009.02859.x; (3) Grote and Niinemets, 2008: Plant Biol., 10, 8, doi:10.1055/s-2007-964975; (4) Best et al., 2011: Geosci. Model Dev., 4, 677, doi:10.5194/gmd-4-677-2011; (5) Clark et al., 2011: Geosci. Model Dev., 4, 701, doi:10.5194/gmd-4-701-2011; (6) Pacifico et al., 2011: Atmos. Chem. Phys., 11, 4371, doi:10.5194/acp-11-4371-2011; [7] Simpson et al., 2012: Atmos. Chem. Phys., 12, 7825, doi: 10.5194/acp-12-7825-2012; [8] Vieno et al., 2016: Atmos. Chem. Phys., 16, 265, doi: 10.5194/acp-16-265-2016.

A Simple Model of Global Aerosol Indirect Effects

NASA Technical Reports Server (NTRS)

Ghan, Steven J.; Smith, Steven J.; Wang, Minghuai; Zhang, Kai; Pringle, Kirsty; Carslaw, Kenneth; Pierce, Jeffrey; Bauer, Susanne; Adams, Peter

2013-01-01

Most estimates of the global mean indirect effect of anthropogenic aerosol on the Earth's energy balance are from simulations by global models of the aerosol lifecycle coupled with global models of clouds and the hydrologic cycle. Extremely simple models have been developed for integrated assessment models, but lack the flexibility to distinguish between primary and secondary sources of aerosol. Here a simple but more physically based model expresses the aerosol indirect effect (AIE) using analytic representations of cloud and aerosol distributions and processes. Although the simple model is able to produce estimates of AIEs that are comparable to those from some global aerosol models using the same global mean aerosol properties, the estimates by the simple model are sensitive to preindustrial cloud condensation nuclei concentration, preindustrial accumulation mode radius, width of the accumulation mode, size of primary particles, cloud thickness, primary and secondary anthropogenic emissions, the fraction of the secondary anthropogenic emissions that accumulates on the coarse mode, the fraction of the secondary mass that forms new particles, and the sensitivity of liquid water path to droplet number concentration. Estimates of present-day AIEs as low as 5 W/sq m and as high as 0.3 W/sq m are obtained for plausible sets of parameter values. Estimates are surprisingly linear in emissions. The estimates depend on parameter values in ways that are consistent with results from detailed global aerosol-climate simulation models, which adds to understanding of the dependence on AIE uncertainty on uncertainty in parameter values.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nyhan, Marguerite; Sobolevsky, Stanislav; Kang, Chaogui

Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters inmore » high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset Results demonstrated that high resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain.« less

U.S. ENVIRONMENTAL PROTECTION AGENCY'S LANDFILL GAS EMISSION MODEL (LANDGEM)

EPA Science Inventory

The paper discusses EPA's available software for estimating landfill gas emissions. This software is based on a first-order decomposition rate equation using empirical data from U.S. landfills. The software provides a relatively simple approach to estimating landfill gas emissi...

Urban scale air quality modelling using detailed traffic emissions estimates

NASA Astrophysics Data System (ADS)

Borrego, C.; Amorim, J. H.; Tchepel, O.; Dias, D.; Rafael, S.; Sá, E.; Pimentel, C.; Fontes, T.; Fernandes, P.; Pereira, S. R.; Bandeira, J. M.; Coelho, M. C.

2016-04-01

The atmospheric dispersion of NOx and PM10 was simulated with a second generation Gaussian model over a medium-size south-European city. Microscopic traffic models calibrated with GPS data were used to derive typical driving cycles for each road link, while instantaneous emissions were estimated applying a combined Vehicle Specific Power/Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (VSP/EMEP) methodology. Site-specific background concentrations were estimated using time series analysis and a low-pass filter applied to local observations. Air quality modelling results are compared against measurements at two locations for a 1 week period. 78% of the results are within a factor of two of the observations for 1-h average concentrations, increasing to 94% for daily averages. Correlation significantly improves when background is added, with an average of 0.89 for the 24 h record. The results highlight the potential of detailed traffic and instantaneous exhaust emissions estimates, together with filtered urban background, to provide accurate input data to Gaussian models applied at the urban scale.

Building a Multivariable Linear Regression Model of On-road Traffic for Creation of High Resolution Emission Inventories

NASA Astrophysics Data System (ADS)

Powell, James Eckhardt

Emissions inventories are an important tool, often built by governments, and used to manage emissions. To build an inventory of urban CO2 emissions and other fossil fuel combustion products in the urban atmosphere, an inventory of on-road traffic is required. In particular, a high resolution inventory is necessary to capture the local characteristics of transport emissions. These emissions vary widely due to the local nature of the fleet, fuel, and roads. Here we show a new model of ADT for the Portland, OR metropolitan region. The backbone is traffic counter recordings made by the Portland Bureau of Transportation at 7,767 sites over 21 years (1986-2006), augmented with PORTAL (The Portland Regional Transportation Archive Listing) freeway traffic count data. We constructed a regression model to fill in traffic network gaps using GIS data such as road class and population density. An EPA-supplied emissions factor was used to estimate transportation CO2 emissions, which is compared to several other estimates for the city's CO2 footprint.

One decade of space-based isoprene emission estimates: Interannual variations and emission trends between 2005 and 2014

NASA Astrophysics Data System (ADS)

Bauwens, Maite; Stavrakou, Trissevgeni; Müller, Jean-François; De Smedt, Isabelle; Van Roozendael, Michel

2016-04-01

Isoprene is one of the most largely emitted hydrocarbons in the atmosphere, with global annual emissions estimated at about 500 Tg, but with large uncertainties (Arneth et al., 2011). Here we use the source inversion approach to derive top-down biogenic isoprene emission estimates for the period between 2005 and 2014 constrained by formaldehyde observations, a high-yield intermediate in the oxidation of isoprene in the atmosphere. Formaldehyde columns retrieved from the Ozone Monitoring Instrument (OMI) are used to constrain the IMAGESv2 global chemistry-transport model and its adjoint code (Stavrakou et al., 2009). The MEGAN-MOHYCAN isoprene emissions (Stavrakou et al., 2014) are used as bottom-up inventory in the model. The inversions are performed separately for each year of the study period, and monthly emissions are derived for every model grid cell. The inversion results are compared to independent isoprene emissions from GUESS-ES (Arneth et al., 2007) and MEGAN-MACC (Sinderalova et al., 2014) and to top-down fluxes based on GOME-2 formaldehyde columns (Bauwens et al., 2014; Stavrakou et al., 2015). The mean global annual OMI-based isoprene flux for the period 2005-2014 is estimated to be 270 Tg, with small interannual variation. This estimate is by 20% lower with regard to the a priori inventory on average, but on the regional scale strong emission updates are inferred. The OMI-based emissions are substantially lower than the MEGAN-MACC and the GUESS-ES inventory, but agree well with the isoprene fluxes constrained by GOME-2 formaldehyde columns. Strong emission reductions are derived over tropical regions. The seasonal pattern of isoprene emissions is generally well preserved after inversion and relatively consistent with other inventories, lending confidence to the MEGAN parameterization of the a priori inventory. In boreal regions the isoprene emission trend is positive and reinforced after inversion, whereas the inversion suggests negative trends in the rainforests of Equatorial Africa and South America. The top-down isoprene fluxes are available at a resolution of 0.5°x0.5° between 2005 and 2014 at the GlobEmission website (http://www.globemission.eu). References: Arneth, A., et al.: Process-based estimates of terrestrial ecosystem isoprene emissions: incorporating the effects of a direct CO 2-isoprene interaction, Atmos. Chem. Phys., 7(1), 31-53, 2007. Arneth, A., et al.: Global terrestrial isoprene emission models: sensitivity to variability in climate and vegetation, Atmos. Chem. Phys., 11(15), 8037-8052, 2011. Bauwens, M., et al.: Satellite-based isoprene emission estimates (2007-2012) from the GlobEmission project, in ACCENT-Plus Symposium 2013 Proceedings., 2014. Stavrakou, T., et al.: Isoprene emissions over Asia 1979 - 2012: impact of climate and land-use changes, Atmos. Chem. Phys., 14(9), 4587-4605, doi:10.5194/acp-14-4587-2014, 2014. Stavrakou, T., et al.: How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI?, Atmos. Chem. Phys., 15(20), 11861-11884, doi:10.5194/acp-15-11861-2015, 2015. Stavrakou, T., et al.: Evaluating the performance of pyrogenic and biogenic emission inventories against one decade of space-based formaldehyde columns, Atmos. Chem. Phys., 9(3), 1037-1060, doi:10.5194/acp-9-1037-2009, 2009.

Universal Approach to Estimate Perfluorocarbons Emissions During Individual High-Voltage Anode Effect for Prebaked Cell Technologies

NASA Astrophysics Data System (ADS)

Dion, Lukas; Gaboury, Simon; Picard, Frédéric; Kiss, Laszlo I.; Poncsak, Sandor; Morais, Nadia

2018-04-01

Recent investigations on aluminum electrolysis cell demonstrated limitations to the commonly used tier-3 slope methodology to estimate perfluorocarbon (PFC) emissions from high-voltage anode effects (HVAEs). These limitations are greater for smelters with a reduced HVAE frequency. A novel approach is proposed to estimate the specific emissions using a tier 2 model resulting from individual HVAE instead of estimating monthly emissions for pot lines with the slope methodology. This approach considers the nonlinear behavior of PFC emissions as a function of the polarized anode effect duration but also integrates the change in behavior attributed to cell productivity. Validation was performed by comparing the new approach and the slope methodology with measurement campaigns from different smelters. The results demonstrate a good agreement between measured and estimated emissions as well as more accurately reflect individual HVAE dynamics occurring over time. Finally, the possible impact of this approach for the aluminum industry is discussed.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

Impact of Surface Emissions to the Zonal Variability of Tropical Tropospheric Ozone and Carbon Monoxide for November 2004

NASA Technical Reports Server (NTRS)

Bowman, K. W.; Jones, D.; Logan, J.; Worden, H.; Boersma, F.; Chang, R.; Kulawik, S.; Osterman, G.; Worden, J.

2008-01-01

The chemical and dynamical processes governing the zonal variability of tropical tropospheric ozone and carbon monoxide are investigated for November 2004 using satellite observations, in-situ measurements, and chemical transport models in conjunction with inverse-estimated surface emissions. Vertical ozone profile estimates from the Tropospheric Emission Spectrometer (TES) and ozone sonde measurements from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network show the so called zonal 'wave-one' pattern, which is characterized by peak ozone concentrations (70-80 ppb) centered over the Atlantic, as well as elevated concentrations of ozone over Indonesia and Australia (60-70 ppb) in the lower troposphere. Observational evidence from TES CO vertical profiles and Ozone Monitoring Instrument (OMI) NO2 columns point to regional surface emissions as an important contributor to the elevated ozone over Indonesia. This contribution is investigated with the GEOS-Chem chemistry and transport model using surface emission estimates derived from an optimal inverse model, which was constrained by TES and Measurements Of Pollution In The Troposphere (MOPITT) CO profiles (Jones et al., 2007). These a posteriori estimates, which were over a factor of 2 greater than climatological emissions, reduced differences between GEOS-Chem and TES ozone observations by 30-40% and led to changes in GEOS-Chem upper tropospheric ozone of up to 40% over Indonesia. The remaining residual differences can be explained in part by upper tropospheric ozone produced from lightning NOx in the South Atlantic. Furthermore, model simulations from GEOS-Chem indicate that ozone over Indonesian/Australian is more sensitive to changes in surface emissions of NOx than ozone over the tropical Atlantic.

Worldwide biogenic soil NOx emission estimates from OMI NO2 observations and the GEOS-Chem model

NASA Astrophysics Data System (ADS)

Vinken, Geert; Boersma, Folkert; Maasakkers, Bram; Martin, Randall

2014-05-01

Bacteria in soils are an important source of biogenic nitrogen oxides (NOx = NO + NO2), which are important precursors for ozone (O3) formation. Furthermore NOx emissions contribute to increased nitrogen deposition and particulate matter formation. Bottom-up estimates of global soil NOx emissions range from 4 to 27 Tg N / yr, reflecting our incomplete knowledge of emission factors and processes driving these emissions. In this study we used, for the first time, OMI NO2 columns on all continents to reduce the uncertainty in soil NOx emissions. Regions and months dominated by soil NOx emissions were identified using a filtering scheme in the GEOS-Chem chemistry transport model. Consequently, we compared OMI observed NO2 observed columns to GEOS-Chem simulated columns and provide constraints for these months in 11 regions. This allows us to provide a top-down emission inventory for 2005 for soil NOx emissions from all continents. Our total global soil NOx emission inventory amounts to 10 Tg N / yr. Our estimate is 4% higher than the GEOS-Chem a priori (Hudman et al., 2012), but substantial regional differences exist (e.g. +20% for Sahel and India; and -40% for mid-USA). We furthermore observed a stronger seasonal cycle in the Sahel region, indicating directions for possible future improvements to the parameterization currently used in GEOS-Chem. We validated NO2 concentrations simulated with this new top-down inventory against surface NO2 measurements from monitoring stations in Africa, the USA and Europe. On the whole, we conclude that simulations with our new top-down inventory better agree with measurements. Our work shows that satellite retrieved NO2 columns can improve estimates of soil NOx emissions over sparsely monitored remote rural areas. We show that the range in previous estimates of soil NOx emissions is too large, and global emissions are most likely around 10 Tg N/yr, in agreement with the most recent parameterizations.

Modeling methane and nitrous oxide emissions from direct-seeded rice systems

NASA Astrophysics Data System (ADS)

Simmonds, Maegen B.; Li, Changsheng; Lee, Juhwan; Six, Johan; van Kessel, Chris; Linquist, Bruce A.

2015-10-01

Process-based modeling of CH4 and N2O emissions from rice fields is a practical tool for conducting greenhouse gas inventories and estimating mitigation potentials of alternative practices at the scale of management and policy making. However, the accuracy of these models in simulating CH4 and N2O emissions in direct-seeded rice systems under various management practices remains a question. We empirically evaluated the denitrification-decomposition model for estimating CH4 and N2O fluxes in California rice systems. Five and nine site-year combinations were used for calibration and validation, respectively. The model was parameterized for two cultivars, M206 and Koshihikari, and able to simulate 30% and 78% of the variation in measured yields, respectively. Overall, modeled and observed seasonal CH4 emissions were similar (R2 = 0.85), but there was poor correspondence in fallow period CH4 emissions and in seasonal and fallow period N2O emissions. Furthermore, management effects on seasonal CH4 emissions were highly variable and not well represented by the model (0.2-465% absolute relative deviation). Specifically, simulated CH4 emissions were oversensitive to fertilizer N rate but lacked sensitivity to the type of seeding system (dry seeding versus water seeding) and prior fallow period straw management. Additionally, N2O emissions were oversensitive to fertilizer N rate and field drainage. Sensitivity analysis showed that CH4 emissions were highly sensitive to changes in the root to total plant biomass ratio, suggesting that it is a significant source of model uncertainty. These findings have implications for model-directed field research that could improve model representation of paddy soils for application at larger spatial scales.

Constraining CO emission estimates using atmospheric observations

NASA Astrophysics Data System (ADS)

Hooghiemstra, P. B.

2012-06-01

We apply a four-dimensional variational (4D-Var) data assimilation system to optimize carbon monoxide (CO) emissions and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. In the first study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-Var system. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, since the observations only constrain total CO emissions, the 4D-Var system has difficulties separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10%. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes. In the second study, we compare two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from NOAA or CO total columns from the MOPITT instrument are assimilated in a 4D-Var framework. In the Southern Hemisphere (SH) three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions (mainly CO from oxidation of NMVOCs) that are 185 Tg CO/yr higher compared to the stations-only inversion. Second, MOPITT-only derived biomass burning emissions are reduced with respect to the prior which is in contrast to previous (inverse) modeling studies. Finally, MOPITT derived total emissions are significantly higher for South America and Africa compared to the stations-only inversion. This is likely due to a positive bias in the MOPITT V4 product. This bias is also apparent from validation with surface stations and ground-truth FTIR columns. In the final study we present the first inverse modeling study to estimate CO emissions constrained by both surface (NOAA) and satellite (MOPITT) observations using a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations are underestimated in current inventories by 50-100%.

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

NASA Astrophysics Data System (ADS)

Susiluoto, Jouni; Raivonen, Maarit; Backman, Leif; Laine, Marko; Makela, Jarmo; Peltola, Olli; Vesala, Timo; Aalto, Tuula

2018-03-01

Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.

Model simulations of cooking organic aerosol (COA) over the UK using estimates of emissions based on measurements at two sites in London

NASA Astrophysics Data System (ADS)

Ots, Riinu; Vieno, Massimo; Allan, James D.; Reis, Stefan; Nemitz, Eiko; Young, Dominique E.; Coe, Hugh; Di Marco, Chiara; Detournay, Anais; Mackenzie, Ian A.; Green, David C.; Heal, Mathew R.

2016-11-01

Cooking organic aerosol (COA) is currently not included in European emission inventories. However, recent positive matrix factorization (PMF) analyses of aerosol mass spectrometer (AMS) measurements have suggested important contributions of COA in several European cities. In this study, emissions of COA were estimated for the UK, based on hourly AMS measurements of COA made at two sites in London (a kerbside site in central London and an urban background site in a residential area close to central London) for the full calendar year of 2012 during the Clean Air for London (ClearfLo) campaign. Iteration of COA emissions estimates and subsequent evaluation and sensitivity experiments were conducted with the EMEP4UK atmospheric chemistry transport modelling system with a horizontal resolution of 5 km × 5 km. The spatial distribution of these emissions was based on workday population density derived from the 2011 census data. The estimated UK annual COA emission was 7.4 Gg per year, which is an almost 10 % addition to the officially reported UK national total anthropogenic emissions of PM2.5 (82 Gg in 2012), corresponding to 320 mg person-1 day-1 on average. Weekday and weekend diurnal variation in COA emissions were also based on the AMS measurements. Modelled concentrations of COA were then independently evaluated against AMS-derived COA measurements from another city and time period (Manchester, January-February 2007), as well as with COA estimated by a chemical mass balance model of measurements for a 2-week period at the Harwell rural site (˜ 80 km west of central London). The modelled annual average contribution of COA to ambient particulate matter (PM) in central London was between 1 and 2 µg m-3 (˜ 20 % of total measured OA1) and between 0.5 and 0.7 µg m-3 in other major cities in England (Manchester, Birmingham, Leeds). It was also shown that cities smaller than London can have a central hotspot of population density of smaller area than the computational grid cell, in which case higher localized COA concentrations than modelled here may be expected. Modelled COA concentrations dropped rapidly outside of major urban areas (annual average of 0.12 µg m-3 for the Harwell location), indicating that although COA can be a notable component in urban air, it does not have a significant effect on PM concentrations on rural areas. The possibility that the AMS-PMF apportionment measurements overestimate COA concentrations by up to a factor of 2 is discussed. Since COA is a primary emission, any downward adjustments in COA emissions would lead to a proportional linear downward scaling in the absolute magnitudes of COA concentrations simulated in the model.

Improved modelling of ship SO 2 emissions—a fuel-based approach

NASA Astrophysics Data System (ADS)

Endresen, Øyvind; Bakke, Joachim; Sørgård, Eirik; Flatlandsmo Berglen, Tore; Holmvang, Per

Significant variations are apparent between the various reported regional and global ship SO 2 emission inventories. Important parameters for SO 2 emission modelling are sulphur contents and marine fuel consumption. Since 1993, the global average sulphur content for heavy fuel has shown an overall downward trend, while the bunker sale has increased. We present an improved bottom up approach to estimate marine sulphur emissions from ship transportation, including the geographical distribution. More than 53,000 individual bunker samples are used to establish regionally and globally (volume) weighted average sulphur contents for heavy and distillate marine fuels. We find that the year 2002 sulphur content in heavy fuels varies regionally from 1.90% (South America) to 3.07% (Asia), with a globally weighted average of 2.68% sulphur. The calculated globally weighted average content for heavy fuels is found to be 5% higher than the average (arithmetic mean) sulphur content commonly used. The reason for this is likely that larger bunker stems are mainly of high-viscosity heavy fuel, which tends to have higher sulphur values compared to lower viscosity fuels. The uncertainties in SO 2 inventories are significantly reduced using our updated SO 2 emission factors (volume-weighted sulphur content). Regional marine bunker sales figures are combined with volume-weighted sulphur contents for each region to give a global SO 2 emission estimate in the range of 5.9-7.2 Tg (SO 2) for international marine transportation. Also taking into account the domestic sales, the total emissions from all ocean-going transportation is estimated to be 7.0-8.5 Tg (SO 2). Our estimate is significantly lower than recent global estimate reported by Corbett and Koehler [2003. Journal of Geophysical Research: Atmospheres 108] (6.49 Tg S or about 13.0 Tg SO 2). Endresen et al. [2004. Journal of Geophysical Research 109, D23302] claim that uncertainties in input data for the activity-based method will give too high emission estimates. We also indicate that this higher estimate will almost give doubling of regional emissions, compared to detailed movement-based estimates. The paper presents an alternative approach to estimate present overall SO 2 ship emissions with improved accuracy.

The global distribution of ammonia emissions from seabird colonies

NASA Astrophysics Data System (ADS)

Riddick, S. N.; Dragosits, U.; Blackall, T. D.; Daunt, F.; Wanless, S.; Sutton, M. A.

2012-08-01

Seabird colonies represent a significant source of atmospheric ammonia (NH3) in remote maritime systems, producing a source of nitrogen that may encourage plant growth, alter terrestrial plant community composition and affect the surrounding marine ecosystem. To investigate seabird NH3 emissions on a global scale, we developed a contemporary seabird database including a total seabird population of 261 million breeding pairs. We used this in conjunction with a bioenergetics model to estimate the mass of nitrogen excreted by all seabirds at each breeding colony. The results combined with the findings of mid-latitude field studies of volatilization rates estimate the global distribution of NH3 emissions from seabird colonies on an annual basis. The largest uncertainty in our emission estimate concerns the potential temperature dependence of NH3 emission. To investigate this we calculated and compared temperature independent emission estimates with a maximum feasible temperature dependent emission, based on the thermodynamic dissociation and solubility equilibria. Using the temperature independent approach, we estimate global NH3 emissions from seabird colonies at 404 Gg NH3 per year. By comparison, since most seabirds are located in relatively cold circumpolar locations, the thermodynamically dependent estimate is 136 Gg NH3 per year. Actual global emissions are expected to be within these bounds, as other factors, such as non-linear interactions with water availability and surface infiltration, moderate the theoretical temperature response. Combining sources of error from temperature (±49%), seabird population estimates (±36%), variation in diet composition (±23%) and non-breeder attendance (±13%), gives a mid estimate with an overall uncertainty range of NH3 emission from seabird colonies of 270 [97-442] Gg NH3 per year. These emissions are environmentally relevant as they primarily occur as "hot-spots" in otherwise pristine environments with low anthropogenic emissions.

Estimation of wetland methane emissions in a biogeochemical model integrated in CESM: sensitivity analysis and comparison against surface and atmospheric measurements

NASA Astrophysics Data System (ADS)

Meng, L.; Mahowald, N. M.; Hess, P. G.; Yavitt, J. B.; Riley, W. J.; Subin, Z. M.; Lawrence, D. M.; Swenson, S. C.; Jauhiainen, J.; Fuka, D. R.

2012-12-01

Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources is still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al. 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model because there are large differences between simulated fractional inundation and satellite observations and thus we do not use CLM4 simulated inundated area. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993-2004 were 256 Tg CH4 y-1 (including the soil sink). Tropical wetlands contributed 201 Tg CH4 y-1, or 78% of the global wetland flux. Northern latitude (>50N) systems contributed 12 Tg CH4 y-1. Our sensitivity studies show a large range (150-346 Tg CH4 y-1) in predicted global methane emissions. In order to evaluate our methane emissions on the regional and global scales against atmospheric measurements, we conducted simulations with the Community Atmospheric Model with chemistry (CAM-chem) forced with our baseline simulation of wetland and rice paddy emissions along with other methane sources (e.g. anthropogenic, fire, and termite emissions) and compared model simulations against measured atmospheric concentrations obtained from the World Data Centre for Greenhouse Gases (WDCGG) at http://ds.data.jma.go.jp/gmd/wdcgg/. Overall, using our estimated wetland and rice paddy emissions, CAM-chem model can produce seasonal and interannual variations of observed atmospheric concentration performs well. Thus, within the current level of uncertainty, our emissions appear to be reasonable.

Monthly and spatially resolved black carbon emission inventory of India: uncertainty analysis

NASA Astrophysics Data System (ADS)

Paliwal, Umed; Sharma, Mukesh; Burkhart, John F.

2016-10-01

Black carbon (BC) emissions from India for the year 2011 are estimated to be 901.11 ± 151.56 Gg yr-1 based on a new ground-up, GIS-based inventory. The grid-based, spatially resolved emission inventory includes, in addition to conventional sources, emissions from kerosene lamps, forest fires, diesel-powered irrigation pumps and electricity generators at mobile towers. The emissions have been estimated at district level and were spatially distributed onto grids at a resolution of 40 × 40 km2. The uncertainty in emissions has been estimated using a Monte Carlo simulation by considering the variability in activity data and emission factors. Monthly variation of BC emissions has also been estimated to account for the seasonal variability. To the total BC emissions, domestic fuels contributed most significantly (47 %), followed by industry (22 %), transport (17 %), open burning (12 %) and others (2 %). The spatial and seasonal resolution of the inventory will be useful for modeling BC transport in the atmosphere for air quality, global warming and other process-level studies that require greater temporal resolution than traditional inventories.

Measurement and prediction of enteric methane emission

NASA Astrophysics Data System (ADS)

Sejian, Veerasamy; Lal, Rattan; Lakritz, Jeffrey; Ezeji, Thaddeus

2011-01-01

The greenhouse gas (GHG) emissions from the agricultural sector account for about 25.5% of total global anthropogenic emission. While CO2 receives the most attention as a factor relative to global warming, CH4, N2O and chlorofluorocarbons (CFCs) also cause significant radiative forcing. With the relative global warming potential of 25 compared with CO2, CH4 is one of the most important GHGs. This article reviews the prediction models, estimation methodology and strategies for reducing enteric CH4 emissions. Emission of CH4 in ruminants differs among developed and developing countries, depending on factors like animal species, breed, pH of rumen fluid, ratio of acetate:propionate, methanogen population, composition of diet and amount of concentrate fed. Among the ruminant animals, cattle contribute the most towards the greenhouse effect through methane emission followed by sheep, goats and buffalos, respectively. The estimated CH4 emission rate per cattle, buffaloe, sheep and goat in developed countries are 150.7, 137, 21.9 and 13.7 (g/animal/day) respectively. However, the estimated rates in developing countries are significantly lower at 95.9 and 13.7 (g/animal/day) per cattle and sheep, respectively. There exists a strong interest in developing new and improving the existing CH4 prediction models to identify mitigation strategies for reducing the overall CH4 emissions. A synthesis of the available literature suggests that the mechanistic models are superior to empirical models in accurately predicting the CH4 emission from dairy farms. The latest development in prediction model is the integrated farm system model which is a process-based whole-farm simulation technique. Several techniques are used to quantify enteric CH4 emissions starting from whole animal chambers to sulfur hexafluoride (SF6) tracer techniques. The latest technology developed to estimate CH4 more accurately is the micrometeorological mass difference technique. Because the conditions under which animals are managed vary greatly by country, CH4 emissions reduction strategies must be tailored to country-specific circumstances. Strategies that are cost effective, improve productivity, and have limited potential negative effects on livestock production hold a greater chance of being adopted by producers. It is also important to evaluate CH4 mitigation strategies in terms of the total GHG budget and to consider the economics of various strategies. Although reductions in GHG emissions from livestock industries are seen as high priorities, strategies for reducing emissions should not reduce the economic viability of enterprises.

Top-down NOX Emissions of European Cities Derived from Modelled and Spaceborne Tropospheric NO2 Columns

NASA Astrophysics Data System (ADS)

Verstraeten, W. W.; Boersma, K. F.; Douros, J.; Williams, J. E.; Eskes, H.; Delcloo, A. W.

2017-12-01

High nitrogen oxides (NOX = NO + NO2) concentrations near the surface impact humans and ecosystems badly and play a key role in tropospheric chemistry. NO2 is an important precursor of tropospheric ozone (O3) which in turn affects the production of the hydroxyl radical controlling the chemical lifetime of key atmospheric pollutants and reactive greenhouse gases. Combustion from industrial, traffic and household activities in large and densely populated urban areas result in high NOX emissions. Accurate mapping of these emissions is essential but hard to do since reported emissions factors may differ from real-time emissions in order of magnitude. Modelled NO2 levels and lifetimes also have large associated uncertainties and overestimation in the chemical lifetime which may mask missing NOX chemistry in current chemistry transport models (CTM's). The simultaneously estimation of both the NO2 lifetime and as well as the concentrations by applying the Exponentially Modified Gaussian (EMG) method on tropospheric NO2 columns lines densities should improve the surface NOX emission estimates. Here we evaluate if the EMG methodology applied on the tropospheric NO2 columns simulated by the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) CTM can reproduce the NOX emissions used as model input. First we process both the modelled tropospheric NO2 columns for the period April-September 2013 for 21 selected European urban areas under windy conditions (averaged vertical wind speeds between surface and 500 m from ECMWF > 2 m s-1) as well as the accompanying OMI (Ozone Monitoring Instrument) data providing us with real-time observation-based estimates of midday NO2 columns. Then we compare the top-down derived surface NOX emissions with the 2011 MACC-III emission inventory, used in the CTM as input to simulate the NO2 columns. For cities where NOX emissions can be assumed as originating from one large source good agreement is found between the top-down derived NOX emissions from CTM and OMI with the MACC-III inventory. For cities where multiple sources of NOX are observed (e.g. Brussels, London), an adapted methodology is required. For some cities such as St-Petersburg and Moscow the top-down NOX estimates from 2013 OMI data are biased low compared to the MACC-III inventory which uses a 2011 NOX emissions update.

[Measurement model of carbon emission from forest fire: a review].

PubMed

Hu, Hai-Qing; Wei, Shu-Jing; Jin, Sen; Sun, Long

2012-05-01

Forest fire is the main disturbance factor for forest ecosystem, and an important pathway of the decrease of vegetation- and soil carbon storage. Large amount of carbonaceous gases in forest fire can release into atmosphere, giving remarkable impacts on the atmospheric carbon balance and global climate change. To scientifically and effectively measure the carbonaceous gases emission from forest fire is of importance in understanding the significance of forest fire in the carbon balance and climate change. This paper reviewed the research progress in the measurement model of carbon emission from forest fire, which covered three critical issues, i. e., measurement methods of forest fire-induced total carbon emission and carbonaceous gases emission, affecting factors and measurement parameters of measurement model, and cause analysis of the uncertainty in the measurement of the carbon emissions. Three path selections to improve the quantitative measurement of the carbon emissions were proposed, i. e., using high resolution remote sensing data and improving algorithm and estimation accuracy of burned area in combining with effective fuel measurement model to improve the accuracy of the estimated fuel load, using high resolution remote sensing images combined with indoor controlled environment experiments, field measurements, and field ground surveys to determine the combustion efficiency, and combining indoor controlled environment experiments with field air sampling to determine the emission factors and emission ratio.

Ammonia emission inventory for the state of Wyoming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirchstetter, Thomas W.; Maser, Colette R.; Brown, Nancy J.

2003-12-17

Ammonia (NH{sub 3}) is the only significant gaseous base in the atmosphere and it has a variety of impacts as an atmospheric pollutant, including the formation of secondary aerosol particles: ammonium sulfate and ammonium nitrate. NH{sub 3} preferentially forms ammonium sulfate; consequently ammonium nitrate aerosol formation may be limited by the availability of NH{sub 3}. Understanding the impact of emissions of oxides of sulfur and nitrogen on visibility, therefore, requires accurately determined ammonia emission inventories for use in air quality models, upon which regulatory and policy decisions increasingly depend. This report presents an emission inventory of NH{sub 3} for themore » state of Wyoming. The inventory is temporally and spatially resolved at the monthly and county level, and is comprised of emissions from individual sources in ten categories: livestock, fertilizer, domestic animals, wild animals, wildfires, soil, industry, mobile sources, humans, and publicly owned treatment works. The Wyoming NH{sub 3} inventory was developed using the Carnegie Mellon University (CMU) Ammonia Model as framework. Current Wyoming-specific activity data and emissions factors obtained from state agencies and published literature were assessed and used as inputs to the CMU Ammonia Model. Biogenic emissions from soils comprise about three-quarters of the Wyoming NH{sub 3} inventory, though emission factors from soils are highly uncertain. Published emission factors are scarce and based on limited measurements. In Wyoming, agricultural land, rangeland, and forests comprise 96% of the land area and essentially all of the estimated emissions from soils. Future research on emission rates of NH{sub 3} for these land categories may lead to a substantial change in the magnitude of soil emissions, a different inventory composition, and reduced uncertainty in the inventory. While many NH{sub 3} inventories include annual emissions, air quality modeling studies require finer temporal resolution. Published studies indicate higher emission rates from soils and animal wastes at higher temperatures, and temporal variation in fertilizer application. A recent inverse modeling study indicates temporal variation in regional NH{sub 3} emissions. Monthly allocation factors were derived to estimate monthly emissions from soils, livestock and wild animal waste based on annual emission estimates. Monthly resolution of NH{sub 3} emissions from fertilizers is based on fertilizer sales to farmers. Statewide NH{sub 3} emissions are highest in the late spring and early summer months.« less

Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks

NASA Astrophysics Data System (ADS)

Zhu, Xudong; Zhuang, Qianlai; Qin, Zhangcai; Glagolev, Mikhail; Song, Lulu

2013-04-01

Methane (CH4) emissions from wetland ecosystems in nothern high latitudes provide a potentially positive feedback to global climate warming. Large uncertainties still remain in estimating wetland CH4 emisions at regional scales. Here we develop a statistical model of CH4 emissions using an artificial neural network (ANN) approach and field observations of CH4 fluxes. Six explanatory variables (air temperature, precipitation, water table depth, soil organic carbon, soil total porosity, and soil pH) are included in the development of ANN models, which are then extrapolated to the northern high latitudes to estimate monthly CH4 emissions from 1990 to 2009. We estimate that the annual wetland CH4 source from the northern high latitudes (north of 45°N) is 48.7 Tg CH4 yr-1 (1 Tg = 1012 g) with an uncertainty range of 44.0 53.7 Tg CH4 yr-1. The estimated wetland CH4 emissions show a large spatial variability over the northern high latitudes, due to variations in hydrology, climate, and soil conditions. Significant interannual and seasonal variations of wetland CH4 emissions exist in the past 2 decades, and the emissions in this period are most sensitive to variations in water table position. To improve future assessment of wetland CH4 dynamics in this region, research priorities should be directed to better characterizing hydrological processes of wetlands, including temporal dynamics of water table position and spatial dynamics of wetland areas.

Fire and deforestation dynamics in Amazonia (1973–2014)

PubMed Central

Field, Robert D.; van der Werf, Guido R.; Estrada de Wagt, Ivan A.; Houghton, Richard A.; Rizzo, Luciana V.; Artaxo, Paulo; Tsigaridis, Kostas

2017-01-01

Abstract Consistent long‐term estimates of fire emissions are important to understand the changing role of fire in the global carbon cycle and to assess the relative importance of humans and climate in shaping fire regimes. However, there is limited information on fire emissions from before the satellite era. We show that in the Amazon region, including the Arc of Deforestation and Bolivia, visibility observations derived from weather stations could explain 61% of the variability in satellite‐based estimates of bottom‐up fire emissions since 1997 and 42% of the variability in satellite‐based estimates of total column carbon monoxide concentrations since 2001. This enabled us to reconstruct the fire history of this region since 1973 when visibility information became available. Our estimates indicate that until 1987 relatively few fires occurred in this region and that fire emissions increased rapidly over the 1990s. We found that this pattern agreed reasonably well with forest loss data sets, indicating that although natural fires may occur here, deforestation and degradation were the main cause of fires. Compared to fire emissions estimates based on Food and Agricultural Organization's Global Forest and Resources Assessment data, our estimates were substantially lower up to the 1990s, after which they were more in line. These visibility‐based fire emissions data set can help constrain dynamic global vegetation models and atmospheric models with a better representation of the complex fire regime in this region. PMID:28286373

Greenhouse gas contribution of municipal solid waste collection: A case study in the city of Istanbul, Turkey.

PubMed

Korkut, Nafiz E; Yaman, Cevat; Küçükağa, Yusuf; Jaunich, Megan K; Demir, İbrahim

2018-02-01

This article estimates greenhouse gas emissions and global warming factors resulting from collection of municipal solid waste to the transfer stations or landfills in Istanbul for the year of 2015. The aim of this study is to quantify and compare diesel fuel consumption and estimate the greenhouse gas emissions and global warming factors associated with municipal solid waste collection of the 39 districts of Istanbul. Each district's greenhouse gas emissions resulting from the provision and combustion of diesel fuel was estimated by considering the number of collection trips and distances to municipal solid waste facilities. The estimated greenhouse gases and global warming factors for the districts varied from 61.2 to 2759.1 t CO 2 -eq and from 4.60 to 15.20 kg CO 2 -eq t -1 , respectively. The total greenhouse gas emission was estimated as 46.4E3 t CO 2 -eq. Lastly, the collection data from the districts was used to parameterise a collection model that can be used to estimate fuel consumption associated with municipal solid waste collection. This mechanistic model can then be used to predict future fuel consumption and greenhouse gas emissions associated with municipal solid waste collection based on projected population, waste generation, and distance to transfer stations and landfills. The greenhouse gas emissions can be reduced by decreasing the trip numbers and trip distances, building more transfer stations around the city, and making sure that the collection trucks are full in each trip.

Global methane emission estimates for 2000-2012 from CarbonTracker Europe-CH4 v1.0

NASA Astrophysics Data System (ADS)

Tsuruta, Aki; Aalto, Tuula; Backman, Leif; Hakkarainen, Janne; van der Laan-Luijkx, Ingrid T.; Krol, Maarten C.; Spahni, Renato; Houweling, Sander; Laine, Marko; Dlugokencky, Ed; Gomez-Pelaez, Angel J.; van der Schoot, Marcel; Langenfelds, Ray; Ellul, Raymond; Arduini, Jgor; Apadula, Francesco; Gerbig, Christoph; Feist, Dietrich G.; Kivi, Rigel; Yoshida, Yukio; Peters, Wouter

2017-03-01

We present a global distribution of surface methane (CH4) emission estimates for 2000-2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000-2012 are 516 ± 51 Tg CH4 yr-1, with an increase of 18 Tg CH4 yr-1 from 2000-2006 to 2007-2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations ( < 2 Tg CH4 yr-1 difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH4 yr-1), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16-20 Tg CH4 yr-1). However, this study could not conclusively separate a small trend in biospheric emissions (-5 to +6.9 Tg CH4 yr-1) from the much larger trend in anthropogenic emissions (15-27 Tg CH4 yr-1). Finally, we find that the global and North American CH4 balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH4 emissions in the United States. With further developments, especially on the treatment of the atmospheric CH4 sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.

Assessing concentrations and health impacts of air quality management strategies: Framework for Rapid Emissions Scenario and Health impact ESTimation (FRESH-EST).

PubMed

Milando, Chad W; Martenies, Sheena E; Batterman, Stuart A

2016-09-01

In air quality management, reducing emissions from pollutant sources often forms the primary response to attaining air quality standards and guidelines. Despite the broad success of air quality management in the US, challenges remain. As examples: allocating emissions reductions among multiple sources is complex and can require many rounds of negotiation; health impacts associated with emissions, the ultimate driver for the standards, are not explicitly assessed; and long dispersion model run-times, which result from the increasing size and complexity of model inputs, limit the number of scenarios that can be evaluated, thus increasing the likelihood of missing an optimal strategy. A new modeling framework, called the "Framework for Rapid Emissions Scenario and Health impact ESTimation" (FRESH-EST), is presented to respond to these challenges. FRESH-EST estimates concentrations and health impacts of alternative emissions scenarios at the urban scale, providing efficient computations from emissions to health impacts at the Census block or other desired spatial scale. In addition, FRESH-EST can optimize emission reductions to meet specified environmental and health constraints, and a convenient user interface and graphical displays are provided to facilitate scenario evaluation. The new framework is demonstrated in an SO2 non-attainment area in southeast Michigan with two optimization strategies: the first minimizes emission reductions needed to achieve a target concentration; the second minimizes concentrations while holding constant the cumulative emissions across local sources (e.g., an emissions floor). The optimized strategies match outcomes in the proposed SO2 State Implementation Plan without the proposed stack parameter modifications or shutdowns. In addition, the lower health impacts estimated for these strategies suggest that FRESH-EST could be used to identify potentially more desirable pollution control alternatives in air quality management planning. Copyright © 2016 Elsevier Ltd. All rights reserved.

ESTIMATES OF ALPHA-PINENE EMISSIONS FROM A LOBLOLLY PINE FOREST USING AN ATMOSPHERIC DIFFUSION MODEL

EPA Science Inventory

The body of information presented in this paper is directed to atmospheric chemists and modelers who are concerned with assessing the impact of biogenic hydrocarbon emissions. A field study was conducted to determine the emission rate of alpha-pinene from a loblolly pine forest u...

A MECHANISTIC MODEL FOR ESTIMATING VOC EMISSIONS FROM INDUSTRIAL PROCESS DRAINS PART I: THE UNDERLYING CHANNEL. (R823335)

EPA Science Inventory

Recent research has indicated the potential for emissions of volatile organic compound
(VOCs) from industrial process drains, and a need for better understanding of the mass transfer
kinetics associated with such emissions. rn this study, a two-zone model was developed in a...

Modeling indoor air pollution from cookstove emissions in developing countries using a Monte Carlo single-box model

NASA Astrophysics Data System (ADS)

Johnson, Michael; Lam, Nick; Brant, Simone; Gray, Christen; Pennise, David

2011-06-01

A simple Monte Carlo single-box model is presented as a first approach toward examining the relationship between emissions of pollutants from fuel/cookstove combinations and the resulting indoor air pollution (IAP) concentrations. The model combines stove emission rates with expected distributions of kitchen volumes and air exchange rates in the developing country context to produce a distribution of IAP concentration estimates. The resulting distribution can be used to predict the likelihood that IAP concentrations will meet air quality guidelines, including those recommended by the World Health Organization (WHO) for fine particulate matter (PM2.5) and carbon monoxide (CO). The model can also be used in reverse to estimate the probability that specific emission factors will result in meeting air quality guidelines. The modeled distributions of indoor PM2.5 concentration estimated that only 4% of homes using fuelwood in a rocket-style cookstove, even under idealized conditions, would meet the WHO Interim-1 annual PM2.5 guideline of 35 μg m-3. According to the model, the PM2.5 emissions that would be required for even 50% of homes to meet this guideline (0.055 g MJ-delivered-1) are lower than those for an advanced gasifier fan stove, while emissions levels similar to liquefied petroleum gas (0.018 g MJ-delivered-1) would be required for 90% of homes to meet the guideline. Although the predicted distribution of PM concentrations (median = 1320 μg m-3) from inputs for traditional wood stoves was within the range of reported values for India (108-3522 μg m-3), the model likely overestimates IAP concentrations. Direct comparison with simultaneously measured emissions rates and indoor concentrations of CO indicated the model overestimated IAP concentrations resulting from charcoal and kerosene emissions in Kenyan kitchens by 3 and 8 times respectively, although it underestimated the CO concentrations resulting from wood-burning cookstoves in India by approximately one half. The potential overestimation of IAP concentrations is thought to stem from the model's assumption that all stove emissions enter the room and are completely mixed. Future versions of the model may be improved by incorporating these factors into the model, as well as more comprehensive and representative data on stove emissions performance, daily cooking energy requirements, and kitchen characteristics.

Estimating Lightning NOx Emissions for Regional Air Quality Modeling

NASA Astrophysics Data System (ADS)

Holloway, T.; Scotty, E.; Harkey, M.

2014-12-01

Lightning emissions have long been recognized as an important source of nitrogen oxides (NOx) on a global scale, and an essential emission component for global atmospheric chemistry models. However, only in recent years have regional air quality models incorporated lightning NOx emissions into simulations. The growth in regional modeling of lightning emissions has been driven in part by comparisons with satellite-derived estimates of column NO2, especially from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite. We present and evaluate a lightning inventory for the EPA Community Multiscale Air Quality (CMAQ) model. Our approach follows Koo et al. [2010] in the approach to spatially and temporally allocating a given total value based on cloud-top height and convective precipitation. However, we consider alternate total NOx emission values (which translate into alternate lightning emission factors) based on a review of the literature and performance evaluation against OMI NO2 for July 2007 conditions over the U.S. and parts of Canada and Mexico. The vertical distribution of lightning emissions follow a bimodal distribution from Allen et al. [2012] calculated over 27 vertical model layers. Total lightning NO emissions for July 2007 show the highest above-land emissions in Florida, southeastern Texas and southern Louisiana. Although agreement with OMI NO2 across the domain varied significantly depending on lightning NOx assumptions, agreement among the simulations at ground-based NO2 monitors from the EPA Air Quality System database showed no meaningful sensitivity to lightning NOx. Emissions are compared with prior studies, which find similar distribution patterns, but a wide range of calculated magnitudes.

The Use of Satellite-Measured Aerosol Optical Depth to Constrain Biomass Burning Emissions Source Strength in a Global Model GOCART

NASA Technical Reports Server (NTRS)

Petrenko, Mariya; Kahn, Ralph; Chin, Mian; Soja, Amber; Kuesera, Tom; harshvardhan, E. M.

2012-01-01

Small particles in the atmosphere, called "atmospheric aerosol" have a direct effect on Earth climate through scattering and absorbing sunlight, and also an indirect effect by changing the properties of clouds, as they interact with solar radiation as well. Aerosol typically stays in the atmosphere for several days, and can be transported long distances, affecting air quality, visibility, and human health not only near the source, but also far downwind. Smoke from vegetation fires is one of the main sources of atmospheric aerosol; other sources include anthropogenic pollution, dust, and sea salt. Chemistry transport models (CTMs) are among the major tools for studying the atmospheric and climate effects of aerosol. Due to the considerable variation of aerosol concentrations and properties on many temporal and spatial scales, and the complexity of the processes involved, the uncertainties in aerosol effects on climate are large, as is featured in the latest report of Intergovernmental Panel on Climate Change (IPCC) in 2007. Reducing this uncertainty in the models is very important both for predicting future climate scenarios and for regional air quality forecasting and mitigation. During vegetation fires, also called biomass burning (BB) events, complex mixture of gases and particles is emitted. The amount of BB emissions is usually estimated taking into account the intensity and size of the fire and the properties of burning vegetation. These estimates are input into CTMs to simulate BB aerosol. Unfortunately, due to large variability of fire and vegetation properties, the quantity of BB emissions is very difficult to estimate and BB emission inventories provide numbers that can differ by up to the order of magnitude in some regions. Larger uncertainties in data input make uncertainties in model output larger as well. A powerful way to narrow the range of possible model estimates is to compare model output to observations. We use satellite observations of aerosol properties, specifically aerosol optical depth, which is directly proportional to the amount of aerosol in the atmosphere, and compare it to the model output. Assuming the model represents aerosol transport and particle properties correctly, the amount of BB emissions determines the simulated aerosol optical depth. In this study, we explore the regional performance of 13 commonly used emission estimates. These are each input to global Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. We then evaluate how well each emission estimate reproduces the smoke aerosol optical depth measured by the MODIS instrument. We compared GOCART-simulate aerosol optical depth with that measured from the satellite for 124 fire cases around the world during 2006 and 2007. We summarize the regional performance of each emission inventory and discuss reasons for their differences by considering the assumptions made during their development. We also show that because stronger wind disperses smoke plumes more readily, in cases with stronger wind, a larger increase in emission amount is needed to increase aerosol optical depth. In quiet, low-wind-speed environments, BB emissions produce a more significant increase in aerosol optical depth, other things being equal. Using the region-specific, quantitative relationships derived in our paper, together with the wind speed obtained from another source for a given fire case, we can constrain the amount of emission required in the model to reproduce the observations. The results of this paper are useful to the developers of BB emission inventories, as they show the strengths and weaknesses of individual emission inventories in different regions of the globe, and also for modelers who use these inventories and wish to improve their model results.

Hourly disaggregation of industrial CO2 emissions from Shenzhen, China.

PubMed

Ma, Li; Cai, Bofeng; Wu, Feng; Zeng, Hui

2018-05-01

Shenzhen's total industrial CO 2 emission was calculated using the IPCC recommended bottom-up approach and data obtained from the China High Resolution Emission Gridded Data (CHRED). Monthly product yield was then used as the proxy to disaggregate a facility's total emission into monthly emissions. Since a thermal power unit's emission changes with daily and hourly power loads, typical power load curves were used as the proxy to disaggregate the monthly emissions on a daily and hourly basis. The daily and hourly emissions of other facilities were calculated according to two specially designed models: the "weekdays + Spring Festival holidays" model for February and the "weekdays + weekends" model for non-February months. The uncertainty ranges associated with the process of the total amount calculation, monthly disaggregation, daily disaggregation and hourly disaggregation were quantitatively estimated. The total combined uncertainty of the hourly disaggregation of "weekdays + weekends" mode was ±26.19%, and that of the "weekdays + Spring Festival holidays" mode was ±33.06%. These temporal-disaggregation methods and uncertainty estimate approaches could also be used for the industrial air pollutant emission inventory and easily reproduced in the whole country. Copyright © 2018 Elsevier Ltd. All rights reserved.

A technology-based mass emission factors of gases and aerosol precursor and spatial distribution of emissions from on-road transport sector in India

NASA Astrophysics Data System (ADS)

Prakash, Jai; Habib, Gazala

2018-05-01

This study presents a new emission estimate of gaseous pollutants including CO, CO2, and NOX from on-road transport sector of India for the base year 2013. For the first time, a detailed vintage-wise on-road measured emission factors used for reducing uncertainties in emission estimates. The consumptions of diesel, gasoline, and compressed natural gas (CNG) were also estimated at the national level and disaggregated at the state level. The national average use of diesel, gasoline, and CNG and their 95% confidence interval estimated as 52 (39-66), 24 (18-30), and 1.6 (1.2-2.0) MTy-1 for the year 2013. The CO, CO2, and NOX emissions were estimated as 7349 (3220-11477) Gg y-1, 261 (179-343) Tg y-1, and 4052 (2127-5977) Gg y-1, respectively from on-road transport sector for the year 2013. New vehicles registered after 2005 emit 70-80% of national level CO2, and NOX, while rest 20-30% were emitted by old vehicles registered before 2005. Old and new vehicles both equally contributed to CO emissions. Superemitters accounted for 14% of total traffic volume, but they were responsible for 17-57% of total CO2, CO and NOX emissions. The uncertainties in emission estimates were reduced to 48-56% compared to previous estimates (62-136%). The comparison with recent studies for nationwide emission estimates from 4-wheelers indicated that use of emission factors from dynamometer studies can underestimate the emissions by 32-92% for various pollutants, while an overestimation by 20-82% was seen with the use of emission model derived emission factors. Similarly for Delhi city recent CO and NOx emission estimates for 4-wheelers based on emission factors reported from dynamometer studies were 23-89% lower than present work. The present work revealed the need for representative vintage wise emission factor database development from on-road measurement and the more comprehensive assessment of activity data through survey.

Lidar method to estimate emission rates from extended sources

USDA-ARS?s Scientific Manuscript database

Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. However, these methods often fall short in their spatial and temporal resolution and accuracy. In recent years, lidar has emerged as a suitable to...

ESTIMATE OF METHANE EMISSIONS FROM U.S. LANDFILLS

EPA Science Inventory

The report describes the development of a statistical regression model used for estimating methane (CH4) emissions, which relates landfill gas (LFG) flow rates to waste-in-place data from 105 landfills with LFG recovery projects. (NOTE: CH4 flow rates from landfills with LFG reco...

An audit of the global carbon budget: identifying and reducing sources of uncertainty

NASA Astrophysics Data System (ADS)

Ballantyne, A. P.; Tans, P. P.; Marland, G.; Stocker, B. D.

2012-12-01

Uncertainties in our carbon accounting practices may limit our ability to objectively verify emission reductions on regional scales. Furthermore uncertainties in the global C budget must be reduced to benchmark Earth System Models that incorporate carbon-climate interactions. Here we present an audit of the global C budget where we try to identify sources of uncertainty for major terms in the global C budget. The atmospheric growth rate of CO2 has increased significantly over the last 50 years, while the uncertainty in calculating the global atmospheric growth rate has been reduced from 0.4 ppm/yr to 0.2 ppm/yr (95% confidence). Although we have greatly reduced global CO2 growth rate uncertainties, there remain regions, such as the Southern Hemisphere, Tropics and Arctic, where changes in regional sources/sinks will remain difficult to detect without additional observations. Increases in fossil fuel (FF) emissions are the primary factor driving the increase in global CO2 growth rate; however, our confidence in FF emission estimates has actually gone down. Based on a comparison of multiple estimates, FF emissions have increased from 2.45 ± 0.12 PgC/yr in 1959 to 9.40 ± 0.66 PgC/yr in 2010. Major sources of increasing FF emission uncertainty are increased emissions from emerging economies, such as China and India, as well as subtle differences in accounting practices. Lastly, we evaluate emission estimates from Land Use Change (LUC). Although relative errors in emission estimates from LUC are quite high (2 sigma ~ 50%), LUC emissions have remained fairly constant in recent decades. We evaluate the three commonly used approaches to estimating LUC emissions- Bookkeeping, Satellite Imagery, and Model Simulations- to identify their main sources of error and their ability to detect net emissions from LUC.; Uncertainties in Fossil Fuel Emissions over the last 50 years.

Estimating State-Specific Contributions to PM2.5- and O3-Related Health Burden from Residential Combustion and Electricity Generating Unit Emissions in the United States.

PubMed

Penn, Stefani L; Arunachalam, Saravanan; Woody, Matthew; Heiger-Bernays, Wendy; Tripodis, Yorghos; Levy, Jonathan I

2017-03-01

Residential combustion (RC) and electricity generating unit (EGU) emissions adversely impact air quality and human health by increasing ambient concentrations of fine particulate matter (PM 2.5 ) and ozone (O 3 ). Studies to date have not isolated contributing emissions by state of origin (source-state), which is necessary for policy makers to determine efficient strategies to decrease health impacts. In this study, we aimed to estimate health impacts (premature mortalities) attributable to PM 2.5 and O 3 from RC and EGU emissions by precursor species, source sector, and source-state in the continental United States for 2005. We used the Community Multiscale Air Quality model employing the decoupled direct method to quantify changes in air quality and epidemiological evidence to determine concentration-response functions to calculate associated health impacts. We estimated 21,000 premature mortalities per year from EGU emissions, driven by sulfur dioxide emissions forming PM 2.5 . More than half of EGU health impacts are attributable to emissions from eight states with significant coal combustion and large downwind populations. We estimate 10,000 premature mortalities per year from RC emissions, driven by primary PM 2.5 emissions. States with large populations and significant residential wood combustion dominate RC health impacts. Annual mortality risk per thousand tons of precursor emissions (health damage functions) varied significantly across source-states for both source sectors and all precursor pollutants. Our findings reinforce the importance of pollutant-specific, location-specific, and source-specific models of health impacts in design of health-risk minimizing emissions control policies. Citation: Penn SL, Arunachalam S, Woody M, Heiger-Bernays W, Tripodis Y, Levy JI. 2017. Estimating state-specific contributions to PM 2.5 - and O 3 -related health burden from residential combustion and electricity generating unit emissions in the United States. Environ Health Perspect 125:324-332; http://dx.doi.org/10.1289/EHP550.

Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates

NASA Astrophysics Data System (ADS)

Zhang, Lin; Chen, Youfan; Zhao, Yuanhong; Henze, Daven K.; Zhu, Liye; Song, Yu; Paulot, Fabien; Liu, Xuejun; Pan, Yuepeng; Lin, Yi; Huang, Binxiang

2018-01-01

Current estimates of agricultural ammonia (NH3) emissions in China differ by more than a factor of 2, hindering our understanding of their environmental consequences. Here we apply both bottom-up statistical and top-down inversion methods to quantify NH3 emissions from agriculture in China for the year 2008. We first assimilate satellite observations of NH3 column concentration from the Tropospheric Emission Spectrometer (TES) using the GEOS-Chem adjoint model to optimize Chinese anthropogenic NH3 emissions at the 1/2° × 2/3° horizontal resolution for March-October 2008. Optimized emissions show a strong summer peak, with emissions about 50 % higher in summer than spring and fall, which is underestimated in current bottom-up NH3 emission estimates. To reconcile the latter with the top-down results, we revisit the processes of agricultural NH3 emissions and develop an improved bottom-up inventory of Chinese NH3 emissions from fertilizer application and livestock waste at the 1/2° × 2/3° resolution. Our bottom-up emission inventory includes more detailed information on crop-specific fertilizer application practices and better accounts for meteorological modulation of NH3 emission factors in China. We find that annual anthropogenic NH3 emissions are 11.7 Tg for 2008, with 5.05 Tg from fertilizer application and 5.31 Tg from livestock waste. The two sources together account for 88 % of total anthropogenic NH3 emissions in China. Our bottom-up emission estimates also show a distinct seasonality peaking in summer, consistent with top-down results from the satellite-based inversion. Further evaluations using surface network measurements show that the model driven by our bottom-up emissions reproduces the observed spatial and seasonal variations of NH3 gas concentrations and ammonium (NH4+) wet deposition fluxes over China well, providing additional credibility to the improvements we have made to our agricultural NH3 emission inventory.

Assessing Uncertainties in Gridded Emissions: A Case Study for Fossil Fuel Carbon Dioxide (FFCO2) Emission Data

NASA Technical Reports Server (NTRS)

Oda, T.; Ott, L.; Lauvaux, T.; Feng, S.; Bun, R.; Roman, M.; Baker, D. F.; Pawson, S.

2017-01-01

Fossil fuel carbon dioxide (CO2) emissions (FFCO2) are the largest input to the global carbon cycle on a decadal time scale. Because total emissions are assumed to be reasonably well constrained by fuel statistics, FFCO2 often serves as a reference in order to deduce carbon uptake by poorly understood terrestrial and ocean sinks. Conventional atmospheric CO2 flux inversions solve for spatially explicit regional sources and sinks and estimate land and ocean fluxes by subtracting FFCO2. Thus, errors in FFCO2 can propagate into the final inferred flux estimates. Gridded emissions are often based on disaggregation of emissions estimated at national or regional level. Although national and regional total FFCO2 are well known, gridded emission fields are subject to additional uncertainties due to the emission disaggregation. Assessing such uncertainties is often challenging because of the lack of physical measurements for evaluation. We first review difficulties in assessing uncertainties associated with gridded FFCO2 emission data and present several approaches for evaluation of such uncertainties at multiple scales. Given known limitations, inter-emission data differences are often used as a proxy for the uncertainty. The popular approach allows us to characterize differences in emissions, but does not allow us to fully quantify emission disaggregation biases. Our work aims to vicariously evaluate FFCO2 emission data using atmospheric models and measurements. We show a global simulation experiment where uncertainty estimates are propagated as an atmospheric tracer (uncertainty tracer) alongside CO2 in NASA's GEOS model and discuss implications of FFCO2 uncertainties in the context of flux inversions. We also demonstrate the use of high resolution urban CO2 simulations as a tool for objectively evaluating FFCO2 data over intense emission regions. Though this study focuses on FFCO2 emission data, the outcome of this study could also help improve the knowledge of similar gridded emissions data for non-CO2 compounds with similar emission characteristics.

Assessing uncertainties in gridded emissions: A case study for fossil fuel carbon dioxide (FFCO2) emission data

NASA Astrophysics Data System (ADS)

Oda, T.; Ott, L. E.; Lauvaux, T.; Feng, S.; Bun, R.; Roman, M. O.; Baker, D. F.; Pawson, S.

2017-12-01

Fossil fuel carbon dioxide (CO2) emissions (FFCO2) are the largest input to the global carbon cycle on a decadal time scale. Because total emissions are assumed to be reasonably well constrained by fuel statistics, FFCO2 often serves as a reference in order to deduce carbon uptake by poorly understood terrestrial and ocean sinks. Conventional atmospheric CO2 flux inversions solve for spatially explicit regional sources and sinks and estimate land and ocean fluxes by subtracting FFCO2. Thus, errors in FFCO2 can propagate into the final inferred flux estimates. Gridded emissions are often based on disaggregation of emissions estimated at national or regional level. Although national and regional total FFCO2 are well known, gridded emission fields are subject to additional uncertainties due to the emission disaggregation. Assessing such uncertainties is often challenging because of the lack of physical measurements for evaluation. We first review difficulties in assessing uncertainties associated with gridded FFCO2 emission data and present several approaches for evaluation of such uncertainties at multiple scales. Given known limitations, inter-emission data differences are often used as a proxy for the uncertainty. The popular approach allows us to characterize differences in emissions, but does not allow us to fully quantify emission disaggregation biases. Our work aims to vicariously evaluate FFCO2 emission data using atmospheric models and measurements. We show a global simulation experiment where uncertainty estimates are propagated as an atmospheric tracer (uncertainty tracer) alongside CO2 in NASA's GEOS model and discuss implications of FFCO2 uncertainties in the context of flux inversions. We also demonstrate the use of high resolution urban CO2 simulations as a tool for objectively evaluating FFCO2 data over intense emission regions. Though this study focuses on FFCO2 emission data, the outcome of this study could also help improve the knowledge of similar gridded emissions data for non-CO2 compounds that share emission sectors.

Global anthropogenic emissions of particulate matter including black carbon

NASA Astrophysics Data System (ADS)

Klimont, Zbigniew; Kupiainen, Kaarle; Heyes, Chris; Purohit, Pallav; Cofala, Janusz; Rafaj, Peter; Borken-Kleefeld, Jens; Schöpp, Wolfgang

2017-07-01

This paper presents a comprehensive assessment of historical (1990-2010) global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calculation of mass-based size distribution (PM1, PM2. 5, PM10), as well as primary carbonaceous aerosols including black carbon (BC) and organic carbon (OC). The estimates were developed with the integrated assessment model GAINS, where source- and region-specific technology characteristics are explicitly included. This assessment includes a number of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total estimate. Spatially, emissions were calculated for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5° × 0.5° longitude-latitude grids. No independent estimates of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included. We estimate that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g. European, North American, and Pacific contributions to global emissions dropped from nearly 30 % in 1990 to well below 15 % in 2010, while Asia's contribution grew from just over 50 % to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60 % of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60 % for BC and OC, 45 % for PM2. 5, and less than 40 % for PM10, where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estimated at about 6.6 and 7.2 Tg in 2000 and 2010, respectively, and represent about 15 % of PM2. 5 but for some sources reach nearly 50 %, i.e. for the transport sector. Our global BC numbers are higher than previously published owing primarily to the inclusion of new sources. This PM estimate fills the gap in emission data and emission source characterization required in air quality and climate modelling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atmospheric transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyond, serves better parameterization of the global integrated assessment models with respect to representation of black carbon and organic carbon emissions, and built a basis for recently published global particulate number estimates.

Contribution of Anthropogenic and Natural Emissions to Global CH4 Balances by Utilizing δ13C-CH4 Observations in CarbonTracker Data Assimilation System (CTDAS)

NASA Astrophysics Data System (ADS)

Kangasaho, V. E.; Tsuruta, A.; Aalto, T.; Backman, L. B.; Houweling, S.; Krol, M. C.; Peters, W.; van der Laan-Luijkx, I. T.; Lienert, S.; Joos, F.; Dlugokencky, E. J.; Michael, S.; White, J. W. C.

2017-12-01

The atmospheric burden of CH4 has more than doubled since preindustrial time. Evaluating the contribution from anthropogenic and natural emissions to the global methane budget is of great importance to better understand the significance of different sources at the global scale, and their contribution to changes in growth rate of atmospheric CH4 before and after 2006. In addition, observations of δ13C-CH4 suggest an increase in natural sources after 2006, which matches the observed increase and variation of CH4 abudance. Methane emission sources can be identified using δ13C-CH4, because different sources produce methane with process-specific isotopic signatures. This study focuses on inversion model based estimates of global anthropogenic and natural methane emission rates to evaluate the existing methane emission estimates with a new δ13C-CH4 inversion system. In situ measurements of atmospheric methane and δ13C-CH4 isotopic signature, provided by the NOAA Global Monitoring Division and the Institute of Arctic and Alpine Research, will be assimilated into the CTDAS-13C-CH4. The system uses the TM5 atmospheric transport model as an observation operator, constrained by ECMWF ERA Interim meteorological fields, and off-line TM5 chemistry fields to account for the atmospheric methane sink. LPX-Bern DYPTOP ecosystem model is used for prior natural methane emissions from wetlands, peatlands and mineral soils, GFED v4 for prior fire emissions and EDGAR v4.2 FT2010 inventory for prior anthropogenic emissions. The EDGAR antropogenic emissions are re-divided into enteric fermentation and manure management, landfills and waste water, rice, coal, oil and gas, and residential emissions, and the trend of total emissions is scaled to match optimized anthropogenic emissions from CTE-CH4. In addition to these categories, emissions from termites and oceans are included. Process specific δ13C-CH4 isotopic signatures are assigned to each emission source to estimate 13CH4 fraction in CH4 emissions. Among the priors, anthropogenic and natural emissions are optimized and others are directly imposed from the prior. A detailed emission estimates of antropogenic and natural CH4 emissions will be constructed in order to provide a more comprehensive understanding of methane emission source divisions.

A historical reconstruction of ships' fuel consumption and emissions

NASA Astrophysics Data System (ADS)

Endresen, Øyvind; Sørgârd, Eirik; Behrens, Hanna Lee; Brett, Per Olaf; Isaksen, Ivar S. A.

2007-06-01

Shipping activity has increased considerably over the last century and currently represents a significant contribution to the global emissions of pollutants and greenhouse gases. Despite this, information about the historical development of fuel consumption and emissions is generally limited, with little data published pre-1950 and large deviations reported for estimates covering the last 3 decades. To better understand the historical development in ship emissions and the uncertainties associated with the estimates, we present fuel-based CO2 and SO2 emission inventories from 1925 up to 2002 and activity-based estimates from 1970 up to 2000. The global CO2 emissions from ships in 1925 have been estimated to 229 Tg (CO2), growing to about 634 Tg (CO2) in 2002. The corresponding SO2 emissions are about 2.5 Tg (SO2) and 8.5 Tg (SO2), respectively. Our activity-based estimates of fuel consumption from 1970 to 2000, covering all oceangoing civil ships above or equal to 100 gross tonnage (GT), are lower compared to previous activity-based studies. We have applied a more detailed model approach, which includes variation in the demand for sea transport, as well as operational and technological changes of the past. This study concludes that the main reason for the large deviations found in reported inventories is the applied number of days at sea. Moreover, our modeling indicates that the ship size and the degree of utilization of the fleet, combined with the shift to diesel engines, have been the major factors determining yearly fuel consumption. Interestingly, the model results from around 1973 suggest that the fleet growth is not necessarily followed by increased fuel consumption, as technical and operational characteristics have changed. Results from this study indicate that reported sales over the last 3 decades seems not to be significantly underreported as previous simplified activity-based studies have suggested. The results confirm our previously reported modeling estimates for year 2000. Previous activity-based studies have not considered ships less than 100 GT (e.g., today some 1.3 million fishing vessels), and we suggest that this fleet could account for an important part of the total fuel consumption (˜10%).

Uncertainties and implications of applying aggregated data for spatial modelling of atmospheric ammonia emissions.

PubMed

Hellsten, S; Dragosits, U; Place, C J; Dore, A J; Tang, Y S; Sutton, M A

2018-05-09

Ammonia emissions vary greatly at a local scale, and effects (eutrophication, acidification) occur primarily close to sources. Therefore it is important that spatially distributed emission estimates are located as accurately as possible. The main source of ammonia emissions is agriculture, and therefore agricultural survey statistics are the most important input data to an ammonia emission inventory alongside per activity estimates of emission potential. In the UK, agricultural statistics are collected at farm level, but are aggregated to parish level, NUTS-3 level or regular grid resolution for distribution to users. In this study, the Modifiable Areal Unit Problem (MAUP), associated with such amalgamation, is investigated in the context of assessing the spatial distribution of ammonia sources for emission inventories. England was used as a test area to study the effects of the MAUP. Agricultural survey data at farm level (point data) were obtained under license and amalgamated to different areal units or zones: regular 1-km, 5-km, 10-km grids and parish level, before they were imported into the emission model. The results of using the survey data at different levels of amalgamation were assessed to estimate the effects of the MAUP on the spatial inventory. The analysis showed that the size and shape of aggregation zones applied to the farm-level agricultural statistics strongly affect the location of the emissions estimated by the model. If the zones are too small, this may result in false emission "hot spots", i.e., artificially high emission values that are in reality not confined to the zone to which they are allocated. Conversely, if the zones are too large, detail may be lost and emissions smoothed out, which may give a false impression of the spatial patterns and magnitude of emissions in those zones. The results of the study indicate that the MAUP has a significant effect on the location and local magnitude of emissions in spatial inventories where amalgamated, zonal data are used. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting gaseous emissions from small-scale combustion of agricultural biomass fuels.

PubMed

Fournel, S; Marcos, B; Godbout, S; Heitz, M

2015-03-01

A prediction model of gaseous emissions (CO, CO2, NOx, SO2 and HCl) from small-scale combustion of agricultural biomass fuels was developed in order to rapidly assess their potential to be burned in accordance to current environmental threshold values. The model was established based on calculation of thermodynamic equilibrium of reactive multicomponent systems using Gibbs free energy minimization. Since this method has been widely used to estimate the composition of the syngas from wood gasification, the model was first validated by comparing its prediction results with those of similar models from the literature. The model was then used to evaluate the main gas emissions from the combustion of four dedicated energy crops (short-rotation willow, reed canary grass, switchgrass and miscanthus) previously burned in a 29-kW boiler. The prediction values revealed good agreement with the experimental results. The model was particularly effective in estimating the influence of harvest season on SO2 emissions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Emission inventory estimation of an intercity bus terminal.

PubMed

Qiu, Zhaowen; Li, Xiaoxia; Hao, Yanzhao; Deng, Shunxi; Gao, H Oliver

2016-06-01

Intercity bus terminals are hotspots of air pollution due to concentrated activities of diesel buses. In order to evaluate the bus terminals' impact on air quality, it is necessary to estimate the associated mobile emission inventories. Since the vehicles' operating condition at the bus terminal varies significantly, conventional calculation of the emissions based on average emission factors suffers the loss of accuracy. In this study, we examined a typical intercity bus terminal-the Southern City Bus Station of Xi'an, China-using a multi-scale emission model-(US EPA's MOVES model)-to quantity the vehicle emission inventory. A representative operating cycle for buses within the station is constructed. The emission inventory was then estimated using detailed inputs including vehicle ages, operating speeds, operating schedules, and operating mode distribution, as well as meteorological data (temperature and humidity). Five functional areas (bus yard, platforms, disembarking area, bus travel routes within the station, and bus entrance/exit routes) at the terminal were identified, and the bus operation cycle was established using the micro-trip cycle construction method. Results of our case study showed that switching to compressed natural gas (CNG) from diesel fuel could reduce PM2.5 and CO emissions by 85.64 and 6.21 %, respectively, in the microenvironment of the bus terminal. When CNG is used, tail pipe exhaust PM2.5 emission is significantly reduced, even less than brake wear PM2.5. The estimated bus operating cycles can also offer researchers and policy makers important information for emission evaluation in the planning and design of any typical intercity bus terminals of a similar scale.

Estimation of Phosphorus Emissions in the Upper Iguazu Basin (brazil) Using GIS and the More Model

NASA Astrophysics Data System (ADS)

Acosta Porras, E. A.; Kishi, R. T.; Fuchs, S.; Hilgert, S.

2016-06-01

Pollution emissions into the drainage basin have direct impact on surface water quality. These emissions result from human activities that turn into pollution loads when they reach the water bodies, as point or diffuse sources. Their pollution potential depends on the characteristics and quantity of the transported materials. The estimation of pollution loads can assist decision-making in basin management. Knowledge about the potential pollution sources allows for a prioritization of pollution control policies to achieve the desired water quality. Consequently, it helps avoiding problems such as eutrophication of water bodies. The focus of the research described in this study is related to phosphorus emissions into river basins. The study area is the upper Iguazu basin that lies in the northeast region of the State of Paraná, Brazil, covering about 2,965 km2 and around 4 million inhabitants live concentrated on just 16% of its area. The MoRE (Modeling of Regionalized Emissions) model was used to estimate phosphorus emissions. MoRE is a model that uses empirical approaches to model processes in analytical units, capable of using spatially distributed parameters, covering both, emissions from point sources as well as non-point sources. In order to model the processes, the basin was divided into 152 analytical units with an average size of 20 km2. Available data was organized in a GIS environment. Using e.g. layers of precipitation, the Digital Terrain Model from a 1:10000 scale map as well as soils and land cover, which were derived from remote sensing imagery. Further data is used, such as point pollution discharges and statistical socio-economic data. The model shows that one of the main pollution sources in the upper Iguazu basin is the domestic sewage that enters the river as point source (effluents of treatment stations) and/or as diffuse pollution, caused by failures of sanitary sewer systems or clandestine sewer discharges, accounting for about 56% of the emissions. Second significant shares of emissions come from direct runoff or groundwater, being responsible for 32% of the total emissions. Finally, agricultural erosion and industry pathways represent 12% of emissions. This study shows that MoRE is capable of producing valid emission calculation on a relatively reduced input data basis.

Modeling study of natural emissions, source apportionment, and emission control of atmospheric mercury

NASA Astrophysics Data System (ADS)

Shetty, Suraj K.

Mercury (Hg) is a toxic pollutant and is important to understand its cycling in the environment. In this dissertation, a number of modeling investigations were conducted to better understand the emission from natural surfaces, the source-receptor relationship of the emissions, and emission reduction of atmospheric mercury. The first part of this work estimates mercury emissions from vegetation, soil and water surfaces using a number of natural emission processors and detailed (LAI) Leaf Area Index data from GIS (Geographic Information System) satellite products. East Asian domain was chosen as it contributes nearly 50% of the global anthropogenic mercury emissions into the atmosphere. The estimated annual natural mercury emissions (gaseous elemental mercury) in the domain are 834 Mg yr-1 with 462 Mg yr-1 contributing from China. Compared to anthropogenic sources, natural sources show greater seasonal variability (highest in simmer). The emissions are significant, sometimes dominant, contributors to total mercury emission in the regions. The estimates provide possible explanation for the gaps between the anthropogenic emission estimates based on activity data and the emission inferred from field observations in the regions. To understand the contribution of domestic emissions to mercury deposition in the United States, the second part of the work applies the mercury model of Community Multi-scale Air Quality Modeling system (CMAQ-Hg v4.6) to apportion the various emission sources attributing to the mercury wet and dry deposition in the 6 United States receptor regions. Contributions to mercury deposition from electric generating units (EGU), iron and steel industry (IRST), industrial point sources excluding EGU and IRST (OIPM), the remaining anthropogenic sources (RA), natural processes (NAT), and out-of-boundary transport (BC) in domain was estimated. The model results for 2005 compared reasonably well to field observations made by MDN (Mercury Deposition Network) and CAMNet (Canadian Atmospheric Mercury Measurement Network). The model estimated a total deposition of 474 Mg yr-1 to the CONUS (Contiguous United States) domain, with two-thirds being dry deposited. Reactive gaseous mercury contributed the most to 60% of deposition. Emission speciation distribution is a key factor for local deposition as contribution from large point sources can be as high as 75% near (< 100 km) the emission sources, indicating that emission reduction may result in direct deposition decrease near the source locations. Among the sources, BC contributes to about 68% to 91% of total deposition. Excluding the BC's contribution, EGU contributes to nearly 50% of deposition caused by CONUS emissions in the Northeast, Southeast and East Central regions, while emissions from natural processes are more important in the Pacific and West Central regions (contributing up to 40% of deposition). The modeling results implies that implementation of the new emission standards proposed by USEPA (United States Environmental Protection Agency) would significantly benefit regions that have larger contributions from EGU sources. Control of mercury emissions from coal combustion processes has attracted great attention due to its toxicity and the emission-control regulations and has lead to advancement in state-of-the-art control technologies that alleviate the impact of mercury on ecosystem and human health. This part of the work applies a sorption model to simulate adsorption of mercury in flue gases, onto a confined-bed of activated carbon. The model's performances were studied at various flue gas flow rates, inlet mercury concentrations and adsorption bed temperatures. The process simulated a flue gas, with inlet mercury concentration of 300 ppb, entering at a velocity of 0.3 m s-1 from the bottom into a fixed bed (inside bed diameter of 1 m and 3 m bed height; bed temperature of 25 °C) of activated carbon (particle size of 0.004 m with density of 0.5 g cm-3 and surface area of 90.25 cm2 g -1). The model result demonstrated that a batch of activated carbon bed was capable of controlling mercury emission for approximately 275 days after which further mercury uptake starts to decrease till it reaches about 500 days when additional control ceases. An increase in bed temperature significantly reduces mercury sorption capacity of the activated carbon. Increase in flue gas flow rate may result in faster consumption of sorption capacity initially but at a later stage, the sorption rate decreases due to reduced sorption capacity. Thus, overall sorption rate remains unaffected. The activated carbon's effective life (time to reach saturation) is not affected by inlet mercury concentration, implying that the designing and operation of a mercury sorption process can be done independently. The results provide quantitative indication for designing efficient confined-bed process to remove mercury from flue gases.

Estimation of ambient BVOC emissions using remote sensing techniques

NASA Astrophysics Data System (ADS)

Nichol, Janet; Wong, Man Sing

2011-06-01

The contribution of Biogenic Volatile Organic Compounds (BVOCs) to local air quality modelling is often ignored due to the difficulty of obtaining accurate spatial estimates of emissions. Yet their role in the formation of secondary aerosols and photochemical smog is thought to be significant, especially in hot tropical cities such as Hong Kong, which are situated downwind from dense forests. This paper evaluates Guenther et al.'s [Guenther, A., Hewitt, C.N., Erickson, D., Fall, R., Geron, C., Graedel, T.E., Harley, P., Klinger, L., Lerdau, M., McKay, W.A., Pierce, T., Scholes, B., Steinbrecher, R., Tallamraju, R., Taylor, J., Zimmerman, P., 1995. A global model of natural volatile organic compound emissions. Journal of Geophysical Research 100, 8873-8892] global model of BVOC emissions, for application at a spatially detailed level to Hong Kong's tropical forested landscape using high resolution remote sensing and ground data. The emission estimates are based on a landscape approach which assigns emission rates directly to ecosystem types not to individual species, since unlike in temperate regions where one or two single species may dominate over large regions, Hong Kong's vegetation is extremely diverse with up to 300 different species in one hectare. The resulting BVOC emission maps are suitable for direct input to regional and local air quality models giving 10 m raster output on an hourly basis over the whole of the Hong Kong territory, an area of 1100 km 2. Due to the spatially detailed mapping of isoprene emissions over the study area, it was possible to validate the model output using field data collected at a precise time and place by replicating those conditions in the model. The field measurement of emissions used for validating the model was based on a canister sampling technique, undertaken under different climatic conditions for Hong Kong's main ecosystem types in both urban and rural areas. The model-derived BVOC flux distributions appeared to be consistent with the field observations, indicating the robustness of the landscape modelling approach when applied to tropical forests at detailed level, as well as the promising role of remote sensing in BVOC mapping.

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

DOE PAGES

Kim, Young-Min; Zhou, Ying; Gao, Yang; ...

2014-11-16

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5)more » and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3-related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.« less

Spatially resolved estimation of ozone-related mortality in the United States under two representative concentration pathways (RCPs) and their uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Young-Min; Zhou, Ying; Gao, Yang

We report that the spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O 3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O 3 level at 12 km resolution between the future (2057 and 2059) and base years (2001–2004) under a low-to-medium emission scenario (RCP4.5)more » and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O 3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O 3-related premature deaths in the continental U.S. were estimated to be 1312 deaths/year under RCP8.5 (95 % confidence interval (CI): 427 to 2198) and ₋2118 deaths/year under RCP4.5 (95 % CI: ₋3021 to ₋1216), when allowing for climate change and emissions reduction. The uncertainty of O 3-related excess mortality estimates was mainly caused by RCP emissions pathways. Finally, excess mortality estimates attributable to the combined effect of climate and emission changes on O 3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.« less

Enhanced representation of soil NO emissions in the ...

EPA Pesticide Factsheets

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12 km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and

Quantification of point sources of carbon monoxide using satellite measurements

NASA Astrophysics Data System (ADS)

Dekker, Iris; Houweling, Sander; Aben, Ilse; Roeckmann, Thomas; Krol, Maarten

2017-04-01

The growth of mega-cities leads to air quality problems directly affecting the citizens. With satellite measurements becoming of higher quality and quantity, satellite instruments can more accurately retrieve the enhanced air pollutant concentrations over large cities. The aim of this research is to quantify carbon monoxide emissions from megacities and their trends using satellite retrievals, combined with an atmospheric chemistry and transport model. Earlier emission estimations of cities have been done using MOPITT satellite data only. To improve the reliability of the emission estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRF-Chem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. A reasonable agreement is obtained between the yearly averaged model output and satellite measurements (R2=0.75) for Madrid. After optimization, the emission of Madrid is reduced by 48% for 2002 and by 17% for 2006 compared with EdgarV4.2. The MOPITT derived emission adjustments lead to a better agreement with a European emission inventory TNO-MAC-III for both years. This suggested that the downward trend in CO emissions over Madrid is overestimated in EdgarV4.2 and more realistically represented in TNO-MAC-III. However, uncertainties remain large using our satellite-based emission estimation method, in the order of 20% for 2002 and 50% for 2006. Therefore, different options to increase the degrees of freedom in the optimization are investigated, to account for the noise in the MOPITT data. We also show comparisons with IASI data, which have a higher temporal resolution. The method is developed for application to Sentinel 5P TROPOMI, to be launched in June 2017.

Surface Nitrification: A Major Uncertainty in Marine N2O Emissions

NASA Technical Reports Server (NTRS)

Zamora, Lauren M.; Oschlies, Andreas

2014-01-01

The ocean is responsible for up to a third of total global nitrous oxide (N2O) emissions, but uncertainties in emission rates of this potent greenhouse gas are high (approaching 100%). Here we use a marine biogeochemical model to assess six major uncertainties in estimates of N2O production, thereby providing guidance in how future studies may most effectively reduce uncertainties in current and future marine N2O emissions. Potential surface N2O production from nitrification causes the largest uncertainty in N2O emissions (estimated up to approximately 1.6 Tg N/yr (sup -1) or 48% of modeled values), followed by the unknown oxygen concentration at which N2O production switches to N2O consumption (0.8 Tg N/yr (sup -1)or 24% of modeled values). Other uncertainties are minor, cumulatively changing regional emissions by less than 15%. If production of N2O by surface nitrification could be ruled out in future studies, uncertainties in marine N2O emissions would be halved.

Land cover maps, BVOC emissions, and SOA burden in a global aerosol-climate model

NASA Astrophysics Data System (ADS)

Stanelle, Tanja; Henrot, Alexandra; Bey, Isaelle

2015-04-01

It has been reported that different land cover representations influence the emission of biogenic volatile organic compounds (BVOC) (e.g. Guenther et al., 2006). But the land cover forcing used in model simulations is quite uncertain (e.g. Jung et al., 2006). As a consequence the simulated emission of BVOCs depends on the applied land cover map. To test the sensitivity of global and regional estimates of BVOC emissions on the applied land cover map we applied 3 different land cover maps into our global aerosol-climate model ECHAM6-HAM2.2. We found a high sensitivity for tropical regions. BVOCs are a very prominent precursor for the production of Secondary Organic Aerosols (SOA). Therefore the sensitivity of BVOC emissions on land cover maps impacts the SOA burden in the atmosphere. With our model system we are able to quantify that impact. References: Guenther et al. (2006), Estimates of global terrestrial isoprene emissions using MEGAN, Atmos. Chem. Phys., 6, 3181-3210, doi:10.5194/acp-6-3181-2006. Jung et al. (2006), Exploiting synergies of global land cover products for carbon cycle modeling, Rem. Sens. Environm., 101, 534-553, doi:10.1016/j.rse.2006.01.020.

An algorithm to estimate aircraft cruise black carbon emissions for use in developing a cruise emissions inventory.

PubMed

Peck, Jay; Oluwole, Oluwayemisi O; Wong, Hsi-Wu; Miake-Lye, Richard C

2013-03-01

To provide accurate input parameters to the large-scale global climate simulation models, an algorithm was developed to estimate the black carbon (BC) mass emission index for engines in the commercial fleet at cruise. Using a high-dimensional model representation (HDMR) global sensitivity analysis, relevant engine specification/operation parameters were ranked, and the most important parameters were selected. Simple algebraic formulas were then constructed based on those important parameters. The algorithm takes the cruise power (alternatively, fuel flow rate), altitude, and Mach number as inputs, and calculates BC emission index for a given engine/airframe combination using the engine property parameters, such as the smoke number, available in the International Civil Aviation Organization (ICAO) engine certification databank. The algorithm can be interfaced with state-of-the-art aircraft emissions inventory development tools, and will greatly improve the global climate simulations that currently use a single fleet average value for all airplanes. An algorithm to estimate the cruise condition black carbon emission index for commercial aircraft engines was developed. Using the ICAO certification data, the algorithm can evaluate the black carbon emission at given cruise altitude and speed.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

PubMed

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

Estimates of spatially and temporally resolved constrained black carbon emission over the Indian region using a strategic integrated modelling approach

NASA Astrophysics Data System (ADS)

Verma, S.; Reddy, D. Manigopal; Ghosh, S.; Kumar, D. Bharath; Chowdhury, A. Kundu

2017-10-01

We estimated the latest spatially and temporally resolved gridded constrained black carbon (BC) emissions over the Indian region using a strategic integrated modelling approach. This was done extracting information on initial bottom-up emissions and atmospheric BC concentration from a general circulation model (GCM) simulation in conjunction with the receptor modelling approach. Monthly BC emission (83-364 Gg) obtained from the present study exhibited a spatial and temporal variability with this being the highest (lowest) during February (July). Monthly BC emission flux was considerably high (> 100 kg km- 2) over the entire Indo-Gangetic plain (IGP), east and the west coast during winter months. This was relatively higher over the central and western India than over the IGP during summer months. Annual BC emission rate was 2534 Gg y- 1 with that over the IGP and central India respectively comprising 50% and 40% of the total annual BC emissions over India. A high relative increase was observed in modified BC emissions (more than five times the initial emissions) over the most part of the IGP, east coast, central/northwestern India. The relative predominance of monthly BC emission flux over a region (as depicted from z-score distribution maps) was inferred being consistent with the prevalence of region- and season-specific anthropogenic activity.

Costs of IQ Loss from Leaded Aviation Gasoline Emissions

PubMed Central

Wolfe, Philip J.; Giang, Amanda; Ashok, Akshay; Selin, Noelle E.; Barrett, Steven R. H.

2017-01-01

In the United States, general aviation piston-driven aircraft are now the largest source of lead emitted to the atmosphere. Elevated lead concentrations impair children’s IQ and can lead to lower earnings potentials. This study is the first assessment of the nationwide annual costs of IQ losses from aircraft lead emissions. We develop a general aviation emissions inventory for the continental United States and model its impact on atmospheric concentrations using the Community Multi-Scale Air Quality Model (CMAQ). We use these concentrations to quantify the impacts of annual aviation lead emissions on the U.S. population using two methods: through static estimates of cohort-wide IQ deficits and through dynamic economy-wide effects using a computational general equilibrium model. We also examine the sensitivity of these damage estimates to different background lead concentrations, showing the impact of lead controls and regulations on marginal costs. We find that aircraft-attributable lead contributes to $1.06 billion 2006 USD ($0.01 – $11.6) in annual damages from lifetime earnings reductions, and that dynamic economy-wide methods result in damage estimates that are 54% larger. Because the marginal costs of lead are dependent on background concentration, the costs of piston-driven aircraft lead emissions are expected to increase over time as regulations on other emissions sources are tightened. PMID:27494542

Costs of IQ Loss from Leaded Aviation Gasoline Emissions.

PubMed

Wolfe, Philip J; Giang, Amanda; Ashok, Akshay; Selin, Noelle E; Barrett, Steven R H

2016-09-06

In the United States, general aviation piston-driven aircraft are now the largest source of lead emitted to the atmosphere. Elevated lead concentrations impair children's IQ and can lead to lower earnings potentials. This study is the first assessment of the nationwide annual costs of IQ losses from aircraft lead emissions. We develop a general aviation emissions inventory for the continental United States and model its impact on atmospheric concentrations using the community multi-scale air quality model (CMAQ). We use these concentrations to quantify the impacts of annual aviation lead emissions on the U.S. population using two methods: through static estimates of cohort-wide IQ deficits and through dynamic economy-wide effects using a computational general equilibrium model. We also examine the sensitivity of these damage estimates to different background lead concentrations, showing the impact of lead controls and regulations on marginal costs. We find that aircraft-attributable lead contributes to $1.06 billion 2006 USD ($0.01-$11.6) in annual damages from lifetime earnings reductions, and that dynamic economy-wide methods result in damage estimates that are 54% larger. Because the marginal costs of lead are dependent on background concentration, the costs of piston-driven aircraft lead emissions are expected to increase over time as regulations on other emissions sources are tightened.

Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand.

PubMed

Appuhamy, Jayasooriya A D R N; France, James; Kebreab, Ermias

2016-09-01

There are several models in the literature for predicting enteric methane (CH4 ) emissions. These models were often developed on region or country-specific data and may not be able to predict the emissions successfully in every region. The majority of extant models require dry matter intake (DMI) of individual animals, which is not routinely measured. The objectives of this study were to (i) evaluate performance of extant models in predicting enteric CH4 emissions from dairy cows in North America (NA), Europe (EU), and Australia and New Zealand (AUNZ) and (ii) explore the performance using estimated DMI. Forty extant models were challenged on 55, 105, and 52 enteric CH4 measurements (g per lactating cow per day) from NA, EU, and AUNZ, respectively. The models were ranked using root mean square prediction error as a percentage of the average observed value (RMSPE) and concordance correlation coefficient (CCC). A modified model of Nielsen et al. (Acta Agriculturae Scand Section A, 63, 2013 and 126) using DMI, and dietary digestible neutral detergent fiber and fatty acid contents as predictor variables, were ranked highest in NA (RMSPE = 13.1% and CCC = 0.78). The gross energy intake-based model of Yan et al. (Livestock Production Science, 64, 2000 and 253) and the updated IPCC Tier 2 model were ranked highest in EU (RMSPE = 11.0% and CCC = 0.66) and AUNZ (RMSPE = 15.6% and CCC = 0.75), respectively. DMI of cows in NA and EU was estimated satisfactorily with body weight and fat-corrected milk yield data (RMSPE < 12.0% and CCC > 0.60). Using estimated DMI, the Nielsen et al. (2013) (RMSPE = 12.7 and CCC = 0.79) and Yan et al. (2000) (RMSPE = 13.7 and CCC = 0.50) models still predicted emissions in respective regions well. Enteric CH4 emissions from dairy cows can be predicted successfully (i.e., RMSPE < 15%), if DMI can be estimated with reasonable accuracy (i.e., RMSPE < 10%). © 2016 John Wiley & Sons Ltd.

Model study of the ship emissions impact on the air quality in the Adriatic/Ionian area

NASA Astrophysics Data System (ADS)

Karagiannidis, Athanasios; Poupkou, Anastasia; Liora, Natalia; Dimopoulos, Spiros; Giannaros, Christos; Melas, Dimitrios; Argiriou, Athanassios

2015-04-01

The increase of the ship traffic for touristic and commercial purposes is one of the EU Blue Growth targets. The Adriatic/Ionian is one of the sea-basin strategic areas for this target. The purpose of the study is the examination of the impact of the ship emissions on the gaseous and particulate pollutants concentrations in the Adriatic/Ionian area for which the current scientific knowledge is limited. The impact is simulated over a domain covering the Central and Eastern Mediterranean in 10 km resolution during a summer period (July) and a winter period (January) of the year 2012. The modeling system used consists of the photochemical model CAMx off line coupled with the meteorological model WRF. The zero-out modeling method is implemented involving CAMx simulations performed while including and omitting the ship emission data. The simulations are based on the European scale anthropogenic emission inventory of The Netherlands Organisation (TNO) for the reference year 2009. Natural emissions (NMVOCs from the vegetation, sea salt, wind-blown dust), estimated with the use of the Natural Emission MOdel (NEMO) developed by the Aristotle University of Thessaloniki, are accounted for in the photochemical model runs. The spatial distribution of the resulting differences in the gaseous and particulate pollutant concentration fields for both emission scenarios are presented and discussed, providing an estimation of the contribution of ship emissions on the determination of the air quality in the Adriatic/Ionian countries

Using Satellite Remote Sensing and Modelling for Insights into N02 Air Pollution and NO2 Emissions

NASA Technical Reports Server (NTRS)

Lamsal, L. N.; Martin, R. V.; Krotkov, N. A.; Bucsela, E. J.; Celarier, E. A.; vanDonkelaar, A.; Parrish, D.

2012-01-01

Nitrogen oxides (NO(x)) are key actors in air quality and climate change. Satellite remote sensing of tropospheric NO2 has developed rapidly with enhanced spatial and temporal resolution since initial observations in 1995. We have developed an improved algorithm and retrieved tropospheric NO2 columns from Ozone Monitoring Instrument. Column observations of tropospheric NO2 from the nadir-viewing satellite sensors contain large contributions from the boundary layer due to strong enhancement of NO2 in the boundary layer. We infer ground-level NO2 concentrations from the OMI satellite instrument which demonstrate significant agreement with in-situ surface measurements. We examine how NO2 columns measured by satellite, ground-level NO2 derived from satellite, and NO(x) emissions obtained from bottom-up inventories relate to world's urban population. We perform inverse modeling analysis of NO2 measurements from OMI to estimate "top-down" surface NO(x) emissions, which are used to evaluate and improve "bottom-up" emission inventories. We use NO2 column observations from OMI and the relationship between NO2 columns and NO(x) emissions from a GEOS-Chem model simulation to estimate the annual change in bottom-up NO(x) emissions. The emission updates offer an improved estimate of NO(x) that are critical to our understanding of air quality, acid deposition, and climate change.

The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning

Treesearch

C. Wiedinmyer; S. K. Akagi; R. J. Yokelson; L. K. Emmons; J. A. Al-Saadi; J. J. Orlando; A. J. Soja

2010-01-01

The Fire INventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include 5 biofuel use and trash burning. Emission factors used in the calculations have been updated with recent data,...

Estimation of automobile emissions and control strategies in India.

PubMed

Nesamani, K S

2010-03-15

Rapid, but unplanned urban development and the consequent urban sprawl coupled with economic growth have aggravated auto dependency in India over the last two decades. This has resulted in congestion and pollution in cities. The central and state governments have taken many ameliorative measures to reduce vehicular emissions. However, evolution of scientific methods for emission inventory is crucial. Therefore, an attempt has been made to estimate the emissions (running and start) from on-road vehicles in Chennai using IVE model in this paper. GPS was used to collect driving patterns. The estimated emissions from motor vehicles in Chennai in 2005 were 431, 119, 46, 7, 4575, 29, and 0.41 tons/days respectively for CO, VOC, NO(x), PM, CO(2,) CH(4) and N(2)O. It is observed from the results that air quality in Chennai has degraded. The estimation revealed that two and three-wheelers emitted about 64% of the total CO emissions and heavy-duty vehicles accounted for more than 60% and 36% of the NO(x) and PM emissions respectively. About 19% of total emissions were that of start emissions. It is also estimated that on-road transport contributes about 6637 tons/day CO(2) equivalent in Chennai. This paper has further examined various mitigation options to reduce vehicular emissions. The study has concluded that advanced vehicular technology and augmentation of public transit would have significant impact on reducing vehicular emissions.

Validation of a novel air toxic risk model with air monitoring.

PubMed

Pratt, Gregory C; Dymond, Mary; Ellickson, Kristie; Thé, Jesse

2012-01-01

Three modeling systems were used to estimate human health risks from air pollution: two versions of MNRiskS (for Minnesota Risk Screening), and the USEPA National Air Toxics Assessment (NATA). MNRiskS is a unique cumulative risk modeling system used to assess risks from multiple air toxics, sources, and pathways on a local to a state-wide scale. In addition, ambient outdoor air monitoring data were available for estimation of risks and comparison with the modeled estimates of air concentrations. Highest air concentrations and estimated risks were generally found in the Minneapolis-St. Paul metropolitan area and lowest risks in undeveloped rural areas. Emissions from mobile and area (nonpoint) sources created greater estimated risks than emissions from point sources. Highest cancer risks were via ingestion pathway exposures to dioxins and related compounds. Diesel particles, acrolein, and formaldehyde created the highest estimated inhalation health impacts. Model-estimated air concentrations were generally highest for NATA and lowest for the AERMOD version of MNRiskS. This validation study showed reasonable agreement between available measurements and model predictions, although results varied among pollutants, and predictions were often lower than measurements. The results increased confidence in identifying pollutants, pathways, geographic areas, sources, and receptors of potential concern, and thus provide a basis for informing pollution reduction strategies and focusing efforts on specific pollutants (diesel particles, acrolein, and formaldehyde), geographic areas (urban centers), and source categories (nonpoint sources). The results heighten concerns about risks from food chain exposures to dioxins and PAHs. Risk estimates were sensitive to variations in methodologies for treating emissions, dispersion, deposition, exposure, and toxicity. © 2011 Society for Risk Analysis.

Hybrid Air Quality Modeling Approach For Use in the Near ...

EPA Pesticide Factsheets

The Near-road EXposures to Urban air pollutant Study (NEXUS) investigated whether children with asthma living in close proximity to major roadways in Detroit, MI, (particularly near roadways with high diesel traffic) have greater health impacts associated with exposure to air pollutants than those living farther away. A major challenge in such health and exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. This paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the AERMOD and R-LINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multiscale Air Quality (CMAQ) model and the Space/Time Ordinary Kriging (STOK) model. To capture the near-road pollutant gradients, refined “mini-grids” of model recep

Estimating Landfill Methane Oxidation Using the Information of CO2/CH4 Fluxes Measured By the Eddy Covariance Method

NASA Astrophysics Data System (ADS)

Xu, L.; McDermitt, D. K.; Li, J.; Green, R. B.

2016-12-01

Methane plays a critical role in the radiation balance and chemistry of the atmosphere. Globally, landfill methane emission contributes about 10-19% of the anthropogenic methane burden into the atmosphere. In the United States, 18% of annual anthropogenic methane emissions come from landfills, which represent the third largest source of anthropogenic methane emissions, behind enteric fermentation and natural gas and oil production. One uncertainty in estimating landfill methane emissions is the fraction of methane oxidized when methane produced under anaerobic conditions passes through the cover soil. We developed a simple stoichiometric model to estimate the landfill methane oxidation fraction when the anaerobic CO2/CH4 production ratio is known. The model predicts a linear relationship between CO2 emission rates and CH4 emission rates, where the slope depends on anaerobic CO2/CH4 production ratio and the fraction of methane oxidized, and the intercept depends on non-methane-dependent oxidation processes. The model was tested with eddy covariance CO2 and CH4 emission rates at Bluff Road Landfill in Lincoln Nebraska. It predicted zero oxidation rate in the northern portion of this landfill where a membrane and vents were present. The zero oxidation rate was expected because there would be little opportunity for methane to encounter oxidizing conditions before leaving the vents. We also applied the model at the Turkey Run Landfill in Georgia to estimate the CH4 oxidation rate over a one year period. In contrast to Bluff Road Landfill, the Turkey Run Landfill did not have a membrane or vents. Instead, methane produced in the landfill had to diffuse through a 0.5 m soil cap before release to the atmosphere. We observed evidence for methane oxidation ranging from about 18% to above 60% depending upon the age of deposited waste material. The model will be briefly described, and results from the two contrasting landfills will be discussed in this presentation.

A probabilistic approach to emissions from transportation sector in the coming decades

NASA Astrophysics Data System (ADS)

Yan, F.; Winijkul, E.; Bond, T. C.; Streets, D. G.

2010-12-01

Future emission estimates are necessary for understanding climate change, designing national and international strategies for air quality control and evaluating mitigation policies. Emission inventories are uncertain and future projections even more so. Most current emission projection models are deterministic; in other words, there is only single answer for each scenario. As a result, uncertainties have not been included in the estimation of climate forcing or other environmental effects, but it is important to quantify the uncertainty inherent in emission projections. We explore uncertainties of emission projections from transportation sector in the coming decades by sensitivity analysis and Monte Carlo simulations. These projections are based on a technology driven model: the Speciated Pollutants Emission Wizard (SPEW)-Trend, which responds to socioeconomic conditions in different economic and mitigation scenarios. The model contains detail about technology stock, including consumption growth rates, retirement rates, timing of emission standards, deterioration rates and transition rates from normal vehicles to vehicles with extremely high emission factors (termed “superemitters”). However, understanding of these parameters, as well as relationships with socioeconomic conditions, is uncertain. We project emissions from transportation sectors under four different IPCC scenarios (A1B, A2, B1, and B2). Due to the later implementation of advanced emission standards, Africa has the highest annual growth rate (1.2-3.1%) from 2010 to 2050. Superemitters begin producing more than 50% of global emissions around year 2020. We estimate uncertainties from the relationships between technological change and socioeconomic conditions and examine their impact on future emissions. Sensitivities to parameters governing retirement rates are highest, causing changes in global emissions from-26% to +55% on average from 2010 to 2050. We perform Monte Carlo simulations to examine how these uncertainties will affect total emissions if any input parameter that has inherent the uncertainties is substituted by a range of values-probability distribution and varies at the same time; the 95% confidence interval of global emission annual growth rate is -1.9% to +0.2% per year.

Timing of carbon emissions from global forest clearance

Treesearch

J. Mason Earles; Sonia Yeh; Kenneth E. Skog

2012-01-01

Land-use change, primarily from conventional agricultural expansion and deforestation, contributes to approximately 17% of global greenhouse-gas emissions1. The fate of cleared wood and subsequent carbon storage as wood products, however, has not been consistently estimated, and is largely ignored or oversimplified by most models estimating...

ESTIMATION OF THE RATE OF VOC EMISSIONS FROM SOLVENT-BASED INDOOR COATING MATERIALS BASED ON PRODUCT FORMULATION

EPA Science Inventory

Two computational methods are proposed for estimation of the emission rate of volatile organic compounds (VOCs) from solvent-based indoor coating materials based on the knowledge of product formulation. The first method utilizes two previously developed mass transfer models with ...

A New Global Anthropogenic SO2 Emission Inventory for the Last Decade: A Mosaic of Satellite-derived and Bottom-up Emissions

NASA Astrophysics Data System (ADS)

Liu, F.; Joiner, J.; Choi, S.; Krotkov, N. A.; Li, C.; Fioletov, V. E.; McLinden, C. A.

2017-12-01

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources using an innovative estimation technique. Emissions from about 500 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO2 emissions. We developed a new emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory. OMI-HTAP includes OMI-based estimates for over 400 point sources and is gap-filled with the emission grid map of the latest available global bottom-up emission inventory (HTAP v2.2) for the rest of sources. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO2 concentrations driven by both the HTAP and the OMI-HTAP inventory were compared against in-situ and satellite measurements. Results show that the OMI-HTAP inventory improves the model agreement with observations, in particular over the US, India and the Middle East. Additionally, simulations with the OMI-HTAP inventory capture the major trends of anthropogenic SO2 emissions over the world and highlight the influence of missing sources in the bottom-up inventory.

Top-down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin

DOE PAGES

Cui, Yu Yan; Brioude, Jerome; McKeen, Stuart A.; ...

2015-07-28

Methane (CH 4) is the primary component of natural gas and has a larger global warming potential than CO 2. Some recent top-down studies based on observations showed CH 4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH 4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use themore » FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH 4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R 2 = 0.5). The emission estimates for CH 4 in the posterior, 46.3 ± 9.2 Mg CH 4/h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH 4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. Furthermore, we estimate that dairies contributed 5.9 ± 1.7 Mg CH 4/h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH 4/h in the SoCAB.« less

Investigating the Impact of Marine Ship Emissions on Regional Air Quality using OMI Satellite NO2 Observations and the CMAQ Model

NASA Astrophysics Data System (ADS)

Ring, A.; Canty, T. P.; He, H.; Vinciguerra, T.; Lamsal, L. N.; Dickerson, R. R.; Salawitch, R. J.; Cohen, M.; Montgomery, L. N.; Dreessen, J.

2015-12-01

Commercial marine vessels (CMVs) emit significant amounts of NOx, an ozone precursor, which may contribute to negative health consequences for people living in areas near shipping lanes. In coastal US states, many metropolitan areas such as Baltimore and New York City are located near ports with CMVs. Many studies estimate that ships account for ~15-30% of the global anthropogenic NOx emissions. EPA developed emissions inventories are widely used by states to construct model scenarios for testing air quality attainment strategies. Currently, CMV emissions are generated by simply applying growth factors to aggregated emissions data from much earlier years. Satellite retrievals from the Ozone Monitoring Instrument (OMI) have been successfully used to improve the veracity of marine emissions by incorporating observational data from the inventory year. In this study we use OMI NO2 observations and Community Multiscale Air Quality (CMAQ) model outputs to improve the EPA marine emission estimates for the Mid-Atlantic region. Back trajectories from the NOAA Air Resources Laboratory HYSPLIT model are used to identify days with minimal continental influence on OMI tropospheric column NO2 over shipping lanes. We perform sensitivity analyses to quantify the impact of marine emissions on air quality and suggest strategies to better meet the EPA mandated ozone standard.

An inventory of nitrous oxide emissions from agriculture in the UK using the IPCC methodology: emission estimate, uncertainty and sensitivity analysis

NASA Astrophysics Data System (ADS)

Brown, L.; Armstrong Brown, S.; Jarvis, S. C.; Syed, B.; Goulding, K. W. T.; Phillips, V. R.; Sneath, R. W.; Pain, B. F.

Nitrous oxide emission from UK agriculture was estimated, using the IPCC default values of all emission factors and parameters, to be 87 Gg N 2O-N in both 1990 and 1995. This estimate was shown, however, to have an overall uncertainty of 62%. The largest component of the emission (54%) was from the direct (soil) sector. Two of the three emission factors applied within the soil sector, EF1 (direct emission from soil) and EF3 PRP (emission from pasture range and paddock) were amongst the most influential on the total estimate, producing a ±31 and +11% to -17% change in emissions, respectively, when varied through the IPCC range from the default value. The indirect sector (from leached N and deposited ammonia) contributed 29% of the total emission, and had the largest uncertainty (126%). The factors determining the fraction of N leached (Frac LEACH) and emissions from it (EF5), were the two most influential. These parameters are poorly specified and there is great potential to improve the emission estimate for this component. Use of mathematical models (NCYCLE and SUNDIAL) to predict Frac LEACH suggested that the IPCC default value for this parameter may be too high for most situations in the UK. Comparison with other UK-derived inventories suggests that the IPCC methodology may overestimate emission. Although the IPCC approach includes additional components to the other inventories (most notably emission from indirect sources), estimates for the common components (i.e. fertiliser and animals), and emission factors used, are higher than those of other inventories. Whilst it is recognised that the IPCC approach is generalised in order to allow widespread applicability, sufficient data are available to specify at least two of the most influential parameters, i.e. EF1 and Frac LEACH, more accurately, and so provide an improved estimate of nitrous oxide emissions from UK agriculture.

Simulation of atmospheric oxidation capacity in Houston, Texas

EPA Science Inventory

Air quality model simulations are performed and evaluated for Houston using the Community Multiscale Air Quality (CMAQ) model. The simulations use two different emissions estimates: the EPA 2005 National Emissions Inventory (NEI) and the Texas Commission on Environmental Quality ...

Future anthropogenic pollutant emissions in a Mediterranean port city with emphasis on the maritime sector emissions - Study of the impact on the city air quality

NASA Astrophysics Data System (ADS)

Liora, Natalia; Poupkou, Anastasia; Markakis, Konstantinos; Giannaros, Theodoros; Karagiannidis, Athanasios; Melas, Dimitrios

2013-04-01

The aim of this study is the estimation of the future emissions in the area of the large urban center of Thessaloniki (Greece) with emphasis on the emissions originated from the maritime sector within the port area of the city which are presented in detail. In addition, the contribution of the future anthropogenic emissions to atmospheric pollution levels in Thessaloniki focusing on PM levels is studied. A 2km spatial resolution anthropogenic gaseous and particulate matter emission inventory has been compiled for the port city of Thessaloniki for the year 2010 with the anthropogenic emission model MOSESS, developed by Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki. MOSESS was used for the estimation of emissions from several emission sources (road transport, central heating, industries, maritime sector etc) while the natural emission model NEMO was implemented for the calculation of dust, sea salt and biogenic emissions. Maritime emissions originated from the various processes inside the area of the port (harbor operations such as stockpiles, loading/unloading operations, machineries etc) as well as from the maritime transport sector including passenger ships, cargo shipping, inland waterways vessels (e.g. pleasure crafts) and fish catching ships. Ship emissions were estimated for the three operation modes; cruising, maneuvering and hotelling. For the calculation of maritime emissions, the activity data used were provided by local and national authorities (e.g.Thessaloniki Port Authority S.A.). Pollutant anthropogenic emissions were projected to the year 2020. The emissions from all the anthropogenic sources except for the maritime sector were projected using factors provided by the GAINS model. Future emissions from the maritime activities were estimated on the basis of the future activity data provided by the Port Authority and of the legislation for shipping in the future. Future maritime emissions are determined by the vessels traffic changes as foreseen for the year 2020 by the Port Authority Investment Plan and by the reduction of the sulfur content in fuels used by ships in cruising mode to 0.5% m/m according to a revision of the MARPOL Annex VI. Based on the above, an approximately 60% increase in the future maritime sector PM10 emissions is expected due to the high increase of the traffic of vessels. The impact of future emissions on the air quality of Thessaloniki is examined with the use of the modelling system WRF-CAMx applied with 2km spatial resolution over the study area. Simulations of the modelling system are performed for a summertime (July 2011) and a wintertime (15 November to 15 December 2011) period accounting for present time (scenario A) and future time (scenario B) pollutant emissions. The differences in pollutant levels (mainly PM) between the scenarios examined are presented and discussed.

Modeled occupational exposures to gas-phase medical laser-generated air contaminants.

PubMed

Lippert, Julia F; Lacey, Steven E; Jones, Rachael M

2014-01-01

Exposure monitoring data indicate the potential for substantive exposure to laser-generated air contaminants (LGAC); however the diversity of medical lasers and their applications limit generalization from direct workplace monitoring. Emission rates of seven previously reported gas-phase constituents of medical laser-generated air contaminants (LGAC) were determined experimentally and used in a semi-empirical two-zone model to estimate a range of plausible occupational exposures to health care staff. Single-source emission rates were generated in an emission chamber as a one-compartment mass balance model at steady-state. Clinical facility parameters such as room size and ventilation rate were based on standard ventilation and environmental conditions required for a laser surgical facility in compliance with regulatory agencies. All input variables in the model including point source emission rates were varied over an appropriate distribution in a Monte Carlo simulation to generate a range of time-weighted average (TWA) concentrations in the near and far field zones of the room in a conservative approach inclusive of all contributing factors to inform future predictive models. The concentrations were assessed for risk and the highest values were shown to be at least three orders of magnitude lower than the relevant occupational exposure limits (OELs). Estimated values do not appear to present a significant exposure hazard within the conditions of our emission rate estimates.

MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP

DOE PAGES

Li, Meng; Zhang, Qiang; Kurokawa, Jun-ichi; ...

2017-01-20

Here, the MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the totalmore » Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO 2, 52.1 Tg NO x, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH 3, 31.7 Tg PM 10, 22.7 Tg PM 2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO 2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: –8.1 % for SO 2, +19.2 % for NO x, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH 3, –3.4 % for PM 10, –1.6 % for PM 2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO 2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach.« less

Simulating N2O emissions under different tillage systems of irrigated corn using RZ-Shaw model

USDA-ARS?s Scientific Manuscript database

Nitrous oxide (N2O) is potent greenhouse gas (GHG) and agriculture is a global source of N2O emissions from soil fertility management. Yet emissions vary by agronomic practices and environmental factors that govern soil moisture and temperature. Ecosystem models are important tools to estimate N2O e...

Geographic Inventory Framework (GiF) for estimating N2O and CH4 emissions from agriculture in the province of Alberta, Canada

NASA Astrophysics Data System (ADS)

Dimitrov, D. D.; Wang, J.

2016-12-01

A Geographic Information Framework (GiF) has been created to estimate and map agricultural N2O and CH4 emissions of the province of Alberta, Canada. The GiF consists of a modelling component, a GIS component, and application software to communicate between the model, database and census data. For compatibility, GiF follows the IPCC Tier 1 method and contains census data for animal populations, crop areas, and farms for the main IPCC animal and plant types (dairy cows, cattle cows, pigs, sheep, poultry, other animals, grasses, legumes, other crops), and estimated N2O and CH4 emissions from manure management, enteric fermentation, direct soil emissions (with applied manure, synthetic fertilizer, crop residue degradation, biological fixation) and indirect soil emissions (with atmospheric deposition and leaching). Methane emissions from enteric fermentation (609.24 Gg) prevailed over those from manure (44.99 Gg), and nitrous oxide emission from manure (22.01 Gg) prevailed over those from soil (17.73 Gg), with cattle cows emitting most N2O and CH4, followed by plant N2O emissions, and pigs and dairy cows CH4 emissions. The GIS maps showed discernible pattern of N2O and CH4 emissions increasing from North and West to the central Alberta and then slightly declining to South and East, which could be useful to address various mitigation strategies. The framework allows easy replacement of Tier 1 emission factors by Tire 2 or 3 ones from process-based models. Future applying of the latter will allow accounting for CO2 source/sink strength of agricultural ecosystems, hence their complete GHG balance affected by soil, water, and climate.

Determining Source Strength of Semivolatile Organic Compounds using Measured Concentrations in Indoor Dust

PubMed Central

Shin, Hyeong-Moo; McKone, Thomas E.; Nishioka, Marcia G.; Fallin, M. Daniele; Croen, Lisa A.; Hertz-Picciotto, Irva; Newschaffer, Craig J.; Bennett, Deborah H.

2014-01-01

Consumer products and building materials emit a number of semivolatile organic compounds (SVOCs) in the indoor environment. Because indoor SVOCs accumulate in dust, we explore the use of dust to determine source strength and report here on analysis of dust samples collected in 30 U.S. homes for six phthalates, four personal care product ingredients, and five flame retardants. We then use a fugacity-based indoor mass-balance model to estimate the whole house emission rates of SVOCs that would account for the measured dust concentrations. Di-2-ethylhexyl phthalate (DEHP) and di-iso-nonyl phthalate (DiNP) were the most abundant compounds in these dust samples. On the other hand, the estimated emission rate of diethyl phthalate (DEP) is the largest among phthalates, although its dust concentration is over two orders of magnitude smaller than DEHP and DiNP. The magnitude of the estimated emission rate that corresponds to the measured dust concentration is found to be inversely correlated with the vapor pressure of the compound, indicating that dust concentrations alone cannot be used to determine which compounds have the greatest emission rates. The combined dust-assay modeling approach shows promise for estimating indoor emission rates for SVOCs. PMID:24118221

General circulation model response to production-limited fossil fuel emission estimates.

NASA Astrophysics Data System (ADS)

Bowman, K. W.; Rutledge, D.; Miller, C.

2008-12-01

The differences in emissions scenarios used to drive IPCC climate projections are the largest sources of uncertainty in future temperature predictions. These estimates are critically dependent on oil, gas, and coal production where the extremal variations in fossil fuel production used in these scenarios is roughly 10:1 after 2100. The development of emission scenarios based on production-limited fossil fuel estimates, i.e., total fossil fuel reserves can be reliably predicted from cumulative production, offers the opportunity to significantly reduce this uncertainty. We present preliminary results of the response of the NASA GISS atmospheric general circulation model to input forcings constrained by production-limited cumulative future fossil-fuel CO2 emissions estimates that reach roughly 500 GtC by 2100, which is significantly lower than any of the IPCC emission scenarios. For climate projections performed from 1958 through 2400 and a climate sensitivity of 5C/2xCO2, the change in globally averaged annual mean temperature relative to fixed CO2 does not exceed 3C with most changes occurring at high latitudes. We find that from 2100-2400 other input forcings such as increased in N2O play an important role in maintaining increase surface temperatures.

Uncertainties of fluxes and 13C / 12C ratios of atmospheric reactive-gas emissions

NASA Astrophysics Data System (ADS)

Gromov, Sergey; Brenninkmeijer, Carl A. M.; Jöckel, Patrick

2017-07-01

We provide a comprehensive review of the proxy data on the 13C / 12C ratios and uncertainties of emissions of reactive carbonaceous compounds into the atmosphere, with a focus on CO sources. Based on an evaluated set-up of the EMAC model, we derive the isotope-resolved data set of its emission inventory for the 1997-2005 period. Additionally, we revisit the calculus required for the correct derivation of uncertainties associated with isotope ratios of emission fluxes. The resulting δ13C of overall surface CO emission in 2000 of -(25. 2 ± 0. 7) ‰ is in line with previous bottom-up estimates and is less uncertain by a factor of 2. In contrast to this, we find that uncertainties of the respective inverse modelling estimates may be substantially larger due to the correlated nature of their derivation. We reckon the δ13C values of surface emissions of higher hydrocarbons to be within -24 to -27 ‰ (uncertainty typically below ±1 ‰), with an exception of isoprene and methanol emissions being close to -30 and -60 ‰, respectively. The isotope signature of ethane surface emission coincides with earlier estimates, but integrates very different source inputs. δ13C values are reported relative to V-PDB.

Estimating the Additional Greenhouse Gas Emissions in Korea: Focused on Demolition of Asbestos Containing Materials in Building

PubMed Central

Kim, Young-Chan; Hong, Won-Hwa; Zhang, Yuan-Long; Son, Byeung-Hun; Seo, Youn-Kyu; Choi, Jun-Ho

2016-01-01

When asbestos containing materials (ACM) must be removed from the building before demolition, additional greenhouse gas (GHG) emissions are generated. However, precedent studies have not considered the removal of ACM from the building. The present study aimed to develop a model for estimating GHG emissions created by the ACM removal processes, specifically the removal of asbestos cement slates (ACS). The second objective was to use the new model to predict the total GHG emission produced by ACM removal in the entire country of Korea. First, an input-equipment inventory was established for each step of the ACS removal process. Second, an energy consumption database for each equipment type was established. Third, the total GHG emission contributed by each step of the process was calculated. The GHG emissions generated from the 1,142,688 ACS-containing buildings in Korea was estimated to total 23,778 tonCO2eq to 132,141 tonCO2eq. This study was meaningful in that the emissions generated by ACS removal have not been studied before. Furthermore, the study deals with additional problems that can be triggered by the presence of asbestos in building materials. The method provided in this study is expected to contribute greatly to the calculation of GHG emissions caused by ACM worldwide. PMID:27626433

Regional carbon fluxes from land use and land cover change in Asia, 1980–2009

DOE Office of Scientific and Technical Information (OSTI.GOV)

Calle, Leonardo; Canadell, Josep G.; Patra, Prabir

We present a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia using multiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food and Agriculture Organization-Forest Resource Assessment (FAO-FRA 2015; country-level inventory estimates), the Emission Database for Global Atmospheric Research (EDGARv4.3), the 'Houghton' bookkeeping model that incorporates FAO-FRA data, an ensemble of 8 state-of-the-art Dynamic Global Vegetation Models (DGVM), and 2 recently published independent studies using primarily remote sensing techniques. The estimates are aggregated spatially to Southeast, East, and Southmore » Asia and temporally for three decades, 1980–1989, 1990–1999 and 2000–2009. Since 1980, net carbon emissions from LULCC in Asia were responsible for 20%–40% of global LULCC emissions, with emissions from Southeast Asia alone accounting for 15%–25% of global LULCC emissions during the same period. In the 2000s and for all Asia, three estimates (FAO-FRA, DGVM, Houghton) were in agreement of a net source of carbon to the atmosphere, with mean estimates ranging between 0.24 to 0.41 Pg C yr -1, whereas EDGARv4.3 suggested a net carbon sink of -0.17 Pg C yr -1. Three of 4 estimates suggest that LULCC carbon emissions declined by at least 34% in the preceding decade (1990–2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to include emissions from carbon rich peatlands and land management, such as shifting cultivation and wood harvesting, which appear to be consistently underreported.« less

Regional carbon fluxes from land use and land cover change in Asia, 1980–2009

DOE PAGES

Calle, Leonardo; Canadell, Josep G.; Patra, Prabir; ...

2016-07-08

We present a synthesis of the land-atmosphere carbon flux from land use and land cover change (LULCC) in Asia using multiple data sources and paying particular attention to deforestation and forest regrowth fluxes. The data sources are quasi-independent and include the U.N. Food and Agriculture Organization-Forest Resource Assessment (FAO-FRA 2015; country-level inventory estimates), the Emission Database for Global Atmospheric Research (EDGARv4.3), the 'Houghton' bookkeeping model that incorporates FAO-FRA data, an ensemble of 8 state-of-the-art Dynamic Global Vegetation Models (DGVM), and 2 recently published independent studies using primarily remote sensing techniques. The estimates are aggregated spatially to Southeast, East, and Southmore » Asia and temporally for three decades, 1980–1989, 1990–1999 and 2000–2009. Since 1980, net carbon emissions from LULCC in Asia were responsible for 20%–40% of global LULCC emissions, with emissions from Southeast Asia alone accounting for 15%–25% of global LULCC emissions during the same period. In the 2000s and for all Asia, three estimates (FAO-FRA, DGVM, Houghton) were in agreement of a net source of carbon to the atmosphere, with mean estimates ranging between 0.24 to 0.41 Pg C yr -1, whereas EDGARv4.3 suggested a net carbon sink of -0.17 Pg C yr -1. Three of 4 estimates suggest that LULCC carbon emissions declined by at least 34% in the preceding decade (1990–2000). Spread in the estimates is due to the inclusion of different flux components and their treatments, showing the importance to include emissions from carbon rich peatlands and land management, such as shifting cultivation and wood harvesting, which appear to be consistently underreported.« less

Development of a high temporal-spatial resolution vehicle emission inventory based on NRT traffic data and its impact on air pollution in Beijing - Part 1: Development and evaluation of vehicle emission inventory

NASA Astrophysics Data System (ADS)

Jing, B. Y.; Wu, L.; Mao, H. J.; Gong, S. L.; He, J. J.; Zou, C.; Song, G. H.; Li, X. Y.; Wu, Z.

2015-10-01

As the ownership of vehicles and frequency of utilization increase, vehicle emissions have become an important source of air pollution in Chinese cities. An accurate emission inventory for on-road vehicles is necessary for numerical air quality simulation and the assessment of implementation strategies. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near real time (NRT) traffic data on road segments to develop a high temporal-spatial resolution vehicle emission inventory (HTSVE) for the urban Beijing area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NOx, CO, HC and PM were 10.54 × 104, 42.51 × 104 and 2.13 × 104 and 0.41 × 104 Mg, respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Additionally, the on-road vehicle emission inventory model and control effect assessment system in Beijing, a vehicle emission inventory model, was established based on this study in a companion paper (He et al., 2015).

Indirect estimation of emission factors for phosphate surface mining using air dispersion modeling.

PubMed

Tartakovsky, Dmitry; Stern, Eli; Broday, David M

2016-06-15

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance. Copyright © 2016 Elsevier B.V. All rights reserved.

Emission-dominated gas exchange of elemental mercury vapor over natural surfaces in China

NASA Astrophysics Data System (ADS)

Wang, Xun; Lin, Che-Jen; Yuan, Wei; Sommar, Jonas; Zhu, Wei; Feng, Xinbin

2016-09-01

Mercury (Hg) emission from natural surfaces plays an important role in global Hg cycling. The present estimate of global natural emission has large uncertainty and remains unverified against field data, particularly for terrestrial surfaces. In this study, a mechanistic model is developed for estimating the emission of elemental mercury vapor (Hg0) from natural surfaces in China. The development implements recent advancements in the understanding of air-soil and air-foliage exchange of Hg0 and redox chemistry in soil and on surfaces, incorporates the effects of soil characteristics and land use changes by agricultural activities, and is examined through a systematic set of sensitivity simulations. Using the model, the net exchange of Hg0 between the atmosphere and natural surfaces of mainland China is estimated to be 465.1 Mg yr-1, including 565.5 Mg yr-1 from soil surfaces, 9.0 Mg yr-1 from water bodies, and -100.4 Mg yr-1 from vegetation. The air-surface exchange is strongly dependent on the land use and meteorology, with 9 % of net emission from forest ecosystems; 50 % from shrubland, savanna, and grassland; 33 % from cropland; and 8 % from other land uses. Given the large agricultural land area in China, farming activities play an important role on the air-surface exchange over farmland. Particularly, rice field shift from a net sink (3.3 Mg uptake) during April-October (rice planting) to a net source when the farmland is not flooded (November-March). Summing up the emission from each land use, more than half of the total emission occurs in summer (51 %), followed by spring (28 %), autumn (13 %), and winter (8 %). Model verification is accomplished using observational data of air-soil/air-water fluxes and Hg deposition through litterfall for forest ecosystems in China and Monte Carlo simulations. In contrast to the earlier estimate by Shetty et al. (2008) that reported large emission from vegetative surfaces using an evapotranspiration approach, the estimate in this study shows natural emissions are primarily from grassland and dry cropland. Such an emission pattern may alter the current understanding of Hg emission outflow from China as reported by Lin et al. (2010b) because a substantial natural Hg emission occurs in West China.

Challenges in modelling isoprene and monoterpene emission dynamics of Arctic plants: a case study from a subarctic tundra heath

NASA Astrophysics Data System (ADS)

Tang, Jing; Schurgers, Guy; Valolahti, Hanna; Faubert, Patrick; Tiiva, Päivi; Michelsen, Anders; Rinnan, Riikka

2016-12-01

The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOCs) emissions from Arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged increasingly by field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to Arctic climate warming. Field observations in a subarctic tundra heath with long-term (13-year) warming treatments were extensively used for parameterizing and evaluating BVOC-related processes (photosynthesis, emission responses to temperature and vegetation composition). We propose an adjusted temperature (T) response curve for Arctic plants with much stronger T sensitivity than the commonly used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves were evaluated against the observed warming responses (WRs) at short-term scales. Moreover, the model responses to warming by 4 and 8 °C were also investigated as a sensitivity test. The model showed reasonable agreement to the observed vegetation CO2 fluxes in the main growing season as well as day-to-day variability of isoprene and monoterpene emissions. The observed relatively high WRs were better captured by the adjusted T response curve than by the common one. During 1999-2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m-2 yr-1, with an increase by 55 and 57 % for 2 °C summertime warming, respectively. Warming by 4 and 8 °C for the same period further elevated isoprene emission for all years, but the impacts on monoterpene emissions levelled off during the last few years. At hour-day scale, the WRs seem to be strongly impacted by canopy air T, while at the day-year scale, the WRs are a combined effect of plant functional type (PFT) dynamics and instantaneous BVOC responses to warming. The identified challenges in estimating Arctic BVOC emissions are (1) correct leaf T estimation, (2) PFT parameterization accounting for plant emission features as well as physiological responses to warming, and (3) representation of long-term vegetation changes in the past and the future.

Municipal solid waste characterization and its assessment for potential methane generation: a case study.

PubMed

Mor, Suman; Ravindra, Khaiwal; De Visscher, Alex; Dahiya, R P; Chandra, A

2006-12-01

There has been a significant increase in municipal solid waste (MSW) generation in India during the last few decades and its management has become a major issue because the poor waste management practices affect the health and amenity of the cities. In the present study, various physico-chemical parameters of the MSW were analyzed to characterize the waste dumped at Gazipur landfill site in Delhi, India, which shows that it contains a high fraction of degradable organic components. The decomposition of organic components produces methane, a significant contributor to global warming. Based on the waste composition, waste age and the total amount dumped, a first-order decay model (FOD) was applied to estimate the methane generation potential of the Gazipur landfill site, which yields an estimate of 15.3 Gg/year. This value accounts to about 1-3% of existing Indian landfill methane emission estimates. Based on the investigation of Gazipur landfill, we estimate Indian landfill methane emissions at 1.25 Tg/year or 1.68 Tg/year of methane generation potential. These values are within the range of existing estimates. A comparison of FOD with a recently proposed triangular model was also performed and it shows that both models can be used for the estimation of methane generation. However, the decrease of the emission after closure is more gradual in the case of the first-order model, leading to larger gas production predictions after more than 10 years of closure. The regional and global implications of national landfill methane emission are also discussed.

Evaluating Bay Area Methane Emission Inventory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fischer, Marc; Jeong, Seongeun

As a regulatory agency, evaluating and improving estimates of methane (CH4) emissions from the San Francisco Bay Area is an area of interest to the Bay Area Air Quality Management District (BAAQMD). Currently, regional, state, and federal agencies generally estimate methane emissions using bottom-up inventory methods that rely on a combination of activity data, emission factors, biogeochemical models and other information. Recent atmospheric top-down measurement estimates of methane emissions for the US as a whole (e.g., Miller et al., 2013) and in California (e.g., Jeong et al., 2013; Peischl et al., 2013) have shown inventories underestimate total methane emissions bymore » ~ 50% in many areas of California, including the SF Bay Area (Fairley and Fischer, 2015). The goal of this research is to provide information to help improve methane emission estimates for the San Francisco Bay Area. The research effort builds upon our previous work that produced methane emission maps for each of the major source sectors as part of the California Greenhouse Gas Emissions Measurement (CALGEM) project (http://calgem.lbl.gov/prior_emission.html; Jeong et al., 2012; Jeong et al., 2013; Jeong et al., 2014). Working with BAAQMD, we evaluate the existing inventory in light of recently published literature and revise the CALGEM CH4 emission maps to provide better specificity for BAAQMD. We also suggest further research that will improve emission estimates. To accomplish the goals, we reviewed the current BAAQMD inventory, and compared its method with those from the state inventory from the California Air Resources Board (CARB), the CALGEM inventory, and recent published literature. We also updated activity data (e.g., livestock statistics) to reflect recent changes and to better represent spatial information. Then, we produced spatially explicit CH4 emission estimates on the 1-km modeling grid used by BAAQMD. We present the detailed activity data, methods and derived emission maps by sector. In total, we estimate the anthropogenic emissions for BAAQMD to be 116.4 Gg (1 Gg = 109 g) CH4/yr, with a likely uncertainty of ~ 50% or more (e.g., NRC, 2010; US-EPA, 2015). Including the emissions from wetland (Jeong et al., 2013), the total CH4 emission estimate for BAAQMD is 120.1 Gg CH4/yr. Table 1 summarizes the estimated CH4 emissions for 2011 by sector. The sectors were categorized following those that are used in recent regional emission quantification studies (e.g., Jeong et al., 2013; Peischl et al., 2013; Wecht et al., 2014). However, we note that this result is marginally lower than the top-down estimate of 240 ± 60 Gg CH4/yr (at 95% confidence) reported by Fairley and Fischer (2015), suggesting some combination of systematic error in the top-down estimate, underestimation of emissions from known sources, or as yet unidentified sources may be present. With respect to the relative contributions from different source sectors, the CH4 emissions from the region are dominated by urban activities. Landfill emissions represent 53% of the District’s total emission followed by livestock (16%) and natural gas (15%). These three dominant sectors account for 84% of the total anthropogenic emission in BAAQMD. This suggests that mitigation efforts need to focus on these three sources. Figure 1 shows the gridded anthropogenic CH4 emissions on the BAAQMD’s 1-km grid. In general, the spatial pattern of emissions follows the density of population while strong point sources are also distributed in the rural areas of the District. Detailed methods and emissions for each sector and county are described in the following sections.« less

Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

NASA Astrophysics Data System (ADS)

Dekker, Iris N.; Houweling, Sander; Aben, Ilse; Röckmann, Thomas; Krol, Maarten; Martínez-Alonso, Sara; Deeter, Merritt N.; Worden, Helen M.

2017-12-01

The growth of mega-cities leads to air quality problems directly affecting the citizens. Satellite measurements are becoming of higher quality and quantity, which leads to more accurate satellite retrievals of enhanced air pollutant concentrations over large cities. In this paper, we compare and discuss both an existing and a new method for estimating urban-scale trends in CO emissions using multi-year retrievals from the MOPITT satellite instrument. The first method is mainly based on satellite data, and has the advantage of fewer assumptions, but also comes with uncertainties and limitations as shown in this paper. To improve the reliability of urban-to-regional scale emission trend estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRF-Chem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. This method has the advantage over the existing method in that it allows both a trend analysis of CO concentrations and a quantification of CO emissions. Our analysis confirms that MOPITT is capable of detecting CO enhancements over Madrid, although significant differences remain between the yearly averaged model output and satellite measurements (R2 = 0.75) over the city. After optimization, we find Madrid CO emissions to be lower by 48 % for 2002 and by 17 % for 2006 compared with the EdgarV4.2 emission inventory. The MOPITT-derived emission adjustments lead to better agreement with the European emission inventory TNO-MAC-III for both years. This suggests that the downward trend in CO emissions over Madrid is overestimated in EdgarV4.2 and more realistically represented in TNO-MACC-III. However, our satellite and model based emission estimates have large uncertainties, around 20 % for 2002 and 50 % for 2006.

A first European scale multimedia fate modelling of BDE-209 from 1970 to 2020.

PubMed

Earnshaw, Mark R; Jones, Kevin C; Sweetman, Andy J

2015-01-01

The European Variant Berkeley Trent (EVn-BETR) multimedia fugacity model is used to test the validity of previously derived emission estimates and predict environmental concentrations of the main decabromodiphenyl ether congener, BDE-209. The results are presented here and compared with measured environmental data from the literature. Future multimedia concentration trends are predicted using three emission scenarios (Low, Realistic and High) in the dynamic unsteady state mode covering the period 1970-2020. The spatial and temporal distributions of emissions are evaluated. It is predicted that BDE-209 atmospheric concentrations peaked in 2004 and will decline to negligible levels by 2025. Freshwater concentrations should have peaked in 2011, one year after the emissions peak with sediment concentrations peaking in 2013. Predicted atmospheric concentrations are in good agreement with measured data for the Realistic (best estimate of emissions) and High (worst case scenario) emission scenarios. The Low emission scenario consistently underestimates measured data. The German unilateral ban on the use of DecaBDE in the textile industry is simulated in an additional scenario, the effects of which are mainly observed within Germany with only a small effect on the surrounding areas. Overall, the EVn-BTER model predicts atmospheric concentrations reasonably well, within a factor of 5 and 1.2 for the Realistic and High emission scenarios respectively, providing partial validation for the original emission estimate. Total mean MEC:PEC shows the High emission scenario predicts the best fit between air, freshwater and sediment data. An alternative spatial distribution of emissions is tested, based on higher consumption in EBFRIP member states, resulting in improved agreement between MECs and PECs in comparison with the Uniform spatial distribution based on population density. Despite good agreement between modelled and measured point data, more long-term monitoring datasets are needed to compare predicted trends in concentration to determine the rate of change of POPs within the environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards a Novel Integrated Approach for Estimating Greenhouse Gas Emissions in Support of International Agreements

NASA Astrophysics Data System (ADS)

Reimann, S.; Vollmer, M. K.; Henne, S.; Brunner, D.; Emmenegger, L.; Manning, A.; Fraser, P. J.; Krummel, P. B.; Dunse, B. L.; DeCola, P.; Tarasova, O. A.

2016-12-01

In the recently adopted Paris Agreement the community of signatory states has agreed to limit the future global temperature increase between +1.5 °C and +2.0 °C, compared to pre-industrial times. To achieve this goal, emission reduction targets have been submitted by individual nations (called Intended Nationally Determined Contributions, INDCs). Inventories will be used for checking progress towards these envisaged goals. These inventories are calculated by combining information on specific activities (e.g. passenger cars, agriculture) with activity-related, typically IPCC-sanctioned, emission factors - the so-called bottom-up method. These calculated emissions are reported on an annual basis and are checked by external bodies by using the same method. A second independent method estimates emissions by translating greenhouse gas measurements made at regionally representative stations into regional/global emissions using meteorologically-based transport models. In recent years this so-called top-down approach has been substantially advanced into a powerful tool and emission estimates at the national/regional level have become possible. This method is already used in Switzerland, in the United Kingdom and in Australia to estimate greenhouse gas emissions and independently support the national bottom-up emission inventories within the UNFCCC framework. Examples of the comparison of the two independent methods will be presented and the added-value will be discussed. The World Meteorological Organization (WMO) and partner organizations are currently developing a plan to expand this top-down approach and to expand the globally representative GAW network of ground-based stations and remote-sensing platforms and integrate their information with atmospheric transport models. This Integrated Global Greenhouse Gas Information System (IG3IS) initiative will help nations to improve the accuracy of their country-based emissions inventories and their ability to evaluate the success of emission reductions strategies. This could foster trans-national collaboration on methodologies for estimation of emissions. Furthermore, more accurate emission knowledge will clarify the value of emission reduction efforts and could encourage countries to strengthen their reduction pledges.

Determination of Summertime VOC Emission Rates from Produced Water Ponds in the Uintah Basin

NASA Astrophysics Data System (ADS)

Martin, R. S.; Woods, C.; Lyman, S.

2013-12-01

The observance of excess ozone concentrations in Utah's Uintah Basin over past several years has prompted several investigations into the extent and causes of the elevated ozone. Among these is the assessment of potential emissions of reactive VOCs. Evaporation ponds, used a remediation technique for treatment of contaminated production and other waters, are one potential source of significant VOC emissions and is estimated that there are around 160 such ponds within the Uintah Basin's oil and gas production areas. In June 2012 VOC emission rates for several reactive VOCs were derived for an evaporation facility consisting of a small inlet pond (≈0.03 acres) and two larger, serial ponds (≈4.3 acres each). The emission rates were determined over three sampling periods using an inverse modeling approach. Under this methodology, ambient VOC concentrations are determined at several downwind locations through whole-air collection into SUMMA canisters, followed by GC/MS quantification and compared with predicted concentrations using an EPA-approved dispersion model, AERMOD. The presumed emission rates used within the model were then adjusted until the modeled concentrations approach the observed concentrations. The derived emission rates for the individual VOCs were on the order of 10-3 g/s/m2 from the inlet pond and 10-6 g/s/m2 from the larger ponds. The emissions from the 1st pond in series after the inlet pond were about 3-4x the emissions from the 2nd pond. These combined emission rates are about an order of magnitude those reported for a single study in Colorado (Thoma, 2009). It should be noted, however, that the variability about each of the VOC emission rates was significant (often ×100% at the 95% confidence interval). Extrapolating these emission rates to the estimated total areas of all the evaporation ponds within Basin resulted in calculated Basin-wide VOC emissions 292,835 tons/yr. However, Bar-Ilan et al. (2009) estimated 2012 VOC oil and gas related emissions within the Uintah Basin to be 119,974 tons/yr. Given the large observed variabilities and the uncertainties with extrapolating the derived emission rates across varying pond types and differing climatic conditions, the comparisons are not unreasonable. If the lower, literature emission rates of Thoma (2009) are used the estimated Basin-wide evaporation emissions, the pond emissions would still be approximately 30% of the total emissions compiled by Bar-Ilan et al. (2009). Although the study described herein only represents a single facility and a single set of seasonal conditions, extrapolating these rates can give potential insight into the significance of VOC emissions into the Basin atmosphere from evaporation ponds.

Process-based Modeling of Ammonia Emission from Beef Cattle Feedyards with the Integrated Farm Systems Model.

PubMed

Waldrip, Heidi M; Rotz, C Alan; Hafner, Sasha D; Todd, Richard W; Cole, N Andy

2014-07-01

Ammonia (NH) volatilization from manure in beef cattle feedyards results in loss of agronomically important nitrogen (N) and potentially leads to overfertilization and acidification of aquatic and terrestrial ecosystems. In addition, NH is involved in the formation of atmospheric fine particulate matter (PM), which can affect human health. Process-based models have been developed to estimate NH emissions from various livestock production systems; however, little work has been conducted to assess their accuracy for large, open-lot beef cattle feedyards. This work describes the extension of an existing process-based model, the Integrated Farm Systems Model (IFSM), to include simulation of N dynamics in this type of system. To evaluate the model, IFSM-simulated daily per capita NH emission rates were compared with emissions data collected from two commercial feedyards in the Texas High Plains from 2007 to 2009. Model predictions were in good agreement with observations and were sensitive to variations in air temperature and dietary crude protein concentration. Predicted mean daily NH emission rates for the two feedyards had 71 to 81% agreement with observations. In addition, IFSM estimates of annual feedyard emissions were within 11 to 24% of observations, whereas a constant emission factor currently in use by the USEPA underestimated feedyard emissions by as much as 79%. The results from this study indicate that IFSM can quantify average feedyard NH emissions, assist with emissions reporting, provide accurate information for legislators and policymakers, investigate methods to mitigate NH losses, and evaluate the effects of specific management practices on farm nutrient balances. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Estimating PM2.5-associated mortality increase in California due to the Volkswagen emission control defeat device

NASA Astrophysics Data System (ADS)

Wang, Tianyang; Jerrett, Michael; Sinsheimer, Peter; Zhu, Yifang

2016-11-01

The Volkswagen Group of America (VW) was found by the US Environmental Protection Agency (EPA) and the California Air Resources Board (CARB) to have installed "defeat devices" and emit more oxides of nitrogen (NOx) than permitted under current EPA standards. In this paper, we quantify the hidden NOx emissions from this so-called VW scandal and the resulting public health impacts in California. The NOx emissions are calculated based on VW road test data and the CARB Emission Factors (EMFAC) model. Cumulative hidden NOx emissions from 2009 to 2015 were estimated to be over 3500 tons. Adult mortality changes were estimated based on ambient fine particulate matter (PM2.5) change due to secondary nitrate formation and the related concentration-response functions. We estimated that hidden NOx emissions from 2009 to 2015 have resulted in a total of 12 PM2.5-associated adult mortality increases in California. Most of the mortality increase happened in metropolitan areas, due to their high population and vehicle density.

Comparison of Field Measurements to Methane Emissions Models at a New Landfill

EPA Science Inventory

Due to both technical and economic limitations, estimates of methane emissions from landfills rely primarily on models. While models are easy to implement, there is uncertainty due to the use of parameters that are difficult to validate. The objective of this research was to comp...

ESTIMATION OF EMISSION ADJUSTMENTS FROM THE APPLICATION OF FOUR-DIMENSIONAL DATA ASSIMILATION TO PHOTOCHEMICAL AIR QUALITY MODELING. (R826372)

EPA Science Inventory

Four-dimensional data assimilation applied to photochemical air quality modeling is used to suggest adjustments to the emissions inventory of the Atlanta, Georgia metropolitan area. In this approach, a three-dimensional air quality model, coupled with direct sensitivity analys...

Methane emission to the atmosphere from landfills in the Canary Islands

NASA Astrophysics Data System (ADS)

Hernández, Pedro A.; Asensio-Ramos, María; Rodríguez, Fátima; Alonso, Mar; García-Merino, Marta; Amonte, Cecilia; Melián, Gladys V.; Barrancos, José; Rodríguez-Delgado, Miguel A.; Hernández-Abad, Marta; Pérez, Erica; Alonso, Monica; Tassi, Franco; Raco, Brunella; Pérez, Nemesio M.

2017-04-01

Methane (CH4) is one of the most powerful greenhouse gases, and is increasing in the atmosphere by 0.6% each year (Intergovernmental Panel on Climate Change, IPCC, 2013). This gas is produced in landfills in large quantities following the anaerobic degradation of organic matter. The IPCC has estimated that more than 10% of the total anthropogenic emissions of CH4 are originated in landfills. Even after years of being no operative (closed), a significant amount of landfill gas could be released to the atmosphere through its surface as diffuse or fugitive degassing. Many landfills currently report their CH4 emissions to the atmosphere using model-based methods, which are based on the rate of production of CH4, the oxidation rate of CH4 and the amount of CH4 recovered (Bingemer and Crutzen, 1987). This approach often involves large uncertainties due to inaccuracies of input data and many assumptions in the estimation. In fact, the estimated CH4 emissions from landfills in the Canary Islands published by the Spanish National Emission and Pollutant Sources Registration (PRTR-Spain) seem to be overestimated due to the use of protocols and analytical methodologies based on mathematical models. For this reason, direct measurements to estimate CH4 emissions in landfills are essential to reduce this uncertainty. In order to estimate the CH4 emissions to the atmosphere from landfills in the Canary Islands 23 surveys have been performed since 1999. Each survey implies hundreds of CO2and CH4 efflux measurements covering the landfill surface area. Surface landfill CO2 efflux measurements were carried out at each sampling site by means of a portable non-dispersive infrared spectrophotometer (NDIR) model LICOR Li800 following the accumulation chamber method. Samples of landfill gases were taken in the gas accumulated in the chamber and CO2 and CH4 were analyzed using a double channel VARIAN 4900 micro-GC. The CH4 efflux measurent was computed combining CO2 efflux and CH4/CO2 ratio. To quantify the the diffuse or fugitive CO2 and CH4 emission, gas efflux contour maps were constructed using sequential Gaussian simulation (sGs) as interpolation method. Considering that (a) there are 5 controlled landfills in the Canary Islands, (b) the average area of the 23 studied cells is 0.17 km2 and (c) the mean value of the CH4 emission estimated for the studied cells range between 6.9 and 8.1 kt km-2 y-1, the estimated CH4 emission to the atmosphere from landfills in the Canary Islands showed a range of 7.0 - 7.8 kt y-1. On the contrary and for the same period of time, the PRTR-Spain estimates CH4 emission in the order of 10.3 - 14.9 kt y-1, nearly two times our estimated value. This result demonstrates the need to perform direct measurements to estimate the surface fugitive emission of CH4 from landfills. Bingemer, H. G., and P. J. Crutzen (1987). The production of methane from solid wastes, J. Geophys. Res. 92, 2182-2187

Methods for Analysis of Urban Energy Systems: A New York City Case Study

NASA Astrophysics Data System (ADS)

Howard, Bianca

This dissertation describes methods developed for analysis of the New York City energy system. The analysis specifically aims to consider the built environment and its' impacts on greenhouse gas (GHG) emissions. Several contributions to the urban energy systems literature were made. First, estimates of annual energy intensities of the New York building stock were derived using a statistical analysis that leveraged energy consumption and tax assessor data collected by the Office of the Mayor. These estimates provided the basis for an assessment of the spatial distribution of building energy consumption. The energy consumption estimates were then leveraged to estimate the potential for combined heat and power (CHP) systems in New York City at both the building and microgrid scales. In aggregate, given the 2009 non-baseload GHG emissions factors for electricity production, these systems could reduce citywide GHG emissions by 10%. The operational characteristics of CHP systems were explored further considering different prime movers, climates, and GHG emissions factors. A combination of mixed integer linear programing and controlled random search algorithms were the methods used to determine the optimal capacity and operating strategies for the CHP systems under the various scenarios. Lastly a multi-regional unit commitment model of electricity and GHG emissions production for New York State was developed using data collected from several publicly available sources. The model was used to estimate average and marginal GHG emissions factors for New York State and New York City. The analysis found that marginal GHG emissions factors could reduce by 30% to 370 g CO2e/kWh in the next 10 years.

2014 National Emissions Inventory (NEI) Plan

EPA Pesticide Factsheets

The NEI is prepared at least every three years by the U.S. EPA based primarily upon emissions estimates and emissions model inputs provided by State, Local and Tribal (SLT) air agencies, and supplemented by data developed by the EPA.

Transient simulations of historical climate change including interactive carbon emissions from land-use change.

NASA Astrophysics Data System (ADS)

Matveev, A.; Matthews, H. D.

2009-04-01

Carbon fluxes from land conversion are among the most uncertain variables in our understanding of the contemporary carbon cycle, which limits our ability to estimate both the total human contribution to current climate forcing and the net effect of terrestrial biosphere changes on atmospheric CO2 increases. The current generation of coupled climate-carbon models have made significant progress in simulating the coupled climate and carbon cycle response to anthropogenic CO2 emissions, but do not typically include land-use change as a dynamic component of the simulation. In this work we have incorporated a book-keeping land-use carbon accounting model into the University of Victoria Earth System Climate Model (UVic ESCM), and intermediate-complexity coupled climate-carbon model. The terrestrial component of the UVic ESCM allows an aerial competition of five plant functional types (PFTs) in response to climatic conditions and area availability, and tracks the associated changes in affected carbon pools. In order to model CO2 emissions from land conversion in the terrestrial component of the model, we calculate the allocation of carbon to short and long-lived wood products following specified land-cover change, and use varying decay timescales to estimate CO2 emissions. We use recently available spatial datasets of both crop and pasture distributions to drive a series of transient simulations and estimate the net contribution of human land-use change to historical carbon emissions and climate change.

The global methane budget 2000-2012

NASA Astrophysics Data System (ADS)

Saunois, Marielle; Bousquet, Philippe; Poulter, Ben; Peregon, Anna; Ciais, Philippe; Canadell, Josep G.; Dlugokencky, Edward J.; Etiope, Giuseppe; Bastviken, David; Houweling, Sander; Janssens-Maenhout, Greet; Tubiello, Francesco N.; Castaldi, Simona; Jackson, Robert B.; Alexe, Mihai; Arora, Vivek K.; Beerling, David J.; Bergamaschi, Peter; Blake, Donald R.; Brailsford, Gordon; Brovkin, Victor; Bruhwiler, Lori; Crevoisier, Cyril; Crill, Patrick; Covey, Kristofer; Curry, Charles; Frankenberg, Christian; Gedney, Nicola; Höglund-Isaksson, Lena; Ishizawa, Misa; Ito, Akihiko; Joos, Fortunat; Kim, Heon-Sook; Kleinen, Thomas; Krummel, Paul; Lamarque, Jean-François; Langenfelds, Ray; Locatelli, Robin; Machida, Toshinobu; Maksyutov, Shamil; McDonald, Kyle C.; Marshall, Julia; Melton, Joe R.; Morino, Isamu; Naik, Vaishali; O'Doherty, Simon; Parmentier, Frans-Jan W.; Patra, Prabir K.; Peng, Changhui; Peng, Shushi; Peters, Glen P.; Pison, Isabelle; Prigent, Catherine; Prinn, Ronald; Ramonet, Michel; Riley, William J.; Saito, Makoto; Santini, Monia; Schroeder, Ronny; Simpson, Isobel J.; Spahni, Renato; Steele, Paul; Takizawa, Atsushi; Thornton, Brett F.; Tian, Hanqin; Tohjima, Yasunori; Viovy, Nicolas; Voulgarakis, Apostolos; van Weele, Michiel; van der Werf, Guido R.; Weiss, Ray; Wiedinmyer, Christine; Wilton, David J.; Wiltshire, Andy; Worthy, Doug; Wunch, Debra; Xu, Xiyan; Yoshida, Yukio; Zhang, Bowen; Zhang, Zhen; Zhu, Qiuan

2016-12-01

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (˜ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 Tg CH4 yr-1, range 540-568. About 60 % of global emissions are anthropogenic (range 50-65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 Tg CH4 yr-1, range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (˜ 64 % of the global budget, < 30° N) as compared to mid (˜ 32 %, 30-60° N) and high northern latitudes (˜ 4 %, 60-90° N). Top-down inversions consistently infer lower emissions in China (˜ 58 Tg CH4 yr-1, range 51-72, -14 %) and higher emissions in Africa (86 Tg CH4 yr-1, range 73-108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

The Global Methane Budget 2000-2012

NASA Technical Reports Server (NTRS)

Saunois, Marielle; Bousquet, Philippe; Poulter, Benjamin; Peregon, Anna; Ciais, Philippe; Canadell, Josep G.; Dlugokencky, Edward J.; Etiope, Giuseppe; Bastviken, David; Houweling, Sander;

Estimation of GHG Emissions from Water Reclamation Plants in Beijing.

PubMed

Fan, Yupeng; Bai, Yanying; Jiao, Wentao

  A procedure for estimating Greenhouse gas (GHG) emissions from a wastewater reclamation plant in Beijing was developed based on the process chain model. GHG emissions under two typical water reclamation treatment processes, the coagulation-sedimentation-filtration traditional process and advanced biological treatment process, were examined. The total on-site GHG emissions were estimated to be 0.0056 kg/m 3 and 0.6765 kg/m 3 respectively, while total off-site GHG emissions were estimated to be 0.3699 kg/m 3 and 0.4816 kg/m 3 . The overall GHG emissions were 0.3755 kg/m 3 under the type 1 treatment, which is much lower than that under the type 2 of 1.1581 kg/m 3 . Emissions from both processes were lower than that from the tap water production. Wastewater reclamation and reuse should be promoted as it not only saves the water resources but also can reduce the GHG emissions. Energy consumption was the most significant source of GHG emissions. Biogas recovery should be employed as it can significantly reduce the GHG emissions, especially under the type 2 treatment process. Considering the wastewater treatment and reclamation process as a whole, the type 2 treatment process has advantages in reducing the GHG emissions per unit of pollutant. This paper provides scientific basis for decision making.

Switchgrass Biofuel Research: Carbon Sequestration and Life Cycle Analysis (a.k.a. Second Generation Biofuels: Carbon Sequestration and Life Cycle Analysis)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liska, Adam J.; Suyker, Andrew E.; Arkebauer, Timothy J.

2013-12-20

Soil emissions have been inadequately characterized in life cycle assessment of biofuels (see section 3.2.3). This project measures the net differences in field-level greenhouse gas emissions (CO 2, N 2O, and CH 4) due to corn residue removal for cellulosic ethanol production. Gas measurements are then incorporated into life cycle assessment of the final biofuel product to determine whether it is in compliance with federal greenhouse gas emissions standards for biofuels (Renewable Fuel Standard 2, RFS2). The field measurements have been conducted over three years on two, quarter-section, production-scale, irrigated corn fields (both roughly 50 hectares, as this size ofmore » field is necessary for reproducible eddy covariance flux measurements of CO 2; chamber measurements are used to determine N 2O and CH 4 emissions). Due to a large hail storm in 2010, estimates of the emission from residue could not be separated from the total CO 2 flux in 2011. This led us to develop soil organic carbon (SOC) modeling techniques to estimate changes in CO 2 emissions from residue removal. Modeling has predicted emissions of CO 2 from oxidation of SOC that are consistent (<12%) with 9 years of CO 2 flux measurements at the two production field sites, and modeling is also consistent with other field measurements (Liska et al., submitted). The model was then used to estimate the average change in SOC and CO 2 emissions from nine years of simulated residue removal (6 Mg biomass per hectare per year) at the sites; a loss of 0.43 Mg C ha -1 yr -1 resulted. The model was then used to estimate SOC changes over 10 years across Nebraska using supercomputing, based on 61 million, 30 x 30 meter, grid cells to account for regional variability in initial SOC, crop yield, and temperature; an average loss of 0.47 Mg C ha -1 yr -1 resulted. When these CO 2 emissions are included in simple life cycle assessment calculations, emissions from cellulosic ethanol from crop residue are above mandated levels of 60% reduction compared to gasoline (Liska, in press). These approaches are both technically effective and economically feasible. This work has been extensively peer reviewed.« less

GOSAT observations of anthropogenic emission of carbon dioxide and methane

NASA Astrophysics Data System (ADS)

Janardanan, Rajesh; Maksyutov, Shamil; Oda, Tomohiro; Saito, Makoto; Ito, Akihiko; Kaiser, Johannes W.; Ganshin, Alexander; Yoshida, Yukio; Yokota, Tatsuya; Matsunaga, Tsuneo

2017-04-01

Carbon dioxide (CO2) and methane (CH4) are the most important greenhouse gases in terms of radiative forcing. Human activities such as combustion of fossil fuel (for CO2), and gas leakage, animal agriculture, rice cultivation and landfill emissions (for CH4), are considered to be major sources of their emissions. Global emissions datasets usually depend on emission estimates reported by countries, which are seldom evaluated in an objective way. Here we present a method for delineating anthropogenic contributions to global atmospheric CO2 and CH4 (2009-2014) concentration fields using GOSAT observations of column-average dry air mole fractions (XCO2 and XCH4) and atmospheric transport model simulations using high-resolution emissions datasets (ODIAC for CO2 and EDGAR for CH4). The XCO2 and XCH4 concentration enhancements due to anthropogenic emissions are estimated at all GOSAT observation locations using the transport model simulation. We calculated threshold values to classify GOSAT observations into two categories: (1) data influenced by the anthropogenic sources and (2) those not influenced. We defined a clean background (averaged concentrations of GOSAT data that are free from contamination) in 10˚ ×10˚ regions over the globe and subtracted the background values from individual GOSAT observations. The anomalies (GOSAT observed values minus background values) were binned and compared to model-based anomalies over continental regions and selected countries. For CO2, we have found global and regional linear relationships between model and observed anomalies especially for Eurasia and North America. The analysis for East Asian region showed a systematic bias that is somewhat comparable in magnitude to the uncertainties in emission inventories in that region, which were reported by recent studies. In the case of CH4, we found a good match between inventory-based estimates and GOSAT observations for continental regions and large countries. The inventory-based estimate over North American region is biased which is in agreement with recent studies. Currently, our method is limited by the numbers of GOSAT observations available. If sufficient numbers of satellite observations are available from a instrument like GOSAT, our method could be a useful tool for monitoring greenhouse gas emissions using the regression slope between modeled and observed anomalies as a correction factor for nationally reported emissions.

Modeling of pesticide emissions from agricultural ecosystems

NASA Astrophysics Data System (ADS)

Li, Rong

2012-04-01

Pesticides are applied to crops and soils to improve agricultural yields, but the use of pesticides has become highly regulated because of concerns about their adverse effects on human health and environment. Estimating pesticide emission rates from soils and crops is a key component for risk assessment for pesticide registration, identification of pesticide sources to the contamination of sensitive ecosystems, and appreciation of transport and fate of pesticides in the environment. Pesticide emission rates involve processes occurring in the soil, in the atmosphere, and on vegetation surfaces and are highly dependent on soil texture, agricultural practices, and meteorology, which vary significantly with location and/or time. To take all these factors into account for simulating pesticide emissions from large agricultural ecosystems, this study coupled a comprehensive meteorological model with a dynamic pesticide emission model. The combined model calculates hourly emission rates from both emission sources: current applications and soil residues resulting from historical use. The coupled modeling system is used to compute a gridded (36 × 36 km) hourly toxaphene emission inventory for North America for the year 2000 using a published U.S. toxaphene residue inventory and a Mexican toxaphene residue inventory developed using its historical application rates and a cropland inventory. To my knowledge, this is the first such hourly toxaphene emission inventory for North America. Results show that modeled emission rates have strong diurnal and seasonal variations at a given location and over the entire domain. The simulated total toxaphene emission from contaminated agricultural soils in North America in 2000 was about 255 t, which compares reasonably well to a published annual estimate. Most emissions occur in spring and summer, with domain-wide emission rates in April, May and, June of 36, 51, and 35 t/month, respectively. The spatial distribution of emissions depends on the distribution of toxaphene soil residues, and high emission rates coincide with heavily contaminated areas.

Nitrous oxide emissions from intensively managed agroecosystems: The role of carbon inputs

USDA-ARS?s Scientific Manuscript database

In agroecosystems, many reports demonstrate a positive relationship between N2O emissions and N fertilizer inputs. This relationship has been incorporated into IPCC model estimates of N2O emissions and implies that inorganic N limits N2O emissions. However, evidence indicates that denitrification ac...

Biogenic Emission Inventories: Scaling Local Biogenic Measurements to Regions

NASA Astrophysics Data System (ADS)

Lamb, B.; Pressley, S.; Westberg, H.; Guenther, A.

2002-12-01

Biogenic Hydrocarbons, such as isoprene, are important trace gas species that are naturally emitted by vegetation and that affect the oxidative capacity of the atmosphere. Biogenic emissions are regulated by many environmental variables; the most important variables are thought to be temperature and light. Long-term isoprene flux measurements are useful for verifying existing canopy models and exploring other correlations between isoprene fluxes and environmental parameters. Biogenic Emission Models, such as BEIS (Biogenic Emission Inventory System) rely on above canopy environmental parameters and below canopy scaling factors to estimate canopy scale biogenic hydrocarbon fluxes. Other models, which are more complex, are coupled micrometeorological and physiological modules that provide feedback mechanisms present in a canopy environment. These types of models can predict biogenic emissions well, however, the required input is extensive, and for regional applications, they can be cumbersome. This paper presents analyses based on long-term isoprene flux measurements that have been collected since 1999 at the AmeriFlux site located at the University of Michigan Biological Station (UMBS) as part of the Program for Research on Oxidants: PHotochemistry, Emissions, and Transport (PROPHET). The goals of this research were to explore a potential relationship between the surface energy budget (primarily sensible heat flux) and isoprene emissions. Our hypothesis is that the surface energy flux is a better model parameter for isoprene emissions at the canopy scale than temperature and light levels, and the link to the surface energy budget will provide a significant improvement in isoprene emission models. Preliminary results indicate a significant correlation between daily isoprene emissions and sensible heat fluxes for a predominantly aspen/oak stand located in northern Michigan. Since surface energy budgets are an integral part of mesoscale meteorological models, this could potentially be a useful tool for including biogenic emissions into regional atmospheric models. Comparison of measured isoprene fluxes with current BEIS estimates will also be shown as an example of where emission inventories currently stand.

Implications of Uncertainty in Fossil Fuel Emissions for Terrestrial Ecosystem Modeling

NASA Astrophysics Data System (ADS)

King, A. W.; Ricciuto, D. M.; Mao, J.; Andres, R. J.

2017-12-01

Given observations of the increase in atmospheric CO2, estimates of anthropogenic emissions and models of oceanic CO2 uptake, one can estimate net global CO2 exchange between the atmosphere and terrestrial ecosystems as the residual of the balanced global carbon budget. Estimates from the Global Carbon Project 2016 show that terrestrial ecosystems are a growing sink for atmospheric CO2 (averaging 2.12 Gt C y-1 for the period 1959-2015 with a growth rate of 0.03 Gt C y-1 per year) but with considerable year-to-year variability (standard deviation of 1.07 Gt C y-1). Within the uncertainty of the observations, emissions estimates and ocean modeling, this residual calculation is a robust estimate of a global terrestrial sink for CO2. A task of terrestrial ecosystem science is to explain the trend and variability in this estimate. However, "within the uncertainty" is an important caveat. The uncertainty (2σ; 95% confidence interval) in fossil fuel emissions is 8.4% (±0.8 Gt C in 2015). Combined with uncertainty in other carbon budget components, the 2σ uncertainty surrounding the global net terrestrial ecosystem CO2 exchange is ±1.6 Gt C y-1. Ignoring the uncertainty, the estimate of a general terrestrial sink includes 2 years (1987 and 1998) in which terrestrial ecosystems are a small source of CO2 to the atmosphere. However, with 2σ uncertainty, terrestrial ecosystems may have been a source in as many as 18 years. We examine how well global terrestrial biosphere models simulate the trend and interannual variability of the global-budget estimate of the terrestrial sink within the context of this uncertainty (e.g., which models fall outside the 2σ uncertainty and in what years). Models are generally capable of reproducing the trend in net terrestrial exchange, but are less able to capture interannual variability and often fall outside the 2σ uncertainty. The trend in the residual carbon budget estimate is primarily associated with the increase in atmospheric CO2, while interannual variation is related to variations in global land-surface temperature with weaker sinks in warmer years. We examine whether these relationships are reproduced in models. Their absence might explain weaknesses in model simulations or in the reconstruction of historical climate used as drivers in model intercomparison projects (MIPs).

Predicting emissions from oil and gas operations in the Uinta Basin, Utah.

PubMed

Wilkey, Jonathan; Kelly, Kerry; Jaramillo, Isabel Cristina; Spinti, Jennifer; Ring, Terry; Hogue, Michael; Pasqualini, Donatella

2016-05-01

In this study, emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin are predicted (with uncertainty estimates) from 2015-2019 using a Monte-Carlo model of (a) drilling and production activity, and (b) emission factors. Cross-validation tests against actual drilling and production data from 2010-2014 show that the model can accurately predict both types of activities, returning median results that are within 5% of actual values for drilling, 0.1% for oil production, and 4% for gas production. A variety of one-time (drilling) and ongoing (oil and gas production) emission factors for greenhouse gases, methane, and volatile organic compounds (VOCs) are applied to the predicted oil and gas operations. Based on the range of emission factor values reported in the literature, emissions from well completions are the most significant source of emissions, followed by gas transmission and production. We estimate that the annual average VOC emissions rate for the oil and gas industry over the 2010-2015 time period was 44.2E+06 (mean) ± 12.8E+06 (standard deviation) kg VOCs per year (with all applicable emissions reductions). On the same basis, over the 2015-2019 period annual average VOC emissions from oil and gas operations are expected to drop 45% to 24.2E+06 ± 3.43E+06 kg VOCs per year, due to decreases in drilling activity and tighter emission standards. This study improves upon previous methods for estimating emissions of ozone precursors from oil and gas operations in Utah's Uinta Basin by tracking one-time and ongoing emission events on a well-by-well basis. The proposed method has proven highly accurate at predicting drilling and production activity and includes uncertainty estimates to describe the range of potential emissions inventory outcomes. If similar input data are available in other oil and gas producing regions, then the method developed here could be applied to those regions as well.

Analysis of Biogenic VOCs Emissions During the MAPS-Seoul Aircraft Field Campaign

NASA Astrophysics Data System (ADS)

Lee, Y.; Woo, J. H.; Kim, Y.; Bu, C.; Kim, J.; Kim, H. K.; Lee, M. H.; Eo, Y.

2016-12-01

The MAPS-Seoul (Megacity Air Pollution Studies-Seoul) aircraft mission was conducted in May - June 2016 to understand atmospheric environment over the South Korea. BVOCs emissions forecasting, along with other components, were conducted daily in support of the aircraft mission planning. The biogenic emissions as well as anthropogenic ones were very important factor to model and analyze atmospheric environment since more than 80% of global VOCs emission comes from biogenic sources. This also could be true for South Korea, since more than 70% of its land area are vegetated such as forest, cropland. For modeling-based BVOC emission estimation, geographical distribution of PFT (plant functional type) and LAI (Leaf Area Index) are considered as very important driving variables. Most of cases, PFTs and LAI were derived from the low-resolution satellite-based information which are not quite ideal for relatively small area like South Korea. In this study, we developed the more reliable Korean PFT and LAI cover derived from Korean landcover maps and modeled satellite images. The WRF-MEGAN modeling framework over South Korea for the period of May to June 2016 was used to estimate re-analysis BVOCs emission field. Analysis of different PFT and LAI inputs affected local and national biogenic emission estimations will be presented at site. Acknowledgements : This subject is supported by Korea Ministry of Environment as "Climate Change Correspondence Program". This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea.

Quantifying methane emissions from coal and natural gas sources along the northwestern Appalachian

NASA Astrophysics Data System (ADS)

Barkley, Z.; Lauvaux, T.; Davis, K. J.; Fried, A.

2017-12-01

According to the EPA's 2012 gridded inventory (Maasakkers et al., 2016), more than 10% of all CH4 emissions in the U.S. are located along the western edge of the Appalachian with the majority of these emissions coming from natural gas infrastructure and coal mines. However, top-down studies of unconventional wells in southwestern Pennsylvania have found emission rates to be much higher than EPA estimates (Caulton et al., 2014, Ren et al., 2017). Furthermore, although 9 of the 10 largest sources of CH4 in the EPA Greenhouse Gas Reporting Program are coal mines located in this region, no top down studies have been performed to assess the accuracy of these enormous point sources. This study uses aircraft data from the ACT-America flight campaign in conjunction with techniques previously used to solve for CH4 emissions from the northeastern Marcellus (Barkley et al., 2017) to quantify the total CH4 flux from the western Pennsylvania/West Virginia region and constrain emissions from natural gas and coal with an upper limit for each source. We use the WRF-Chem mesoscale model at 3 km resolution to simulate CH4 enhancements from a customized emissions inventory and compare the modelled enhancements to observations from 7 flights that were downwind of coal and gas sources. Coal and natural gas emissions are adjusted in the model to minimize a cost function that accounts for the difference between the modelled and observed CH4 values, and a range of likely combinations for natural gas and coal emission rates are obtained for each flight. We then overlap this range of likely emission rates across all flights to further limit the range of possible emission rates. Influence functions created using a lagrangian particle dispersion model for segments of each flight provide information on what area emissions are being optimized for. Preliminary results find that CH4 emissions from gas and coal along the northwestern Appalachian are lower than EPA estimates by 20-50%. In particular, upper limits on CH4 emissions from unconventional natural gas are less than 1% of total production, significantly lower than previous top-down estimates in the region. Future work will use ethane data to better distinguish between coal and natural gas emissions, and expand these analyses to other study regions explored in the ACT-America aircraft campaign.

Modeling the impact of chlorine emissions from coal combustion and prescribed waste incineration on tropospheric ozone formation in China

NASA Astrophysics Data System (ADS)

Liu, Yiming; Fan, Qi; Chen, Xiaoyang; Zhao, Jun; Ling, Zhenhao; Hong, Yingying; Li, Weibiao; Chen, Xunlai; Wang, Mingjie; Wei, Xiaolin

2018-02-01

Chlorine radicals can enhance atmospheric oxidation, which potentially increases tropospheric ozone concentration. However, few studies have been done to quantify the impact of chlorine emissions on ozone formation in China due to the lack of a chlorine emission inventory used in air quality models with sufficient resolution. In this study, the Anthropogenic Chlorine Emissions Inventory for China (ACEIC) was developed for the first time, including emissions of hydrogen chloride (HCl) and molecular chlorine (Cl2) from coal combustion and prescribed waste incineration (waste incineration plant). The HCl and Cl2 emissions from coal combustion in China in 2012 were estimated to be 232.9 and 9.4 Gg, respectively, while HCl emission from prescribed waste incineration was estimated to be 2.9 Gg. Spatially the highest emissions of HCl and Cl2 were found in the North China Plain, the Yangtze River Delta, and the Sichuan Basin. Air quality model simulations with the Community Multiscale Air Quality (CMAQ) modeling system were performed for November 2011, and the modeling results derived with and without chlorine emissions were compared. The magnitude of the simulated HCl, Cl2 and ClNO2 agreed reasonably with the observation when anthropogenic chlorine emissions were included in the model. The inclusion of the ACEIC increased the concentration of fine particulate Cl-, leading to enhanced heterogeneous reactions between Cl- and N2O5, which resulted in the higher production of ClNO2. Photolysis of ClNO2 and Cl2 in the morning and the reaction of HCl with OH in the afternoon produced chlorine radicals which accelerated tropospheric oxidation. When anthropogenic chlorine emissions were included in the model, the monthly mean concentrations of fine particulate Cl-, daily maximum 1 h ClNO2, and Cl radicals were estimated to increase by up to about 2.0 µg m-3, 773 pptv, and 1.5 × 103 molecule cm-3 in China, respectively. Meanwhile, the monthly mean daily maximum 8 h O3 concentration was found to increase by up to 2.0 ppbv (4.1 %), while the monthly mean NOx concentration decreased by up to 0.5 ppbv (6.1 %). The anthropogenic chlorine emissions potentially increased the 1 h O3 concentration by up to 7.7 ppbv in China. This study highlights the need for the inclusion of anthropogenic chlorine emission in air quality modeling and demonstrated its importance in tropospheric ozone formation.

REDD+ emissions estimation and reporting: dealing with uncertainty

NASA Astrophysics Data System (ADS)

Pelletier, Johanne; Martin, Davy; Potvin, Catherine

2013-09-01

The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries’ emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki et al (2012 Environ. Res. Lett. 7 024005) to perform sensitivity and uncertainty analysis distinguishing between ‘modelling sources’ of uncertainty, which refers to model-specific parameters and assumptions, and ‘recurring sources’ of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology used to evaluate reference level and emission reductions would strengthen the credibility of the system by promoting accountability and transparency. To secure conservativeness and deal with uncertainty, we consider the need for further research using real data available to developing countries to test the applicability of conservative discounts including the trend uncertainty and other possible options that would allow real incentives and stimulate improvements over time. Finally, we argue that REDD+ result-based actions assessed on the basis of a dashboard of performance indicators, not only in ‘tonnes CO2 equ. per year’ might provide a more holistic approach, at least until better accuracy and certainty of forest carbon stocks emission and removal estimates to support a REDD+ policy can be reached.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Meng; Zhang, Qiang; Kurokawa, Jun-ichi

Here, the MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the totalmore » Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO 2, 52.1 Tg NO x, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH 3, 31.7 Tg PM 10, 22.7 Tg PM 2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO 2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: –8.1 % for SO 2, +19.2 % for NO x, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH 3, –3.4 % for PM 10, –1.6 % for PM 2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO 2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach.« less

Reconciling Basin-Scale Top-Down and Bottom-Up Methane Emission Measurements for Onshore Oil and Gas Development: Cooperative Research and Development Final Report, CRADA Number CRD-14-572

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heath, Garvin A.

The overall objective of the Research Partnership to Secure Energy for America (RPSEA)-funded research project is to develop independent estimates of methane emissions using top-down and bottom-up measurement approaches and then to compare the estimates, including consideration of uncertainty. Such approaches will be applied at two scales: basin and facility. At facility scale, multiple methods will be used to measure methane emissions of the whole facility (controlled dual tracer and single tracer releases, aircraft-based mass balance and Gaussian back-trajectory), which are considered top-down approaches. The bottom-up approach will sum emissions from identified point sources measured using appropriate source-level measurement techniquesmore » (e.g., high-flow meters). At basin scale, the top-down estimate will come from boundary layer airborne measurements upwind and downwind of the basin, using a regional mass balance model plus approaches to separate atmospheric methane emissions attributed to the oil and gas sector. The bottom-up estimate will result from statistical modeling (also known as scaling up) of measurements made at selected facilities, with gaps filled through measurements and other estimates based on other studies. The relative comparison of the bottom-up and top-down estimates made at both scales will help improve understanding of the accuracy of the tested measurement and modeling approaches. The subject of this CRADA is NREL's contribution to the overall project. This project resulted from winning a competitive solicitation no. RPSEA RFP2012UN001, proposal no. 12122-95, which is the basis for the overall project. This Joint Work Statement (JWS) details the contributions of NREL and Colorado School of Mines (CSM) in performance of the CRADA effort.« less

Estimating Uncertainty in N2O Emissions from US Cropland Soils

USDA-ARS?s Scientific Manuscript database

A Monte Carlo analysis was combined with an empirically-based approach to quantify uncertainties in soil N2O emissions from US croplands estimated with the DAYCENT simulation model. Only a subset of croplands was simulated in the Monte Carlo analysis which was used to infer uncertainties across the ...

TRACKING THE EMISSION OF CARBON DIOXIDE BY NATION, SECTOR, AND FUEL TYPE: A TRACE GAS ACCOUNTING SYSTEM (TGAS)

EPA Science Inventory

The paper describes a new way to estimate an efficient econometric model of global emissions of carbon dioxide (CO2) by nation, sector, and fuel type. Equations for fuel intensity are estimated for coal, oil, natural gas, electricity, and heat for six sectors: agricultural, indus...

VALIDATION OF A METHOD FOR ESTIMATING POLLUTION EMISSION RATES FROM AREA SOURCES USING OPEN-PATH FTIR SEPCTROSCOPY AND DISPERSION MODELING TECHNIQUES

EPA Science Inventory

The paper describes a methodology developed to estimate emissions factors for a variety of different area sources in a rapid, accurate, and cost effective manner. he methodology involves using an open-path Fourier transform infrared (FTIR) spectrometer to measure concentrations o...

Canopy Level Emissions of 2-methyl-3-buten-2-ol, monoterpenes, and sesquiterpenes from a Pinus taeda Experimental Plantation

EPA Science Inventory

Emissions of biogenic volatile organic compounds (BVOC) observed during 2007 from a Pinus taeda experimental plantation in Central North Carolina are compared with model estimates from MEGAN 2.1. Relaxed Eddy Accumulation (REA) estimates of 2-methyl-3-buten-2-ol (MBO) fluxes are ...

Estimating methane emissions from dairies in the Los Angeles Basin

NASA Astrophysics Data System (ADS)

Viatte, C.; Lauvaux, T.; Hedelius, J.; Parker, H. A.; Chen, J.; Jones, T.; Franklin, J.; Deng, A.; Gaudet, B.; Duren, R. M.; Verhulst, K. R.; Wunch, D.; Roehl, C. M.; Dubey, M. K.; Wofsy, S.; Wennberg, P. O.

2015-12-01

Inventory estimates of methane (CH4) emissions among the individual sources (mainly agriculture, energy production, and waste management) remain highly uncertain at regional and urban scales. Accurate atmospheric measurements can provide independent estimates to evaluate bottom-up inventories, especially in urban region, where many different CH4 sources are often confined in relatively small areas. Among these sources, livestock emissions, which are mainly originating from dairy cows, account for ~55% of the total CH4 emissions in California in 2013. This study aims to rigorously estimate the amount of CH4 emitted by the largest dairies in the Southern California region by combining measurements from four mobile ground-based spectrometers (EM27/SUN), in situ isotopic methane measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport model (the Weather Research and Forecasting model) in Large-Eddy Simulation mode. The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information about total emissions of the dairies. Gradients measured by the four EM27 ranged from 0.2 to 22 ppb and from 0.7 to 3 ppm for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, measurements of these gradients are used in conjunction with the local atmospheric dynamics simulated at 111 m resolution. Inverse modelling from WRF-LES is employed to resolve the spatial distribution of CH4 emissions in the domain. A Bayesian inversion and a Monte-Carlo approach were used to provide the CH4 emissions over the dairy with their associated uncertainties. The isotopic δ13C sampled at different locations in the area ranges from -40 ‰ to -55 ‰, indicating a mixture of anthropogenic and biogenic sources.

A two-step combination of top-down and bottom-up fire emission estimates at regional and global scales: strengths and main uncertainties

NASA Astrophysics Data System (ADS)

Sofiev, Mikhail; Soares, Joana; Kouznetsov, Rostislav; Vira, Julius; Prank, Marje

2016-04-01

Top-down emission estimation via inverse dispersion modelling is used for various problems, where bottom-up approaches are difficult or highly uncertain. One of such areas is the estimation of emission from wild-land fires. In combination with dispersion modelling, satellite and/or in-situ observations can, in principle, be used to efficiently constrain the emission values. This is the main strength of the approach: the a-priori values of the emission factors (based on laboratory studies) are refined for real-life situations using the inverse-modelling technique. However, the approach also has major uncertainties, which are illustrated here with a few examples of the Integrated System for wild-land Fires (IS4FIRES). IS4FIRES generates the smoke emission and injection profile from MODIS and SEVIRI active-fire radiative energy observations. The emission calculation includes two steps: (i) initial top-down calibration of emission factors via inverse dispersion problem solution that is made once using training dataset from the past, (ii) application of the obtained emission coefficients to individual-fire radiative energy observations, thus leading to bottom-up emission compilation. For such a procedure, the major classes of uncertainties include: (i) imperfect information on fires, (ii) simplifications in the fire description, (iii) inaccuracies in the smoke observations and modelling, (iv) inaccuracies of the inverse problem solution. Using examples of the fire seasons 2010 in Russia, 2012 in Eurasia, 2007 in Australia, etc, it is pointed out that the top-down system calibration performed for a limited number of comparatively moderate cases (often the best-observed ones) may lead to errors in application to extreme events. For instance, the total emission of 2010 Russian fires is likely to be over-estimated by up to 50% if the calibration is based on the season 2006 and fire description is simplified. Longer calibration period and more sophisticated parameterization (including the smoke injection model and distinguishing all relevant vegetation types) can improve the predictions. The other significant parameter, so far weakly addressed in fire emission inventories, is the size spectrum of the emitted aerosols. Direct size-resolving measurements showed, for instance, that smoke from smouldering fires has smaller particles as compares with smoke from flaming fires. Due to dependence of the smoke optical thickness on the size distribution, such variability can lead to significant changes in the top-down calibration step. Experiments with IS4FIRES-SILAM system manifested up to a factor of two difference in AOD, depending on the assumption on particle spectrum.

Impact of forest fires on particulate matter and ozone levels during the 2003, 2004 and 2005 fire seasons in Portugal.

PubMed

Martins, V; Miranda, A I; Carvalho, A; Schaap, M; Borrego, C; Sá, E

2012-01-01

The main purpose of this work is to estimate the impact of forest fires on air pollution applying the LOTOS-EUROS air quality modeling system in Portugal for three consecutive years, 2003-2005. Forest fire emissions have been included in the modeling system through the development of a numerical module, which takes into account the most suitable parameters for Portuguese forest fire characteristics and the burnt area by large forest fires. To better evaluate the influence of forest fires on air quality the LOTOS-EUROS system has been applied with and without forest fire emissions. Hourly concentration results have been compared to measure data at several monitoring locations with better modeling quality parameters when forest fire emissions were considered. Moreover, hourly estimates, with and without fire emissions, can reach differences in the order of 20%, showing the importance and the influence of this type of emissions on air quality. Copyright © 2011 Elsevier B.V. All rights reserved.

Confirmation of Elevated Methane Emissions in Utah's Uintah Basin With Ground-Based Observations and a High-Resolution Transport Model

NASA Astrophysics Data System (ADS)

Foster, C. S.; Crosman, E. T.; Holland, L.; Mallia, D. V.; Fasoli, B.; Bares, R.; Horel, J.; Lin, J. C.

2017-12-01

Large CH4 leak rates have been observed in the Uintah Basin of eastern Utah, an area with over 10,000 active and producing natural gas and oil wells. In this paper, we model CH4 concentrations at four sites in the Uintah Basin and compare the simulated results to in situ observations at these sites during two spring time periods in 2015 and 2016. These sites include a baseline location (Fruitland), two sites near oil wells (Roosevelt and Castlepeak), and a site near natural gas wells (Horsepool). To interpret these measurements and relate observed CH4 variations to emissions, we carried out atmospheric simulations using the Stochastic Time-Inverted Lagrangian Transport model driven by meteorological fields simulated by the Weather Research and Forecasting and High Resolution Rapid Refresh models. These simulations were combined with two different emission inventories: (1) aircraft-derived basin-wide emissions allocated spatially using oil and gas well locations, from the National Oceanic and Atmospheric Administration (NOAA), and (2) a bottom-up inventory for the entire U.S., from the Environmental Protection Agency (EPA). At both Horsepool and Castlepeak, the diurnal cycle of modeled CH4 concentrations was captured using NOAA emission estimates but was underestimated using the EPA inventory. These findings corroborate emission estimates from the NOAA inventory, based on daytime mass balance estimates, and provide additional support for a suggested leak rate from the Uintah Basin that is higher than most other regions with natural gas and oil development.

GHGfrack: An Open-Source Model for Estimating Greenhouse Gas Emissions from Combustion of Fuel during Drilling and Hydraulic Fracturing.

PubMed

Vafi, Kourosh; Brandt, Adam

2016-07-19

This paper introduces GHGfrack, an open-source engineering-based model that estimates energy consumption and associated GHG emissions from drilling and hydraulic fracturing operations. We describe verification and calibration of GHGfrack against field data for energy and fuel consumption. We run GHGfrack using data from 6927 wells in Eagle Ford and 4431 wells in Bakken oil fields. The average estimated energy consumption in Eagle Ford wells using lateral hole diameters of 8 (3)/4 and 6 (1)/8 in. are 2.25 and 2.73 TJ/well, respectively. The average estimated energy consumption in Bakken wells using hole diameters of 6 in. for horizontal section is 2.16 TJ/well. We estimate average greenhouse gas (GHG) emissions of 419 and 510 tonne of equivalent CO2 per well (tonne of CO2 eq/well) for the two aforementioned assumed geometries in Eagle Ford, respectively, and 417 tonne of CO2 eq/well for the case of Bakken. These estimates are limited only to GHG emissions from combustion of diesel fuel to supply energy only for rotation of drill string, drilling mud circulation, and fracturing pumps. Sensitivity analysis of the model shows that the top three key variables in driving energy intensity in drilling are the lateral hole diameter, drill pipe internal diameter, and mud flow rate. In hydraulic fracturing, the top three are lateral casing diameter, fracturing fluid volume, and length of the lateral.

OBSERVABLE INDICATORS OF THE SENSITIVITY OF PM2.5 NITRATE TO EMISSION REDUCTIONS PART I: DERIVATION OF THE ADJUSTED GAS RATIO AND APPLICABILITY AT REGULATORY-RELEVANT TIME SCALES

EPA Science Inventory

Chemical transport models have frequently been used to evaluate the impacts of emission reductions on inorganic PM2.5. However, such models are limited in their accuracy by uncertain estimates of the spatial and temporal characterization of emissions and meteorology. Site-speci...

Estimating Sources and Sinks of Methane from Soils in the Contiguous United States (CONUS)

NASA Astrophysics Data System (ADS)

Shu, S.; Jain, A. K.; Kheshgi, H. S.

2017-12-01

The global methane (CH4) budget estimated based on state-of-the-art models remains highly uncertain. Sources and sinks of CH4 from soils, including wetlands, are the most important source of uncertainty. Soils are estimated to account for about 45% of global CH4 emissions. At the same time oxidation of CH4 by soils is a significant sink, representing about 10% of the total sink. However, most regional and global scale modeling studies of soil CH4 fluxes have ignored the sink through soil oxidation and the source of CH4 emissions from the wet soils with shallow water tables. In this study, we link a bottom-up soil gas diffusion and CH4 biogeochemistry model to a land surface model, ISAM, to calculate the sources, emissions from both wetlands and non-wetlands, and sinks, soil oxidation, of CH4 from soils for the CONUS over the period 1900-2100. The newly developed soil CH4 model framework consists of a gas diffusion module with the vertically resolved soil hydrology (depth up to 3.5 m soil) and soil organic carbon (SOC) and CH4 biogeochemistry module. SOC profile is estimated by modeling vertical soil mixing and thus can represent the deep SOC content and estimate CH4 production from the deep non-wetland soil. For the diffusion calculations, we separately consider both the dissolved and gaseous O2 and CH4 at each soil layer. For CH4 biogeochemistry, we parameterize the production, soil oxidation, ebullition and aerenchyma transportation of CH4 for both seasonal/permanent wetland and wet soil. The SWAMP inundated fraction dataset with 8-day temporal resolution is incorporated to prescribe the extent of permanent and seasonal wetland extent for the recent decade. The model is first evaluated using a compilation of published CH4 site measurement data for CONUS. We then perform two different model experiments: 1) forced by the CRUNCEP climate data from 1900 to 2010 to estimate the contemporary CH4 emission and 2) forced by a climate projection of IPCC's highest representative concentration pathway (RCP8.5) from 2011 to 2100. Our study shows that soil oxidation has an important role attenuating the estimated natural CH4 source. We also find a wetter and warmer climate affects the dry soil CH4 sink and wet soil CH4 emissions and increases the estimated CH4 source over the CONUS.

Emissions implications of downscaled electricity generation scenarios for the western United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nsanzineza, Rene; O’Connell, Matthew; Brinkman, Gregory

This study explores how emissions from electricity generation in the Western Interconnection region of the U.S. might respond in circa 2030 to contrasting scenarios for fuel prices and greenhouse gas (GHG) emissions fees. We examine spatial and temporal variations in generation mix across the region and year using the PLEXOS unit commitment and dispatch model with a production cost model database adapted from the Western Electricity Coordinating Council. Emissions estimates are computed by combining the dispatch model results with unit-specific, emissions-load relationships. Wind energy displaces natural gas and coal in scenarios with relatively expensive natural gas or with GHG fees.more » Correspondingly, annual emissions of NOx, SO2, and CO2 are reduced by 20-40% in these cases. NOx emissions, which are a concern as a precursor of ground-level ozone, are relatively high and consistent across scenarios during summer, when peak electricity loads occur and wind resources in the region are comparatively weak. Accounting for the difference in start-up versus stabilized NOx emissions rates for natural gas plants had little impact on region-wide emissions estimates due to the dominant contribution from coal-fired plants, but would be more important in the vicinity of the natural gas units.« less

Emission Projections for Long-Haul Freight Trucks and Rail in the United States through 2050.

PubMed

Liu, Liang; Hwang, Taesung; Lee, Sungwon; Ouyang, Yanfeng; Lee, Bumsoo; Smith, Steven J; Yan, Fang; Daenzer, Kathryn; Bond, Tami C

2015-10-06

This work develops an integrated model approach for estimating emissions from long-haul freight truck and rail transport in the United States between 2010 and 2050. We connect models of macroeconomic activity, freight demand by commodity, transportation networks, and emission technology to represent different pathways of future freight emissions. Emissions of particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and total hydrocarbon (THC) decrease by 60%-70% from 2010 to 2030, as older vehicles built to less-stringent emission standards retire. Climate policy, in the form of carbon tax that increases apparent fuel prices, causes a shift from truck to rail, resulting in a 30% reduction in fuel consumption and a 10%-28% reduction in pollutant emissions by 2050, if rail capacity is sufficient. Eliminating high-emitting conditions in the truck fleet affects air pollutants by 20% to 65%; although these estimates are highly uncertain, they indicate the importance of durability in vehicle engines and emission control systems. Future infrastructure investment will be required both to meet transport demand and to enable actions that reduce emissions of air and climate pollutants. By driving the integrated model framework with two macroeconomic scenarios, we show that the effect of carbon tax on air pollution is robust regardless of growth levels.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality

PubMed Central

Hondula, David M.; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-01-01

Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634 PMID:28885979

Modelling the spatial distribution of ammonia emissions in the UK.

PubMed

Hellsten, S; Dragosits, U; Place, C J; Vieno, M; Dore, A J; Misselbrook, T H; Tang, Y S; Sutton, M A

2008-08-01

Ammonia emissions (NH3) are characterised by a high spatial variability at a local scale. When modelling the spatial distribution of NH3 emissions, it is important to provide robust emission estimates, since the model output is used to assess potential environmental impacts, e.g. exceedance of critical loads. The aim of this study was to provide a new, updated spatial NH3 emission inventory for the UK for the year 2000, based on an improved modelling approach and the use of updated input datasets. The AENEID model distributes NH3 emissions from a range of agricultural activities, such as grazing and housing of livestock, storage and spreading of manures, and fertilizer application, at a 1-km grid resolution over the most suitable landcover types. The results of the emission calculation for the year 2000 are analysed and the methodology is compared with a previous spatial emission inventory for 1996.

A Pilot Study to Evaluate California's Fossil Fuel CO2 Emissions Using Atmospheric Observations

NASA Astrophysics Data System (ADS)

Graven, H. D.; Fischer, M. L.; Lueker, T.; Guilderson, T.; Brophy, K. J.; Keeling, R. F.; Arnold, T.; Bambha, R.; Callahan, W.; Campbell, J. E.; Cui, X.; Frankenberg, C.; Hsu, Y.; Iraci, L. T.; Jeong, S.; Kim, J.; LaFranchi, B. W.; Lehman, S.; Manning, A.; Michelsen, H. A.; Miller, J. B.; Newman, S.; Paplawsky, B.; Parazoo, N.; Sloop, C.; Walker, S.; Whelan, M.; Wunch, D.

2016-12-01

Atmospheric CO2 concentration is influenced by human activities and by natural exchanges. Studies of CO2 fluxes using atmospheric CO2 measurements typically focus on natural exchanges and assume that CO2 emissions by fossil fuel combustion and cement production are well-known from inventory estimates. However, atmospheric observation-based or "top-down" studies could potentially provide independent methods for evaluating fossil fuel CO2 emissions, in support of policies to reduce greenhouse gas emissions and mitigate climate change. Observation-based estimates of fossil fuel-derived CO2 may also improve estimates of biospheric CO2 exchange, which could help to characterize carbon storage and climate change mitigation by terrestrial ecosystems. We have been developing a top-down framework for estimating fossil fuel CO2 emissions in California that uses atmospheric observations and modeling. California is implementing the "Global Warming Solutions Act of 2006" to reduce total greenhouse gas emissions to 1990 levels by 2020, and it has a diverse array of ecosystems that may serve as CO2 sources or sinks. We performed three month-long field campaigns in different seasons in 2014-15 to collect flask samples from a state-wide network of 10 towers. Using measurements of radiocarbon in CO2, we estimate the fossil fuel-derived CO2 present in the flask samples, relative to marine background air observed at coastal sites. Radiocarbon (14C) is not present in fossil fuel-derived CO2 because of radioactive decay over millions of years, so fossil fuel emissions cause a measurable decrease in the 14C/C ratio in atmospheric CO2. We compare the observations of fossil fuel-derived CO2 to simulations based on atmospheric modeling and published fossil fuel flux estimates, and adjust the fossil fuel flux estimates in a statistical inversion that takes account of several uncertainties. We will present the results of the top-down technique to estimate fossil fuel emissions for our field campaigns in California, and we will give an outlook for future development of the technique in California.

Modeling of Control Costs, Emissions, and Control Retrofits for Cost Effectiveness and Feasibility Analyses

EPA Pesticide Factsheets

Learn about EPA’s use of the Integrated Planning Model (IPM) to develop estimates of SO2 and NOx emission control costs, projections of futureemissions, and projections of capacity of future control retrofits, assuming controls on EGUs.

High-Mileage Light-Duty Fleet Vehicle Emissions: Their Potentially Overlooked Importance.

PubMed

Bishop, Gary A; Stedman, Donald H; Burgard, Daniel A; Atkinson, Oscar

2016-05-17

State and local agencies in the United States use activity-based computer models to estimate mobile source emissions for inventories. These models generally assume that vehicle activity levels are uniform across all of the vehicle emission level classifications using the same age-adjusted travel fractions. Recent fuel-specific emission measurements from the SeaTac Airport, Los Angeles, and multi-year measurements in the Chicago area suggest that some high-mileage fleets are responsible for a disproportionate share of the fleet's emissions. Hybrid taxis at the airport show large increases in carbon monoxide, hydrocarbon, and oxide of nitrogen emissions in their fourth year when compared to similar vehicles from the general population. Ammonia emissions from the airport shuttle vans indicate that catalyst reduction capability begins to wane after 5-6 years, 3 times faster than is observed in the general population, indicating accelerated aging. In Chicago, the observed, on-road taxi fleet also had significantly higher emissions and an emissions share that was more than double their fleet representation. When compounded by their expected higher than average mileage accumulation, we estimate that these small fleets (<1% of total) may be overlooked as a significant emission source (>2-5% of fleet emissions).

2017 National Emissions Inventory (NEI) Plan

EPA Pesticide Factsheets

The 2017 NEI Plan is prepared at least every three years by the U.S. EPA based primarily upon emissions estimates and emissions model inputs provided by State, Local and Tribal (SLT) air agencies, and supplemented by data developed by the EPA.

Fuel-cycle emissions for conventional and alternative fuel vehicles : an assessment of air toxics

DOT National Transportation Integrated Search

2000-08-01

This report provides information on recent efforts to use the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) fuel-cycle model to estimate air toxics emissions. GREET, developed at Argonne National Laboratory, currentl...

Gaining ground in the modeling of land-use change greenhouse gas emissions associated with biofuel production

NASA Astrophysics Data System (ADS)

Dunn, J.; Mueller, S.; Kwon, H.; Wang, M.; Wander, M.

2012-12-01

Land-use change (LUC) resulting from biofuel feedstock production and the associated greenhouse gas (GHG) emissions are a hotly-debated aspect of biofuels. Certainly, LUC GHG emissions are one of the most uncertain elements in life cycle analyses (LCA) of biofuels. To estimate LUC GHG emissions, two sets of data are necessary. First, information on the amount and type of land that is converted to biofuel feedstock production is required. These data are typically generated through application of computable general equilibrium (CGE) models such as Purdue University's Global Trade Analysis Project (GTAP) model. Second, soil carbon content data for the affected land types is essential. Recently, Argonne National Laboratory's Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) has been updated with CGE modeling results that estimate the amount and type of LUC world-wide from production of ethanol from corn, corn stover, miscanthus, and switchgrass (Mueller et al. 2012). Moreover, we have developed state-specific carbon content data, determined through modeling with CENTURY, for the two most dominant soil types in the conterminous 48 U.S. states (Kwon et al. 2012) to enable finer-resolution results for domestic LUC GHG emissions for these ethanol production scenarios. Of the feedstocks examined, CCLUB estimates that LUC GHG emissions are highest for corn ethanol (9.1 g CO2e/MJ ethanol) and lowest for miscanthus (-12 g CO2e/MJ ethanol). We will present key observations from CCLUB results incorporated into Argonne National Laboratory's Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model, which is a LCA tool for transportation fuels and advanced vehicle technologies. We will discuss selected issues in this modeling, including the sensitivity of domestic soil carbon emission factors to modeling parameters and assumptions about the fate of harvested wood products. Further, we will discuss efforts to update CCLUB with county-level soil carbon emission factors and updated international soil carbon emission factors. Finally, we will examine data needs for improved LUC GHG calculations in both the modeling of land conversion and soil carbon content. Kwon, H. Y., Wander, M. M., Mueller, S., Dunn, J. B. "Modeling state-level soil carbon emission factors under various scenarios for direct land use change associated with United States biofuel feedstock production." Biomass and Bioenergy. Under Review. Mueller, S., Dunn, J. B., Wang, M. "Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) Users' Manual and Technical Documentation." May 2012. ANL/ESD/12-5. Available at http://greet.es.anl.gov/publication-cclub-manual.

Systematic Review of Life Cycle Greenhouse Gas Emissions from Geothermal Electricity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eberle, Annika; Heath, Garvin A.; Carpenter Petri, Alberta C.

The primary goal of this work was to assess the magnitude and variability of published life cycle greenhouse gas (GHG) emission estimates for three types of geothermal electricity generation technologies: enhanced geothermal systems (EGS) binary, hydrothermal (HT) flash, and HT binary. These technologies were chosen to align the results of this report with technologies modeled in National Renewable Energy Laboratory's (NREL's) Regional Energy Deployment Systems (ReEDs) model. Although we did gather and screen life cycle assessment (LCA) literature on hybrid systems, dry steam, and two geothermal heating technologies, we did not analyze published GHG emission estimates for these technologies. Inmore » our systematic literature review of the LCA literature, we screened studies in two stages based on a variety of criteria adapted from NREL's Life Cycle Assessment (LCA) Harmonization study (Heath and Mann 2012). Of the more than 180 geothermal studies identified, only 29 successfully passed both screening stages and only 26 of these included estimates of life cycle GHG emissions. We found that the median estimate of life cycle GHG emissions (in grams of carbon dioxide equivalent per kilowatt-hour generated [g CO2eq/kWh]) reported by these studies are 32.0, 47.0, and 11.3 for EGS binary, HT flash, and HT binary, respectively (Figure ES-1). We also found that the total life cycle GHG emissions are dominated by different stages of the life cycle for different technologies. For example, the GHG emissions from HT flash plants are dominated by the operations phase owing to the flash cycle being open loop whereby carbon dioxide entrained in the geothermal fluids is released to the atmosphere. This is in contrast to binary plants (using either EGS or HT resources), whose GHG emissions predominantly originate in the construction phase, owing to its closed-loop process design. Finally, by comparing this review's literature-derived range of HT flash GHG emissions to data from currently operating geothermal plants, we found that emissions from operational plants exhibit more variability and the median of emissions from operational plants is twice the median of operational emissions reported by LCAs. Further investigation is warranted to better understand the cause of differences between published LCAs and estimates from operational plants and to develop LCA analytical approaches that can yield estimates closer to actual emissions.« less

Satellite-derived methane hotspot emission estimates using a fast data-driven method

NASA Astrophysics Data System (ADS)

Buchwitz, Michael; Schneising, Oliver; Reuter, Maximilian; Heymann, Jens; Krautwurst, Sven; Bovensmann, Heinrich; Burrows, John P.; Boesch, Hartmut; Parker, Robert J.; Somkuti, Peter; Detmers, Rob G.; Hasekamp, Otto P.; Aben, Ilse; Butz, André; Frankenberg, Christian; Turner, Alexander J.

2017-05-01

Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. XCH4, can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH4. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003-2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5-8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.

Influence of updating global emission inventory of black carbon on evaluation of the climate and health impact

NASA Astrophysics Data System (ADS)

Wang, Rong; Tao, Shu; Balkanski, Yves; Ciais, Philippe

2013-04-01

Black carbon (BC) is an air component of particular concern in terms of air quality and climate change. Black carbon emissions are often estimated based on the fuel data and emission factors. However, large variations in emission factors reported in the literature have led to a high uncertainty in previous inventories. Here, we develop a new global 0.1°×0.1° BC emission inventory for 2007 with full uncertainty analysis based on updated source and emission factor databases. Two versions of LMDz-OR-INCA models, named as INCA and INCA-zA, are run to evaluate the new emission inventory. INCA is built up based on a regular grid system with a resolution of 1.27° in latitude and 2.50° in longitude, while INCA-zA is specially zoomed to 0.51°×0.66° (latitude×longitude) in Asia. By checking against field observations, we compare our inventory with ACCMIP, which is used by IPCC in the 5th assessment report, and also evaluate the influence of model resolutions. With the newly calculated BC air concentrations and the nested model, we estimate the direct radiative forcing of BC and the premature death and mortality rate induced by BC exposure with Asia emphasized. Global BC direct radiative forcing at TOA is estimated to be 0.41 W/m2 (0.2 - 0.8 as inter-quartile range), which is 17% higher than that derived from the inventory adopted by IPCC-AR5 (0.34 W/m2). The estimated premature deaths induced by inhalation exposure to anthropogenic BC (0.36 million in 2007) and the percentage of high risk population are higher than those previously estimated. Ninety percents of the global total anthropogenic PD occur in Asia with 0.18 and 0.08 million deaths in China and India, respectively.

Using the Fire Weather Index (FWI) to improve the estimation of fire emissions from fire radiative power (FRP) observations

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca; Rémy, Samuel; Pappenberger, Florian; Wetterhall, Fredrik

2018-04-01

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

Estimation of Pre-industrial Nitrous Oxide Emission from the Terrestrial Biosphere

NASA Astrophysics Data System (ADS)

Xu, R.; Tian, H.; Lu, C.; Zhang, B.; Pan, S.; Yang, J.

2015-12-01

Nitrous oxide (N2O) is currently the third most important greenhouse gases (GHG) after methane (CH4) and carbon dioxide (CO2). Global N2O emission increased substantially primarily due to reactive nitrogen (N) enrichment through fossil fuel combustion, fertilizer production, and legume crop cultivation etc. In order to understand how climate system is perturbed by anthropogenic N2O emissions from the terrestrial biosphere, it is necessary to better estimate the pre-industrial N2O emissions. Previous estimations of natural N2O emissions from the terrestrial biosphere range from 3.3-9.0 Tg N2O-N yr-1. This large uncertainty in the estimation of pre-industrial N2O emissions from the terrestrial biosphere may be caused by uncertainty associated with key parameters such as maximum nitrification and denitrification rates, half-saturation coefficients of soil ammonium and nitrate, N fixation rate, and maximum N uptake rate. In addition to the large estimation range, previous studies did not provide an estimate on preindustrial N2O emissions at regional and biome levels. In this study, we applied a process-based coupled biogeochemical model to estimate the magnitude and spatial patterns of pre-industrial N2O fluxes at biome and continental scales as driven by multiple input data, including pre-industrial climate data, atmospheric CO2 concentration, N deposition, N fixation, and land cover types and distributions. Uncertainty associated with key parameters is also evaluated. Finally, we generate sector-based estimates of pre-industrial N2O emission, which provides a reference for assessing the climate forcing of anthropogenic N2O emission from the land biosphere.

How much do direct livestock emissions actually contribute to global warming?

PubMed

Reisinger, Andy; Clark, Harry

2018-04-01

Agriculture directly contributes about 10%-12% of current global anthropogenic greenhouse gas emissions, mostly from livestock. However, such percentage estimates are based on global warming potentials (GWPs), which do not measure the actual warming caused by emissions and ignore the fact that methane does not accumulate in the atmosphere in the same way as CO 2 . Here, we employ a simple carbon cycle-climate model, historical estimates and future projections of livestock emissions to infer the fraction of actual warming that is attributable to direct livestock non-CO 2 emissions now and in future, and to CO 2 from pasture conversions, without relying on GWPs. We find that direct livestock non-CO 2 emissions caused about 19% of the total modelled warming of 0.81°C from all anthropogenic sources in 2010. CO 2 from pasture conversions contributed at least another 0.03°C, bringing the warming directly attributable to livestock to 23% of the total warming in 2010. The significance of direct livestock emissions to future warming depends strongly on global actions to reduce emissions from other sectors. Direct non-CO 2 livestock emissions would contribute only about 5% of the warming in 2100 if emissions from other sectors increase unabated, but could constitute as much as 18% (0.27°C) of the warming in 2100 if global CO 2 emissions from other sectors are reduced to near or below zero by 2100, consistent with the goal of limiting warming to well below 2°C. These estimates constitute a lower bound since indirect emissions linked to livestock feed production and supply chains were not included. Our estimates demonstrate that expanding the mitigation potential and realizing substantial reductions of direct livestock non-CO 2 emissions through demand and supply side measures can make an important contribution to achieve the stringent mitigation goals set out in the Paris Agreement, including by increasing the carbon budget consistent with the 1.5°C goal. © 2017 John Wiley & Sons Ltd.

Using box models to quantify zonal distributions and emissions of halocarbons in the background atmosphere.

NASA Astrophysics Data System (ADS)

Elkins, J. W.; Nance, J. D.; Dutton, G. S.; Montzka, S. A.; Hall, B. D.; Miller, B.; Butler, J. H.; Mondeel, D. J.; Siso, C.; Moore, F. L.; Hintsa, E. J.; Wofsy, S. C.; Rigby, M. L.

2015-12-01

The Halocarbons and other Atmospheric Trace Species (HATS) of NOAA's Global Monitoring Division started measurements of the major chlorofluorocarbons and nitrous oxide in 1977 from flask samples collected at five remote sites around the world. Our program has expanded to over 40 compounds at twelve sites, which includes six in situ instruments and twelve flask sites. The Montreal Protocol for Substances that Deplete the Ozone Layer and its subsequent amendments has helped to decrease the concentrations of many of the ozone depleting compounds in the atmosphere. Our goal is to provide zonal emission estimates for these trace gases from multi-box models and their estimated atmospheric lifetimes in this presentation and make the emission values available on our web site. We plan to use our airborne measurements to calibrate the exchange times between the boxes for 5-box and 12-box models using sulfur hexafluoride where emissions are better understood.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saide, Pablo E.; Peterson, David A.; de Silva, Arlindo

We couple airborne, ground-based, and satellite observations; conduct regional simulations; and develop and apply an inversion technique to constrain hourly smoke emissions from the Rim Fire, the third largest observed in California, USA. Emissions constrained with multiplatform data show notable nocturnal enhancements (sometimes over a factor of 20), correlate better with daily burned area data, and are a factor of 2–4 higher than a priori estimates, highlighting the need for improved characterization of diurnal profiles and day-to-day variability when modeling extreme fires. Constraining only with satellite data results in smaller enhancements mainly due to missing retrievals near the emissions source,more » suggesting that top-down emission estimates for these events could be underestimated and a multiplatform approach is required to resolve them. Predictions driven by emissions constrained with multiplatform data present significant variations in downwind air quality and in aerosol feedback on meteorology, emphasizing the need for improved emissions estimates during exceptional events.« less

Estimation of Methane Emissions from Slurry Pits below Pig and Cattle Confinements

PubMed Central

Petersen, Søren O.; Olsen, Anne B.; Elsgaard, Lars; Triolo, Jin Mi; Sommer, Sven G.

2016-01-01

Quantifying in-house emissions of methane (CH4) from liquid manure (slurry) is difficult due to high background emissions from enteric processes, yet of great importance for correct estimation of CH4 emissions from manure management and effects of treatment technologies such as anaerobic digestion. In this study CH4 production rates were determined in 20 pig slurry and 11 cattle slurry samples collected beneath slatted floors on six representative farms; rates were determined within 24 h at temperatures close to the temperature in slurry pits at the time of collection. Methane production rates in pig and cattle slurry differed significantly at 0.030 and 0.011 kg CH4 kg-1 VS (volatile solids). Current estimates of CH4 emissions from pig and cattle manure management correspond to 0.032 and 0.015 kg CH4 kg-1, respectively, indicating that slurry pits under animal confinements are a significant source. Fractions of degradable volatile solids (VSd, kg kg-1 VS) were estimated using an aerobic biodegradability assay and total organic C analyses. The VSd in pig and cattle slurry averaged 0.51 and 0.33 kg kg-1 VS, and it was estimated that on average 43 and 28% of VSd in fresh excreta from pigs and cattle, respectively, had been lost at the time of sampling. An empirical model of CH4 emissions from slurry was reparameterised based on experimental results. A sensitivity analysis indicated that predicted CH4 emissions were highly sensitive to uncertainties in the value of lnA of the Arrhenius equation, but much less sensitive to uncertainties in VSd or slurry temperature. A model application indicated that losses of carbon in VS as CO2 may be much greater than losses as CH4. Implications of these results for the correct estimation of CH4 emissions from manure management, and for the mitigation potential of treatments such as anaerobic digestion, are discussed. PMID:27529692

Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications

NASA Astrophysics Data System (ADS)

Ringeval, B.; Houweling, S.; van Bodegom, P. M.; Spahni, R.; van Beek, R.; Joos, F.; Röckmann, T.

2014-03-01

Tropical wetlands are estimated to represent about 50% of the natural wetland methane (CH4) emissions and explain a large fraction of the observed CH4 variability on timescales ranging from glacial-interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH4 emissions. This publication documents a first step in the development of a process-based model of CH4 emissions from tropical floodplains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH4 emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH4 flux densities were evaluated against field observations and regional flux inventories. Simulated CH4 emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH4 flux densities for the Amazon Basin. However, the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH4 emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH4 emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr-1. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20%. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the full dynamics in CH4 emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is accounted for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH4 emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH4 emissions, and they stress the need for more research to constrain floodplain CH4 emissions and their temporal variability, even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes.

The climate response to five trillion tonnes of carbon

NASA Astrophysics Data System (ADS)

Tokarska, Katarzyna B.; Gillett, Nathan P.; Weaver, Andrew J.; Arora, Vivek K.; Eby, Michael

2016-09-01

Concrete actions to curtail greenhouse gas emissions have so far been limited on a global scale, and therefore the ultimate magnitude of climate change in the absence of further mitigation is an important consideration for climate policy. Estimates of fossil fuel reserves and resources are highly uncertain, and the amount used under a business-as-usual scenario would depend on prevailing economic and technological conditions. In the absence of global mitigation actions, five trillion tonnes of carbon (5 EgC), corresponding to the lower end of the range of estimates of the total fossil fuel resource, is often cited as an estimate of total cumulative emissions. An approximately linear relationship between global warming and cumulative CO2 emissions is known to hold up to 2 EgC emissions on decadal to centennial timescales; however, in some simple climate models the predicted warming at higher cumulative emissions is less than that predicted by such a linear relationship. Here, using simulations from four comprehensive Earth system models, we demonstrate that CO2-attributable warming continues to increase approximately linearly up to 5 EgC emissions. These models simulate, in response to 5 EgC of CO2 emissions, global mean warming of 6.4-9.5 °C, mean Arctic warming of 14.7-19.5 °C, and mean regional precipitation increases by more than a factor of four. These results indicate that the unregulated exploitation of the fossil fuel resource could ultimately result in considerably more profound climate changes than previously suggested.

Constraining the 2012-2014 growing season Alaskan methane budget using CARVE aircraft measurements

NASA Astrophysics Data System (ADS)

Hartery, S.; Chang, R. Y. W.; Commane, R.; Lindaas, J.; Miller, S. M.; Wofsy, S. C.; Karion, A.; Sweeney, C.; Miller, C. E.; Dinardo, S. J.; Steiner, N.; McDonald, K. C.; Watts, J. D.; Zona, D.; Oechel, W. C.; Kimball, J. S.; Henderson, J.; Mountain, M. E.

2015-12-01

Soil in northen latitudes contains rich carbon stores which have been historically preserved via permafrost within the soil bed; however, recent surface warming in these regions is allowing deeper soil layers to thaw, influencing the net carbon exchange from these areas. Due to the extreme nature of its climate, these eco-regions remain poorly understood by most global models. In this study we analyze methane fluxes from Alaska using in situ aircraft observations from the 2012-2014 Carbon in Arctic Reservoir Vulnerability Experiment (CARVE). These observations are coupled with an atmospheric particle transport model which quantitatively links surface emissions to atmospheric observations to make regional methane emission estimates. The results of this study are two-fold. First, the inter-annual variability of the methane emissions was found to be <1 Tg over the area of interest and is largely influenced by the length of time the deep soil remains unfrozen. Second, the resulting methane flux estimates and mean soil parameters were used to develop an empirical emissions model to help spatially and temporally constrain the methane exchange at the Alaskan soil surface. The empirical emissions model will provide a basis for exploring the sensitivity of methane emissions to subsurface soil temperature, soil moisture, organic carbon content, and other parameters commonly used in process-based models.

Upscaling NZ-DNDC using a regression based meta-model to estimate direct N2O emissions from New Zealand grazed pastures.

PubMed

Giltrap, Donna L; Ausseil, Anne-Gaëlle E

2016-01-01

The availability of detailed input data frequently limits the application of process-based models at large scale. In this study, we produced simplified meta-models of the simulated nitrous oxide (N2O) emission factors (EF) using NZ-DNDC. Monte Carlo simulations were performed and the results investigated using multiple regression analysis to produce simplified meta-models of EF. These meta-models were then used to estimate direct N2O emissions from grazed pastures in New Zealand. New Zealand EF maps were generated using the meta-models with data from national scale soil maps. Direct emissions of N2O from grazed pasture were calculated by multiplying the EF map with a nitrogen (N) input map. Three meta-models were considered. Model 1 included only the soil organic carbon in the top 30cm (SOC30), Model 2 also included a clay content factor, and Model 3 added the interaction between SOC30 and clay. The median annual national direct N2O emissions from grazed pastures estimated using each model (assuming model errors were purely random) were: 9.6GgN (Model 1), 13.6GgN (Model 2), and 11.9GgN (Model 3). These values corresponded to an average EF of 0.53%, 0.75% and 0.63% respectively, while the corresponding average EF using New Zealand national inventory values was 0.67%. If the model error can be assumed to be independent for each pixel then the 95% confidence interval for the N2O emissions was of the order of ±0.4-0.7%, which is much lower than existing methods. However, spatial correlations in the model errors could invalidate this assumption. Under the extreme assumption that the model error for each pixel was identical the 95% confidence interval was approximately ±100-200%. Therefore further work is needed to assess the degree of spatial correlation in the model errors. Copyright © 2015 Elsevier B.V. All rights reserved.

Using Satellite Observations to Evaluate the AeroCOM Volcanic Emissions Inventory and the Dispersal of Volcanic SO2 Clouds in MERRA

NASA Technical Reports Server (NTRS)

Hughes, Eric J.; Krotkov, Nickolay; da Silva, Arlindo; Colarco, Peter

2015-01-01

Simulation of volcanic emissions in climate models requires information that describes the eruption of the emissions into the atmosphere. While the total amount of gases and aerosols released from a volcanic eruption can be readily estimated from satellite observations, information about the source parameters, like injection altitude, eruption time and duration, is often not directly known. The AeroCOM volcanic emissions inventory provides estimates of eruption source parameters and has been used to initialize volcanic emissions in reanalysis projects, like MERRA. The AeroCOM volcanic emission inventory provides an eruptions daily SO2 flux and plume top altitude, yet an eruption can be very short lived, lasting only a few hours, and emit clouds at multiple altitudes. Case studies comparing the satellite observed dispersal of volcanic SO2 clouds to simulations in MERRA have shown mixed results. Some cases show good agreement with observations Okmok (2008), while for other eruptions the observed initial SO2 mass is half of that in the simulations, Sierra Negra (2005). In other cases, the initial SO2 amount agrees with the observations but shows very different dispersal rates, Soufriere Hills (2006). In the aviation hazards community, deriving accurate source terms is crucial for monitoring and short-term forecasting (24-h) of volcanic clouds. Back trajectory methods have been developed which use satellite observations and transport models to estimate the injection altitude, eruption time, and eruption duration of observed volcanic clouds. These methods can provide eruption timing estimates on a 2-hour temporal resolution and estimate the altitude and depth of a volcanic cloud. To better understand the differences between MERRA simulations and volcanic SO2 observations, back trajectory methods are used to estimate the source term parameters for a few volcanic eruptions and compared to their corresponding entry in the AeroCOM volcanic emission inventory. The nature of these mixed results is discussed with respect to the source term estimates.

How carbon-friendly is nuclear energy? A hybrid MRIO-LCA model of a Spanish facility.

PubMed

Zafrilla, Jorge E; Cadarso, María-Ángeles; Monsalve, Fabio; de la Rúa, Cristina

2014-12-16

Spain faces the challenge of 80-95% greenhouse gas emissions reduction by 2050 (European Energy Roadmap). As a possible first step to fulfill this objective, this paper presents a two-level analysis. First, we estimate the carbon footprint of a hypothetical nuclear facility in Spain. Using a hybrid multiregional input-output model, to avoid truncation while diminishing sector aggregation problems and to improve environmental leakages estimations, we calculate the CO2 equivalent emissions associated with the different phases of the nuclear life-cycle--construction, fuel processing and operation and maintenance--taking into account the countries or regions where the emissions have been generated. Our results estimate a nuclear carbon footprint of 21.30 gCO2e/kWh, of which 89% comes from regions outside Spain. In some regions, the highest impacts are mostly direct (92%, 95%, and 92% of total carbon emissions in the U.S., France, and UK, respectively), meaning that these emissions are linked to the inputs directly required for nuclear energy production; in other regions, indirect emissions are higher (83% in China), which becomes relevant for policy measures. Second, through the analyses of different scenarios, we unravel and quantify how different assumptions that are often taken in the literature result in different carbon emissions.

Top-down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS-Chem adjoint model

NASA Astrophysics Data System (ADS)

Wang, Jun; Xu, Xiaoguang; Henze, Daven K.; Zeng, Jing; Ji, Qiang; Tsay, Si-Chee; Huang, Jianping

2012-04-01

Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOS-Chem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health.

Top-down Estimate of Dust Emissions Through Integration of MODIS and MISR Aerosol Retrievals With the Geos-chem Adjoint Model

NASA Technical Reports Server (NTRS)

Wang, Jun; Xu, Xiaoguang; Henze, Daven K.; Zeng, Jing; Ji, Qiang; Tsay, Si-Chee; Huang, Jianping

2012-01-01

Predicting the influences of dust on atmospheric composition, climate, and human health requires accurate knowledge of dust emissions, but large uncertainties persist in quantifying mineral sources. This study presents a new method for combined use of satellite-measured radiances and inverse modeling to spatially constrain the amount and location of dust emissions. The technique is illustrated with a case study in May 2008; the dust emissions in Taklimakan and Gobi deserts are spatially optimized using the GEOSChem chemical transport model and its adjoint constrained by aerosol optical depth (AOD) that are derived over the downwind dark-surface region in China from MODIS (Moderate Resolution Imaging Spectroradiometer) reflectance with the aerosol single scattering properties consistent with GEOS-chem. The adjoint inverse modeling yields an overall 51% decrease in prior dust emissions estimated by GEOS-Chem over the Taklimakan-Gobi area, with more significant reductions south of the Gobi Desert. The model simulation with optimized dust emissions shows much better agreement with independent observations from MISR (Multi-angle Imaging SpectroRadiometer) AOD and MODIS Deep Blue AOD over the dust source region and surface PM10 concentrations. The technique of this study can be applied to global multi-sensor remote sensing data for constraining dust emissions at various temporal and spatial scales, and hence improving the quantification of dust effects on climate, air quality, and human health.

Magnitude and Seasonality of Wetland Methane Emissions from the Hudson Bay Lowlands (Canada)

NASA Technical Reports Server (NTRS)

Pickett-Heaps, C. A.; Jacob, D. J.; Wecht, K. J.; Kort, E. A.; Wofsy, S. C.; Diskin, G. S.; Worthy, D. E. J.; Kaplan, J. O.; Bey, I.; Drevet, J.

2011-01-01

The Hudson Bay Lowlands (HBL) is the second largest boreal wetland ecosystem in the world and an important natural source of global atmospheric methane. We quantify the HBL methane emissions by using the GEOS-Chem chemical transport model to simulate aircraft measurements over the HBL from the ARCTAS and pre-HIPPO campaigns in May-July 2008, together with continuous 2004-2008 surface observations at Fraserdale (southern edge of HBL) and Alert (Arctic background). The difference in methane concentrations between Fraserdale and Alert is shown to be a good indicator of HBL emissions, and implies a sharp seasonal onset of emissions in late May (consistent with the aircraft data), a peak in July-August, and a seasonal shut-off in September. The model, in which seasonal variation of emission is mainly driven by surface temperature, reproduces well the observations in summer but its seasonal shoulders are too broad. We suggest that this reflects the suppression of emissions by snow cover and greatly improve the model simulation by accounting for this effect. Our resulting best estimate for HBL methane emissions is 2.3 Tg/a, several-fold higher than previous estimates (Roulet et al., 1994; Worthy et al., 2000).

DYNAMIC EVALUATION OF REGIONAL AIR QUALITY MODELS: ASSESSING CHANGES TO O 3 STEMMING FROM CHANGES IN EMISSIONS AND METEOROLOGY

EPA Science Inventory

Regional-scale air quality models are used to estimate the response of air pollutants to potential emission control strategies as part of the decision-making process. Traditionally, the model predicted pollutant concentrations are evaluated for the “base case” to assess a model’s...

Development of an Agricultural Fertilizer Modeling System for Bi-Directional Ammonia Fluxes in the Community Multiscale Air Quality (CMAQ) Model

EPA Science Inventory

Atmospheric ammonia (NH₃) plays an important role in fine-mode aerosol formation. Accurate estimates of ammonia from both human and natural emissions can reduce uncertainties in air quality modeling. The majority of ammonia anthropogenic emissions come from the agricul...

Evaluating environmental modeling and sampling data with biomarker data to identify sources and routes of exposure

NASA Astrophysics Data System (ADS)

Shin, Hyeong-Moo; McKone, Thomas E.; Bennett, Deborah H.

2013-04-01

Exposure to environmental chemicals results from multiple sources, environmental media, and exposure routes. Ideally, modeled exposures should be compared to biomonitoring data. This study compares the magnitude and variation of modeled polycyclic aromatic hydrocarbons (PAHs) exposures resulting from emissions to outdoor and indoor air and estimated exposure inferred from biomarker levels. Outdoor emissions result in both inhalation and food-based exposures. We modeled PAH intake doses using U.S. EPA's 2002 National Air Toxics Assessment (NATA) county-level emissions data for outdoor inhalation, the CalTOX model for food ingestion (based on NATA emissions), and indoor air concentrations from field studies for indoor inhalation. We then compared the modeled intake with the measured urine levels of hydroxy-PAH metabolites from the 2001-2002 National Health and Nutrition Examination Survey (NHANES) survey as quantifiable human intake of PAH parent-compounds. Lognormal probability plots of modeled intakes and estimated intakes inferred from biomarkers suggest that a primary route of exposure to naphthalene, fluorene, and phenanthrene for the U.S. population is likely inhalation from indoor sources. For benzo(a)pyrene, the predominant exposure route is likely from food ingestion resulting from multi-pathway transport and bioaccumulation due to outdoor emissions. Multiple routes of exposure are important for pyrene. We also considered the sensitivity of the predicted exposure to the proportion of the total naphthalene production volume emitted to the indoor environment. The comparison of PAH biomarkers with exposure variability estimated from models and sample data for various exposure pathways supports that both indoor and outdoor models are needed to capture the sources and routes of exposure to environmental contaminants.

Estimating source-attributable health impacts of ambient fine particulate matter exposure: global premature mortality from surface transportation emissions in 2005

NASA Astrophysics Data System (ADS)

Chambliss, S. E.; Silva, R.; West, J. J.; Zeinali, M.; Minjares, R.

2014-10-01

Exposure to ambient fine particular matter (PM2.5) was responsible for 3.2 million premature deaths in 2010 and is among the top ten leading risk factors for early death. Surface transportation is a significant global source of PM2.5 emissions and a target for new actions. The objective of this study is to estimate the global and national health burden of ambient PM2.5 exposure attributable to surface transportation emissions. This share of health burden is called the transportation attributable fraction (TAF), and is assumed equal to the proportional decrease in modeled ambient particulate matter concentrations when surface transportation emissions are removed. National population-weighted TAFs for 190 countries are modeled for 2005 using the MOZART-4 global chemical transport model. Changes in annual average concentration of PM2.5 at 0.5 × 0.67 degree horizontal resolution are based on a global emissions inventory and removal of all surface transportation emissions. Global population-weighted average TAF was 8.5 percent or 1.75 μg m-3 in 2005. Approximately 242 000 annual premature deaths were attributable to surface transportation emissions, dominated by China, the United States, the European Union and India. This application of TAF allows future Global Burden of Disease studies to estimate the sector-specific burden of ambient PM2.5 exposure. Additional research is needed to capture intraurban variations in emissions and exposure, and to broaden the range of health effects considered, including the effects of other pollutants.

Consumer Vehicle Choice Model Documentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Changzheng; Greene, David L

In response to the Fuel Economy and Greenhouse Gas (GHG) emissions standards, automobile manufacturers will need to adopt new technologies to improve the fuel economy of their vehicles and to reduce the overall GHG emissions of their fleets. The U.S. Environmental Protection Agency (EPA) has developed the Optimization Model for reducing GHGs from Automobiles (OMEGA) to estimate the costs and benefits of meeting GHG emission standards through different technology packages. However, the model does not simulate the impact that increased technology costs will have on vehicle sales or on consumer surplus. As the model documentation states, “While OMEGA incorporates functionsmore » which generally minimize the cost of meeting a specified carbon dioxide (CO2) target, it is not an economic simulation model which adjusts vehicle sales in response to the cost of the technology added to each vehicle.” Changes in the mix of vehicles sold, caused by the costs and benefits of added fuel economy technologies, could make it easier or more difficult for manufacturers to meet fuel economy and emissions standards, and impacts on consumer surplus could raise the costs or augment the benefits of the standards. Because the OMEGA model does not presently estimate such impacts, the EPA is investigating the feasibility of developing an adjunct to the OMEGA model to make such estimates. This project is an effort to develop and test a candidate model. The project statement of work spells out the key functional requirements for the new model.« less

Greenhouse gas emissions from reservoir water surfaces: A ...

EPA Pesticide Factsheets

Collectively, reservoirs created by dams are thought to be an important source ofgreenhouse gases (GHGs) to the atmosphere. So far, efforts to quantify, model, andmanage these emissions have been limited by data availability and inconsistenciesin methodological approach. Here we synthesize worldwide reservoir methane,carbon dioxide, and nitrous oxide emission data with three main objectives: (1) togenerate a global estimate of GHG emissions from reservoirs, (2) to identify the bestpredictors of these emissions, and (3) to consider the effect of methodology onemission estimates. We estimate that GHG emission from reservoir water surfacesaccount for 0.8 (0.5-1.2) Pg CO2-equivalents per year, equal to ~1.3 % of allanthropogenic GHG emissions, with the majority (79%) of this forcing due tomethane. We also discuss the potential for several alternative pathways such as damdegassing and downstream emissions to contribute significantly to overall GHGemissions. Although prior studies have linked reservoir GHG emissions to systemage and latitude, we find that factors related to reservoir productivity are betterpredictors of emission. Finally, as methane contributed the most to total reservoirGHG emissions, it is important that future monitoring campaigns incorporatemethane emission pathways, especially ebullition. To inform the public.

Population and Activity of On-road Vehicles in MOVES2014

EPA Science Inventory

This report describes the sources and derivation for on-road vehicle population and activity information and associated adjustments as stored in the MOVES2014 default databases. Motor Vehicle Emission Simulator, the MOVES2014 model, is a set of modeling tools for estimating emiss...

Characterization of Methane Emission Sources Using Genetic Algorithms and Atmospheric Transport Modeling

NASA Astrophysics Data System (ADS)

Cao, Y.; Cervone, G.; Barkley, Z.; Lauvaux, T.; Deng, A.; Miles, N.; Richardson, S.

2016-12-01

Fugitive methane emission rates for the Marcellus shale area are estimated using a genetic algorithm that finds optimal weights to minimize the error between simulated and observed concentrations. The overall goal is to understand the relative contribution of methane due to Shale gas extraction. Methane sensors were installed on four towers located in northeastern Pennsylvania to measure atmospheric concentrations since May 2015. Inverse Lagrangian dispersion model runs are performed from each of these tower locations for each hour of 2015. Simulated methane concentrations at each of the four towers are computed by multiplying the resulting footprints from the atmospheric simulations by thousands of emission sources grouped into 11 classes. The emission sources were identified using GIS techniques, and include conventional and unconventional wells, different types of compressor stations, pipelines, landfills, farming and wetlands. Initial estimates for each source are calculated based on emission factors from EPA and few regional studies. A genetic algorithm is then used to identify optimal emission rates for the 11 classes of methane emissions and to explore extreme events and spatial and temporal structures in the emissions associated with natural gas activities.

Investigating model deficiencies in the global budget of ethane

NASA Astrophysics Data System (ADS)

Tzompa Sosa, Z. A.; Keller, C. A.; Turner, A. J.; Mahieu, E.; Franco, B.; Fischer, E. V.

2015-12-01

Many locations in the Northern Hemisphere show a statistically-significant sharp increase in measurements of ethane (C2H6) since 2009. It is hypothesized that the recent massive growth of shale gas exploitation in North America could be the source of this change. However, state-of-the-science chemical transport models are currently unable to reproduce the hemispheric burden of C2H6 or the recent sharp increase, pointing to a potential problem with current emission inventories. To resolve this, we used space-borne CH4 observations from the Greenhouse Gases Observing SATellite (GOSAT) to derive C2H6 emissions. By using known emission ratios to CH4, we estimated emissions of C2H6 from oil and gas activities, biofuels, and biomass burning over North America. The GEOS-Chem global chemical transport model was used to simulate atmospheric abundances of C2H6 with the new emissions estimates. The model is able to reproduce Northern Hemisphere surface concentrations. However, the model significantly under-predicts the amount of C2H6 throughout the column and the observed Northern Hemispheric gradient as diagnosed by comparisons to aircraft observations from the Hiaper Pole-to-Pole (HIPPO) Campaign.

Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty

NASA Astrophysics Data System (ADS)

Ito, A.; Inatomi, M.

2012-02-01

We assessed the global terrestrial budget of methane (CH4) by using a process-based biogeochemical model (VISIT) and inventory data for components of the budget that were not included in the model. Emissions from wetlands, paddy fields, biomass burning, and plants, as well as oxidative consumption by upland soils, were simulated by the model. Emissions from ruminant livestock and termites were evaluated by using an inventory approach. These CH4 flows were estimated for each of the model's 0.5° × 0.5° grid cells from 1901 to 2009, while accounting for atmospheric composition, meteorological factors, and land-use changes. Estimation uncertainties were examined through ensemble simulations using different parameterization schemes and input data (e.g., different wetland maps and emission factors). From 1996 to 2005, the average global terrestrial CH4 budget was estimated on the basis of 1152 simulations, and terrestrial ecosystems were found to be a net source of 308.3 ± 20.7 Tg CH4 yr-1. Wetland and livestock ruminant emissions were the primary sources. The results of our simulations indicate that sources and sinks are distributed highly heterogeneously over the Earth's land surface. Seasonal and interannual variability in the terrestrial budget was also assessed. The trend of increasing net emission from terrestrial sources and its relationship with temperature variability imply that terrestrial CH4 feedbacks will play an increasingly important role as a result of future climatic change.

Estimating State-Specific Contributions to PM2.5- and O3-Related Health Burden from Residential Combustion and Electricity Generating Unit Emissions in the United States

PubMed Central

Penn, Stefani L.; Arunachalam, Saravanan; Woody, Matthew; Heiger-Bernays, Wendy; Tripodis, Yorghos; Levy, Jonathan I.

2016-01-01

Background: Residential combustion (RC) and electricity generating unit (EGU) emissions adversely impact air quality and human health by increasing ambient concentrations of fine particulate matter (PM2.5) and ozone (O3). Studies to date have not isolated contributing emissions by state of origin (source-state), which is necessary for policy makers to determine efficient strategies to decrease health impacts. Objectives: In this study, we aimed to estimate health impacts (premature mortalities) attributable to PM2.5 and O3 from RC and EGU emissions by precursor species, source sector, and source-state in the continental United States for 2005. Methods: We used the Community Multiscale Air Quality model employing the decoupled direct method to quantify changes in air quality and epidemiological evidence to determine concentration–response functions to calculate associated health impacts. Results: We estimated 21,000 premature mortalities per year from EGU emissions, driven by sulfur dioxide emissions forming PM2.5. More than half of EGU health impacts are attributable to emissions from eight states with significant coal combustion and large downwind populations. We estimate 10,000 premature mortalities per year from RC emissions, driven by primary PM2.5 emissions. States with large populations and significant residential wood combustion dominate RC health impacts. Annual mortality risk per thousand tons of precursor emissions (health damage functions) varied significantly across source-states for both source sectors and all precursor pollutants. Conclusions: Our findings reinforce the importance of pollutant-specific, location-specific, and source-specific models of health impacts in design of health-risk minimizing emissions control policies. Citation: Penn SL, Arunachalam S, Woody M, Heiger-Bernays W, Tripodis Y, Levy JI. 2017. Estimating state-specific contributions to PM2.5- and O3-related health burden from residential combustion and electricity generating unit emissions in the United States. Environ Health Perspect 125:324–332; http://dx.doi.org/10.1289/EHP550 PMID:27586513

Estimating the electron energy distribution during ionospheric modification from spectrographic airglow measurements

NASA Astrophysics Data System (ADS)

Hysell, D. L.; Varney, R. H.; Vlasov, M. N.; Nossa, E.; Watkins, B.; Pedersen, T.; Huba, J. D.

2012-02-01

The electron energy distribution during an F region ionospheric modification experiment at the HAARP facility near Gakona, Alaska, is inferred from spectrographic airglow emission data. Emission lines at 630.0, 557.7, and 844.6 nm are considered along with the absence of detectable emissions at 427.8 nm. Estimating the electron energy distribution function from the airglow data is a problem in classical linear inverse theory. We describe an augmented version of the method of Backus and Gilbert which we use to invert the data. The method optimizes the model resolution, the precision of the mapping between the actual electron energy distribution and its estimate. Here, the method has also been augmented so as to limit the model prediction error. Model estimates of the suprathermal electron energy distribution versus energy and altitude are incorporated in the inverse problem formulation as representer functions. Our methodology indicates a heater-induced electron energy distribution with a broad peak near 5 eV that decreases approximately exponentially by 30 dB between 5-50 eV.

Statistical atmospheric inversion of local gas emissions by coupling the tracer release technique and local-scale transport modelling: a test case with controlled methane emissions

NASA Astrophysics Data System (ADS)

Ars, Sébastien; Broquet, Grégoire; Yver Kwok, Camille; Roustan, Yelva; Wu, Lin; Arzoumanian, Emmanuel; Bousquet, Philippe

2017-12-01

This study presents a new concept for estimating the pollutant emission rates of a site and its main facilities using a series of atmospheric measurements across the pollutant plumes. This concept combines the tracer release method, local-scale atmospheric transport modelling and a statistical atmospheric inversion approach. The conversion between the controlled emission and the measured atmospheric concentrations of the released tracer across the plume places valuable constraints on the atmospheric transport. This is used to optimise the configuration of the transport model parameters and the model uncertainty statistics in the inversion system. The emission rates of all sources are then inverted to optimise the match between the concentrations simulated with the transport model and the pollutants' measured atmospheric concentrations, accounting for the transport model uncertainty. In principle, by using atmospheric transport modelling, this concept does not strongly rely on the good colocation between the tracer and pollutant sources and can be used to monitor multiple sources within a single site, unlike the classical tracer release technique. The statistical inversion framework and the use of the tracer data for the configuration of the transport and inversion modelling systems should ensure that the transport modelling errors are correctly handled in the source estimation. The potential of this new concept is evaluated with a relatively simple practical implementation based on a Gaussian plume model and a series of inversions of controlled methane point sources using acetylene as a tracer gas. The experimental conditions are chosen so that they are suitable for the use of a Gaussian plume model to simulate the atmospheric transport. In these experiments, different configurations of methane and acetylene point source locations are tested to assess the efficiency of the method in comparison to the classic tracer release technique in coping with the distances between the different methane and acetylene sources. The results from these controlled experiments demonstrate that, when the targeted and tracer gases are not well collocated, this new approach provides a better estimate of the emission rates than the tracer release technique. As an example, the relative error between the estimated and actual emission rates is reduced from 32 % with the tracer release technique to 16 % with the combined approach in the case of a tracer located 60 m upwind of a single methane source. Further studies and more complex implementations with more advanced transport models and more advanced optimisations of their configuration will be required to generalise the applicability of the approach and strengthen its robustness.

Evaluation and application of site-specific data to revise the first-order decay model for estimating landfill gas generation and emissions at Danish landfills.

PubMed

Mou, Zishen; Scheutz, Charlotte; Kjeldsen, Peter

2015-06-01

Methane (CH₄) generated from low-organic waste degradation at four Danish landfills was estimated by three first-order decay (FOD) landfill gas (LFG) generation models (LandGEM, IPCC, and Afvalzorg). Actual waste data from Danish landfills were applied to fit model (IPCC and Afvalzorg) required categories. In general, the single-phase model, LandGEM, significantly overestimated CH₄generation, because it applied too high default values for key parameters to handle low-organic waste scenarios. The key parameters were biochemical CH₄potential (BMP) and CH₄generation rate constant (k-value). In comparison to the IPCC model, the Afvalzorg model was more suitable for estimating CH₄generation at Danish landfills, because it defined more proper waste categories rather than traditional municipal solid waste (MSW) fractions. Moreover, the Afvalzorg model could better show the influence of not only the total disposed waste amount, but also various waste categories. By using laboratory-determined BMPs and k-values for shredder, sludge, mixed bulky waste, and street-cleaning waste, the Afvalzorg model was revised. The revised model estimated smaller cumulative CH₄generation results at the four Danish landfills (from the start of disposal until 2020 and until 2100). Through a CH₄mass balance approach, fugitive CH₄emissions from whole sites and a specific cell for shredder waste were aggregated based on the revised Afvalzorg model outcomes. Aggregated results were in good agreement with field measurements, indicating that the revised Afvalzorg model could provide practical and accurate estimation for Danish LFG emissions. This study is valuable for both researchers and engineers aiming to predict, control, and mitigate fugitive CH₄emissions from landfills receiving low-organic waste. Landfill operators use the first-order decay (FOD) models to estimate methane (CH₄) generation. A single-phase model (LandGEM) and a traditional model (IPCC) could result in overestimation when handling a low-organic waste scenario. Site-specific data were important and capable of calibrating key parameter values in FOD models. The comparison study of the revised Afvalzorg model outcomes and field measurements at four Danish landfills provided a guideline for revising the Pollutants Release and Transfer Registers (PRTR) model, as well as indicating noteworthy waste fractions that could emit CH₄at modern landfills.

Simulating CH4 and N2O emissions from direct-seeded rice systems using the DeNitrification DeComposition (DNDC) model

NASA Astrophysics Data System (ADS)

Simmonds, M.; Li, C.; Lee, J.; Six, J.; Van Kessel, C.; Linquist, B.

2015-12-01

Process-based modeling of CH4 and N2O emissions from rice fields is a practical tool for conducting greenhouse gas inventories and estimating mitigation potentials of alternative practices at the scales of management and policy-making. However, few studies have evaluated site-level model performance in side-by-side field trials of various management practices during both the growing season and fallow periods. We empirically evaluated the DeNitrification-DeComposition (DNDC) model for estimating CH4 and N2O fluxes in California rice systems under varying management (N fertilizer application rate, type of seeding system, fallow period straw and water management), soil environments, and weather conditions. Five and nine site-year combinations were used for calibration and validation, respectively. The model was parameterized for two cultivars, M206 and Koshihikari, and able to simulate 30% and 78% of the measured variation in yields, respectively. A major strength of DNDC was in estimating general site-level seasonal CH4 emissions (R2 = 0.85). However, a major limitation was in simulating finer resolution of differences in CH4 emissions (or lack thereof) among side-by-side management treatments (range of 0.2-465% relative absolute deviation). Additionally, DNDC did not satisfactorily simulate fallow period CH4 emissions, or seasonal and fallow period N2O emissions across all sites with the exception of a few cases. Specifically, simulated CH4 emissions were oversensitive to fertilizer N rates, but lacked sensitivity to the type of seeding system and prior fallow period straw management. Additionally, N2O emissions were oversensitive to fertilizer N rates and field drainage. Sensitivity analysis showed that CH4 emissions were highly sensitive to changes in the root to total plant biomass ratio. Overall, uncertainty in model predictions was attributed to uncertainty in both the input parameters due to in-field spatiotemporal variability of soil properties, and in the model structure (e.g., genotype by environment interactions, clay effects, and simulation routines for field drainage, and diffusion and ebullition of gasses). These findings have implications for model-directed field research that could improve model uncertainty for application at larger spatial scales.

Estimation of saltation emission in the Kubuqi Desert, North China.

PubMed

Du, Heqiang; Xue, Xian; Wang, Tao

2014-05-01

The Kubuqi Desert suffered more severe wind erosion hazard. Every year, a mass of aeolian sand was blown in the Ten Tributaries that are tributaries of the Yellow River. To estimate the quantity of aeolian sediment blown into the Ten Tributaries from the Kubuqi Desert, it is necessary to simulate the saltation processes of the Kubuqi Desert. A saltation submodel of the IWEMS (Integrated Wind-Erosion Modeling System) and its accompanying RS (Remote Sensing) and GIS (Geographic Information System) methods were used to model saltation emissions in the Kubuqi Desert. To calibrate the saltation submodel, frontal area of vegetation, soil moisture, wind velocity and saltation sediment were observed synchronously on several points in 2011 and 2012. In this study, a model namely BEACH (Bridge Event And Continuous Hydrological) was introduced to simulate the daily soil moisture. Using the surface parameters (frontal area of vegetation and soil moisture) along with the observed wind velocities and saltation sediments for the observed points, the saltation model was calibrated and validated. To reduce the simulate error, a subdaily wind velocity program, WINDGEN was introduced in this model to simulate the hourly wind velocity of the Kubuqi Desert. By incorporating simulated hourly wind velocity, and model variables, the saltation emission of the Kubuqi Desert was modeled. The model results show that the total sediment flow rate was 1-30.99 tons/m over the last 10years (2001-2010). The saltation emission mainly occurs in the north central part of the Kubuqi Desert in winter and spring. Integrating the wind directions, the quantity of the aeolian sediment that deposits in the Ten Tributaries was estimated. Compared with the observed data by the local government and hydrometric stations, our estimation is reasonable. Copyright © 2014 Elsevier B.V. All rights reserved.

Wall Paint Exposure Assessment Model (WPEM)

EPA Pesticide Factsheets

WPEM uses mathematical models developed from small chamber data to estimate the emissions of chemicals from oil-based (alkyd) and latex wall paint which is then combined with detailed use, workload and occupancy data to estimate user exposure.

Global atmospheric emissions and transport of polycyclic aromatic hydrocarbons: Evaluation of modeling and transboundary pollution

NASA Astrophysics Data System (ADS)

Shen, Huizhong; Tao, Shu

2014-05-01

Global atmospheric emissions of 16 polycyclic aromatic hydrocarbons (PAHs) from 69 major sources were estimated for a period from 1960 to 2030. Regression models and a technology split method were used to estimated country and time specific emission factors, resulting in a new estimate of PAH emission factor variation among different countries and over time. PAH emissions in 2007 were spatially resolved to 0.1° × 0.1° grids based on a newly developed global high-resolution fuel combustion inventory (PKU-FUEL-2007). MOZART-4 (The Model for Ozone and Related Chemical Tracers, version 4) was applied to simulate the global tropospheric transport of Benzo(a)pyrene, one of the high molecular weight carcinogenic PAHs, at a horizontal resolution of 1.875° (longitude) × 1.8947° (latitude). The reaction with OH radical, gas/particle partitioning, wet deposition, dry deposition, and dynamic soil/ocean-air exchange of PAHs were considered. The simulation was validated by observations at both background and non-background sites, including Alert site in Canadian High Arctic, EMEP sites in Europe, and other 254 urban/rural sites reported from literatures. Key factors effecting long-range transport of BaP were addressed, and transboundary pollution was discussed.

Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010

NASA Astrophysics Data System (ADS)

Hooghiemstra, P. B.; Krol, M. C.; van Leeuwen, T. T.; van der Werf, G. R.; Novelli, P. C.; Deeter, M. N.; Aben, I.; Röckmann, T.

2012-08-01

We present the first inverse modeling study to estimate CO emissions constrained by both surface and satellite observations. Our 4D-Var system assimilates National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) surface and Measurements Of Pollution In The Troposphere (MOPITT) satellite observations jointly by fitting a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. South American dry season (August and September) biomass burning emission estimates amount to 60, 92, 42, 16 and 93 Tg CO/yr for 2006 to 2010, respectively. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations in São Paulo state are underestimated in current inventories by 50-100%. We conclude that climatic conditions (such as the widespread drought in 2010) seem the most likely cause for the IAV in biomass burning CO emissions. However, socio-economic factors (such as the growing global demand for soy, beef and sugar cane ethanol) and associated deforestation fires, are also likely as drivers for the IAV of CO emissions, but are difficult to link directly to CO emissions.

Estimation of CO2 emission from water treatment plant--model development and application.

PubMed

Kyung, Daeseung; Kim, Dongwook; Park, Nosuk; Lee, Woojin

2013-12-15

A comprehensive mathematical model developed for this study was used to compare estimates of on-site and off-site CO2 emissions, from conventional and advanced water treatment plants (WTPs). When 200,000 m(3) of raw water at 10 NTU (Nepthelometric Turbidity Unit) was treated by a conventional WTP to 0.1 NTU using aluminum sulfate as a coagulant, the total CO2 emissions were estimated to be 790 ± 228 (on-site) and 69,596 ± 3950 (off-site) kg CO2e/d. The emissions from an advanced WTP containing micro-filtration (MF) membrane and ozone disinfection processes; treating the same raw water to 0.005 NTU, were estimated to be 395 ± 115 (on-site) and 38,197 ± 2922 (off-site) kg CO2e/d. The on-site CO2 emissions from the advanced WTP were half that from the conventional WTP due to much lower use of coagulant. On the other hand, off-site CO2 emissions due to consumption of electricity were 2.14 times higher for the advanced WTP, due to the demands for operation of the MF membrane and ozone disinfection processes. However, the lower use of chemicals in the advanced WTP decreased off-site CO2 emissions related to chemical production and transportation. Overall, total CO2 emissions from the conventional WTP were 1.82 times higher than that from the advanced WTP. A sensitivity analysis was performed for the advanced WTP to suggest tactics for simultaneously reducing CO2 emissions further and enhancing water quality. Copyright © 2013 Elsevier Ltd. All rights reserved.

Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

NASA Technical Reports Server (NTRS)

Mu, M.; Randerson, J. T.; vanderWerf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; DeFries, R. S.; Hyer, E. J.; Prins, E. M.;

Comparison of Techniques to Estimate Ammonia Emissions at Cattle Feedlots Using Time-Averaged and Instantaneous Concentration Measurements

NASA Astrophysics Data System (ADS)

Shonkwiler, K. B.; Ham, J. M.; Williams, C. M.

2013-12-01

Ammonia (NH3) that volatilizes from confined animal feeding operations (CAFOs) can form aerosols that travel long distances where such aerosols can deposit in sensitive regions, potentially causing harm to local ecosystems. However, quantifying the emissions of ammonia from CAFOs through direct measurement is very difficult and costly to perform. A system was therefore developed at Colorado State University for conditionally sampling NH3 concentrations based on weather parameters measured using inexpensive equipment. These systems use passive diffusive cartridges (Radiello, Sigma-Aldrich, St. Louis, MO, USA) that provide time-averaged concentrations representative of a two-week deployment period. The samplers are exposed by a robotic mechanism so they are only deployed when wind is from the direction of the CAFO at 1.4 m/s or greater. These concentration data, along with other weather variables measured during each sampler deployment period, can then be used in a simple inverse model (FIDES, UMR Environnement et Grandes Cultures, Thiverval-Grignon, France) to estimate emissions. There are not yet any direct comparisons of the modeled emissions derived from time-averaged concentration data to modeled emissions from more sophisticated backward Lagrangian stochastic (bLs) techniques that utilize instantaneous measurements of NH3 concentration. In the summer and autumn of 2013, a suite of robotic passive sampler systems were deployed at a 25,000-head cattle feedlot at the same time as an open-path infrared (IR) diode laser (GasFinder2, Boreal Laser Inc., Edmonton, Alberta, Canada) which continuously measured ammonia concentrations instantaneously over a 225-m path. This particular laser is utilized in agricultural settings, and in combination with a bLs model (WindTrax, Thunder Beach Scientific, Inc., Halifax, Nova Scotia, Canada), has become a common method for estimating NH3 emissions from a variety of agricultural and industrial operations. This study will first compare the ammonia concentrations measured with the Radiello system to that measured with the long-path IR laser. Second, NH3 emissions estimated using the simple inverse model (FIDES) and the time-averaged data will be compared to emissions derived from the bLS model (WindTrax) using the laser-based NH3 data. Results could lead to a more cost-efficient and simpler technique for monitoring ammonia fluxes from of CAFOs and other strong areal sources.

Mercury emission estimates from fires: an initial inventory for the United States.

PubMed

Wiedinmyer, Christine; Friedli, Hans

2007-12-01

Recent studies have shown that emissions of mercury (Hg), a hazardous air pollutant, from fires can be significant. However, to date, these emissions have not been well-quantified for the entire United States. Daily emissions of Hg from fires in the lower 48 states of the United States (LOWER48) and in Alaska were estimated for 2002-2006 using a simple fire emissions model. Emission factors of Hg from fires in different ecosystems were compiled from published plume studies and from soil-based assessments. Annual averaged emissions of Hg from fires in the LOWER48 and Alaska were 44 (20-65) metric tons yr(-1), equivalent to approximately 30% of the U.S. EPA 2002 National Emissions Inventory for Hg. Alaska had the highest averaged monthly emissions of all states; however, the emissions have a high temporal variability. Emissions from forests dominate the inventory, suggesting that Hg emissions from agricultural fires are not significant on an annual basis. The uncertainty in the Hg emission factors due to limited data leads to an uncertainty in the emission estimates on the order of +/-50%. Research is still needed to better constrain Hg emission factors from fires, particularly in the eastern U.S. and for ecosystems other than forests.

An economic analysis of localized pollution: rendering emissions in a residential setting

Treesearch

J. Michael Bowker; H.F. MacDonald

1991-01-01

The contingent value method is employed to estimate economic damages to households resulting from rendering plant emissions in a small town. Household willingness to accept (WTA) and willingness to pay (WTP) are estimated individually and in aggregate. The influence of household characteristics on WTP and WTA is examined via regression models. The perception of health...

"Peer Review: Nonroad (NR) Updates to Population Growth, Compression Ignition (CI) Criteria, Toxic Emission Factors and Speciation Profiles"

EPA Science Inventory

This report focuses on the methodology for estimating growth in NR engine populations as used in the MOVES201X-NONROAD emission inventory model. MOVES NR growth rates start with base year engine populations and estimate growth in the populations of NR engines, while applying cons...

Gaussian model for emission rate measurement of heated plumes using hyperspectral data

NASA Astrophysics Data System (ADS)

Grauer, Samuel J.; Conrad, Bradley M.; Miguel, Rodrigo B.; Daun, Kyle J.

2018-02-01

This paper presents a novel model for measuring the emission rate of a heated gas plume using hyperspectral data from an FTIR imaging spectrometer. The radiative transfer equation (RTE) is used to relate the spectral intensity of a pixel to presumed Gaussian distributions of volume fraction and temperature within the plume, along a line-of-sight that corresponds to the pixel, whereas previous techniques exclusively presume uniform distributions for these parameters. Estimates of volume fraction and temperature are converted to a column density by integrating the local molecular density along each path. Image correlation velocimetry is then employed on raw spectral intensity images to estimate the volume-weighted normal velocity at each pixel. Finally, integrating the product of velocity and column density along a control surface yields an estimate of the instantaneous emission rate. For validation, emission rate estimates were derived from synthetic hyperspectral images of a heated methane plume, generated using data from a large-eddy simulation. Calculating the RTE with Gaussian distributions of volume fraction and temperature, instead of uniform distributions, improved the accuracy of column density measurement by 14%. Moreover, the mean methane emission rate measured using our approach was within 4% of the ground truth. These results support the use of Gaussian distributions of thermodynamic properties in calculation of the RTE for optical gas diagnostics.

UPDATED PHOTOCHEMICAL MODELING FOR CALIFORNIA'S SOUTH COAST AIR BASIN: COMPARISON OF CHEMICAL MECHANISMS AND MOTOR VEHICLE EMISSION INVENTORIES. (R824792)

EPA Science Inventory

Large uncertainties remain in photochemical models used
to relate emissions of VOC and NO_x to ambient
O₃
concentrations. Bias in motor vehicle emission
estimates
for VOC has been a long-standing concern. An improved
Eul...

Use of a land-use-based emissions inventory in delineating clean-air zones

Treesearch

Victor S. Fahrer; Howard A. Peters

1977-01-01

Use of a land-use-based emissions inventory from which air-pollution estimates can be projected was studied. First the methodology used to establish a land-use-based emission inventory is described. Then this inventory is used as input in a simple model that delineates clean air and buffer zones. The model is applied to the town of Burlington, Massachusetts....

Estimating methane emissions in California's urban and rural regions using multitower observations

DOE PAGES

Jeong, Seongeun; Newman, Sally; Zhang, Jingsong; ...

2016-11-05

Here, we present an analysis of methane (CH 4) emissions using atmospheric observations from 36 thirteen sites in California during June 2013 – May 2014. A hierarchical Bayesian inversion 37 method is used to estimate CH 4 emissions for spatial regions (0.3° pixels for major regions) by 38 comparing measured CH 4 mixing ratios with transport model (WRF-STILT) predictions based 39 on seasonally varying California-specific CH 4 prior emission models. The transport model is 40 assessed using a combination of meteorological and carbon monoxide (CO) measurements 41 coupled with the gridded California Air Resources Board (CARB) carbon monoxide (CO) 42more » emission inventory. Hierarchical Bayesian inversion suggests that state annual anthropogenic 43 CH 4 emissions are 2.42 ± 0.49 Tg CH 4/yr (at 95% confidence, including transport bias 44 uncertainty), higher (1.2 - 1.8 times) than the CARB current inventory (1.64 Tg CH 4/yr in 2013). 45 We note that the estimated CH 4 emissions drop to 1.0 - 1.6 times the CARB inventory if we 46 correct for the 10% median CH 4 emissions assuming the bias in CO analysis is applicable to 47 CH 4. The CH 4 emissions from the Central Valley and urban regions (San Francisco Bay and 48 South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, 49 respectively. This study suggests that the livestock sector is likely the major contributor to the 50 state total CH 4 emissions, in agreement with CARB’s inventory. Attribution to source sectors for 51 sub-regions of California using additional trace gas species would further improve the 52 quantification of California’s CH 4 emissions and mitigation efforts towards the California Global 53 Warming Solutions Act of 2006 (AB-32).« less

Estimating methane emissions in California's urban and rural regions using multitower observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeong, Seongeun; Newman, Sally; Zhang, Jingsong

Here, we present an analysis of methane (CH 4) emissions using atmospheric observations from 36 thirteen sites in California during June 2013 – May 2014. A hierarchical Bayesian inversion 37 method is used to estimate CH 4 emissions for spatial regions (0.3° pixels for major regions) by 38 comparing measured CH 4 mixing ratios with transport model (WRF-STILT) predictions based 39 on seasonally varying California-specific CH 4 prior emission models. The transport model is 40 assessed using a combination of meteorological and carbon monoxide (CO) measurements 41 coupled with the gridded California Air Resources Board (CARB) carbon monoxide (CO) 42more » emission inventory. Hierarchical Bayesian inversion suggests that state annual anthropogenic 43 CH 4 emissions are 2.42 ± 0.49 Tg CH 4/yr (at 95% confidence, including transport bias 44 uncertainty), higher (1.2 - 1.8 times) than the CARB current inventory (1.64 Tg CH 4/yr in 2013). 45 We note that the estimated CH 4 emissions drop to 1.0 - 1.6 times the CARB inventory if we 46 correct for the 10% median CH 4 emissions assuming the bias in CO analysis is applicable to 47 CH 4. The CH 4 emissions from the Central Valley and urban regions (San Francisco Bay and 48 South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, 49 respectively. This study suggests that the livestock sector is likely the major contributor to the 50 state total CH 4 emissions, in agreement with CARB’s inventory. Attribution to source sectors for 51 sub-regions of California using additional trace gas species would further improve the 52 quantification of California’s CH 4 emissions and mitigation efforts towards the California Global 53 Warming Solutions Act of 2006 (AB-32).« less

Transportation Sector Model of the National Energy Modeling System. Volume 2 -- Appendices: Part 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

The attachments contained within this appendix provide additional details about the model development and estimation process which do not easily lend themselves to incorporation in the main body of the model documentation report. The information provided in these attachments is not integral to the understanding of the model`s operation, but provides the reader with opportunity to gain a deeper understanding of some of the model`s underlying assumptions. There will be a slight degree of replication of materials found elsewhere in the documentation, made unavoidable by the dictates of internal consistency. Each attachment is associated with a specific component of themore » transportation model; the presentation follows the same sequence of modules employed in Volume 1. The following attachments are contained in Appendix F: Fuel Economy Model (FEM)--provides a discussion of the FEM vehicle demand and performance by size class models; Alternative Fuel Vehicle (AFV) Model--describes data input sources and extrapolation methodologies; Light-Duty Vehicle (LDV) Stock Model--discusses the fuel economy gap estimation methodology; Light Duty Vehicle Fleet Model--presents the data development for business, utility, and government fleet vehicles; Light Commercial Truck Model--describes the stratification methodology and data sources employed in estimating the stock and performance of LCT`s; Air Travel Demand Model--presents the derivation of the demographic index, used to modify estimates of personal travel demand; and Airborne Emissions Model--describes the derivation of emissions factors used to associate transportation measures to levels of airborne emissions of several pollutants.« less

A Modeling Framework for Inference of Surface Emissions Using Mobile Observations

NASA Astrophysics Data System (ADS)

Fasoli, B.; Mitchell, L.; Crosman, E.; Mendoza, D. L.; Lin, J. C.

2016-12-01

Our ability to quantify surface emissions depends on the precision of observations and the spatial density of measurement networks. Mobile measurement techniques offer a cost effective strategy for quantifying atmospheric conditions over space without requiring a dense network of in-situ sites. However, interpretation of these data and inversion of dispersed measurements to estimate surface emissions can be difficult. We introduce a framework using the Stochastic Time-Inverted Lagrangian Transport (STILT) model that assimilates both spatially resolved observations and an emissions inventory to better estimate surface fluxes. Salt Lake City is a unique laboratory for the study of urban carbon emissions. It is the only U.S. city that utilizes light-rail trains to continuously measure high frequency carbon dioxide (CO2) and methane (CH4); it is home to one of the longest and most spatially resolved high precision CO2 measurement networks (air.utah.edu); and it is one of four cities in the world for which the Hestia anthropogenic emissions inventory has been produced which characterizes CO2 emissions at the scale of individual buildings and roadways. Using these data and modeling resources, we evaluate spatially resolved CO2 measurements and transported CO2 emissions on hourly timescales at a dense spatial resolution across Salt Lake City.

A new approach to evaluate regional methane emission from irrigated rice paddies: Combining process study, modeling and remote sensing into GIS

NASA Astrophysics Data System (ADS)

Ding, Aiju

2000-10-01

A large seasonal variation in methane emission from Texas rice fields was observed in most of the growing seasons from 1989 through 1997. In general, the pattern showed small fluxes in the early season of cultivation and reached maximum at post-heading time, then declined and stopped after fields were drained. The amount of methane emission positively relates to the aboveground biomass, the number of effective stems and tillers, and nitrogen addition. The day-to-day pattern of methane emissions was similar among all cultivars. The seasonal total methane emission shows a significant positive correlation with post-heading plant height. The total methane emission from Texas rice fields was estimated as 33.25 × 109 g in 1993, ranging from 25.85 × 109 g/yr to 40.65 × 109 g/yr. A mitigation technique was developed to obtain both high yield and less methane emission from Texas rice fields. A new approach was also developed to evaluate regional to large-scale methane emission from irrigated rice paddies. By combining modeling, ground truth information and remote sensing into a Geographic Information System (GIS)-a computer based system, the seasonal methane emission from a large area can be calculated efficiently and more accurately. The methodology was tested at the Richmond Irrigation District (RID) site in Texas. The average daily methane emission varied from field to field and even within a single field. The calculated seasonal total methane emission from RID rice fields was as low as 3.34 × 108 g CH4 in 1996 and as high as 7.80 × 108 g CH4 in 1998. To support the application of the estimation method in a worldwide study, an algorithm describing the mapping of irrigated rice paddies from Landsat TM data was demonstrated. The accuracy in 1998- supervised classification approached 95% when cloud cover was taken into account. Model uncertainty and data availability are the two major potential problems in worldwide application of the new approach. A potential alternative model is proposed which allows estimation of regional methane emission from rice plant height.

Comparing facility-level methane emission rate estimates at natural gas gathering and boosting stations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vaughn, Timothy L.; Bell, Clay S.; Yacovitch, Tara I.

Coordinated dual-tracer, aircraft-based, and direct component-level measurements were made at midstream natural gas gathering and boosting stations in the Fayetteville shale (Arkansas, USA). On-site component-level measurements were combined with engineering estimates to generate comprehensive facility-level methane emission rate estimates ('study on-site estimates (SOE)') comparable to tracer and aircraft measurements. Combustion slip (unburned fuel entrained in compressor engine exhaust), which was calculated based on 111 recent measurements of representative compressor engines, accounts for an estimated 75% of cumulative SOEs at gathering stations included in comparisons. Measured methane emissions from regenerator vents on glycol dehydrator units were substantially larger than predicted bymore » modelling software; the contribution of dehydrator regenerator vents to the cumulative SOE would increase from 1% to 10% if based on direct measurements. Concurrent measurements at 14 normally-operating facilities show relative agreement between tracer and SOE, but indicate that tracer measurements estimate lower emissions (regression of tracer to SOE = 0.91 (95% CI = 0.83-0.99), R2 = 0.89). Tracer and SOE 95% confidence intervals overlap at 11/14 facilities. Contemporaneous measurements at six facilities suggest that aircraft measurements estimate higher emissions than SOE. Aircraft and study on-site estimate 95% confidence intervals overlap at 3/6 facilities. The average facility level emission rate (FLER) estimated by tracer measurements in this study is 17-73% higher than a prior national study by Marchese et al.« less

Comparing facility-level methane emission rate estimates at natural gas gathering and boosting stations

DOE PAGES

Vaughn, Timothy L.; Bell, Clay S.; Yacovitch, Tara I.; ...

2017-02-09

Coordinated dual-tracer, aircraft-based, and direct component-level measurements were made at midstream natural gas gathering and boosting stations in the Fayetteville shale (Arkansas, USA). On-site component-level measurements were combined with engineering estimates to generate comprehensive facility-level methane emission rate estimates ('study on-site estimates (SOE)') comparable to tracer and aircraft measurements. Combustion slip (unburned fuel entrained in compressor engine exhaust), which was calculated based on 111 recent measurements of representative compressor engines, accounts for an estimated 75% of cumulative SOEs at gathering stations included in comparisons. Measured methane emissions from regenerator vents on glycol dehydrator units were substantially larger than predicted bymore » modelling software; the contribution of dehydrator regenerator vents to the cumulative SOE would increase from 1% to 10% if based on direct measurements. Concurrent measurements at 14 normally-operating facilities show relative agreement between tracer and SOE, but indicate that tracer measurements estimate lower emissions (regression of tracer to SOE = 0.91 (95% CI = 0.83-0.99), R2 = 0.89). Tracer and SOE 95% confidence intervals overlap at 11/14 facilities. Contemporaneous measurements at six facilities suggest that aircraft measurements estimate higher emissions than SOE. Aircraft and study on-site estimate 95% confidence intervals overlap at 3/6 facilities. The average facility level emission rate (FLER) estimated by tracer measurements in this study is 17-73% higher than a prior national study by Marchese et al.« less

WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia

DOE PAGES

Bohn, T. J.; Melton, J. R.; Ito, A.; ...

2015-06-03

Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations.more » Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the performance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH 4 emissions, simulated wetland areas, and CH 4 fluxes per unit wetland area and compared these results to an intensive in situ CH 4 flux data set, several wetland maps, and two satellite surface water products. We found that (a) despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH 4 yr⁻¹), inversions (6.06 ± 1.22 Tg CH 4 yr⁻¹), and in situ observations (3.91 ± 1.29 Tg CH 4 yr⁻¹) largely agreed; (b) forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH 4 emissions; (c) the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models; (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multiyear or multidecade observational records are crucial for evaluating models' responses to long-term climate change.« less

Applying an Inverse Model to Estimate Ammonia Emissions at Cattle Feedlots Using Three Different Observation-Based Approaches

NASA Astrophysics Data System (ADS)

Shonkwiler, K. B.; Ham, J. M.; Nash, C.

2014-12-01

Accurately quantifying emissions of ammonia (NH3) from confined animal feeding operations (CAFOs) is vital not only to the livestock industry, but essential to understanding nitrogen cycling along the Front Range of Colorado, USA, where intensive agriculture, urban sprawl, and pristine ecosystems (e.g., Rocky Mtn Nat'l Park) lie within 100-km of each other. Most observation-based techniques for estimating NH3 emissions can be expensive and highly technical. Many methods rely on concentration observations on location, which implicitly depends on weather conditions. A system for sampling NH3 using on-site weather data was developed to allow remote measurement of NH3 in a simple, cost-effective way. These systems use passive diffusive cartridges (Radiello, Sigma-Aldrich) that provide time-averaged concentrations representative of a typical two-week deployment. Cartridge exposure is robotically managed so they are only visible when winds are 1.4 m/s or greater from the direction of the CAFO. These concentration data can be coupled with stability parameters (measured on-site) in a simple inverse model to estimate emissions (FIDES, UMR Environnement et Grandes Cultures). Few studies have directly compared emissions estimates of NH3 using concentration data obtained from multiple measurement systems at different temporal and spatial scales. Therefore, in the summer and autumn of 2014, several conditional sampler systems were deployed at a 25,000-head cattle feedlot concomitant with an open-path infrared laser (GasFinder2, Boreal Laser Inc.) and a Cavity Ring Down Spectrometer (CRDS) (G1103, Picarro Inc.) which each measured instantaneous NH3 concentrations. This study will test the sampler technology by first comparing concentration data from the three different methods. In livestock research, it is common to estimate NH3 emissions by using such instantaneous data in a backward Lagrangian stochastic (bLs) model (WindTrax, Thunder Beach Sci.) Considering this, NH3 fluxes from the inverse model (FIDES) using all three datasets will be compared to emissions from the bLS model (WindTrax) using only high speed data (laser; CRDS). Results may lend further validity to the conditional sampler approach for more easily and accurately monitoring NH3 fluxes from CAFOs and other strong areal sources.

Further evaluation of wetland emission estimates from the JULES land surface model using SCIAMACHY and GOSAT atmospheric column methane measurements

NASA Astrophysics Data System (ADS)

Hayman, Garry; Comyn-Platt, Edward; McNorton, Joey; Chipperfield, Martyn; Gedney, Nicola

2016-04-01

The atmospheric concentration of methane began rising again in 2007 after a period of near-zero growth [1,2], with the largest increases observed over polar northern latitudes and the Southern Hemisphere in 2007 and in the tropics since then. The observed inter-annual variability in atmospheric methane concentrations and the associated changes in growth rates have variously been attributed to changes in different methane sources and sinks [2,3]. Wetlands are generally accepted as being the largest, but least well quantified, single natural source of CH4, with global emission estimates ranging from 142-284 Tg yr-1 [3]. The modelling of wetlands and their associated emissions of CH4 has become the subject of much current interest [4]. We have previously used the HadGEM2 chemistry-climate model to evaluate the wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including SCIAMACHY total methane columns [5] up to 2007. We have undertaken a series of new HadGEM2 runs using new JULES emission estimates extended in time to the end of 2012, thereby allowing comparison with both SCIAMACHY and GOSAT atmospheric column methane measurements. We will describe the results of these runs and the implications for methane wetland emissions. References [1] Rigby, M., et al.: Renewed growth of atmospheric methane. Geophys. Res. Lett., 35, L22805, 2008; [2] Nisbet, E.G., et al.: Methane on the Rise-Again, Science 343, 493, 2014; [3] Kirschke, S., et al.,: Three decades of global methane sources and sinks, Nature Geosciences, 6, 813-823, 2013; [4] Melton, J. R., et al.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753-788, 2013; [5] Hayman, G.D., et al.: Comparison of the HadGEM2 climate-chemistry model against in situ and SCIAMACHY atmospheric methane data, Atmos. Chem. Phys., 14, 13257-13280, 2014.

A mechanistic modeling system for estimating large scale emissions and transport of pollen and co-allergens

PubMed Central

Efstathiou, Christos; Isukapalli, Sastry

2011-01-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants. PMID:21516207

A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens

NASA Astrophysics Data System (ADS)

Efstathiou, Christos; Isukapalli, Sastry; Georgopoulos, Panos

2011-04-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants.

The impact of residential combustion emissions on atmospheric aerosol, human health, and climate

NASA Astrophysics Data System (ADS)

Butt, E. W.; Rap, A.; Schmidt, A.; Scott, C. E.; Pringle, K. J.; Reddington, C. L.; Richards, N. A. D.; Woodhouse, M. T.; Ramirez-Villegas, J.; Yang, H.; Vakkari, V.; Stone, E. A.; Rupakheti, M.; Praveen, P. S.; van Zyl, P. G.; Beukes, J. P.; Josipovic, M.; Mitchell, E. J. S.; Sallu, S. M.; Forster, P. M.; Spracklen, D. V.

2016-01-01

Combustion of fuels in the residential sector for cooking and heating results in the emission of aerosol and aerosol precursors impacting air quality, human health, and climate. Residential emissions are dominated by the combustion of solid fuels. We use a global aerosol microphysics model to simulate the impact of residential fuel combustion on atmospheric aerosol for the year 2000. The model underestimates black carbon (BC) and organic carbon (OC) mass concentrations observed over Asia, Eastern Europe, and Africa, with better prediction when carbonaceous emissions from the residential sector are doubled. Observed seasonal variability of BC and OC concentrations are better simulated when residential emissions include a seasonal cycle. The largest contributions of residential emissions to annual surface mean particulate matter (PM2.5) concentrations are simulated for East Asia, South Asia, and Eastern Europe. We use a concentration response function to estimate the human health impact due to long-term exposure to ambient PM2.5 from residential emissions. We estimate global annual excess adult (> 30 years of age) premature mortality (due to both cardiopulmonary disease and lung cancer) to be 308 000 (113 300-497 000, 5th to 95th percentile uncertainty range) for monthly varying residential emissions and 517 000 (192 000-827 000) when residential carbonaceous emissions are doubled. Mortality due to residential emissions is greatest in Asia, with China and India accounting for 50 % of simulated global excess mortality. Using an offline radiative transfer model we estimate that residential emissions exert a global annual mean direct radiative effect between -66 and +21 mW m-2, with sensitivity to the residential emission flux and the assumed ratio of BC, OC, and SO2 emissions. Residential emissions exert a global annual mean first aerosol indirect effect of between -52 and -16 mW m-2, which is sensitive to the assumed size distribution of carbonaceous emissions. Overall, our results demonstrate that reducing residential combustion emissions would have substantial benefits for human health through reductions in ambient PM2.5 concentrations.

Modeling and Impacts of Traffic Emissions on Air Toxics Concentrations near Roadways

EPA Science Inventory

The dispersion formulation incorporated in the U.S. Environmental Protection Agency’s AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwin...

Nitrous Oxide Emissions from a Large, Impounded River: The Ohio River

EPA Science Inventory

Models suggest that microbial activity in streams and rivers is a globally significant source of anthropogenic nitrous oxide (N2O), a potent greenhouse gas and the leading cause of stratospheric ozone destruction. However, model estimates of N2O emissions are poorly constrained ...

Modeling crop residue burning experiments to evaluate smoke emissions and plume transport

EPA Science Inventory

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also ...

The utility of the historical record in assessing future carbon budgets

NASA Astrophysics Data System (ADS)

Millar, R.; Friedlingstein, P.; Allen, M. R.

2017-12-01

It has long been known that the cumulative emissions of carbon dioxide (CO2) is the most physically relevant determiner of long-lived anthropogenic climate change, with an approximately linear relationship between CO2-induced global mean surface warming and cumulative emissions. The historical observational record offers a way to constrain the relationship between cumulative carbon dioxide emission and global mean warming using observations to date. Here we show that simple regression analysis indicates that the 1.5°C carbon budget would be exhausted after nearly three decades of current emissions, substantially in excess of many estimates from Earth System Models. However, there are many reasons to be cautious about carbon budget assessments from the historical record alone. Accounting for the uncertainty in non-CO2 radiative forcing using a simple climate model and a standard optimal fingerprinting detection attribution technique gives substantial uncertainty in the contribution of CO2 warming to date, and hence the transient climate response to cumulative emissions. Additionally, the existing balance between CO2 and non-CO2 forcing may change in the future under ambitious mitigation scenarios as non-CO2 emissions become more (or less) important to global mean temperature changes. Natural unforced variability can also have a substantial impact on estimates of remaining carbon budgets. By examining all warmings of a given magnitude in both the historical record and past and future ESM simulations we quantify the impact unforced climate variability may have on estimates of remaining carbon budgets, derived as a function of estimated non-CO2 warming and future emission scenario. In summary, whilst the historical record can act as a useful test of climate models, uncertainties in the response to future cumulative emissions remain large and extrapolations of future carbon budgets from the historical record alone should be treated with caution.

Estimation of vehicular emissions using dynamic emission factors: A case study of Delhi, India

NASA Astrophysics Data System (ADS)

Mishra, Dhirendra; Goyal, P.

2014-12-01

The estimation of vehicular emissions depends mainly on the values of emission factors, which are used for the development of a comprehensive emission inventory of vehicles. In this study the variations of emission factors as well as the emission rates have been studied in Delhi. The implementation of compressed natural gas (CNG), in the diesel and petrol, public vehicles in the year 2001 has changed the complete air quality scenario of Delhi. The dynamic emission factors of criteria pollutants viz. carbon monoxide (CO), nitrogen oxide (NOx) and particulate matter (PM10) for all types of vehicles have been developed after, which are based on the several factors such as regulated emission limits, number of vehicle deterioration, vehicle increment, vehicle age etc. These emission factors are found to be decreased continuously throughout the study years 2003-2012. The International Vehicle Emissions (IVE) model is used to estimate the emissions of criteria pollutants by utilizing a dataset available from field observations at different traffic intersections in Delhi. Thus the vehicular emissions, based on dynamic emission factors have been estimated for the years 2003-2012, which are found to be comparable with the monitored concentrations at different locations in Delhi. It is noticed that the total emissions of CO, NOx, and PM10 are increased by 45.63%, 68.88% and 17.92%, respectively up to the year 2012 and the emissions of NOx and PM10 are grown continuously with an annual average growth rate of 5.4% and 1.7% respectively.

Methane Emissions Estimation from a Dairy Farm using Eddy Covariance Measurements

NASA Astrophysics Data System (ADS)

Guo, Q.; Richardson, S.; Sokol, A. B.; Lauvaux, T.; Hristov, A. N.; Hong, B.; Davis, K. J.

2017-12-01

Dairy farms are a significant source of methane emissions. Accurate quantification of these emissions is important for evaluating and ultimately minimizing the impact of agricultural activity on climate change. We have employed the eddy covariance (EC) technique to attempt to quantify total CH4 emissions from a dairy farm, and compare these emissions to inventory estimates. An eddy covariance (EC) sensor was deployed to monitor CH4 emissions at one dairy manure storage facility from July 2016 through the winter of 2017, at a second manure storage facility from April to mid-July 2017, and at dairy barns during July and August of 2017. A flux footprint model was used to convert the observed methane fluxes into estimates of emissions per unit area from these sources. During April and May, CH4 fluxes from the second lagoon were relatively small and slowly increased with daily mean values growing from 0.45 to 10.75 μmol m-2 s-1. June to mid-July fluxes increased rapidly with a peak daily mean emission of 77.97 μmol m-2 s-1. The fluxes were positively correlated with air temperature. Comparison of emissions from the two lagoons, comparison to an inventory estimate of emissions from these lagoons, and evaluation of methane emissions from the barns are underway. These results will be combined to evaluate total farm emissions, and to test our understanding of the factors that govern emissions from dairy operations.

Quantification of methane fluxes from industrial sites using a combination of a tracer release method and a Gaussian model

NASA Astrophysics Data System (ADS)

Ars, S.; Broquet, G.; Yver-Kwok, C.; Wu, L.; Bousquet, P.; Roustan, Y.

2015-12-01

Greenhouse gas (GHG) concentrations keep on increasing in the atmosphere since industrial revolution. Methane (CH4) is the second most important anthropogenic GHG after carbon dioxide (CO2). Its sources and sinks are nowadays well identified however their relative contributions remain uncertain. The industries and the waste treatment emit an important part of the anthropogenic methane that is difficult to quantify because the sources are fugitive and discontinuous. A better estimation of methane emissions could help industries to adapt their mitigation's politic and encourage them to install methane recovery systems in order to reduce their emissions while saving money. Different methods exist to quantify methane emissions. Among them is the tracer release method consisting in releasing a tracer gas near the methane source at a well-known rate and measuring both their concentrations in the emission plume. The methane rate is calculated using the ratio of methane and tracer concentrations and the emission rate of the tracer. A good estimation of the methane emissions requires a good differentiation between the methane actually emitted by the site and the methane from the background concentration level, but also a good knowledge of the sources distribution over the site. For this purpose, a Gaussian plume model is used in addition to the tracer release method to assess the emission rates calculated. In a first step, the data obtained for the tracer during a field campaign are used to tune the model. Different model's parameterizations have been tested to find the best representation of the atmospheric dispersion conditions. Once these parameters are set, methane emissions are estimated thanks to the methane concentrations measured and a Bayesian inversion. This enables to adjust the position and the emission rate of the different methane sources of the site and remove the methane background concentration.

Quantifying methane emissions from natural gas production in north-eastern Pennsylvania

NASA Astrophysics Data System (ADS)

Barkley, Zachary R.; Lauvaux, Thomas; Davis, Kenneth J.; Deng, Aijun; Miles, Natasha L.; Richardson, Scott J.; Cao, Yanni; Sweeney, Colm; Karion, Anna; Smith, MacKenzie; Kort, Eric A.; Schwietzke, Stefan; Murphy, Thomas; Cervone, Guido; Martins, Douglas; Maasakkers, Joannes D.

2017-11-01

Natural gas infrastructure releases methane (CH4), a potent greenhouse gas, into the atmosphere. The estimated emission rate associated with the production and transportation of natural gas is uncertain, hindering our understanding of its greenhouse footprint. This study presents a new application of inverse methodology for estimating regional emission rates from natural gas production and gathering facilities in north-eastern Pennsylvania. An inventory of CH4 emissions was compiled for major sources in Pennsylvania. This inventory served as input emission data for the Weather Research and Forecasting model with chemistry enabled (WRF-Chem), and atmospheric CH4 mole fraction fields were generated at 3 km resolution. Simulated atmospheric CH4 enhancements from WRF-Chem were compared to observations obtained from a 3-week flight campaign in May 2015. Modelled enhancements from sources not associated with upstream natural gas processes were assumed constant and known and therefore removed from the optimization procedure, creating a set of observed enhancements from natural gas only. Simulated emission rates from unconventional production were then adjusted to minimize the mismatch between aircraft observations and model-simulated mole fractions for 10 flights. To evaluate the method, an aircraft mass balance calculation was performed for four flights where conditions permitted its use. Using the model optimization approach, the weighted mean emission rate from unconventional natural gas production and gathering facilities in north-eastern Pennsylvania approach is found to be 0.36 % of total gas production, with a 2σ confidence interval between 0.27 and 0.45 % of production. Similarly, the mean emission estimates using the aircraft mass balance approach are calculated to be 0.40 % of regional natural gas production, with a 2σ confidence interval between 0.08 and 0.72 % of production. These emission rates as a percent of production are lower than rates found in any other basin using a top-down methodology, and may be indicative of some characteristics of the basin that make sources from the north-eastern Marcellus region unique.

Comparing mass balance and adjoint methods for inverse modeling of nitrogen dioxide columns for global nitrogen oxide emissions

NASA Astrophysics Data System (ADS)

Cooper, Matthew; Martin, Randall V.; Padmanabhan, Akhila; Henze, Daven K.

2017-04-01

Satellite observations offer information applicable to top-down constraints on emission inventories through inverse modeling. Here we compare two methods of inverse modeling for emissions of nitrogen oxides (NOx) from nitrogen dioxide (NO2) columns using the GEOS-Chem chemical transport model and its adjoint. We treat the adjoint-based 4D-Var modeling approach for estimating top-down emissions as a benchmark against which to evaluate variations on the mass balance method. We use synthetic NO2 columns generated from known NOx emissions to serve as "truth." We find that error in mass balance inversions can be reduced by up to a factor of 2 with an iterative process that uses finite difference calculations of the local sensitivity of NO2 columns to a change in emissions. In a simplified experiment to recover local emission perturbations, horizontal smearing effects due to NOx transport are better resolved by the adjoint approach than by mass balance. For more complex emission changes, or at finer resolution, the iterative finite difference mass balance and adjoint methods produce similar global top-down inventories when inverting hourly synthetic observations, both reducing the a priori error by factors of 3-4. Inversions of simulated satellite observations from low Earth and geostationary orbits also indicate that both the mass balance and adjoint inversions produce similar results, reducing a priori error by a factor of 3. As the iterative finite difference mass balance method provides similar accuracy as the adjoint method, it offers the prospect of accurately estimating top-down NOx emissions using models that do not have an adjoint.

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

NASA Astrophysics Data System (ADS)

Milando, Chad W.; Batterman, Stuart A.

2018-05-01

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.

Comparison of Greenhouse Gas Emissions between Two Dairy Farm Systems (Conventional vs. Organic Management) in New Hampshire Using the Manure DNDC Biogeochemical Model

NASA Astrophysics Data System (ADS)

Dorich, C.; Contosta, A.; Li, C.; Brito, A.; Varner, R. K.

2013-12-01

Agriculture contributes 20 to 25 % of the total anthropogenic greenhouse gas (GHG) emissions globally. These agricultural emissions are primarily in the form of methane (CH4) and nitrous oxide (N2O) with these GHG accounting for roughly 40 and 80 % of the total anthropogenic emissions of CH4 and N2O, respectively. Due to varied management and the complexities of agricultural ecosystems, it is difficult to estimate these CH4 and N2O emissions. The IPCC emission factors can be used to yield rough estimates of CH4 and N2O emissions but they are often based on limited data. Accurate modeling validated by measurements is needed in order to identify potential mitigation areas, reduce GHG emissions from agriculture, and improve sustainability of farming practices. The biogeochemical model Manure DNDC was validated using measurements from two dairy farms in New Hampshire, USA in order to quantify GHG emissions under different management systems. One organic and one conventional dairy farm operated by the University of New Hampshire's Agriculture Experiment Station were utilized as the study sites for validation of Manure DNDC. Compilation of management records started in 2011 to provide model inputs. Model results were then compared to field collected samples of soil carbon and nitrogen, above-ground biomass, and GHG fluxes. Fluxes were measured in crop, animal, housing, and waste management sites on the farms in order to examine the entire farm ecosystem and test the validity of the model. Fluxes were measured by static flux chambers, with enteric fermentation measurements being conducted by the SF6 tracer test as well as a new method called Greenfeeder. Our preliminary GHG flux analysis suggests higher emissions than predicted by IPCC emission factors and equations. Results suggest that emissions from manure management is a key concern at the conventional dairy farm while bedded housing at the organic dairy produced large quantities of GHG.

Quantifying Black Carbon emissions in high northern latitudes using an Atmospheric Bayesian Inversion

NASA Astrophysics Data System (ADS)

Evangeliou, Nikolaos; Thompson, Rona; Stohl, Andreas; Shevchenko, Vladimir P.

2016-04-01

Black carbon (BC) is the main light absorbing aerosol species and it has important impacts on air quality, weather and climate. The major source of BC is incomplete combustion of fossil fuels and the burning of biomass or bio-fuels (soot). Therefore, to understand to what extent BC affects climate change and pollutant dynamics, accurate knowledge of the emissions, distribution and variation of BC is required. Most commonly, BC emission inventory datasets are built by "bottom up" approaches based on activity data and emissions factors, but these methods are considered to have large uncertainty (Cao et al, 2006). In this study, we have used a Bayesian Inversion to estimate spatially resolved BC emissions. Emissions are estimated monthly for 2014 and over the domain from 180°W to 180°E and 50°N to 90°N. Atmospheric transport is modeled using the Lagrangian Particle Dispersion Model, FLEXPART (Stohl et al., 1998; 2005), and the inversion framework, FLEXINVERT, developed by Thompson and Stohl, (2014). The study domain is of particular interest concerning the identification and estimation of BC sources. In contrast to Europe and North America, where BC sources are comparatively well documented as a result of intense monitoring, only one station recording BC concentrations exists in the whole of Siberia. In addition, emissions from gas flaring by the oil industry have been geographically misplaced in most emission inventories and may be an important source of BC at high latitudes since a significant proportion of the total gas flared occurs at these high latitudes (Stohl et al., 2013). Our results show large differences with the existing BC inventories, whereas the estimated fluxes improve modeled BC concentrations with respect to observations. References Cao, G. et al. Atmos. Environ., 40, 6516-6527, 2006. Stohl, A. et al. Atmos. Environ., 32(24), 4245-4264, 1998. Stohl, A. et al. Atmos. Chem. Phys., 5(9), 2461-2474, 2005. Stohl, A. et al. Atmos. Chem. Phys., 13, 8833-8855, 2013. Thompson, R. L., and Stohl A. Geosci. Model Dev., 7, 2223-2242, 2014.

MODELING ASSESSMENT OF THE IMPACT OF NITROGEN OXIDES EMISSION REDUCTIONS ON OZONE AIR QUALITY IN THE EASTERN UNITED STATES: OFFSETTING INCREASES IN ENERGY USE

EPA Science Inventory

The objective of this study is to examine changes in ambient ozone concentrations estimated by a photochemical air quality model in response to the NOx emission reductions imposed on the utility sector. To accomplish this task, CMAQ air quality model simulations were performe...