The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Air Quality Modeling Technical Support Document for the 2008 Ozone NAAQS Cross-State Air Pollution Rule Proposal

EPA Pesticide Factsheets

In this technical support document (TSD) we describe the air quality modeling performed to support the proposed Cross-State Air Pollution Rule for the 2008 ozone National Ambient Air Quality Standards (NAAQS)

Evaluation of near surface ozone and particulate matter in air ...

EPA Pesticide Factsheets

In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher-resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000–2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method’s use for future air quality projections. This paper shows that if emissions inputs and coarse-scale meteorological inputs are reasonably accurate, then air quality can be simulated with acceptable accuracy even wi

Meteorological Processes Affecting Air Quality – Research and Model Development Needs

EPA Science Inventory

Meteorology modeling is an important component of air quality modeling systems that defines the physical and dynamical environment for atmospheric chemistry. The meteorology models used for air quality applications are based on numerical weather prediction models that were devel...

Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

Evaluation of the Community Multiscale Air Quality (CMAQ) Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Evaluation of the Community Multi-scale Air Quality Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Evaluation of the Community Multiscale Air Quality Model for Simulating Winter Ozone Formation in the Uinta Basin with Intensive Oil and Gas Production

NASA Astrophysics Data System (ADS)

Matichuk, R.; Tonnesen, G.; Luecken, D.; Roselle, S. J.; Napelenok, S. L.; Baker, K. R.; Gilliam, R. C.; Misenis, C.; Murphy, B.; Schwede, D. B.

2015-12-01

The western United States is an important source of domestic energy resources. One of the primary environmental impacts associated with oil and natural gas production is related to air emission releases of a number of air pollutants. Some of these pollutants are important precursors to the formation of ground-level ozone. To better understand ozone impacts and other air quality issues, photochemical air quality models are used to simulate the changes in pollutant concentrations in the atmosphere on local, regional, and national spatial scales. These models are important for air quality management because they assist in identifying source contributions to air quality problems and designing effective strategies to reduce harmful air pollutants. The success of predicting oil and natural gas air quality impacts depends on the accuracy of the input information, including emissions inventories, meteorological information, and boundary conditions. The treatment of chemical and physical processes within these models is equally important. However, given the limited amount of data collected for oil and natural gas production emissions in the past and the complex terrain and meteorological conditions in western states, the ability of these models to accurately predict pollution concentrations from these sources is uncertain. Therefore, this presentation will focus on understanding the Community Multiscale Air Quality (CMAQ) model's ability to predict air quality impacts associated with oil and natural gas production and its sensitivity to input uncertainties. The results will focus on winter ozone issues in the Uinta Basin, Utah and identify the factors contributing to model performance issues. The results of this study will help support future air quality model development, policy and regulatory decisions for the oil and gas sector.

On Regional Modeling to Support Air Quality Policies

EPA Science Inventory

We examine the use of the Community Multiscale Air Quality (CMAQ) model in simulating the changes in the extreme values of air quality that are of interest to the regulatory agencies. Year-to-year changes in ozone air quality are attributable to variations in the prevailing mete...

Evaluation of the Community Multi-scale Air Quality (CMAQ) Model Version 5.1

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Overview and Evaluation of the Community Multiscale Air Quality Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

Evaluation of the Community Multi-scale Air Quality (CMAQ) Model Version 5.2

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transport and fate of numerous air pollutants, including ozone and particulate matter. The Computational Exposure Division (CED) of the U.S. Environmental Pr...

U.S. EPA MODELS-3/CMAQ - STATUS AND APPLICATIONS

EPA Science Inventory

An advanced third-generation air quality modeling system has been developed by the Atmospheric Modeling Division of the U.S. EPA. The air quality simulation model at the heart of the system is known as the Community Multiscale Air Quality (CMAQ) Model. It is comprehensive in ...

On Regional Modeling to Support Air Quality Policies (book chapter)

EPA Science Inventory

We examine the use of the Community Multiscale Air Quality (CMAQ) model in simulating the changes in the extreme values of air quality that are of interest to the regulatory agencies. Year-to-year changes in ozone air quality are attributable to variations in the prevailing meteo...

Air Pollution Data for Model Evaluation and Application

EPA Science Inventory

One objective of designing an air pollution monitoring network is to obtain data for evaluating air quality models that are used in the air quality management process and scientific discovery.^1.2 A common use is to relate emissions to air quality, including assessing ...

Impact of inherent meteorology uncertainty on air quality model predictions

EPA Science Inventory

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

Evaluating the Vertical Distribution of Ozone and its Relationship to Pollution Events in Air Quality Models using Satellite Data

NASA Astrophysics Data System (ADS)

Osterman, G. B.; Neu, J. L.; Eldering, A.; Pinder, R. W.; Tang, Y.; McQueen, J.

2014-12-01

Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture the distribution of ozone in the mid- and upper troposphere, but it is unclear how this shortcoming relates to their ability to simulate surface ozone. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and a new joint product from TES and the Ozone Monitoring Instrument along with ozonesonde measurements and EPA AirNow ground station ozone data to examine air quality events during August 2006 in the Community Multi-Scale Air Quality (CMAQ) and National Air Quality Forecast Capability (NAQFC) models. We present both aggregated statistics and case-study analyses with the goal of assessing the relationship between the models' ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone. We find that the models lack the mid-tropospheric ozone variability seen in TES and the ozonesonde data, and discuss the conditions under which this variability appears to be important for surface air quality.

“Nitrogen Budgets for the Mississippi River Basin using the ...

EPA Pesticide Factsheets

Presentation on the results from the 3 linked models, EPIC (USDA), CMAQ and NEWS to analyze a scenario of increased corn production related to biofuels together with Clean Air Act emission reductions across the US and the resultant effect on nitrogen loading to the Gulf of Mexico from the Mississippi River Basin. This is a demonstration of a capability to connect the N cascade bringing air, land, water together. EPIC = Environmental Policy Integrated Climate model, CMAQ = Community Multiscale Air Quality model, NEWS = Nutrient Export of WaterSheds model. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Seltzer_et_al_2016

EPA Pesticide Factsheets

This dataset supports the modeling study of Seltzer et al. (2016) published in Atmospheric Environment. In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000-2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method??s use for future air quality projections.This dataset is associated with the following publication:Seltzer, K., C

Impacts of Climate Policy on Regional Air Quality, Health, and Air Quality Regulatory Procedures

NASA Astrophysics Data System (ADS)

Thompson, T. M.; Selin, N. E.

2011-12-01

Both the changing climate, and the policy implemented to address climate change can impact regional air quality. We evaluate the impacts of potential selected climate policies on modeled regional air quality with respect to national pollution standards, human health and the sensitivity of health uncertainty ranges. To assess changes in air quality due to climate policy, we couple output from a regional computable general equilibrium economic model (the US Regional Energy Policy [USREP] model), with a regional air quality model (the Comprehensive Air Quality Model with Extensions [CAMx]). USREP uses economic variables to determine how potential future U.S. climate policy would change emissions of regional pollutants (CO, VOC, NOx, SO2, NH3, black carbon, and organic carbon) from ten emissions-heavy sectors of the economy (electricity, coal, gas, crude oil, refined oil, energy intensive industry, other industry, service, agriculture, and transportation [light duty and heavy duty]). Changes in emissions are then modeled using CAMx to determine the impact on air quality in several cities in the Northeast US. We first calculate the impact of climate policy by using regulatory procedures used to show attainment with National Ambient Air Quality Standards (NAAQS) for ozone and particulate matter. Building on previous work, we compare those results with the calculated results and uncertainties associated with human health impacts due to climate policy. This work addresses a potential disconnect between NAAQS regulatory procedures and the cost/benefit analysis required for and by the Clean Air Act.

An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

NASA Astrophysics Data System (ADS)

Cobourn, W. Geoffrey

2010-08-01

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

MODELED MESOSCALE METEOROLOGICAL FIELDS WITH FOUR-DIMENSIONAL DATA ASSIMILATION IN REGIONAL SCALE AIR QUALITY MODELS

EPA Science Inventory

This paper addresses the need to increase the temporal and spatial resolution of meteorological data currently used in air quality simulation models, AQSMs. ransport and diffusion parameters including mixing heights and stability used in regulatory air quality dispersion models a...

Impact of High Resolution Land-Use Data in Meteorology and Air Quality Modeling Systems

EPA Science Inventory

Accurate land use information is important in meteorology for land surface exchanges, in emission modeling for emission spatial allocation, and in air quality modeling for chemical surface fluxes. Currently, meteorology, emission, and air quality models often use outdated USGS Gl...

EPA RESEARCH HIGHLIGHTS -- MODELS-3/CMAQ OFFERS COMPREHENSIVE APPROACH TO AIR QUALITY MODELING

EPA Science Inventory

Regional and global coordinated efforts are needed to address air quality problems that are growing in complexity and scope. Models-3 CMAQ contains a community multi-scale air quality modeling system for simulating urban to regional scale pollution problems relating to troposphe...

40 CFR 52.60 - Significant deterioration of air quality.

Code of Federal Regulations, 2014 CFR

2014-07-01

... quality. 52.60 Section 52.60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... deterioration of air quality. (a) All applications and other information required pursuant to § 52.21 from... “Guideline on Air Quality Models (Revised)” or other models approved by EPA. [42 FR 22869, May 5, 1977, as...

40 CFR 52.60 - Significant deterioration of air quality.

Code of Federal Regulations, 2013 CFR

2013-07-01

... quality. 52.60 Section 52.60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... deterioration of air quality. (a) All applications and other information required pursuant to § 52.21 from... “Guideline on Air Quality Models (Revised)” or other models approved by EPA. [42 FR 22869, May 5, 1977, as...

40 CFR 52.60 - Significant deterioration of air quality.

Code of Federal Regulations, 2011 CFR

2011-07-01

... quality. 52.60 Section 52.60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... deterioration of air quality. (a) All applications and other information required pursuant to § 52.21 from... “Guideline on Air Quality Models (Revised)” or other models approved by EPA. [42 FR 22869, May 5, 1977, as...

40 CFR 52.60 - Significant deterioration of air quality.

Code of Federal Regulations, 2012 CFR

2012-07-01

... quality. 52.60 Section 52.60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... deterioration of air quality. (a) All applications and other information required pursuant to § 52.21 from... “Guideline on Air Quality Models (Revised)” or other models approved by EPA. [42 FR 22869, May 5, 1977, as...

40 CFR 52.60 - Significant deterioration of air quality.

Code of Federal Regulations, 2010 CFR

2010-07-01

... quality. 52.60 Section 52.60 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... deterioration of air quality. (a) All applications and other information required pursuant to § 52.21 from... “Guideline on Air Quality Models (Revised)” or other models approved by EPA. [42 FR 22869, May 5, 1977, as...

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was ...

A Review and Analysis of Remote Sensing Capability for Air Quality Measurements as a Potential Decision Support Tool Conducted by the NASA DEVELOP Program

NASA Technical Reports Server (NTRS)

Ross, A.; Richards, A.; Keith, K.; Frew, C.; Boseck, J.; Sutton, S.; Watts, C.; Rickman, D.

2007-01-01

This project focused on a comprehensive utilization of air quality model products as decision support tools (DST) needed for public health applications. A review of past and future air quality measurement methods and their uncertainty, along with the relationship of air quality to national and global public health, is vital. This project described current and future NASA satellite remote sensing and ground sensing capabilities and the potential for using these sensors to enhance the prediction, prevention, and control of public health effects that result from poor air quality. The qualitative uncertainty of current satellite remotely sensed air quality, the ground-based remotely sensed air quality, the air quality/public health model, and the decision making process is evaluated in this study. Current peer-reviewed literature suggests that remotely sensed air quality parameters correlate well with ground-based sensor data. A satellite remote-sensed and ground-sensed data complement is needed to enhance the models/tools used by policy makers for the protection of national and global public health communities

Application of Kolomogorov-Zurbenko Filter and the decoupled direct 3D method for the dynamic evaluation of a regional air quality model

EPA Science Inventory

Regional air quality models are being used in a policy-setting to estimate the response of air pollutant concentrations to changes in emissions and meteorology. Dynamic evaluation entails examination of a retrospective case(s) to assess whether an air quality model has properly p...

COMMUNITY MULTISCALE AIR QUALITY MODELING SYSTEM (ONE ATMOSPHERE)

EPA Science Inventory

This task supports ORD's strategy by providing responsive technical support of EPA's mission and provides credible state of the art air quality models and guidance. This research effort is to develop and improve the Community Multiscale Air Quality (CMAQ) modeling system, a mu...

A YEAR-LONG MM5 EVALUATION USING A MODEL EVALUATION TOOLKIT

EPA Science Inventory

Air quality modeling has expanded in both sophistication and application over the past decade. Meteorological and air quality modeling tools are being used for research, forecasting, and regulatory related emission control strategies. Results from air quality simulations have far...

AIR QUALITY FORECAST DATABASE AND ANALYSIS

EPA Science Inventory

In 2003, NOAA and EPA signed a Memorandum of Agreement to collaborate on the design and implementation of a capability to produce daily air quality modeling forecast information for the U.S. NOAA's ETA meteorological model and EPA's Community Multiscale Air Quality (CMAQ) model ...

Local-Scale Air Quality Modeling in Support of Human Health and Exposure Research (Invited)

NASA Astrophysics Data System (ADS)

Isakov, V.

2010-12-01

Spatially- and temporally-sparse information on air quality is a key concern for air-pollution-related environmental health studies. Monitor networks are sparse in both space and time, are costly to maintain, and are often designed purposely to avoid detecting highly localized sources. Recent studies have shown that more narrowly defining the geographic domain of the study populations and improvements in the measured/estimated ambient concentrations can lead to stronger associations between air pollution and hospital admissions and mortality records. Traditionally, ambient air quality measurements have been used as a primary input to support human health and exposure research. However, there is increasing evidence that the current ambient monitoring network is not capturing sharp gradients in exposure due to the presence of high concentration levels near, for example, major roadways. Many air pollutants exhibit large concentration gradients near large emitters such as major roadways, factories, ports, etc. To overcome these limitations, researchers are now beginning to use air quality models to support air pollution exposure and health studies. There are many advantages to using air quality models over traditional approaches based on existing ambient measurements alone. First, models can provide spatially- and temporally-resolved concentrations as direct input to exposure and health studies and thus better defining the concentration levels for the population in the geographic domain. Air quality models have a long history of use in air pollution regulations, and supported by regulatory agencies and a large user community. Also, models can provide bidirectional linkages between sources of emissions and ambient concentrations, thus allowing exploration of various mitigation strategies to reduce risk to exposure. In order to provide best estimates of air concentrations to support human health and exposure studies, model estimates should consider local-scale features, regional-scale transport, and photochemical transformations. Since these needs are currently not met by a single model, hybrid air quality modeling has recently been developed to combine these capabilities. In this paper, we present the results of two studies where we applied the hybrid modeling approach to provide spatial and temporal details in air quality concentrations to support exposure and health studies: a) an urban-scale air quality accountability study involving near-source exposures to multiple ambient air pollutants, and b) an urban-scale epidemiological study involving human health data based on emergency department visits.

Indoor Air Quality Building Education and Assessment Model Forms

EPA Pesticide Factsheets

The Indoor Air Quality Building Education and Assessment Model (I-BEAM) is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

THE ATMOSPHERIC MODEL EVALUATION TOOL (AMET); AIR QUALITY MODULE

EPA Science Inventory

This presentation reviews the development of the Atmospheric Model Evaluation Tool (AMET) air quality module. The AMET tool is being developed to aid in the model evaluation. This presentation focuses on the air quality evaluation portion of AMET. Presented are examples of the...

AIR QUALITY SIMULATION MODEL PERFORMANCE FOR ONE-HOUR AVERAGES

EPA Science Inventory

If a one-hour standard for sulfur dioxide were promulgated, air quality dispersion modeling in the vicinity of major point sources would be an important air quality management tool. Would currently available dispersion models be suitable for use in demonstrating attainment of suc...

Multi-pollutant surface objective analyses and mapping of air quality health index over North America.

PubMed

Robichaud, Alain; Ménard, Richard; Zaïtseva, Yulia; Anselmo, David

2016-01-01

Air quality, like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual. To help protect exposed populations, many countries have put in place real-time air quality nowcasting and forecasting capabilities. We present in this paper an optimal combination of air quality measurements and model outputs and show that it leads to significant improvements in the spatial representativeness of air quality. The product is referred to as multi-pollutant surface objective analyses (MPSOAs). Moreover, based on MPSOA, a geographical mapping of the Canadian Air Quality Health Index (AQHI) is also presented which provides users (policy makers, public, air quality forecasters, and epidemiologists) with a more accurate picture of the health risk anytime and anywhere in Canada and the USA. Since pollutants can also behave as passive atmospheric tracers, they provide information about transport and dispersion and, hence, reveal synoptic and regional meteorological phenomena. MPSOA could also be used to build air pollution climatology, compute local and national trends in air quality, and detect systematic biases in numerical air quality (AQ) models. Finally, initializing AQ models at regular time intervals with MPSOA can produce more accurate air quality forecasts. It is for these reasons that the Canadian Meteorological Centre (CMC) in collaboration with the Air Quality Research Division (AQRD) of Environment Canada has recently implemented MPSOA in their daily operations.

Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

EPA Science Inventory

The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality m...

Indoor Air Quality Building Education and Assessment Model

EPA Pesticide Factsheets

The Indoor Air Quality Building Education and Assessment Model (I-BEAM), released in 2002, is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

Links Related to the Indoor Air Quality Building Education and Assessment Model

EPA Pesticide Factsheets

The Indoor Air Quality Building Education and Assessment Model (I-BEAM) is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

Bibliography for the Indoor Air Quality Building Education and Assessment Model

EPA Pesticide Factsheets

The Indoor Air Quality Building Education and Assessment Model (I-BEAM) is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

40 CFR Appendix W to Part 51 - Guideline on Air Quality Models

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 2 2010-07-01 2010-07-01 false Guideline on Air Quality Models W Appendix W to Part 51 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF IMPLEMENTATION PLANS Pt. 51, App. W Appendix W to Part 51—Guideline on Air Quality Models...

Development of the Next Generation Air Quality Modeling System (20th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA)

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinemen...

Development and application of air quality models at the U.S. EPA

EPA Science Inventory

Overview of the development and application of air quality models at the U.S. EPA, particularly focused on the development and application of the Community Multiscale Air Quality (CMAQ) model developed within the Computation Exposure Division (CED) of the National Exposure Resear...

Diagnostic Analysis of Ozone Concentrations Simulated by Two Regional-Scale Air Quality Models

EPA Science Inventory

Since the Community Multiscale Air Quality modeling system (CMAQ) and the Weather Research and Forecasting with Chemistry model (WRF/Chem) use different approaches to simulate the interaction of meteorology and chemistry, this study compares the CMAQ and WRF/Chem air quality simu...

SIMULATION OF AEROSOL DYNAMICS: A COMPARATIVE REVIEW OF ALGORITHMS USED IN AIR QUALITY MODELS

EPA Science Inventory

A comparative review of algorithms currently used in air quality models to simulate aerosol dynamics is presented. This review addresses coagulation, condensational growth, nucleation, and gas/particle mass transfer. Two major approaches are used in air quality models to repres...

Dynamic Evaluation of Long-Term Air Quality Model Simulations Over the Northeastern U.S.

EPA Science Inventory

Dynamic model evaluation assesses a modeling system's ability to reproduce changes in air quality induced by changes in meteorology and/or emissions. In this paper, we illustrate various approaches to dynamic mode evaluation utilizing 18 years of air quality simulations perform...

Development of the Next Generation Air Quality Modeling System

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinemen...

Air Quality Response Modeling for Decision Support | Science ...

EPA Pesticide Factsheets

Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being use

"Going the Extra Mile in Downscaling: Why Downscaling is not ...

EPA Pesticide Factsheets

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Community Earth System Model (CESM) were shown, both using the out-of-the-box downscaling of CESM and also with a modification to setting the inland lake temperatures. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

"Updates to Model Algorithms & Inputs for the Biogenic ...

EPA Pesticide Factsheets

We have developed new canopy emission algorithms and land use data for BEIS. Simulations with BEIS v3.4 and these updates in CMAQ v5.0.2 are compared these changes to the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and evaluated the simulations against observations. This has resulted in improvements in model evaluations of modeled isoprene, NOx, and O3. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Sensitivity of air quality simulation to smoke plume rise

Treesearch

Yongqiang Liu; Gary Achtemeier; Scott Goodrick

2008-01-01

Plume rise is the height smoke plumes can reach. This information is needed by air quality models such as the Community Multiscale Air Quality (CMAQ) model to simulate physical and chemical processes of point-source fire emissions. This study seeks to understand the importance of plume rise to CMAQ air quality simulation of prescribed burning to plume rise. CMAQ...

Evaluation of the impact of SO₂ and NO₂ emissions on the ambient air-quality in the Çan-Bayramiç region of northwest Turkey during 2007-2008.

PubMed

Ozkurt, Nesimi; Sari, Deniz; Akalin, Nuray; Hilmioglu, Bilgin

2013-07-01

The characterization and assessment of air-quality in this region are essential steps for the implementation of the "Clean Air Action Plan" as this is set by the Turkish Regulation on Ambient Air-Quality Assessment and Management. This study area intends to shed a light on use of modeling tools as an alternative method for the assessment of local atmospheric pollution and the determination of the importance of local emissions. This modeling approach can be also used for the consistent geographic representation of air-quality concentration as well as for assessing the future air-quality condition after the implementation of emission reduction measures in a certain area. With this article we evaluate the impact of sulfur dioxide and nitrogen dioxide emissions on the ambient air-quality in the Çan-Bayramiç region of Turkey. The emission rates of sulfur dioxide and nitrogen dioxide were calculated by using the CALPUFF model. The concentration of these pollutants had also been monitored at ten air-quality locations during 2007-2008 in the research area. The measured data were also utilized for testing the model performance. Results showed that the air-quality in this important rural region of Turkey can be evaluated effectively by using the current numerical modeling system. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Dynamic Evaluation of a Regional Air Quality Model: Assessing the Emissions-Induced Weekly Ozone Cycle

EPA Science Inventory

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the community Mult...

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...

IMPLEMENTATION OF AN URBAN CANOPY PARAMETERIZATION IN MM5 FOR MESO-GAMMA-SCALE AIR QUALITY MODELING APPLICATIONS

EPA Science Inventory

The U.S. Environmental Protection Agency (U.S. EPA) is extending its Models-3/Community Multiscale Air Quality (CMAQ) Modeling System to provide detailed gridded air quality concentration fields and sub-grid variability characterization at neighborhood scales and in urban areas...

Towards the Next Generation Air Quality Modeling System: Current Progress on Implementing Chemistry into MPAS-A

EPA Science Inventory

The community multiscale air quality (CMAQ) model of the U.S. Environmental Protection Agency is one of the most widely used air quality model worldwide; it is employed for both research and regulatory applications at major universities and government agencies for improving under...

Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006

EPA Science Inventory

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories...

A COMPARISON OF THE UCD/CIT AIR QUALITY MODEL AND THE CMB SOURCE-RECEPTOR MODEL FOR PRIMARY AIRBORNE PARTICULATE MATTER. (R831082)

EPA Science Inventory

Source contributions to primary airborne particulate matter calculated using the source-oriented UCD/CIT air quality model and the receptor-oriented chemical mass balance (CMB) model are compared for two air quality episodes in different parts of California. The first episode ...

Examination of the Community Multiscale Air Quality (CMAQ) Model Performance over the North American and European Domains

EPA Science Inventory

The CMAQ modeling system has been used to simulate the air quality for North America and Europe for the entire year of 2006 as part of the Air Quality Model Evaluation International Initiative (AQMEII) and the operational model performance of O₃, fine particulate matte...

ENSEMBLE and AMET: Two Systems and Approaches to a Harmonized, Simplified and Efficient Facility for Air Quality Models Development and Evaluation

EPA Science Inventory

The complexity of air quality modeling systems, air quality monitoring data make ad-hoc systems for model evaluation important aids to the modeling community. Among those are the ENSEMBLE system developed by the EC-Joint Research Center, and the AMET software developed by the US-...

Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate Matter

EPA Science Inventory

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together seventeen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and bound...

Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part 1: Ozone”

EPA Science Inventory

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundar...

Application of Wavelet Filters in an Evaluation of ...

EPA Pesticide Factsheets

Air quality model evaluation can be enhanced with time-scale specific comparisons of outputs and observations. For example, high-frequency (hours to one day) time scale information in observed ozone is not well captured by deterministic models and its incorporation into model performance metrics lead one to devote resources to stochastic variations in model outputs. In this analysis, observations are compared with model outputs at seasonal, weekly, diurnal and intra-day time scales. Filters provide frequency specific information that can be used to compare the strength (amplitude) and timing (phase) of observations and model estimates. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollu

AIR QUALITY MODELING OF PM AND AIR TOXICS AT NEIGHBORHOOD SCALES

EPA Science Inventory

The current interest in fine particles and toxics pollutants provide an impetus for extending air quality modeling capability towards improving exposure modeling and assessments. Human exposure models require information on concentration derived from interpolation of observati...

How Clean is your Local Air? Here's an app for that

NASA Astrophysics Data System (ADS)

Maskey, M.; Yang, E.; Christopher, S. A.; Keiser, K.; Nair, U. S.; Graves, S. J.

2011-12-01

Air quality is a vital element of our environment. Accurate and localized air quality information is critical for characterizing environmental impacts at the local and regional levels. Advances in location-aware handheld devices and air quality modeling have enabled a group of UAHuntsville scientists to develop a mobile app, LocalAQI, that informs users of current conditions and forecasts of up to twenty-four hours, of air quality indices. The air quality index is based on Community Multiscale Air Quality Modeling System (CMAQ). UAHuntsville scientists have used satellite remote sensing products as inputs to CMAQ, resulting in forecast guidance for particulate matter air quality. The CMAQ output is processed to compute a standardized air quality index. Currently, the air quality index is available for the eastern half of the United States. LocalAQI consists of two main views: air quality index view and map view. The air quality index view displays current air quality for the zip code of a location of interest. Air quality index value is translated into a color-coded advisory system. In addition, users are able to cycle through available hourly forecasts for a location. This location-aware app defaults to the current air quality of user's location. The map view displays color-coded air quality information for the eastern US with an ability to animate through the available forecasts. The app is developed using a cross-platform native application development tool, appcelerator; hence LocalAQI is available for iOS and Android-based phones and pads.

MODELING AIR TOXICS AND PM 2.5 CONCENTRATION FIELDS AS A MEANS FOR FACILITATING HUMAN EXPOSURE ASSESSMENTS

EPA Science Inventory

The capability of the US EPA Models-3/Community Multiscale Air Quality (CMAQ) modeling system is extended to provide gridded ambient air quality concentration fields at fine scales. These fields will drive human exposure to air toxics and fine particulate matter (PM2.5) models...

Short-term effects of air quality and thermal stress on non-accidental morbidity-a multivariate meta-analysis comparing indices to single measures.

PubMed

Lokys, Hanna Leona; Junk, Jürgen; Krein, Andreas

2018-01-01

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

Short-term effects of air quality and thermal stress on non-accidental morbidity—a multivariate meta-analysis comparing indices to single measures

NASA Astrophysics Data System (ADS)

Lokys, Hanna Leona; Junk, Jürgen; Krein, Andreas

2018-01-01

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

Reduced-form air quality modeling for community-scale ...

EPA Pesticide Factsheets

Transportation plays an important role in modern society, but its impact on air quality has been shown to have significant adverse effects on public health. Numerous reviews (HEI, CDC, WHO) summarizing findings of hundreds of studies conducted mainly in the last decade, conclude that exposures to traffic emissions near roads are a public health concern. The Community LINE Source Model (C-LINE) is a web-based model designed to inform the community user of local air quality impacts due to roadway vehicles in their region of interest using a simplified modeling approach. Reduced-form air quality modeling is a useful tool for examining what-if scenarios of changes in emissions, such as those due to changes in traffic volume, fleet mix, or vehicle speed. Examining various scenarios of air quality impacts in this way can identify potentially at-risk populations located near roadways, and the effects that a change in traffic activity may have on them. C-LINE computes dispersion of primary mobile source pollutants using meteorological conditions for the region of interest and computes air-quality concentrations corresponding to these selected conditions. C-LINE functionality has been expanded to model emissions from port-related activities (e.g. ships, trucks, cranes, etc.) in a reduced-form modeling system for local-scale near-port air quality analysis. This presentation describes the Community modeling tools C-LINE and C-PORT that are intended to be used by local gove

Air Quality Modeling Technical Support Document for the 2015 Ozone NAAQS Preliminary Interstate Transport Assessment

EPA Pesticide Factsheets

In this technical support document (TSD) EPA describes the air quality modeling performed to support the 2015 ozone National Ambient Air Quality Standards (NAAQS) preliminary interstate transport assessment Notice of Data Availability (NODA).

Evaluation of Day and Nighttime Lower Tropospheric Ozone from Air Quality Models using TES and Ozonesondes

NASA Astrophysics Data System (ADS)

Osterman, G. B.; Neu, J. L.; Eldering, A.; Pinder, R. W.; Tang, Y.; McQueen, J.

2012-12-01

At night, ozone can be transported long distances above the surface inversion layer without chemical destruction or deposition. As the boundary layer breaks up in the morning, this nocturnal ozone can be mixed down to the surface and rapidly increase ozone concentrations at a rate that can rival chemical ozone production. Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture nighttime ozone concentrations and transport. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and other sensors, ozonesonde data collected during the INTEX Ozonesonde Network Study (IONS), EPA AirNow ground station ozone data, the Community Multi-Scale Air Quality (CMAQ) model, and the National Air Quality Forecast Capability (NAQFC) model to examine air quality events during August 2006. We present both aggregated statistics and case-study analyses that assess the relationship between the models' ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone both during the day and at night. We perform the comparisons looking at the geospatial dependence in the differences between the measurements and models under different surface ozone conditions.

DESCRIPTION OF ATMOSPHERIC TRANSPORT PROCESSES IN EULERIAN AIR QUALITY MODELS

EPA Science Inventory

Key differences among many types of air quality models are the way atmospheric advection and turbulent diffusion processes are treated. Gaussian models use analytical solutions of the advection-diffusion equations. Lagrangian models use a hypothetical air parcel concept effecti...

Predicting Future-Year Ozone Concentrations: Integrated Observational-Modeling Approach for Probabilistic Evaluation of the Efficacy of Emission Control strategies

EPA Science Inventory

Regional-scale air quality models are being used to demonstrate attainment of the ozone air quality standard. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological ...

40 CFR 93.158 - Criteria for determining conformity of general Federal actions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... requirements: (i) Specified in paragraph (b) of this section, based on areawide air quality modeling analysis and local air quality modeling analysis; or (ii) Meet the requirements of paragraph (a)(5) of this section and, for local air quality modeling analysis, the requirement of paragraph (b) of this section; (4...

40 CFR 93.158 - Criteria for determining conformity of general Federal actions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... requirements: (i) Specified in paragraph (b) of this section, based on areawide air quality modeling analysis and local air quality modeling analysis; or (ii) Meet the requirements of paragraph (a)(5) of this section and, for local air quality modeling analysis, the requirement of paragraph (b) of this section; (4...

40 CFR 93.158 - Criteria for determining conformity of general Federal actions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... requirements: (i) Specified in paragraph (b) of this section, based on areawide air quality modeling analysis and local air quality modeling analysis; or (ii) Meet the requirements of paragraph (a)(5) of this section and, for local air quality modeling analysis, the requirement of paragraph (b) of this section; (4...

REVIEW OF THE GOVERNING EQUATIONS, COMPUTATIONAL ALGORITHMS, AND OTHER COMPONENTS OF THE MODELS-3 COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM

EPA Science Inventory

This article describes the governing equations, computational algorithms, and other components entering into the Community Multiscale Air Quality (CMAQ) modeling system. This system has been designed to approach air quality as a whole by including state-of-the-science capabiliti...

Evaluating the capability of regional-scale air quality models to capture the vertical distribution of pollutants

EPA Science Inventory

This study is conducted in the framework of the Air Quality Modelling Evaluation International Initiative (AQMEII) and aims at the operational evaluation of an ensemble of 12 regional-scale chemical transport models used to predict air quality over the North American (NA) and Eur...

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

PubMed

So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

2018-05-01

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr. This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.

Validation of smoke plume rise models using ground based lidar

Treesearch

Cyle E. Wold; Shawn Urbanski; Vladimir Kovalev; Alexander Petkov; Wei Min Hao

2010-01-01

Biomass fires can significantly degrade regional air quality. Plume rise height is one of the critical factors determining the impact of fire emissions on air quality. Plume rise models are used to prescribe the vertical distribution of fire emissions which are critical input for smoke dispersion and air quality models. The poor state of model evaluation is due in...

Development and case study of a science-based software platform to support policy making on air quality.

PubMed

Zhu, Yun; Lao, Yanwen; Jang, Carey; Lin, Chen-Jen; Xing, Jia; Wang, Shuxiao; Fu, Joshua S; Deng, Shuang; Xie, Junping; Long, Shicheng

2015-01-01

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling (RSM) methodology and serves as a visualization and analysis tool (VAT) for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S. demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias <2% and assisting in air quality policy making in near real time. Copyright © 2014. Published by Elsevier B.V.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes.

PubMed

Oliveri Conti, Gea; Heibati, Behzad; Kloog, Itai; Fiore, Maria; Ferrante, Margherita

2017-03-01

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

Increasing the Use of Earth Science Data and Models in Air Quality Management.

PubMed

Milford, Jana B; Knight, Daniel

2017-04-01

In 2010, the U.S. National Aeronautics and Space Administration (NASA) initiated the Air Quality Applied Science Team (AQAST) as a 5-year, $17.5-million award with 19 principal investigators. AQAST aims to increase the use of Earth science products in air quality-related research and to help meet air quality managers' information needs. We conducted a Web-based survey and a limited number of follow-up interviews to investigate federal, state, tribal, and local air quality managers' perspectives on usefulness of Earth science data and models, and on the impact AQAST has had. The air quality managers we surveyed identified meeting the National Ambient Air Quality Standards for ozone and particulate matter, emissions from mobile sources, and interstate air pollution transport as top challenges in need of improved information. Most survey respondents viewed inadequate coverage or frequency of satellite observations, data uncertainty, and lack of staff time or resources as barriers to increased use of satellite data by their organizations. Managers who have been involved with AQAST indicated that the program has helped build awareness of NASA Earth science products, and assisted their organizations with retrieval and interpretation of satellite data and with application of global chemistry and climate models. AQAST has also helped build a network between researchers and air quality managers with potential for further collaborations. NASA's Air Quality Applied Science Team (AQAST) aims to increase the use of satellite data and global chemistry and climate models for air quality management purposes, by supporting research and tool development projects of interest to both groups. Our survey and interviews of air quality managers indicate they found value in many AQAST projects and particularly appreciated the connections to the research community that the program facilitated. Managers expressed interest in receiving continued support for their organizations' use of satellite data, including assistance in retrieving and interpreting data from future geostationary platforms meant to provide more frequent coverage for air quality and other applications.

QUANTIFYING SUBGRID POLLUTANT VARIABILITY IN EULERIAN AIR QUALITY MODELS

EPA Science Inventory

In order to properly assess human risk due to exposure to hazardous air pollutants or air toxics, detailed information is needed on the location and magnitude of ambient air toxic concentrations. Regional scale Eulerian air quality models are typically limited to relatively coar...

FINE SCALE AIR QUALITY MODELING USING DISPERSION AND CMAQ MODELING APPROACHES: AN EXAMPLE APPLICATION IN WILMINGTON, DE

EPA Science Inventory

Characterization of spatial variability of air pollutants in an urban setting at fine scales is critical for improved air toxics exposure assessments, for model evaluation studies and also for air quality regulatory applications. For this study, we investigate an approach that su...

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...

AIR QUALITY MODELING AT NEIGHBORHOOD SCALES TO IMPROVE HUMAN EXPOSURE ASSESSMENT

EPA Science Inventory

Air quality modeling is an integral component of risk assessment and of subsequent development of effective and efficient management of air quality. Urban areas introduce of fresh sources of pollutants into regional background producing significant spatial variability of the co...

Recent Advances in WRF Modeling for Air Quality Applications

EPA Science Inventory

The USEPA uses WRF in conjunction with the Community Multiscale Air Quality (CMAQ) for air quality regulation and research. Over the years we have added physics options and geophysical datasets to the WRF system to enhance model capabilities especially for extended retrospective...

Air Quality Modeling

EPA Pesticide Factsheets

In this technical support document (TSD) EPA describes the air quality modeling performed to support the Environmental Protection Agency’s Transport Rule proposal (now known as the Cross-State Air Pollution Rule).

A Multi-Model Assessment for the 2006 and 2010 Simulations under the AirQuality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part I. Indicators of the Sensitivity of O3 and PM2.5 Formation Regimes

EPA Science Inventory

Under the Air Quality Model Evaluation International Initiative, Phase 2 (AQMEII-2), three online coupled air quality model simulations, with six different configurations, are analyzed for their performance, inter-model agreement, and responses to emission and meteorological chan...

77 FR 74355 - Approval of Air Quality Implementation Plans; California; San Joaquin Valley; Attainment Plan for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-14

... update the air quality modeling in the San Joaquin Valley 8-Hour Ozone SIP by December 31, 2014. DATES... modeling in the San Joaquin Valley 8-Hour Ozone SIP to reflect emissions inventory improvements and any...) * * * (396) * * * (ii) * * * (A) * * * (2) * * * (ii) Commitment to update the air quality modeling in the...

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...

APPLICATION OF BIAS AND ADJUSTMENT TECHNIQUES TO THE ETA-CMAQ AIR QUALITY FORECAST

EPA Science Inventory

The current air quality forecast system, based on linking NOAA's Eta meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, consistently overpredicts surface ozone concentrations, but simulates its day-to-day variability quite well. The ability of bias cor...

ROLE OF MODELS IN AIR QUALITY MANAGEMENT DECISIONS

EPA Science Inventory

Within the frame of the US-India bilateral agreement on environmental cooperation, a team of US scientists have been helping India in designing emission control policies to address urban air quality problems. This presentation discusses how air quality models need to be used for ...

Air quality management: evolution of policy and practice in the UK as exemplified by the experience of English local government

NASA Astrophysics Data System (ADS)

Beattie, C. I.; Longhurst, J. W. S.; Woodfield, N. K.

The air quality management (AQM) framework in the UK is designed to be an effects-based solution to air pollutants currently affecting human health. The AQM process has been legislated through The Environment Act 1995, which required the National Air Quality Strategy (NAQS) to be published. AQM practice and capability within local authorities has flourished since the publication of the NAQS in March 1997. This paper outlines the policy framework within which the UK operates, both at a domestic and European level, and reviews the air quality management process relating to current UK policy and EU policy. Data from questionnaire surveys are used to indicate the involvement of various sectors of local government in the air quality management process. These data indicate an increasing use of monitoring, and use of air dispersion modelling by English local authorities. Data relating to the management of air quality, for example, the existence and work of air quality groups, dissemination of information to the public and policy measures in place on a local scale to improve air quality, have also been reported. The UK NAQS has been reviewed in 1999 to reflect developments in European legislation, technological and scientific advances, improved air pollution modelling techniques and an increasingly better understanding of the socio-economic issues involved. The AQM process, as implemented by UK local authorities, provides an effective model for other European member states with regards to the implementation of the Air Quality Framework Directive. The future direction of air quality policy in the UK is also discussed.

Effects of building roof greening on air quality in street canyons

NASA Astrophysics Data System (ADS)

Baik, Jong-Jin; Kwak, Kyung-Hwan; Park, Seung-Bu; Ryu, Young-Hee

2012-12-01

Building roof greening is a successful strategy for improving urban thermal environment. It is of theoretical interest and practical importance to study the effects of building roof greening on urban air quality in a systematic and quantitative way. In this study, we examine the effects of building roof greening on air quality in street canyons using a computational fluid dynamics (CFD) model that includes the thermodynamic energy equation and the transport equation of passive, non-reactive pollutants. For simplicity, building roof greening is represented by specified cooling. Results for a simple building configuration with a street canyon aspect ratio of one show that the cool air produced due to building roof greening flows into the street canyon, giving rise to strengthened street canyon flow. The strengthened street canyon flow enhances pollutant dispersion near the road, which decreases pollutant concentration there. Thus, building roof greening improves air quality near the road. The degree of air quality improvement near the road increases as the cooling intensity increases. In the middle region of the street canyon, the air quality can worsen when the cooling intensity is not too strong. Results for a real urban morphology also show that building roof greening improves air quality near roads. The degree of air quality improvement near roads due to building roof greening depends on the ambient wind direction. These findings provide a theoretical foundation for constructing green roofs for the purpose of improving air quality near roads or at a pedestrian level as well as urban thermal environment. Further studies using a CFD model coupled with a photochemistry model and a surface energy balance model are required to evaluate the effects of building roof greening on air quality in street canyons in a more realistic framework.

Evaluation of regional climate simulations for air quality modelling purposes

NASA Astrophysics Data System (ADS)

Menut, Laurent; Tripathi, Om P.; Colette, Augustin; Vautard, Robert; Flaounas, Emmanouil; Bessagnet, Bertrand

2013-05-01

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional "climate modeling" source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Enhanced data validation strategy of air quality monitoring network.

PubMed

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

2018-01-01

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement). Copyright © 2017 Elsevier Inc. All rights reserved.

CONCENTRATIONS OF TOXIC AIR POLLUTANTS IN THE U.S. SIMULATED BY AN AIR QUALITY MODEL

EPA Science Inventory

As part of the US National Air Toxics Assessment, we have applied the Community Multiscale Air Quality Model, CMAQ, to study the concentrations of twenty gas-phase, toxic, hazardous air pollutants (HAPs) in the atmosphere over the continental United States. We modified the Carbo...

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

PubMed

Yang, Zhongshan; Wang, Jian

2017-10-01

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.

EVALUATION OF THE REAL-TIME AIR-QUALITY MODEL USING THE RAPS (REGIONAL AIR POLLUTION STUDY) DATA BASE. VOLUME 1. OVERVIEW

EPA Science Inventory

The theory and programming of statistical tests for evaluating the Real-Time Air-Quality Model (RAM) using the Regional Air Pollution Study (RAPS) data base are fully documented in four report volumes. Moreover, the tests are generally applicable to other model evaluation problem...

EMISSIONS PROCESSING FOR THE ETA/ CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of the...

COMPUTATIONAL ASPECTS OF THE AIR QUALITY FORECASTING VERSION OF CMAQ (CMAQ-F)

EPA Science Inventory

The air quality forecast version of the Community Modeling Air Quality (CMAQ) model (CMAQ-F) was developed from the public release version of CMAQ (available from http://www.cmascenter.org), and is running operationally at the National Weather Service's National Centers for Envir...

ONE ATMOSPHERE MODELING FOR AIR QUALITY: BUILDING PARTNERSHIPS THAT TRANSITION RESEARCH INTO APPLICATIONS

EPA Science Inventory

The Community Miultiscale Air Quality (CMAQ) modeling system is a "one atmosphere" chemical transport model that simulates the transport and fate of air pollutants from urban to continental scales and from daily to annual time intervals.

COMMUNITY MULTISCALE AIR QUALITY ( CMAQ ) MODEL - QUALITY ASSURANCE AND VERSION CONTROL

EPA Science Inventory

This presentation will be given to the EPA Exposure Modeling Workgroup on January 24, 2006. The quality assurance and version control procedures for the Community Multiscale Air Quality (CMAQ) Model are presented. A brief background of CMAQ is given, then issues related to qual...

Implementation of a WRF-CMAQ Air Quality Modeling System in Bogotá, Colombia

NASA Astrophysics Data System (ADS)

Nedbor-Gross, R.; Henderson, B. H.; Pachon, J. E.; Davis, J. R.; Baublitz, C. B.; Rincón, A.

2014-12-01

Due to a continuous economic growth Bogotá, Colombia has experienced air pollution issues in recent years. The local environmental authority has implemented several strategies to curb air pollution that have resulted in the decrease of PM10 concentrations since 2010. However, more activities are necessary in order to meet international air quality standards in the city. The University of Florida Air Quality and Climate group is collaborating with the Universidad de La Salle to prioritize regulatory strategies for Bogotá using air pollution simulations. To simulate pollution, we developed a modeling platform that combines the Weather Research and Forecasting Model (WRF), local emissions, and the Community Multi-scale Air Quality model (CMAQ). This platform is the first of its kind to be implemented in the megacity of Bogota, Colombia. The presentation will discuss development and evaluation of the air quality modeling system, highlight initial results characterizing photochemical conditions in Bogotá, and characterize air pollution under proposed regulatory strategies. The WRF model has been configured and applied to Bogotá, which resides in a tropical climate with complex mountainous topography. Developing the configuration included incorporation of local topography and land-use data, a physics sensitivity analysis, review, and systematic evaluation. The threshold, however, was set based on synthesis of model performance under less mountainous conditions. We will evaluate the impact that differences in autocorrelation contribute to the non-ideal performance. Air pollution predictions are currently under way. CMAQ has been configured with WRF meteorology, global boundary conditions from GEOS-Chem, and a locally produced emission inventory. Preliminary results from simulations show promising performance of CMAQ in Bogota. Anticipated results include a systematic performance evaluation of ozone and PM10, characterization of photochemical sensitivity, and air quality predictions under proposed regulatory scenarios.

"Total Deposition (TDEP) Maps" | Science Inventory | US EPA

EPA Pesticide Factsheets

The presentation provides an update on the use of a hybrid methodology that relies on measured values from national monitoring networks and modeled values from CMAQ to produce of maps of total deposition for use in critical loads and other ecological assessments. Additionally, comparisons of the deposition values from the hybrid approach are compared with deposition estimates from other methodologies. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

PERFORMANCE AND DIAGNOSTIC EVALUATION OF OZONE PREDICTIONS BY THE ETA-COMMUNITY MULTISCALE AIR QUALITY FORECAST SYSTEM DURING THE 2002 NEW ENGLAND AIR QUALITY STUDY

EPA Science Inventory

A real-time air quality forecasting system (Eta-CMAQ model suite) has been developed by linking the NCEP Eta model to the U.S. EPA CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting O₃ over the northeastern U.S d...

Linking Air Quality and Watershed Models for Environmental Assessments: Analysis of the Effects of Model-Specific Precipitation Estimates on Calculated Water Flux

EPA Science Inventory

Directly linking air quality and watershed models could provide an effective method for estimating spatially-explicit inputs of atmospheric contaminants to watershed biogeochemical models. However, to adequately link air and watershed models for wet deposition estimates, each mod...

GUIDANCE FOR THE PERFORMANCE EVALUATION OF THREE-DIMENSIONAL AIR QUALITY MODELING SYSTEMS FOR PARTICULATE MATTER AND VISIBILITY

EPA Science Inventory

The National Ambient Air Quality Standards for particulate matter (PM) and the federal regional haze regulations place some emphasis on the assessment of fine particle (PM; 5) concentrations. Current air quality models need to be improved and evaluated against observations to a...

THE EMISSION PROCESSING SYSTEM FOR THE ETA/CMAQ AIR QUALITY FORECAST SYSTEM

EPA Science Inventory

NOAA and EPA have created an Air Quality Forecast (AQF) system. This AQF system links an adaptation of the EPA's Community Multiscale Air Quality Model with the 12 kilometer ETA model running operationally at NOAA's National Center for Environmental Predication (NCEP). One of th...

APPLICATION AND EVALUATION OF CMAQ IN THE UNITED STATES: AIR QUALITY FORECASTING AND RETROSPECTIVE MODELING

EPA Science Inventory

Presentation slides provide background on model evaluation techniques. Also included in the presentation is an operational evaluation of 2001 Community Multiscale Air Quality (CMAQ) annual simulation, and an evaluation of PM_2.5 for the CMAQ air quality forecast (AQF) ...

Space-Time Analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 Air Quality Simulations

EPA Science Inventory

This study presents an evaluation of summertime daily maximum ozone concentrations over North America (NA) and Europe (EU) using the database generated during Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying tempor...

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings.

PubMed

Challoner, Avril; Pilla, Francesco; Gill, Laurence

2015-12-01

NO₂ and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person's well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO₂ indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO₂ exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

An Overview of Atmospheric Chemistry and Air Quality Modeling

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.

2017-01-01

This presentation will include my personal research experience and an overview of atmospheric chemistry and air quality modeling to the participants of the NASA Student Airborne Research Program (SARP 2017). The presentation will also provide examples on ways to apply airborne observations for chemical transport (CTM) and air quality (AQ) model evaluation. CTM and AQ models are important tools in understanding tropospheric-stratospheric composition, atmospheric chemistry processes, meteorology, and air quality. This presentation will focus on how NASA scientist currently apply CTM and AQ models to better understand these topics. Finally, the importance of airborne observation in evaluating these topics and how in situ and remote sensing observations can be used to evaluate and improve CTM and AQ model predictions will be highlighted.

Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

NASA Astrophysics Data System (ADS)

Taylan, Osman

2017-02-01

High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

Land Surface Process and Air Quality Research and Applications at MSFC

NASA Technical Reports Server (NTRS)

Quattrochi, Dale; Khan, Maudood

2007-01-01

This viewgraph presentation provides an overview of land surface process and air quality research at MSFC including atmospheric modeling and ongoing research whose objective is to undertake a comprehensive spatiotemporal analysis of the effects of accurate land surface characterization on atmospheric modeling results, and public health applications. Land use maps as well as 10 meter air temperature, surface wind, PBL mean difference heights, NOx, ozone, and O3+NO2 plots as well as spatial growth model outputs are included. Emissions and general air quality modeling are also discussed.

Fractional kalman filter to estimate the concentration of air pollution

NASA Astrophysics Data System (ADS)

Vita Oktaviana, Yessy; Apriliani, Erna; Khusnul Arif, Didik

2018-04-01

Air pollution problem gives important effect in quality environment and quality of human’s life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollution’s problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).

The Use of Regulatory Air Quality Models to Develop Successful Ozone Attainment Strategies

NASA Astrophysics Data System (ADS)

Canty, T. P.; Salawitch, R. J.; Dickerson, R. R.; Ring, A.; Goldberg, D. L.; He, H.; Anderson, D. C.; Vinciguerra, T.

2015-12-01

The Environmental Protection Agency (EPA) recently proposed lowering the 8-hr ozone standard to between 65-70 ppb. Not all regions of the U.S. are in attainment of the current 75 ppb standard and it is expected that many regions currently in attainment will not meet the future, lower surface ozone standard. Ozone production is a nonlinear function of emissions, biological processes, and weather. Federal and state agencies rely on regulatory air quality models such as the Community Multi-Scale Air Quality (CMAQ) model and Comprehensive Air Quality Model with Extensions (CAMx) to test ozone precursor emission reduction strategies that will bring states into compliance with the National Ambient Air Quality Standards (NAAQS). We will describe various model scenarios that simulate how future limits on emission of ozone precursors (i.e. NOx and VOCs) from sources such as power plants and vehicles will affect air quality. These scenarios are currently being developed by states required to submit a State Implementation Plan to the EPA. Projections from these future case scenarios suggest that strategies intended to control local ozone may also bring upwind states into attainment of the new NAAQS. Ground based, aircraft, and satellite observations are used to ensure that air quality models accurately represent photochemical processes within the troposphere. We will highlight some of the improvements made to the CMAQ and CAMx model framework based on our analysis of NASA observations obtained by the OMI instrument on the Aura satellite and by the DISCOVER-AQ field campaign.

US EPA 2012 Air Quality Fused Surface for the Conterminous U.S. Map Service

EPA Pesticide Factsheets

This web service contains a polygon layer that depicts fused air quality predictions for 2012 for census tracts in the conterminous United States. Fused air quality predictions (for ozone and PM2.5) are modeled using a Bayesian space-time downscaling fusion model approach described in a series of three published journal papers: 1) (Berrocal, V., Gelfand, A. E. and Holland, D. M. (2012). Space-time fusion under error in computer model output: an application to modeling air quality. Biometrics 68, 837-848; 2) Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975; and 3) Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 15, 176-197) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr maximum) surfaces for 2012. Summer (O3) and annual (PM2.5) means calculated and published. The downscaling fusion model uses both air quality monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Models-3/Community Multiscale Air Quality (CMAQ). Currently, predictions at the US census tract centroid locations within the 12 km CMAQ domain are archived. Predictions at the CMAQ grid cell centroids, or any desired set of locations co

DEVELOPMENT AND ANALYSIS OF AIR QUALITY MODELING SIMULATIONS FOR HAZARDOUS AIR POLLUTANTS

EPA Science Inventory

The concentrations of five hazardous air pollutants were simulated using the Community Multi Scale Air Quality (CMAQ) modeling system. Annual simulations were performed over the continental United States for the entire year of 2001 to support human exposure estimates. Results a...

LARGE-SCALE PREDICTIONS OF MOBILE SOURCE CONTRIBUTIONS TO CONCENTRATIONS OF TOXIC AIR POLLUTANTS

EPA Science Inventory

This presentation shows concentrations and deposition of toxic air pollutants predicted by a 3-D air quality model, the Community Multi Scale Air Quality (CMAQ) modeling system. Contributions from both on-road and non-road mobile sources are analyzed.

EVALUATION OF THE REAL-TIME AIR-QUALITY MODEL USING THE RAPS (REGIONAL AIR POLLUTION STUDY) DATA BASE. VOLUME 3. PROGRAM USER'S GUIDE

EPA Science Inventory

The theory and programming of statistical tests for evaluating the Real-Time Air-Quality Model (RAM) using the Regional Air Pollution Study (RAPS) data base are fully documented in four volumes. Moreover, the tests are generally applicable to other model evaluation problems. Volu...

EVALUATION OF THE REAL-TIME AIR-QUALITY MODEL USING THE RAPS (REGIONAL AIR POLLUTION STUDY) DATA BASE. VOLUME 4. EVALUATION GUIDE

EPA Science Inventory

The theory and programming of statistical tests for evaluating the Real-Time Air-Quality Model (RAM) using the Regional Air Pollution Study (RAPS) data base are fully documented in four volumes. Moreover, the tests are generally applicable to other model evaluation problems. Volu...

Towards the Next Generation Air Quality Modeling System ...

EPA Pesticide Factsheets

The community multiscale air quality (CMAQ) model of the U.S. Environmental Protection Agency is one of the most widely used air quality model worldwide; it is employed for both research and regulatory applications at major universities and government agencies for improving understanding of the formation and transport of air pollutants. It is noted, however, that air quality issues and climate change assessments need to be addressed globally recognizing the linkages and interactions between meteorology and atmospheric chemistry across a wide range of scales. Therefore, an effort is currently underway to develop the next generation air quality modeling system (NGAQM) that will be based on a global integrated meteorology and chemistry system. The model for prediction across scales-atmosphere (MPAS-A), a global fully compressible non-hydrostatic model with seamlessly refined centroidal Voronoi grids, has been chosen as the meteorological driver of this modeling system. The initial step of adapting MPAS-A for the NGAQM was to implement and test the physics parameterizations and options that are preferred for retrospective air quality simulations (see the work presented by R. Gilliam, R. Bullock, and J. Herwehe at this workshop). The next step, presented herein, would be to link the chemistry from CMAQ to MPAS-A to build a prototype for the NGAQM. Furthermore, the techniques to harmonize transport processes between CMAQ and MPAS-A, methodologies to connect the chemis

Study on an Air Quality Evaluation Model for Beijing City Under Haze-Fog Pollution Based on New Ambient Air Quality Standards

PubMed Central

Li, Li; Liu, Dong-Jun

2014-01-01

Since 2012, China has been facing haze-fog weather conditions, and haze-fog pollution and PM2.5 have become hot topics. It is very necessary to evaluate and analyze the ecological status of the air environment of China, which is of great significance for environmental protection measures. In this study the current situation of haze-fog pollution in China was analyzed first, and the new Ambient Air Quality Standards were introduced. For the issue of air quality evaluation, a comprehensive evaluation model based on an entropy weighting method and nearest neighbor method was developed. The entropy weighting method was used to determine the weights of indicators, and the nearest neighbor method was utilized to evaluate the air quality levels. Then the comprehensive evaluation model was applied into the practical evaluation problems of air quality in Beijing to analyze the haze-fog pollution. Two simulation experiments were implemented in this study. One experiment included the indicator of PM2.5 and was carried out based on the new Ambient Air Quality Standards (GB 3095-2012); the other experiment excluded PM2.5 and was carried out based on the old Ambient Air Quality Standards (GB 3095-1996). Their results were compared, and the simulation results showed that PM2.5 was an important indicator for air quality and the evaluation results of the new Air Quality Standards were more scientific than the old ones. The haze-fog pollution situation in Beijing City was also analyzed based on these results, and the corresponding management measures were suggested. PMID:25170682

Quantifying Co-benefits of Renewable Energy through Integrated Electricity and Air Quality Modeling

NASA Astrophysics Data System (ADS)

Abel, D.

2016-12-01

This work focuses on the coordination of electricity sector changes with air quality and health improvement strategies through the integration of electricity and air quality models. Two energy models are used to calculate emission perturbations associated with changes in generation technology (20% generation from solar photovoltaics) and demand (future electricity use under a warmer climate). Impacts from increased solar PV penetration are simulated with the electricity model GridView, in collaboration with the National Renewable Energy Laboratory (NREL). Generation results are used to scale power plant emissions from an inventory developed by the Lake Michigan Air Directors Consortium (LADCO). Perturbed emissions and are used to calculate secondary particulate matter with the Community Multiscale Air Quality (CMAQ) model. We find that electricity NOx and SO2 emissions decrease at a rate similar to the total fraction of electricity supplied by solar. Across the Eastern U.S. region, average PM2.5 is reduced 5% over the summer, with highest reduction in regions and on days of greater PM2.5. A similar approach evaluates the air quality impacts of elevated electricity demand under a warmer climate. Meteorology is selected from the North American Regional Climate Change Assessment Program (NARCCAP) and input to a building energy model, eQUEST, to assess electricity demand as a function of ambient temperature. The associated generation and emissions are calculated on a plant-by-plant basis by the MyPower power sector model. These emissions are referenced to the 2011 National Emissions Inventory to be modeled in CMAQ for the Eastern U.S. and extended to health impact evaluation with the Environmental Benefits Mapping and Analysis Program (BenMAP). All results focus on the air quality and health consequences of energy system changes, considering grid-level changes to meet climate and air quality goals.

“AQMEII Status Update” | Science Inventory | US EPA

EPA Pesticide Factsheets

“AQMEII Status Update”This presentation provided an overview and status update of the Air Quality Model Evaluation International Initative (AQMEII) to participants of a workshop of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) . In addition, the presentation also outlines the objectives and potential timeline for a possible next phase of AQMEII that would involve a collaboration with the current modeling activities of TF-HTAP. The purpose of the presentation was to provide participants at the HTAP meeting with an overview of current AQMEII activities and timelines and to obtain feedback from HTAP workshop participants regarding HTAP timelines. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air po

Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

EPA Science Inventory

A major challenge in traffic-related air pollution 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 a...

Observations and modeling of air quality trends over 1990-2010 across the northern hemisphere: China, the United States and Europe

EPA Science Inventory

Trends in air quality across the Northern Hemisphere over a 21-year period (1990–2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting WRF) simulations and internally ...

ANALYSIS OF AIR QUALITY DATA NEAR ROADWAYS USING A DISPERSION MODEL

EPA Science Inventory

A dispersion model was used to analyze measurements made during a field study conducted by the U.S. EPA in July and August 2006, to estimate the impact of highway emissions on air quality at distances of tens of meters from an eight-lane highway. The air quality measurements con...

Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States

EPA Science Inventory

The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O₃) and experimental fine particular matter (PM_2...

Statistical Properties of Differences between Low and High Resolution CMAQ Runs with Matched Initial and Boundary Conditions

EPA Science Inventory

The difficulty in assessing errors in numerical models of air quality is a major obstacle to improving their ability to predict and retrospectively map air quality. In this paper, using simulation outputs from the Community Multi-scale Air Quality Model (CMAQ), the statistic...

Modeling the impacts of green infrastructure land use changes on air quality and meteorology case study and sensitivity analysis in Kansas City

EPA Science Inventory

Changes in vegetation cover associated with urban planning efforts may affect regional meteorology and air quality. Here we use a comprehensive coupled meteorology-air quality model (WRF-CMAQ) to simulate the influence of planned land use changes from green infrastructure impleme...

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

2006-01-01

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

ONE-ATMOSPHERE DYNAMICS DESCRIPTION IN THE MODELS-3 COMMUNITY MULTI-SCALE QUALITY (CMAQ) MODELING SYSTEM

EPA Science Inventory

This paper proposes a general procedure to link meteorological data with air quality models, such as U.S. EPA's Models-3 Community Multi-scale Air Quality (CMAQ) modeling system. CMAQ is intended to be used for studying multi-scale (urban and regional) and multi-pollutant (ozon...

Development and application of air quality models at the US ...

EPA Pesticide Factsheets

Overview of the development and application of air quality models at the U.S. EPA, particularly focused on the development and application of the Community Multiscale Air Quality (CMAQ) model developed within the Computation Exposure Division (CED) of the National Exposure Research Laboratory (NERL). This presentation will provide a simple overview of air quality model development and application geared toward a non-technical student audience. 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 support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Feedbacks between Air Pollution and Weather, Part 1: Effects on Weather

EPA Science Inventory

The meteorological predictions of fully coupled air-quality models running in “feedback” versus “nofeedback” simulations were compared against each other as part of Phase 2 of the Air Quality Model Evaluation International Initiative. The model simulations included a “no-feedback...

The AQMEII Two-Continent Regional Air Quality Model Evaluation Study: Fueling Ideas with Unprecedented Data

EPA Science Inventory

Although strong collaborations in the air pollution field have existed among the North American (NA) and European (EU) countries over the past five decades, regional-scale air quality model developments and model performance evaluations have been carried out independently unlike ...

Modeling and Calculator Tools for State and Local Transportation Resources

EPA Pesticide Factsheets

Air quality models, calculators, guidance and strategies are offered for estimating and projecting vehicle air pollution, including ozone or smog-forming pollutants, particulate matter and other emissions that pose public health and air quality concerns.

The Economic Value of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Anderson-Sumo, Tasha

Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with bad air quality warning and forecast air quality forecasts. There is a difficulty presented with evaluating the economic utility of the forecasts. All may not comply and even with a low compliance rate of 5% and 72% as the average probability of detection of poor air quality days by the air quality models, we estimate that the forecasting program saves 412 lives or 412 million dollars per year for the region. The totals we found are great or greater than other typical yearly meteorological hazard programs such as tornado or hurricane forecasting and it is clear that the economic value of air quality forecasting in the Baltimore/Washington region is vital.

CMAQ Involvement in Air Quality Model Evaluation International Initiative

EPA Pesticide Factsheets

Description of Air Quality Model Evaluation International Initiative (AQMEII). Different chemical transport models are applied by different groups over North America and Europe and evaluated against observations.

Emissions and dispersion modeling system (EDMS). Its development and application at airports and airbases

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

Moss, M.T.; Segal, H.M.

1994-06-01

A new complex source microcomputer model has been developed for use at civil airports and Air Force bases. This paper describes both the key features of this model and its application in evaluating the air quality impact of new construction projects at three airports: one in the United States and two in Canada. The single EDMS model replaces the numerous models previously required to assess the air quality impact of pollution sources at airports. EDMS also employs a commercial data base to reduce the time and manpower required to accurately assess and document the air quality impact of airfield operations.more » On July 20, 1993, the U.S. Environmental Protection Agency (EPA) issued the final rule (Federal Register, 7/20/93, page 38816) to add new models to the Guideline on Air Quality Models. At that time EDMS was incorporated into the Guideline as an Appendix A model. 12 refs., 4 figs., 1 tab.« less

30 CFR 285.659 - What requirements must I include in my SAP, COP, or GAP regarding air quality?

Code of Federal Regulations, 2010 CFR

2010-07-01

..., or GAP regarding air quality? 285.659 Section 285.659 Mineral Resources MINERALS MANAGEMENT SERVICE... must I include in my SAP, COP, or GAP regarding air quality? (a) You must comply with the Clean Air Act...) For air quality modeling that you perform in support of the activities proposed in your plan, you...

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings

PubMed Central

Challoner, Avril; Pilla, Francesco; Gill, Laurence

2015-01-01

NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts. PMID:26633448

40 CFR 52.1824 - Review of new sources and modifications.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Quality Models” as supplemented by the “North Dakota Guideline for Air Quality Modeling Analysis”.In a... requirements of the EPA Guideline for air quality modeling demonstrations associated with the permitting of new...

Hybrid Air Quality Modeling Approach for use in the Hear-road Exposures to Urban air pollutant Study(NEXUS)

EPA Science Inventory

The 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, associa...

Space-Time Fusion Under Error in Computer Model Output: An Application to Modeling Air Quality

EPA Science Inventory

In the last two decades a considerable amount of research effort has been devoted to modeling air quality with public health objectives. These objectives include regulatory activities such as setting standards along with assessing the relationship between exposure to air pollutan...

Air Quality Model Evaluation International Initiative (AQMEII) - Utrecht, Netherlands The May 8, 2012

EPA Science Inventory

The 4th workshop of the Air Quality Model Evaluation International Initiative (AQMEII) was held on May 8 in Utrecht, The Netherlands, in conjunction with the NATO/SPS International Technical Meeting on Air Pollution Modeling and Its Application. AQMEII was launched in 2009 as a l...

NEIGHBORHOOD SCALE MODELING OF PM 2.5 AND AIR TOXICS CONCENTRATION DISTRIBUTIONS TO DRIVE HUMAN EXPOSURE MODELS

EPA Science Inventory

Air quality (AQ) simulation models provide a basis for implementing the National Ambient Air Quality Standards (NAAQS) and are a tool for performing risk-based assessments and for developing environmental management strategies. Fine particulate matter (PM 2.5), its constituent...

A multi-model assessment of the co-benefits of climate mitigation for global air quality

NASA Astrophysics Data System (ADS)

Rao, Shilpa; Klimont, Zbigniew; Leitao, Joana; Riahi, Keywan; van Dingenen, Rita; Aleluia Reis, Lara; Calvin, Katherine; Dentener, Frank; Drouet, Laurent; Fujimori, Shinichiro; Harmsen, Mathijs; Luderer, Gunnar; Heyes, Chris; Strefler, Jessica; Tavoni, Massimo; van Vuuren, Detlef P.

2016-12-01

We present a model comparison study that combines multiple integrated assessment models with a reduced-form global air quality model to assess the potential co-benefits of global climate mitigation policies in relation to the World Health Organization (WHO) goals on air quality and health. We include in our assessment, a range of alternative assumptions on the implementation of current and planned pollution control policies. The resulting air pollution emission ranges significantly extend those in the Representative Concentration Pathways. Climate mitigation policies complement current efforts on air pollution control through technology and fuel transformations in the energy system. A combination of stringent policies on air pollution control and climate change mitigation results in 40% of the global population exposed to PM levels below the WHO air quality guideline; with the largest improvements estimated for India, China, and Middle East. Our results stress the importance of integrated multisector policy approaches to achieve the Sustainable Development Goals.

Comparison of CMAQ Modeling Study with Discover-AQ 2014 Aircraft Measurements over Colorado

NASA Astrophysics Data System (ADS)

Tang, Y.; Pan, L.; Lee, P.; Tong, D.; Kim, H. C.; Artz, R. S.

2014-12-01

NASA and NCAR jointly led a recent multiple platform-based (space, air and ground) measurement intensive to study air quality and to validate satellite data. The Discover-AQ/FRAPPE field experiment took place along the Colorado Front Range in July and August, 2014. The air quality modeling team of the NOAA Air Resources Laboratory was one of the three teams that provided real-time air quality forecasting for the campaign. The U.S. EPA Community Multi-scale Air Quality (CMAQ) Model was used with emission inventories based on the data set used by the NOAA National Air Quality Forecasting Capacity (NAQFC). By analyzing the forecast results calculated using aircraft measurements, it was found that CO emissions tended to be overestimated, while ethane emissions were underestimated. Biogenic VOCs were also underpredicted. Due to their relatively high altitude, ozone concentrations in Denver and the surrounding areas are affected by both local emissions and transported ozone. The modeled ozone was highly dependent on the meteorological predictions over this region. The complex terrain over the Rocky Mountains also contributed to the model uncertainty. This study discussed the causes of model biases, the forecast performance under different meteorology, and results from using different model grid resolutions. Several data assimilation techniques were further tested to improve the "post-analysis" performance of the modeling system for the period.

Statistical study of air pollutant concentrations via generalized gamma distribution

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

Marani, A.; Lavagnini, I.; Buttazzoni, C.

1986-11-01

This paper deals with modeling observed frequency distributions of air quality data measured in the area of Venice, Italy. The paper discusses the application of the generalized gamma distribution (ggd) which has not been commonly applied to air quality data notwithstanding the fact that it embodies most distribution models used for air quality analyses. The approach yields important simplifications for statistical analyses. A comparison among the ggd and other relevant models (standard gamma, Weibull, lognormal), carried out on daily sulfur dioxide concentrations in the area of Venice underlines the efficiency of ggd models in portraying experimental data.

Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool

NASA Astrophysics Data System (ADS)

Pisoni, E.; Albrecht, D.; Mara, T. A.; Rosati, R.; Tarantola, S.; Thunis, P.

2018-06-01

Air quality has significantly improved in Europe over the past few decades. Nonetheless we still find high concentrations in measurements mainly in specific regions or cities. This dimensional shift, from EU-wide to hot-spot exceedances, calls for a novel approach to regional air quality management (to complement EU-wide existing policies). The SHERPA (Screening for High Emission Reduction Potentials on Air quality) modelling tool was developed in this context. It provides an additional tool to be used in support to regional/local decision makers responsible for the design of air quality plans. It is therefore important to evaluate the quality of the SHERPA model, and its behavior in the face of various kinds of uncertainty. Uncertainty and sensitivity analysis techniques can be used for this purpose. They both reveal the links between assumptions and forecasts, help in-model simplification and may highlight unexpected relationships between inputs and outputs. Thus, a policy steered SHERPA module - predicting air quality improvement linked to emission reduction scenarios - was evaluated by means of (1) uncertainty analysis (UA) to quantify uncertainty in the model output, and (2) by sensitivity analysis (SA) to identify the most influential input sources of this uncertainty. The results of this study provide relevant information about the key variables driving the SHERPA output uncertainty, and advise policy-makers and modellers where to place their efforts for an improved decision-making process.

Current Applications of OMI Tropospheric NO2 Data for Air Quality and a Look to the Future

NASA Technical Reports Server (NTRS)

Pickering, Kenneth E.; Bucsela, E.; Allen, D.; Prados, A.; Gleason, J.; Kondragunta, S.

2010-01-01

Ozone Monitoring Instrument (OMI) Tropospheric NO2 products are being used to enhance the ability to monitor changes in NO2 air quality, update emission inventories, and evaluate regional air quality models. Trends in tropospheric column NO2 have been examined over the eastern United States in relation to emissions changes mandated by regulatory actions. Decreases of 20 to 40 percent over the period 2005 to 2008 were noted, largely in response to major emission reductions at power plants. The OMI data have been used to identify regions in which the opposite trend has been found. We have also used OMI NO2 in efforts to improve emission inventories for NOx emissions from soil. Lightning NOx emissions have been added to CMAQ, the US Environmental Protection Agency's regional air quality model. Evaluation of the resulting NO2 columns in the model is being conducted using the OMI NO2 observations. Community Multiscale Air Quality (CMAQ) together with the OMI NO2 data comprise a valuable tool for monitoring and predicting air quality. Looking to the future, we expect that the combination of Global Ozone Monitoring Experiment-2 (GOME-2) (morning) and OMI (afternoon) data sets obtained through use of the same retrieval algorithms will substantially increase the possibility of successful integration of satellite information into regional air quality forecast models. Farther down the road, we anticipate the Geostationary Coastal and Air Pollution Events (GEO-CAPE) platform to supply data possibly on an hourly basis, allowing much more comprehensive analysis of air quality from space.

Modeling Aircraft Emissions for Regional-scale Air Quality: Adapting a New Global Aircraft Emissions Database for the U.S

NASA Astrophysics Data System (ADS)

Arunachalam, S.; Baek, B. H.; Vennam, P. L.; Woody, M. C.; Omary, M.; Binkowski, F.; Fleming, G.

2012-12-01

Commercial aircraft emit substantial amounts of pollutants during their complete activity cycle that ranges from landing-and-takeoff (LTO) at airports to cruising in upper elevations of the atmosphere, and affect both air quality and climate. Since these emissions are not uniformly emitted over the earth, and have substantial temporal and spatial variability, it is vital to accurately evaluate and quantify the relative impacts of aviation emissions on ambient air quality. Regional-scale air quality modeling applications do not routinely include these aircraft emissions from all cycles. Federal Aviation Administration (FAA) has developed the Aviation Environmental Design Tool (AEDT), a software system that dynamically models aircraft performance in space and time to calculate fuel burn and emissions from gate-to-gate for all commercial aviation activity from all airports globally. To process in-flight aircraft emissions and to provide a realistic representation of these for treatment in grid-based air quality models, we have developed an interface processor called AEDTproc that accurately distributes full-flight chorded emissions in time and space to create gridded, hourly model-ready emissions input data. Unlike the traditional emissions modeling approach of treating aviation emissions as ground-level sources or processing emissions only from the LTO cycles in regional-scale air quality studies, AEDTproc distributes chorded inventories of aircraft emissions during LTO cycles and cruise activities into a time-variant 3-D gridded structure. We will present results of processed 2006 global emissions from AEDT over a continental U.S. modeling domain to support a national-scale air quality assessment of the incremental impacts of aircraft emissions on surface air quality. This includes about 13.6 million flights within the U.S. out of 31.2 million flights globally. We will focus on assessing spatio-temporal variability of these commercial aircraft emissions, and comparing upper tropospheric budgets of NOx from aircraft and lightning sources in the modeling domain.

The statistical evaluation and comparison of ADMS-Urban model for the prediction of nitrogen dioxide with air quality monitoring network.

PubMed

Dėdelė, Audrius; Miškinytė, Auksė

2015-09-01

In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.

Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Khan, Maudood

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these transportation plans will affect fbture air quality.

Air quality and future energy system planning

NASA Astrophysics Data System (ADS)

Sobral Mourao, Zenaida; Konadu, Dennis; Lupton, Rick

2016-04-01

Ambient air pollution has been linked to an increasing number of premature deaths throughout the world. Projected increases in demand for food, energy resources and manufactured products will likely contribute to exacerbate air pollution with an increasing impact on human health, agricultural productivity and climate change. Current events such as tampering emissions tests by VW car manufacturers, failure to comply with EU Air Quality directives and WHO guidelines by many EU countries, the problem of smog in Chinese cities and new industrial emissions regulations represent unique challenges but also opportunities for regulators, local authorities and industry. However current models and practices of energy and resource use do not consider ambient air impacts as an integral part of the planing process. Furthermore the analysis of drivers, sources and impacts of air pollution is often fragmented, difficult to understand and lacks effective visualization tools that bring all of these components together. This work aims to develop a model that links impacts of air quality on human health and ecosystems to current and future developments in the energy system, industrial and agricultural activity and patterns of land use. The model will be added to the ForeseerTM tool, which is an integrated resource analysis platform that has been developed at the University of Cambridge initially with funding from BP and more recently through the EPSRC funded Whole Systems Energy Modeling (WholeSEM) project. The basis of the tool is a set of linked physical models for energy, water and land, including the technologies that are used to transform these resources into final services such as housing, food, transport and household goods. The new air quality model will explore different feedback effects between energy, land and atmospheric systems with the overarching goal of supporting better communication about the drivers of air quality and to incorporate concerns about air quality into energy system planning. Some example applications of this work are: (1) to discover conflicts and synergies between air quality regulations and future developments in the energy system and land use change; (2) to show the drivers of air quality in a given spatial context; (3) to explore effective ways to visualize impacts of different energy, land use and emissions control policies on air quality. An initial test case for the Bay Area in California will be presented, extending the scope of the existing California ForeseerTM tool to identify impacts of different policies within the water-energy-land nexus on local air quality.

Modeling Applications and Tools

EPA Pesticide Factsheets

The U.S. EPA's Air Quality Modeling Group (AQMG) conducts modeling analyses to support policy and regulatory decisions in OAR and provides leadership and direction on the full range of air quality models and other mathematical simulation techniques used in

Air pollution and public health: a guidance document for risk managers.

PubMed

Craig, Lorraine; Brook, Jeffrey R; Chiotti, Quentin; Croes, Bart; Gower, Stephanie; Hedley, Anthony; Krewski, Daniel; Krupnick, Alan; Krzyzanowski, Michal; Moran, Michael D; Pennell, William; Samet, Jonathan M; Schneider, Jurgen; Shortreed, John; Williams, Martin

2008-01-01

This guidance document is a reference for air quality policymakers and managers providing state-of-the-art, evidence-based information on key determinants of air quality management decisions. The document reflects the findings of five annual meetings of the NERAM (Network for Environmental Risk Assessment and Management) International Colloquium Series on Air Quality Management (2001-2006), as well as the results of supporting international research. The topics covered in the guidance document reflect critical science and policy aspects of air quality risk management including i) health effects, ii) air quality emissions, measurement and modeling, iii) air quality management interventions, and iv) clean air policy challenges and opportunities.

78 FR 29096 - Approval and Promulgation of Implementation Plans; Tennessee; Transportation Conformity Revisions

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

[email protected] . 3. Fax: (404) 562-9019. 4. Mail: ``EPA-R04-OAR-2013-0044,'' Air Quality Modeling and...: Kelly Sheckler, Air Quality Modeling and Transportation Section, Air Planning Branch, Air, Pesticides... of operation. The Regional Office's official hours of business are Monday through Friday, 8:30 to 4...

Atmospheric Boundary Layer Modeling for Combined Meteorology and Air Quality Systems

EPA Science Inventory

Atmospheric Eulerian grid models for mesoscale and larger applications require sub-grid models for turbulent vertical exchange processes, particularly within the Planetary Boundary Layer (PSL). In combined meteorology and air quality modeling systems consistent PSL modeling of wi...

“Summary of the Emission Inventories compiled for the ...

EPA Pesticide Factsheets

We present a summary of the emission inventories from the US, Canada, and Mexico developed for the second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Activities in this second phase are focused on the application and evaluation of coupled meteorology-chemistry models over both North America and Europe using common emissions and boundary conditions for all modeling groups for the years of 2006 and 2010. We will compare the emission inventories developed for these two years focusing on the SO2 and NOx reductions over these years and compare with socio-economic data. In addition we will highlight the differences in the inventories for the US and Canada compared with the inventories used in the phase 1 of this project. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollut

Urban air quality estimation study, phase 1

NASA Technical Reports Server (NTRS)

Diamante, J. M.; Englar, T. S., Jr.; Jazwinski, A. H.

1976-01-01

Possibilities are explored for applying estimation theory to the analysis, interpretation, and use of air quality measurements in conjunction with simulation models to provide a cost effective method of obtaining reliable air quality estimates for wide urban areas. The physical phenomenology of real atmospheric plumes from elevated localized sources is discussed. A fluctuating plume dispersion model is derived. Individual plume parameter formulations are developed along with associated a priori information. Individual measurement models are developed.

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Johnson, Hoyt; Khan, Maudood

2006-01-01

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Remote Sensing and Spatial Growth Modeling Coupled With Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

NASA Astrophysics Data System (ADS)

Quattrochi, D. A.; Estes, M. G.; Crosson, W. L.; Johnson, H.; Khan, M.

2006-05-01

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world's population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta's growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Ambient air pollution and semen quality.

PubMed

Nobles, Carrie J; Schisterman, Enrique F; Ha, Sandie; Kim, Keewan; Mumford, Sunni L; Buck Louis, Germaine M; Chen, Zhen; Liu, Danping; Sherman, Seth; Mendola, Pauline

2018-05-01

Ambient air pollution is associated with systemic increases in oxidative stress, to which sperm are particularly sensitive. Although decrements in semen quality represent a key mechanism for impaired fecundability, prior research has not established a clear association between air pollution and semen quality. To address this, we evaluated the association between ambient air pollution and semen quality among men with moderate air pollution exposure. Of 501 couples in the LIFE study, 467 male partners provided one or more semen samples. Average residential exposure to criteria air pollutants and fine particle constituents in the 72 days before ejaculation was estimated using modified Community Multiscale Air Quality models. Generalized estimating equation models estimated the association between air pollutants and semen quality parameters (volume, count, percent hypo-osmotic swollen, motility, sperm head, morphology and sperm chromatin parameters). Models adjusted for age, body mass index, smoking and season. Most associations between air pollutants and semen parameters were small. However, associations were observed for an interquartile increase in fine particulates ≤2.5 µm and decreased sperm head size, including -0.22 (95% CI -0.34, -0.11) µm 2 for area, -0.06 (95% CI -0.09, -0.03) µm for length and -0.09 (95% CI -0.19, -0.06) µm for perimeter. Fine particulates were also associated with 1.03 (95% CI 0.40, 1.66) greater percent sperm head with acrosome. Air pollution exposure was not associated with semen quality, except for sperm head parameters. Moderate levels of ambient air pollution may not be a major contributor to semen quality. Published by Elsevier Inc.

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...

Evaluation and intercomparison of air quality forecasts over Korea during the KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Lee, Seungun; Park, Rokjin J.; Kim, Soontae; Song, Chul H.; Kim, Cheol-Hee; Woo, Jung-Hun

2017-04-01

We evaluate and intercompare ozone and aerosol simulations over Korea during the KORUS-AQ campaign, which was conducted in May-June 2016. Four global and regional air quality models participated in the campaign and provided daily air quality forecasts over Korea to guide aircraft flight paths for detecting air pollution events over Korean peninsula and its nearby oceans. We first evaluate the model performance by comparing simulated and observed hourly surface ozone and PM2.5 concentrations at ground sites in Korea and find that the models successfully capture intermittent air pollution events and reproduce the daily variation of ozone and PM2.5 concentrations. However, significant underestimates of peak ozone concentrations in the afternoon are also found in most models. Among chemical constituents of PM2.5, the models typically overestimate observed nitrate aerosol concentrations and underestimate organic aerosol concentrations, although the observed mass concentrations of PM2.5 are seemingly reproduced by the models. In particular, all models used the same anthropogenic emission inventory (KU-CREATE) for daily air quality forecast, but they show a considerable discrepancy for ozone and aerosols. Compared to individual model results, the ensemble mean of all models shows the best performance with correlation coefficients of 0.73 for ozone and 0.57 for PM2.5. We here investigate contributing factors to the discrepancy, which will serve as a guidance to improve the performance of the air quality forecast.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Combined comfort model of thermal comfort and air quality on buses in Hong Kong.

PubMed

Shek, Ka Wing; Chan, Wai Tin

2008-01-25

Air-conditioning settings are important factors in controlling the comfort of passengers on buses. The local bus operators control in-bus air quality and thermal environment by conforming to the prescribed levels stated in published standards. As a result, the settings are merely adjusted to fulfill the standards, rather than to satisfy the passengers' thermal comfort and air quality. Such "standard-oriented" practices are not appropriate; the passengers' preferences and satisfaction should be emphasized instead. Thus a "comfort-oriented" philosophy should be implemented to achieve a comfortable in-bus commuting environment. In this study, the achievement of a comfortable in-bus environment was examined with emphasis on thermal comfort and air quality. Both the measurement of physical parameters and subjective questionnaire surveys were conducted to collect practical in-bus thermal and air parameters data, as well as subjective satisfaction and sensation votes from the passengers. By analyzing the correlation between the objective and subjective data, a combined comfort models were developed. The models helped in evaluating the percentage of dissatisfaction under various combinations of passengers' sensation votes towards thermal comfort and air quality. An effective approach integrated the combined comfort model, hardware and software systems and the bus air-conditioning system could effectively control the transient in-bus environment. By processing and analyzing the data from the continuous monitoring system with the combined comfort model, air-conditioning setting adjustment commands could be determined and delivered to the hardware. This system adjusted air-conditioning settings depending on real-time commands along the bus journey. Therefore, a comfortable in-bus air quality and thermal environment could be achieved and efficiently maintained along the bus journey despite dynamic outdoor influences. Moreover, this model can help optimize air-conditioning control by striking a beneficial balance between energy conservation and passengers' satisfaction level.

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 ...

AIR QUALITY MODELING OF AMMONIA: A REGIONAL MODELING PERSPECTIVE

EPA Science Inventory

The talk will address the status of modeling of ammonia from a regional modeling perspective, yet the observations and comments should have general applicability. The air quality modeling system components that are central to modeling ammonia will be noted and a perspective on ...

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

VERIFICATION OF SURFACE LAYER OZONE FORECASTS IN THE NOAA/EPA AIR QUALITY FORECAST SYSTEM IN DIFFERENT REGIONS UNDER DIFFERENT SYNOPTIC SCENARIOS

EPA Science Inventory

An air quality forecast (AQF) system has been established at NOAA/NCEP since 2003 as a collaborative effort of NOAA and EPA. The system is based on NCEP's Eta mesoscale meteorological model and EPA's CMAQ air quality model (Davidson et al, 2004). The vision behind this system is ...

A state of the art regarding urban air quality prediction models

NASA Astrophysics Data System (ADS)

Croitoru, Cristiana; Nastase, Ilinca

2018-02-01

Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.

Modeling prescribed burning experiments and assessing the fire impacts on local to regional air quality

NASA Astrophysics Data System (ADS)

Zhou, L.; Baker, K. R.; Napelenok, S. L.; Elleman, R. A.; Urbanski, S. P.

2016-12-01

Biomass burning, including wildfires and prescribed burns, strongly impact the global carbon cycle and are of increasing concern due to the potential impacts on ambient air quality. This modelling study focuses on the evolution of carbonaceous compounds during a prescribed burning experiment and assesses the impacts of burning on local to regional air quality. The Community Multiscale Air Quality (CMAQ) model is used to conduct 4 and 2 km grid resolution simulations of prescribed burning experiments in southeast Washington state and western Idaho state in summer 2013. The ground and airborne measurements from the field experiment are used to evaluate the model performance in capturing surface and aloft impacts from the burning events. Phase partitioning of organic compounds in the plume are studied as it is a crucial step towards understanding the fate of carbonaceous compounds. The sensitivities of ambient concentrations and deposition to emissions are conducted for organic carbon, elemental carbon and ozone to estimate the impacts of fire on air quality.

“Fine-Scale Application of the coupled WRF-CMAQ System to ...

EPA Pesticide Factsheets

The DISCOVER-AQ project (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality), is a joint collaboration between NASA, U.S. EPA and a number of other local organizations with the goal of characterizing air quality in urban areas using satellite, aircraft, vertical profiler and ground based measurements (http://discover-aq.larc.nasa.gov). In July 2011, the DISCOVER-AQ project conducted intensive air quality measurements in the Baltimore, MD and Washington, D.C. area in the eastern U.S. To take advantage of these unique data, the Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) model is used to simulate the meteorology and air quality in the same region using 12-km, 4-km and 1-km horizontal grid spacings. The goal of the modeling exercise is to demonstrate the capability of the coupled WRF-CMAQ modeling system to simulate air quality at fine grid spacings in an urban area. Development of new data assimilation techniques and the use of higher resolution input data for the WRF model have been implemented to improve the meteorological results, particularly at the 4-km and 1-km grid resolutions. In addition, a number of updates to the CMAQ model were made to enhance the capability of the modeling system to accurately represent the magnitude and spatial distribution of pollutants at fine model resolutions. Data collected during the 2011 DISCOVER-AQ campa

“Application and evaluation of the two-way coupled WRF ...

EPA Pesticide Factsheets

The DISCOVER-AQ project (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality), is a joint collaboration between NASA, U.S. EPA and a number of other local organizations with the goal of characterizing air quality in urban areas using satellite, aircraft, vertical profiler and ground based measurements (http://discover-aq.larc.nasa.gov). In July 2011, the DISCOVER-AQ project conducted intensive air quality measurements in the Baltimore, MD and Washington, D.C. area in the eastern U.S. To take advantage of these unique data, the Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) model is used to simulate the meteorology and air quality in the same region using 12-km, 4-km and 1-km horizontal grid spacings. The goal of the modeling exercise is to demonstrate the capability of the coupled WRF-CMAQ modeling system to simulate air quality at fine grid spacings in an urban area. Development of new data assimilation techniques and the use of higher resolution input data for the WRF model have been implemented to improve the meteorological results, particularly at the 4-km and 1-km grid resolutions. In addition, a number of updates to the CMAQ model were made to enhance the capability of the modeling system to accurately represent the magnitude and spatial distribution of pollutants at fine model resolutions. Data collected during the 2011 DISCOVER-AQ campa

Smart climate ensemble exploring approaches: the example of climate impacts on air pollution in Europe.

NASA Astrophysics Data System (ADS)

Lemaire, Vincent; Colette, Augustin; Menut, Laurent

2016-04-01

Because of its sensitivity to weather patterns, climate change will have an impact on air pollution so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, at present, such impact assessment lack multi-model ensemble approaches to address uncertainties because of the substantial computing cost. Therefore, as a preliminary step towards exploring large climate ensembles with air quality models, we developed an ensemble exploration technique in order to point out the climate models that should be investigated in priority. By using a training dataset from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for 8 regions in Europe and developed statistical models that could be used to estimate future air pollutant concentrations. Applying this statistical model to the whole EuroCordex ensemble of climate projection, we find a climate penalty for six subregions out of eight (Eastern Europe, France, Iberian Peninsula, Mid Europe and Northern Italy). On the contrary, a climate benefit for PM2.5 was identified for three regions (Eastern Europe, Mid Europe and Northern Italy). The uncertainty of this statistical model challenges limits however the confidence we can attribute to associated quantitative projections. This technique allows however selecting a subset of relevant regional climate model members that should be used in priority for future deterministic projections to propose an adequate coverage of uncertainties. We are thereby proposing a smart ensemble exploration strategy that can also be used for other impacts studies beyond air quality.

"Advances in Linked Air Quality, Farm Management and Biogeochemistry Models to Address Bidrectional Ammonia Flux in CMAQ"

EPA Science Inventory

Recent increases in anthropogenic inputs of nitrogen to air, land and water media pose a growing threat to human health and ecosystems. Modeling of air-surface N flux is one area in need of improvement. Implementation of a linked air quality and cropland management system is de...

“Impact of CB6 and CB05TU chemical mechanisms on air quality”

EPA Science Inventory

“Impacts of CB6 and CB05TU chemical mechanisms on air quality”In this study, we incorporate the newly developed Carbon Bond chemical mechanism (CB6) into the Community Multiscale Air Quality modeling system (CMAQv5.0.1) and perform air quality model simulations with the CB6 and t...

Advances in Linked Air Quality, Farm Management and Biogeochemistry Models to Address Bidirectional Ammonia Flux in CMAQ

EPA Science Inventory

Recent increases in anthropogenic inputs of nitrogen to air, land and water media pose a growing threat to human health and ecosystems. Modeling of air-surface N flux is one area in need of improvement. Implementation of a linked air quality and cropland management system is de...

Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling

EPA Science Inventory

The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Predic...

The Third Phase of AQMEII: Evaluation Strategy and Multi-Model Performance Analysis

EPA Science Inventory

AQMEII (Air Quality Model Evaluation International Initiative) is an extraordinary effort promoting policy-relevant research on regional air quality model evaluation across the European and North American atmospheric modelling communities, providing the ideal platform for advanci...

INDOOR AIR QUALITY MODELING (CHAPTER 58)

EPA Science Inventory

The chapter discussses indoor air quality (IAQ) modeling. Such modeling provides a way to investigate many IAQ problems without the expense of large field experiments. Where experiments are planned, IAQ models can be used to help design experiments by providing information on exp...

Methodology and guidelines for regulating traffic flows under air quality constraints in metropolitan areas.

DOT National Transportation Integrated Search

2010-02-01

This project developed a methodology to couple a new pollutant dispersion model with a traffic : assignment process to contain air pollution while maximizing mobility. The overall objective of the air : quality modeling part of the project is to deve...

Study of Regional Downscaled Climate and Air Quality in the United States

NASA Astrophysics Data System (ADS)

Gao, Y.; Fu, J. S.; Drake, J.; Lamarque, J.; Lam, Y.; Huang, K.

2011-12-01

Due to the increasing anthropogenic greenhouse gas emissions, the global and regional climate patterns have significantly changed. Climate change has exerted strong impact on ecosystem, air quality and human life. The global model Community Earth System Model (CESM v1.0) was used to predict future climate and chemistry under projected emission scenarios. Two new emission scenarios, Representative Community Pathways (RCP) 4.5 and RCP 8.5, were used in this study for climate and chemistry simulations. The projected global mean temperature will increase 1.2 and 1.7 degree Celcius for the RCP 4.5 and RCP 8.5 scenarios in 2050s, respectively. In order to take advantage of local detailed topography, land use data and conduct local climate impact on air quality, we downscaled CESM outputs to 4 km by 4 km Eastern US domain using Weather Research and Forecasting (WRF) Model and Community Multi-scale Air Quality modeling system (CMAQ). The evaluations between regional model outputs and global model outputs, regional model outputs and observational data were conducted to verify the downscaled methodology. Future climate change and air quality impact were also examined on a 4 km by 4 km high resolution scale.

A hybrid modeling with data assimilation to evaluate human exposure level

NASA Astrophysics Data System (ADS)

Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

2015-12-01

Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

Maximizing sinter plant operating flexibility through emissions trading and air modeling

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

Schewe, G.J.; Wagner, J.A.; Heron, T.

1998-12-31

This paper provides details on the dispersion modeling analysis performed to demonstrate air quality impacts associated with an emission trading scheme for a sintering operation in Youngstown, Ohio. The emission trade was proposed to allow the sinter plant to expand its current allowable sulfur dioxide (SO2) emissions while being offset with SO{sub 2} emissions from boilers at a nearby shutdown steel mill. While the emission trade itself was feasible and the emissions required for the offset were available (the boiler shutdown and their subsequent SO{sub 2} emission credits were never claimed, banked, or used elsewhere), the second criteria for determiningmore » compliance was a demonstration of minimal air quality impact. The air analysis combined the increased ambient SO{sub 2} concentrations of the relaxed sinter plant emissions with the offsetting air quality of the shutdown boilers to yield the net air quality impacts. To test this net air impact, dispersion modeling was performed treating the sinter plant SO{sub 2} emissions as positive and the shutdown boiler SO{sub 2} emissions as negative. The results of the modeling indicated that the ambient air concentrations due to the proposed emissions increase will be offset by the nearby boiler emissions to levels acceptable under EPA`s offset policy Level 2 significant impact concentrations. Therefore, the dispersion modeling demonstrated that the emission trading scheme would not result in significant air quality impacts and maximum operating flexibility was provided to the sintering facility.« less

Speed estimation for air quality analysis.

DOT National Transportation Integrated Search

2005-05-01

Average speed is an essential input to the air quality analysis model MOBILE6 for emission factor calculation. Traditionally, speed is obtained from travel demand models. However, such models are not usually calibrated to speeds. Furthermore, for rur...

Evaluating Regional-Scale Air Quality Models

EPA Science Inventory

Numerical air quality models are being used to understand the complex interplay among emission loading meteorology, and atmospheric chemistry leading to the formation and accumulation of pollutants in the atmosphere. A model evaluation framework is presented here that considers ...

Analysis of air quality management with emphasis on transportation sources

NASA Technical Reports Server (NTRS)

English, T. D.; Divita, E.; Lees, L.

1980-01-01

The current environment and practices of air quality management were examined for three regions: Denver, Phoenix, and the South Coast Air Basin of California. These regions were chosen because the majority of their air pollution emissions are related to mobile sources. The impact of auto exhaust on the air quality management process is characterized and assessed. An examination of the uncertainties in air pollutant measurements, emission inventories, meteorological parameters, atmospheric chemistry, and air quality simulation models is performed. The implications of these uncertainties to current air quality management practices is discussed. A set of corrective actions are recommended to reduce these uncertainties.

Three-Dimensional Visualization of Ozone Process Data.

DTIC Science & Technology

1997-06-18

Scattered Multivariate Data. IEEE Computer Graphics & Applications. 11 (May), 47-55. Odman, M.T. and Ingram, C.L. (1996) Multiscale Air Quality Simulation...the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. MAQSIP is a modular comprehensive air quality modeling system which MCNC...photolyzed back again to nitric oxide. Finally, oxides of 6 nitrogen are terminated through loss or combination into nitric acid, organic nitrates

*Combining regional - and local-scale air quality models with exposure models for use in environmental health studies - Title changed from Linking air quality and exposure models for use in environmental health studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, w...

Using aircraft and satellite observations to improve regulatory air quality models

NASA Astrophysics Data System (ADS)

Canty, T. P.; Vinciguerra, T.; Anderson, D. C.; Carpenter, S. F.; Goldberg, D. L.; Hembeck, L.; Montgomery, L.; Liu, X.; Salawitch, R. J.; Dickerson, R. R.

2014-12-01

Federal and state agencies rely on EPA approved models to develop attainment strategies that will bring states into compliance with the National Ambient Air Quality Standards (NAAQS). We will describe modifications to the Community Multi-Scale Air Quality (CMAQ) model and Comprehensive Air Quality Model with Extensions (CAMx) frameworks motivated by analysis of NASA satellite and aircraft measurements. Observations of tropospheric column NO2 from OMI have already led to the identification of an important deficiency in the chemical mechanisms used by models; data collected during the DISCOVER-AQ field campaign has been instrumental in devising an improved representation of the chemistry of nitrogen species. Our recent work has focused on the use of: OMI observations of tropospheric O3 to assess and improve the representation of boundary conditions used by AQ models, OMI NO2 to derive a top down NOx emission inventory from commercial shipping vessels that affect air quality in the Eastern U.S., and OMI HCHO to assess the C5H8 emission inventories provided by bioegenic emissions models. We will describe how these OMI-driven model improvements are being incorporated into the State Implementation Plans (SIPs) being prepared for submission to EPA in summer 2015 and how future modeling efforts may be impacted by our findings.

Path Forward for the Air Quality Model Evaluation International Initiative (AQMEII)

EPA Science Inventory

This article lays out the objectives for Phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII). The inhalation of air pollutants such as ozone and fine particles has been linked to adverse impacts on human health, and the atmospheric deposition of pollutan...

Metrics for the Evaluation the Utility of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Sumo, T. M.; Stockwell, W. R.

2013-12-01

Global warming is expected to lead to higher levels of air pollution and therefore the forecasting of both long-term and daily air quality is an important component for the assessment of the costs of climate change and its impact on human health. Some of the risks associated with poor air quality days (where the Air Pollution Index is greater than 100), include hospital visits and mortality. Accurate air quality forecasting has the potential to allow sensitive groups to take appropriate precautions. This research builds metrics for evaluating the utility of air quality forecasting in terms of its potential impacts. Our analysis of air quality models focuses on the Washington, DC/Baltimore, MD region over the summertime ozone seasons between 2010 and 2012. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our evaluation of the performance of air quality forecasts include those forecasts of ozone and particulate matter and data available from the U.S. Environmental Protection Agency (EPA)'s AIRNOW. We also examined observational ozone and particulate matter data available from Clean Air Partners. Overall the forecast models perform well for our region and time interval.

Ambient Air Quality Data Inventory

EPA Pesticide Factsheets

The Office of Air and Radiation's (OAR) Ambient Air Quality Data (Current) contains ambient air pollution data collected by EPA, other federal agencies, as well as state, local, and tribal air pollution control agencies. Its component data sets have been collected over the years from approximately 10,000 monitoring sites, of which approximately 5,000 are currently active. OAR's Office of Air Quality Planning and Standards (OAQPS) and other internal and external users, rely on this data to assess air quality, assist in Attainment/Non-Attainment designations, evaluate State Implementation Plans for Non-Attainment Areas, perform modeling for permit review analysis, and other air quality management functions. Air quality information is also used to prepare reports for Congress as mandated by the Clean Air Act. This data covers air quality data collected after 1980, when the Clean Air Act requirements for monitoring were significantly modified. Air quality data from the Agency's early years (1970s) remains available (see OAR PRIMARY DATA ASSET: Ambient Air Quality Data -- Historical), but because of technical and definitional differences the two data assets are not directly comparable. The Clean Air Act of 1970 provided initial authority for monitoring air quality for Conventional Air Pollutants (CAPs) for which EPA has promulgated National Ambient Air Quality Standards (NAAQS). Requirements for monitoring visibility-related parameters were added in 1977. Requiremen

A model for predicting air quality along highways.

DOT National Transportation Integrated Search

1973-01-01

The subject of this report is an air quality prediction model for highways, AIRPOL Version 2, July 1973. AIRPOL has been developed by modifying the basic Gaussian approach to gaseous dispersion. The resultant model is smooth and continuous throughout...

AN OPERATIONAL EVALUATION OF THE ETA-CMAQ AIR QUALITY FORECAST MODEL

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in collaboration with the Environmental Protection Agency (EPA), are developing an Air Quality Forecasting Program that will eventually result in an operational Nationwide Air Quality Forecasting System. The initial pha...

Air Quality Improvements of Increased Integration of Renewables: Solar Photovoltaics Penetration Scenarios

NASA Astrophysics Data System (ADS)

Duran, P.; Holloway, T.; Brinkman, G.; Denholm, P.; Littlefield, C. M.

2011-12-01

Solar photovoltaics (PV) are an attractive technology because they can be locally deployed and tend to yield high production during periods of peak electric demand. These characteristics can reduce the need for conventional large-scale electricity generation, thereby reducing emissions of criteria air pollutants (CAPs) and improving ambient air quality with regard to such pollutants as nitrogen oxides, sulfur oxides and fine particulates. Such effects depend on the local climate, time-of-day emissions, available solar resources, the structure of the electric grid, and existing electricity production among other factors. This study examines the air quality impacts of distributed PV across the United States Eastern Interconnection. In order to accurately model the air quality impact of distributed PV in space and time, we used the National Renewable Energy Lab's (NREL) Regional Energy Deployment System (ReEDS) model to form three unique PV penetration scenarios in which new PV construction is distributed spatially based upon economic drivers and natural solar resources. Those scenarios are 2006 Eastern Interconnection business as usual, 10% PV penetration, and 20% PV penetration. With the GridView (ABB, Inc) dispatch model, we used historical load data from 2006 to model electricity production and distribution for each of the three scenarios. Solar PV electric output was estimated using historical weather data from 2006. To bridge the gap between dispatch and air quality modeling, we will create emission profiles for electricity generating units (EGUs) in the Eastern Interconnection from historical Continuous Emissions Monitoring System (CEMS) data. Via those emissions profiles, we will create hourly emission data for EGUs in the Eastern Interconnect for each scenario during 2006. Those data will be incorporated in the Community Multi-scale Air Quality (CMAQ) model using the Sparse Matrix Operator Kernel Emissions (SMOKE) model. Initial results indicate that PV penetration significantly reduces conventional peak electricity production and that, due to reduced emissions during periods of extremely active photochemistry, air quality could see benefits.

78 FR 28551 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Canton-Massillon 1997 8-Hour...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

... Promulgation of Air Quality Implementation Plans; Ohio; Canton-Massillon 1997 8-Hour Ozone Maintenance Plan..., Ohio, 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP) under the Clean Air Act... Motor Vehicle Emissions Simulator (MOVES) emissions model. Ohio submitted the SIP revision request to...

Assessing uncertain human exposure to ambient air pollution using environmental models in the Web

NASA Astrophysics Data System (ADS)

Gerharz, L. E.; Pebesma, E.; Denby, B.

2012-04-01

Ambient air quality can have significant impact on human health by causing respiratory and cardio-vascular diseases. Thereby, the pollutant concentration a person is exposed to can differ considerably between individuals depending on their daily routine and movement patterns. Using a straight forward approach this exposure can be estimated by integration of individual space-time paths and spatio-temporally resolved ambient air quality data. To allow a realistic exposure assessment, it is furthermore important to consider uncertainties due to input and model errors. In this work, we present a generic, web-based approach for estimating individual exposure by integration of uncertain position and air quality information implemented as a web service. Following the Model Web initiative envisioning an infrastructure for deploying, executing and chaining environmental models as services, existing models and data sources for e.g. air quality, can be used to assess exposure. Therefore, the service needs to deal with different formats, resolutions and uncertainty representations provided by model or data services. Potential mismatch can be accounted for by transformation of uncertainties and (dis-)aggregation of data under consideration of changes in the uncertainties using components developed in the UncertWeb project. In UncertWeb, the Model Web vision is extended to an Uncertainty-enabled Model Web, where services can process and communicate uncertainties in the data and models. The propagation of uncertainty to the exposure results is quantified using Monte Carlo simulation by combining different realisations of positions and ambient concentrations. Two case studies were used to evaluate the developed exposure assessment service. In a first study, GPS tracks with a positional uncertainty of a few meters, collected in the urban area of Münster, Germany were used to assess exposure to PM10 (particulate matter smaller 10 µm). Air quality data was provided by an uncertainty-enabled air quality model system which provided realisations of concentrations per hour on a 250 m x 250 m resolved grid over Münster. The second case study uses modelled human trajectories in Rotterdam, The Netherlands. The trajectories were provided as realisations in 15 min resolution per 4 digit postal code from an activity model. Air quality estimates were provided for different pollutants as ensembles by a coupled meteorology and air quality model system on a 1 km x 1 km grid with hourly resolution. Both case studies show the successful application of the service to different resolutions and uncertainty representations.

Feedbacks between Air Pollution and Weather, Part 2: Effects on Chemistry.

EPA Science Inventory

Fully-coupled air-quality models running in “feedback” and “no-feedback” configurations were compared against each other and observation network data as part of Phase 2 of the Air Quality Model Evaluation International Initiative. In the “no-feedback” mode, interactions between m...

USE OF REMOTE SENSING AIR QUALITY INFORMATION IN REGIONAL SCALE AIR POLLUTION MODELING: CURRENT USE AND REQUIREMENTS

EPA Science Inventory

In recent years the applications of regional air quality models are continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physic...

MODEL DEVELOPMENT AND TESTING FOR SEMI-VOLATILES (ATRAZINE)

EPA Science Inventory

The Community Multi-Scale Air Quality (CMAQ) model, air quality model within EPA's Models-3 system, can be adapted to simulate the fate of semi-volatile compounds that are emitted into the atmosphere. "Semi-volatile" refers to compounds that partition their mass between two ph...

Global ozone and air quality: a multi-model assessment of risks to human health and crops

NASA Astrophysics Data System (ADS)

Ellingsen, K.; Gauss, M.; van Dingenen, R.; Dentener, F. J.; Emberson, L.; Fiore, A. M.; Schultz, M. G.; Stevenson, D. S.; Ashmore, M. R.; Atherton, C. S.; Bergmann, D. J.; Bey, I.; Butler, T.; Drevet, J.; Eskes, H.; Hauglustaine, D. A.; Isaksen, I. S. A.; Horowitz, L. W.; Krol, M.; Lamarque, J. F.; Lawrence, M. G.; van Noije, T.; Pyle, J.; Rast, S.; Rodriguez, J.; Savage, N.; Strahan, S.; Sudo, K.; Szopa, S.; Wild, O.

2008-02-01

Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5-10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15-20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2-4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and vegetation from changing levels of surface ozone.

30 CFR 285.659 - What requirements must I include in my SAP, COP, or GAP regarding air quality?

Code of Federal Regulations, 2011 CFR

2011-07-01

..., or GAP regarding air quality? 285.659 Section 285.659 Mineral Resources BUREAU OF OCEAN ENERGY... Pipeline Deviations § 285.659 What requirements must I include in my SAP, COP, or GAP regarding air quality..., according to the following table. ER29AP09.130 (b) For air quality modeling that you perform in support of...

Impact of inherent meteorology uncertainty on air quality ...

EPA Pesticide Factsheets

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10–20 ppb

Air Pollution over the States

ERIC Educational Resources Information Center

Environmental Science and Technology, 1972

1972-01-01

State plans for implementing air quality standards are evaluated together with problems in modeling procedures and enforcement. Monitoring networks, standards, air quality regions, and industrial problems are also discussed. (BL)

Evolution of Air Quality Model at the US EPA

EPA Science Inventory

At the US EPA, we have developed an air quality model, CMAQ, in the past 20+ years. Throughout the years, the model has been upgraded with respect to advancement of science. We have extended the model from regional to hemispheric. We have coupled it with meteorological model, WR...

LINKING ETA MODEL WITH THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM: OZONE BOUNDARY CONDITIONS

EPA Science Inventory

A prototype surface ozone concentration forecasting model system for the Eastern U.S. has been developed. The model system is consisting of a regional meteorological and a regional air quality model. It demonstrated a strong prediction dependence on its ozone boundary conditions....

Evaluation of health benefit using BenMAP-CE with an integrated scheme of model and monitor data during Guangzhou Asian Games.

PubMed

Ding, Dian; Zhu, Yun; Jang, Carey; Lin, Che-Jen; Wang, Shuxiao; Fu, Joshua; Gao, Jian; Deng, Shuang; Xie, Junping; Qiu, Xuezhen

2016-04-01

Guangzhou is the capital and largest city (land area: 7287 km(2)) of Guangdong province in South China. The air quality in Guangzhou typically worsens in November due to unfavorable meteorological conditions for pollutant dispersion. During the Guangzhou Asian Games in November 2010, the Guangzhou government carried out a number of emission control measures that significantly improved the air quality. In this paper, we estimated the acute health outcome changes related to the air quality improvement during the 2010 Guangzhou Asian Games using a next-generation, fully-integrated assessment system for air quality and health benefits. This advanced system generates air quality data by fusing model and monitoring data instead of using monitoring data alone, which provides more reliable results. The air quality estimates retain the spatial distribution of model results while calibrating the value with observations. The results show that the mean PM2.5 concentration in November 2010 decreased by 3.5 μg/m(3) compared to that in 2009 due to the emission control measures. From the analysis, we estimate that the air quality improvement avoided 106 premature deaths, 1869 cases of hospital admission, and 20,026 cases of outpatient visits. The overall cost benefit of the improved air quality is estimated to be 165 million CNY, with the avoided premature death contributing 90% of this figure. The research demonstrates that BenMAP-CE is capable of assessing the health and cost benefits of air pollution control for sound policy making. Copyright © 2015. Published by Elsevier B.V.

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation.

PubMed

Bravo, Mercedes A; Fuentes, Montserrat; Zhang, Yang; Burr, Michael J; Bell, Michelle L

2012-07-01

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors. Copyright © 2012 Elsevier Inc. All rights reserved.

EVALUATING AND USING AIR QUALITY MODELS

EPA Science Inventory

Grid-based models are being used to assess the magnitude of the pollution problem and to design emission control strategies to achieve compliance with the relevant air quality standards in the United States.

Spatial Allocator for air quality modeling

EPA Pesticide Factsheets

The Spatial Allocator is a set of tools that helps users manipulate and generate data files related to emissions and air quality modeling without requiring the use of a commercial Geographic Information System.

COMMENTS CONTRIBUTED BY ALAN HUBER TO AWMA AB-3 COMMITTEE FOR POSSIBLE INCLUSION IN THE COMMITTEE'S PRESENTATION AT EPA'S 8TH CONFERENCE ON AIR QUALITY MODELING - A&WMA AB-3 COMMENTS ON NONSTANDARD MODELING APPROACHES

EPA Science Inventory

Technical comments are provided to the Air and waste Management Associations AB-3 committee for potential inclusion into the committee's comments to be made at EPA's 8th Conference on Air Quality Modeling. Computational Fluid Dynamics (CFD) simulations can model specific cases wh...

Preliminary Evaluation of Air Quality Model Performance Utilizing Measurements at the University of Houston Moody Tower and others during the TexAQS-II

NASA Astrophysics Data System (ADS)

Byun, D. W.; Rappenglueck, B.; Lefer, B.

2007-12-01

Accurate meteorological and photochemical modeling efforts are necessary to understand the measurements made during the Texas Air Quality Study (TexAQS-II). The main objective of the study is to understand the meteorological and chemical processes of high ozone and regional haze events in the Eastern Texas, including the Houston-Galveston metropolitan area. Real-time and retrospective meteorological and photochemical model simulations were performed to study key physical and chemical processes in the Houston Galveston Area. In particular, the Vertical Mixing Experiment (VME) at the University of Houston campus was performed on selected days during the TexAQS-II. Results of the MM5 meteorological model and CMAQ air quality model simulations were compared with the VME and other TexAQS-II measurements to understand the interaction of the boundary layer dynamics and photochemical evolution affecting Houston air quality.

Persistence of initial conditions in continental scale air quality simulations

EPA Science Inventory

This study investigates the effect of initial conditions (IC) for pollutant concentrations in the atmosphere and soil on simulated air quality for two continental-scale Community Multiscale Air Quality (CMAQ) model applications. One of these applications was performed for springt...

SPECIATE's VOC and PM Speciation Profiles and Their use to Prepare for Air quality Modeling (2017 EIC)

EPA Pesticide Factsheets

This training provides general concepts on chemical speciation, the SPECIATE database and browser, and how to use the Speciation Tool to create model ready speciation inputs for a photochemical air quality model.

Bayesian Analysis of a Reduced-Form Air Quality Model

EPA Science Inventory

Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level oz...

An Integrated Computer Modeling Environment for Regional Land Use, Air Quality, and Transportation Planning

DOT National Transportation Integrated Search

1997-04-01

The Land Use, Air Quality, and Transportation Integrated Modeling Environment (LATIME) represents an integrated approach to computer modeling and simulation of land use allocation, travel demand, and mobile source emissions for the Albuquerque, New M...

Valuing the health benefits of improving indoor air quality in residences.

PubMed

Chau, C K; Hui, W K; Tse, M S

2008-05-01

Unlike commercial premises, the indoor air quality of residences is more dynamic, uncontrolled, and prone to human behavioral changes. In consequence, measuring the health benefit gains derived from improving indoor air quality in residences is more complicated. To overcome this, a human thermal comfort model was first integrated with indoor microenvironment models, and subsequently linked with appropriate concentration-response and economic data for estimating the economic benefit gains derived from improving indoor air quality in residences for an adult and an elderly person. In this study, the development of the model is illustrated by using a typical residential apartment locating at the worst air quality neighborhood in Hong Kong and the daily weather profiles between 2002 and 2006. Three types of personal intervention measures were examined in the study: (i) using air cleaner in residence, (ii) changing time spent in residence, and (iii) relocating to a better air quality neighborhood. Our results revealed that employing air cleaners with windows closed in residence throughout the entire year was the most beneficial measure as it could provide the greatest annual health benefit gains. It would give a maximum of HK$2072 in 5-year cumulative benefit gain for an adult and HK$1700 for an elderly person. Employing air cleaners with windows closed in only cool season (October through March) could give the highest marginal return per dollar spent. The benefit gains would become smaller when windows were opened to a greater extent. By contrast, relocating to a better air quality neighborhood and changing the time spent in residence did not appeal to be beneficial intervention measures.

MODELS-3 COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODEL AEROSOL COMPONENT 1: MODEL DESCRIPTION

EPA Science Inventory

The aerosol component of the Community Multiscale Air Quality (CMAQ) model is designed to be an efficient and economical depiction of aerosol dynamics in the atmosphere. The approach taken represents the particle size distribution as the superposition of three lognormal subdis...

Trace Gas/Aerosol Boundary Concentrations and their Impacts on Continental-scale AQMEII Modelling Domains

EPA Science Inventory

Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe fo...

EPA Supersites Program-related emissions-based particulate matter modeling: initial applications and advances.

PubMed

Russell, Armistead G

2008-02-01

One objective of the U.S. Environmental Protection Agency's (EPA's) Supersite Program was to provide data that could be used to more thoroughly evaluate and improve air quality models, and then have those models used to address both scientific and policy-related issues dealing with air quality management. In this direction, modeling studies have used Supersites-related data and are reviewed here. Fine temporal resolution data have been used both to test model components (e.g., the inorganic thermodynamic routines) and air quality modeling systems (in particular, Community Multiscale Air Quality [CMAQ] and Comprehensive Air Quality Model with extensions [CAMx] applications). Such evaluations suggest that the inorganic thermodynamic approaches being used are accurate, as well as the description of sulfate production, although there are significant uncertainties in production of nitric acid, biogenic and ammonia emissions, secondary organic aerosol formation, and the ability to follow the formation and evolution of ultrafine particles. Model applications have investigated how PM levels will respond to various emissions controls, suggesting that nitrate will replace some of the reductions in sulfate particulate matter (PM), although the replacement is small in the summer. Although not part of the Supersite program, modeling being conducted by EPA, regional planning organizations, and states for policy purposes has benefited from the detailed data collected, and the PM models have advanced by their more widespread use.

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.

AN INTERDISCIPLINARY APPROACH TO ADDRESSING NEIGHBORHOOD SCALE AIR QUALITY CONCERNS: THE INTEGRATION OF GIS, URBAN MORPHOLOGY, PREDICTIVE METEOROLOGY, AND AIR QUALITY MONITORING TOOLS

EPA Science Inventory

The paper describes a project that combines the capabilities of urban geography, raster-based GIS, predictive meteorological and air pollutant diffusion modeling, to support a neighborhood-scale air quality monitoring pilot study under the U.S. EPA EMPACT Program. The study ha...

Predicting Air Quality Impacts Associated with Oil and Gas Development in the Uinta Basin Using EPA’s Photochemical Air Quality Model

EPA Science Inventory

Rural areas with close proximity to oil and natural gas operations in Utah have experienced winter ozone levels that exceed EPA’s National Ambient Air Quality Standards (NAAQS). Through a collaborative effort, EPA Region 8 – Air Program, ORD, and OAQPS used the Commun...

Linking Meteorology, Air Quality Models and Observations to ...

EPA Pesticide Factsheets

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air pollutants. A major challenge in environmental epidemiology is adequate exposure characterization. Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal variability in ambient concentrations, nor the influence of infiltration and indoor sources. Central-site monitoring becomes even more problematic for certain air pollutants that exhibit significant spatial heterogeneity. Statistical interpolation techniques and passive monitoring methods can provide additional spatial resolution in ambient concentration estimates. In addition, spatio-temporal models, which integrate GIS data and other factors, such as meteorology, have also been developed to produce more resolved estimates of ambient concentrations. Models, such as the Community Multi-Scale Air Quality (CMAQ) model, estimate ambient concentrations by combining information on meteorology, source emissions, and chemical-fate and transport. Hybrid modeling approaches, which integrate regional scale models with local scale dispersion

OVERVIEW OF THE CLIMATE IMPACT ON REGIONAL AIR QUALITY (CIRAQ) PROJECT

EPA Science Inventory

The Climate Impacts on Regional Air Quality (CIRAQ) project will develop model-estimated impacts of global climate changes on ozone and particulate matter (PM) in direct support of the USEPA Global Change Research Program's (GCRP) national air quality assessment. EPA's urban/reg...

Four-dimensional evaluation of regional air quality models

EPA Science Inventory

We present highlights of the results obtained in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3). Activities in AQMEII3 were focused on evaluating the performance of global, hemispheric and regional modeling systems over Europe and North Ame...

INTERCOMPARISON OF ALTERNATIVE VEGETATION DATABASES FOR REGIONAL AIR QUALITY MODELING

EPA Science Inventory

Vegetation cover data are used to characterize several regional air quality modeling processes, including the calculation of heat, moisture, and momentum fluxes with the Mesoscale Meteorological Model (MM5) and the estimate of biogenic volatile organic compound and nitric oxide...

Changes in U.S. Regional-Scale Air Quality at 2030 Simulated Using RCP 6.0

NASA Astrophysics Data System (ADS)

Nolte, C. G.; Otte, T.; Pinder, R. W.; Faluvegi, G.; Shindell, D. T.

2012-12-01

Recent improvements in air quality in the United States have been due to significant reductions in emissions of ozone and particulate matter (PM) precursors, and these downward emissions trends are expected to continue in the next few decades. To ensure that planned air quality regulations are robust under a range of possible future climates and to consider possible policy actions to mitigate climate change, it is important to characterize and understand the effects of climate change on air quality. Recent work by several research groups using global and regional models has demonstrated that there is a "climate penalty," in which climate change leads to increases in surface ozone levels in polluted continental regions. One approach to simulating future air quality at the regional scale is via dynamical downscaling, in which fields from a global climate model are used as input for a regional climate model, and these regional climate data are subsequently used for chemical transport modeling. However, recent studies using this approach have encountered problems with the downscaled regional climate fields, including unrealistic surface temperatures and misrepresentation of synoptic pressure patterns such as the Bermuda High. We developed a downscaling methodology and showed that it now reasonably simulates regional climate by evaluating it against historical data. In this work, regional climate simulations created by downscaling the NASA/GISS Model E2 global climate model are used as input for the Community Multiscale Air Quality (CMAQ) model. CMAQ simulations over the continental United States are conducted for two 11-year time slices, one representing current climate (1995-2005) and one following Representative Concentration Pathway 6.0 from 2025-2035. Anthropogenic emissions of ozone and PM precursors are held constant at year 2006 levels for both the current and future periods. In our presentation, we will examine the changes in ozone and PM concentrations, with particular focus on exceedances of the current U.S. air quality standards, and attempt to relate the changes in air quality to the projected changes in regional climate.

Air pollution and climate response to aerosol direct radiative effects: A modeling study of decadal trends across the northern hemisphere

EPA Science Inventory

Decadal hemispheric Weather Research and Forecast-Community Multiscale Air Quality simulations from 1990 to 2010 were conducted to examine the meteorology and air quality responses to the aerosol direct radiative effects. The model's performance for the simulation of hourly surfa...

Guide to Selected Algorithms, Distributions, and Databases Used in Exposure Models Developed By the Office of Air Quality Planning and Standards

EPA Pesticide Factsheets

In the evaluation of emissions standards, OAQPS frequently uses one or more computer-based models to estimate the number of people who will be exposed to the air pollution levels that are expected to occur under various air quality scenarios.

(AMD) ANALYSIS OF AIR QUALITY DATA NEAR ROADWAYS USING A DISPERSION MODEL

EPA Science Inventory

We used a dispersion model to analyze measurements made during a field study conducted by the U.S. EPA in July-August 2006, to estimate the impact of traffic emissions on air quality at distances of tens of meters from an 8 lane highway located in Raleigh, North Carolina. The air...

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing.

PubMed

McHenry, John N; Vukovich, Jeffery M; Hsu, N Christina

2015-12-01

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new "Deep Blue" retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented. Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

Linking Global and Regional Models to Simulate U.S. Air Quality in the Year 2050

EPA Science Inventory

The potential impact of global climate change on future air quality in the United States is investigated with global and regional-scale models. Regional climate model scenarios are developed by dynamically downscaling the outputs from a global chemistry and climate model and are...

A FRAMEWORK FOR FINE-SCALE COMPUTATIONAL FLUID DYNAMICS AIR QUALITY MODELING AND ANALYSIS

EPA Science Inventory

This paper discusses a framework for fine-scale CFD modeling that may be developed to complement the present Community Multi-scale Air Quality (CMAQ) modeling system which itself is a computational fluid dynamics model. A goal of this presentation is to stimulate discussions on w...

A proof-of-concept for linking the global meteorological model, MPAS-A with the air quality model, CMAQ

EPA Science Inventory

Researchers who perform air quality modeling studies usually do so on a regional scale. Typically, the boundary conditions are generated by another model which might have a different chemical mechanism, spatial resolution, and/or map projection. Hence, a necessary conversion/inte...

PREFACE SPECIAL ISSUE ON MODEL EVALUATION: EVALUATION OF URBAN AND REGIONAL EULERIAN AIR QUALITY MODELS

EPA Science Inventory

The "Preface to the Special Edition on Model Evaluation: Evaluation of Urban and Regional Eulerian Air Quality Models" is a brief introduction to the papers included in a special issue of Atmospheric Environment. The Preface provides a background for the papers, which have thei...

PARADIGM USING JOINT DETERMINISTIC GRID MODELING AND SUB-GRID VARIABILITY STOCHASTIC DESCRIPTION AS A TEMPLATE FOR MODEL EVALUATION

EPA Science Inventory

The goal of achieving verisimilitude of air quality simulations to observations is problematic. Chemical transport models such as the Community Multi-Scale Air Quality (CMAQ) modeling system produce volume averages of pollutant concentration fields. When grid sizes are such tha...

Linking Global to Regional Models to Assess Future Climate Impacts on Surface Ozone Concentrations in the United States

EPA Science Inventory

The UCD sectional aerosol model has been coupled to the CMAQ air quality model and used to simulate air quality in Tampa, Florida. Sea salt emissions are parameterized as a function of modeled wind speed and relative humidity. Modeled aerosol sulfate, nitrate, ammonium, sodium,...

A NEW COMBINED LOCAL AND NON-LOCAL PBL MODEL FOR METEOROLOGY AND AIR QUALITY MODELING

EPA Science Inventory

A new version of the Asymmetric Convective Model (ACM) has been developed to describe sub-grid vertical turbulent transport in both meteorology models and air quality models. The new version (ACM2) combines the non-local convective mixing of the original ACM with local eddy diff...

APPLICATION OF A NEW LAND-SURFACE, DRY DEPOSITION, AND PBL MODEL IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODEL SYSTEM

EPA Science Inventory

Like most air quality modeling systems, CMAQ divides the treatment of meteorological and chemical/transport processes into separate models run sequentially. A potential drawback to this approach is that it creates the illusion that these processes are minimally interdependent an...

Network modeling of PM10 concentration in Malaysia

NASA Astrophysics Data System (ADS)

Supian, Muhammad Nazirul Aiman Abu; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

2017-08-01

Air pollution is not a new phenomenon in Malaysia. The Department of Environment (DOE) monitors the country's ambient air quality through a network of 51 stations. The air quality is measured using the Air Pollution Index (API) which is mainly recorded based on the concentration of particulate matter, PM10 readings. The Continuous Air Quality Monitoring (CAQM) stations are located in various places across the country. In this study, a network model of air quality based on PM10 concen tration for selected CAQM stations in Malaysia has been developed. The model is built using a graph formulation, G = (V, E) where vertex, V is a set of CAQM stations and edges, E is a set of correlation values for each pair of vertices. The network measurements such as degree distributions, closeness centrality, and betweenness centrality are computed to analyse the behaviour of the network. As a result, a rank of CAQM stations has been produced based on their centrality characteristics.

Unusually high soil nitrogen oxide emissions influence air quality in a high-temperature agricultural region

PubMed Central

Oikawa, P. Y.; Ge, C.; Wang, J.; Eberwein, J. R.; Liang, L. L.; Allsman, L. A.; Grantz, D. A.; Jenerette, G. D.

2015-01-01

Fertilized soils have large potential for production of soil nitrogen oxide (NOx=NO+NO2), however these emissions are difficult to predict in high-temperature environments. Understanding these emissions may improve air quality modelling as NOx contributes to formation of tropospheric ozone (O3), a powerful air pollutant. Here we identify the environmental and management factors that regulate soil NOx emissions in a high-temperature agricultural region of California. We also investigate whether soil NOx emissions are capable of influencing regional air quality. We report some of the highest soil NOx emissions ever observed. Emissions vary nonlinearly with fertilization, temperature and soil moisture. We find that a regional air chemistry model often underestimates soil NOx emissions and NOx at the surface and in the troposphere. Adjusting the model to match NOx observations leads to elevated tropospheric O3. Our results suggest management can greatly reduce soil NOx emissions, thereby improving air quality. PMID:26556236

Highway Air Pollution Dispersion Modeling : Preliminary Evaluation of Thirteen Models

DOT National Transportation Integrated Search

1978-06-01

Thirteen highway air pollution dispersion models have been tested, using a portion of the Airedale air quality data base. The Transportation Air Pollution Studies (TAPS) System, a data base management system specifically designed for evaluating dispe...

Highway Air Pollution Dispersion Modeling : Preliminary Evaluation of Thirteen Models

DOT National Transportation Integrated Search

1977-01-01

Thirteen highway air pollution dispersion models have been tested, using a portion of the Airedale air quality data base. The Transportation Air Pollution Studies (TAPS) System, a data base management system specifically designed for evaluating dispe...

Model Evaluation and Ensemble Modelling of Surface-Level Ozone in Europe and North America in the Context of AQMEII

EPA Science Inventory

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full y...

Joint space-time geostatistical model for air quality surveillance

NASA Astrophysics Data System (ADS)

Russo, A.; Soares, A.; Pereira, M. J.

2009-04-01

Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

3D Air Quality and the Clean Air Interstate Rule: Lagrangian Sampling of CMAQ Model Results to Aid Regional Accountability Metrics

NASA Technical Reports Server (NTRS)

Fairlie, T. D.; Szykman, Jim; Pierce, Robert B.; Gilliland, A. B.; Engel-Cox, Jill; Weber, Stephanie; Kittaka, Chieko; Al-Saadi, Jassim A.; Scheffe, Rich; Dimmick, Fred;

Trade transport and environment linkages at the U.S.-Mexico border: which policies matter?

PubMed

Fernandez, Linda; Das, Monica

2011-03-01

We apply a fixed-effects model to examine the impact of trade and environmental policies on air quality at ports along the U.S.-Mexico border. We control for other factors influencing air quality, such as air quality of cities near the border, volume of traffic flows and congestion. Results show the air quality improved after 2004, when the diesel engine policy was applied. We see mixed results for the trade policy, whose implementation time varies across ports along the international border. Controlling for air quality in cities near the border is essential for assessing the policy contributions to air quality. Copyright © 2010. Published by Elsevier Ltd.

Ozone deposition modelling within the Air Quality Model Evaluation International Initiative (AQMEII)

EPA Science Inventory

This presentation provides an overview of the Air Quality Model Evaluation International Initiative (AQMEII). It contains a synopsis of the three phases of AQMEII, including objectives, logistics, and timelines. It also provides a number of examples of analyses conducted through ...

DEVELOPING EMISSION INVENTORIES FOR BIOMASS BURNING FOR REAL-TIME AND RETROSPECTIVE MODELING

EPA Science Inventory

The EPA uses chemical transport models to simulate historic meteorological episodes for developing air quality management strategies. In addition, chemical transport models are now being used operationally to create air quality forecasts. There are currently a number of methods a...

40 CFR 93.152 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... official charged with direct responsibility for management of an area designated as Class I under the Act.... Areawide air quality modeling analysis means an assessment on a scale that includes the entire nonattainment or maintenance area using an air quality dispersion model or photochemical grid model to determine...

Uncertainty, ensembles and air quality dispersion modeling: applications and challenges

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Miller, Erik

The past two decades have seen significant advances in mesoscale meteorological modeling research and applications, such as the development of sophisticated and now widely used advanced mesoscale prognostic models, large eddy simulation models, four-dimensional data assimilation, adjoint models, adaptive and targeted observational strategies, and ensemble and probabilistic forecasts. Some of these advances are now being applied to urban air quality modeling and applications. Looking forward, it is anticipated that the high-priority air quality issues for the near-to-intermediate future will likely include: (1) routine operational forecasting of adverse air quality episodes; (2) real-time high-level support to emergency response activities; and (3) quantification of model uncertainty. Special attention is focused here on the quantification of model uncertainty through the use of ensemble simulations. Application to emergency-response dispersion modeling is illustrated using an actual event that involved the accidental release of the toxic chemical oleum. Both surface footprints of mass concentration and the associated probability distributions at individual receptors are seen to provide valuable quantitative indicators of the range of expected concentrations and their associated uncertainty.

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

NASA Astrophysics Data System (ADS)

Hu, Jianlin; Chen, Jianjun; Ying, Qi; Zhang, Hongliang

2016-08-01

China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

Impacts of Energy Sector Emissions on PM2.5 Air Quality in Northern India

NASA Astrophysics Data System (ADS)

Karambelas, A. N.; Kiesewetter, G.; Heyes, C.; Holloway, T.

2015-12-01

India experiences high concentrations of fine particulate matter (PM2.5), and several Indian cities currently rank among the world's most polluted cities. With ongoing urbanization and a growing economy, emissions from different energy sectors remain major contributors to air pollution in India. Emission sectors impact ambient air quality differently due to spatial distribution (typical urban vs. typical rural sources) as well as source height characteristics (low-level vs. high stack sources). This study aims to assess the impacts of emissions from three distinct energy sectors—transportation, domestic, and electricity—on ambient PM2.5­­ in northern India using an advanced air quality analysis framework based on the U.S. EPA Community Multi-Scale Air Quality (CMAQ) model. Present air quality conditions are simulated using 2010 emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model. Modeled PM2.5 concentrations are compared with satellite observations of aerosol optical depth (AOD) from the Moderate Imaging Spectroradiometer (MODIS) for 2010. Energy sector emissions impacts on future (2030) PM2.5 are evaluated with three sensitivity simulations, assuming maximum feasible reduction technologies for either transportation, domestic, or electricity sectors. These simulations are compared with a business as usual 2030 simulation to assess relative sectoral impacts spatially and temporally. CMAQ is modeled at 12km by 12km and include biogenic emissions from the Community Land Model coupled with the Model of Emissions of Gases and Aerosols in Nature (CLM-MEGAN), biomass burning emissions from the Global Fires Emissions Database (GFED), and ERA-Interim meteorology generated with the Weather Research and Forecasting (WRF) model for 2010 to quantify the impact of modified anthropogenic emissions on ambient PM2.5 concentrations. Energy sector emissions analysis supports decision-making to improve future air quality and public health in India.

Atmospheric Modeling

EPA Science Inventory

Although air quality models have been applied historically to address issues specific to ambient air quality standards (i.e., one criteria pollutant at a time) or welfare (e.g.. acid deposition or visibility impairment). they are inherently multipollutant based. Therefore. in pri...

WILDFIRE EMISSION MODELING: INTEGRATING BLUESKY AND SMOKE

EPA Science Inventory

Atmospheric chemical transport models are used to simulate historic meteorological episodes for developing air quality management strategies. Wildland fire emissions need to be characterized accurately to achieve these air quality management goals. The temporal and spatial esti...

77 FR 60661 - Approval and Promulgation of Air Quality Implementation Plans; Indiana; South Bend-Elkhart...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-04

... Promulgation of Air Quality Implementation Plans; Indiana; South Bend-Elkhart, Indiana Ozone Maintenance Plan..., Indiana 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP) by replacing the... Vehicle Emissions Simulator (MOVES) 2010a emissions model. Indiana submitted this request to EPA for...

40 CFR 93.152 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... official charged with direct responsibility for management of an area designated as Class I under the Act... 301(d) of the Act and which implements the relevant requirements of the Act. Areawide air quality... which uses an air quality dispersion model to determine the effects of emissions on air quality. Cause...

A Comparison of Statistical Techniques for Combining Modeled and Observed Concentrations to Create High-Resolution Ozone Air Quality Surfaces

EPA Science Inventory

Air quality surfaces representing pollutant concentrations across space and time are needed for many applications, including tracking trends and relating air quality to human and ecosystem health. The spatial and temporal characteristics of these surfaces may reveal new informat...

The Contribution of Marine Organics to the Air Quality of the Western United States

EPA Science Inventory

The contribution of marine organic emissions to the air quality in coastal areas of the western United States is studied using the latest version of the US Environmental Protection Agency (EPA) regional-scale Community Multiscale Air Quality (CMAQv4.7) modeling system. Emissions ...

DYNAMIC ELECTRICITY GENERATION FOR ADDRESSING DAILY AIR QUALITY EXCEEDANCES IN THE US

EPA Science Inventory

We will design, demonstrate, and evaluate a dynamic management system for managing daily air quality, exploring different elements of the design of this system such as how air quality forecasts can best be used, and decision rules for the electrical dispatch model. We will ...

Dynamic Evaluation of Regional Air Quality Model's Response to Emission Reductions in the Presence of Uncertain Emission Inventories

EPA Science Inventory

A method is presented and applied for evaluating an air quality model’s changes in pollutant concentrations stemming from changes in emissions while explicitly accounting for the uncertainties in the base emission inventory. Specifically, the Community Multiscale Air Quality (CMA...

Modeling Study on Air Quality Improvement due to Mobile Source Emission control Plan in Seoul Metropolitan Area

NASA Astrophysics Data System (ADS)

Kim, Y. J.; Sunwoo, Y.; Hwang, I.; Song, S.; Sin, J.; Kim, D.

2015-12-01

A very high population and corresponding high number of vehicles in the Seoul Metropolitan Area (SMA) are aggravating the air quality of this region. The Korean government continues to make concerted efforts to improve air quality. One of the major policies that the Ministry of Environment of Korea enforced is "The Special Act for Improvement of Air Quality in SMA" and "The 1st Air Quality Management Plan of SMA". Mobile Source emission controls are an important part of the policy. Thus, it is timely to evaluate the air quality improvement due to the controls. Therefore, we performed a quantitative analysis of the difference in air quality using the Community Multiscale Air Quality (CMAQ) model and December, 2011 was set as the target period to capture the impact of the above control plans. We considered four fuel-type vehicle emission scenarios and compared the air quality improvement differences between them. The scenarios are as follows: no-control, gasoline vehicle control only, diesel vehicle control only, and control of both; utilizing the revised mobile source emissions from the Clean Air Policy Support System (CAPSS), which is the national emission inventory reflecting current policy.In order to improve the accuracy of the modeling data, we developed new temporal allocation coefficients based on traffic volume observation data and spatially reallocated the mobile source emissions using vehicle flow survey data. Furthermore, we calculated the PM10 and PM2.5 emissions of gasoline vehicles which is omitted in CAPSS.The results of the air quality modeling shows that vehicle control plans for both gasoline and diesel lead to a decrease of 0.65ppb~8.75ppb and 0.02㎍/㎥~7.09㎍/㎥ in NO2 and PM10 monthly average concentrations, respectively. The large percentage decreases mainly appear near the center of the metropolis. However, the largest NO2 decrease percentages are found in the northeast region of Gyeonggi-do, which is the province that surrounds the capital of Seoul. Comparing the results between the different scenarios, diesel vehicle control impact dominates relative to the impact of gasoline control. The diesel-only reduction plan shows that NO2 and PM10 improved by 2.93ppb and 3.32㎍/㎥, respectively.

ADAPTATION AND APPLICATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM FOR REAL-TIME AIR QUALITY FORECASTING DURING THE SUMMER OF 2004

EPA Science Inventory

The ability to forecast local and regional air pollution events is challenging since the processes governing the production and sustenance of atmospheric pollutants are complex and often non-linear. Comprehensive atmospheric models, by representing in as much detail as possible t...

Development of Gridded Fields of Urban Canopy Parameters for Advanced Urban Meteorological and Air Quality Models

EPA Science Inventory

Urban dispersion and air quality simulation models applied at various horizontal scales require different levels of fidelity for specifying the characteristics of the underlying surfaces. As the modeling scales approach the neighborhood level (~1 km horizontal grid spacing), the...

Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

EPA Science Inventory

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteo...

EVALUATION OF THE CMAQ - AIM MODEL AGAINST SIZE AND CHEMICALLY-RESOLVED IMPACTOR DATA AT A COASTAL URBAN SITE

EPA Science Inventory

CMAQ-UCD (formerly known as CMAQ-AIM), is a fully dynamic, sectional aerosol model which has been coupled to the Community Multiscale Air Quality (CMAQ) host air quality model. Aerosol sulfate, nitrate, ammonium, sodium, and chloride model outputs are compared against MOUDI data...

DEVELOPMENT OF AN AGGREGATION AND EPISODE SELECTION SCHEME TO SUPPORT THE MODELS-3 COMMUNITY MULTISCALE AIR QUALITY MODEL

EPA Science Inventory

The development of an episode selection and aggregation approach, designed to support distributional estimation of use with the Models-3 Community Multiscale Air Quality (CMAQ) model, is described. The approach utilized cluster analysis of the 700-hPa east-west and north-south...

VOLATILE ORGANIC COMPOUND EMISSIONS FROM LATEX PAINT-PART 2. TEST HOUSE STUDIES AND INDOOR AIR QUALITY (IAQ) MODELING

EPA Science Inventory

Emission models developed using small chamber data were combined with an Indoor Air Quality (IAQ) model to analyze the impact of volatile organic compound (VOC) emissions from latex paint on indoor environments. Test house experiments were conducted to verify the IAQ model's pred...

The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ Modeling System: Updates through MCIPv3.4.1

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) modeling system is a state-of-the science regional air quality modeling system. The CMAQ modeling system has been primarily developed by the U.S. Environmental Protection Agency, and it has been publically and freely available for more...

VERIFICATION AND USES OF THE ENVIRONMENTAL PROTECTION AGENCY (EPA) INDOOR AIR QUALITY MODEL

EPA Science Inventory

The paper describes a set of experiments used to verify an indoor air quality (IAQ) model for estimating the impact of various pollution sources on IAQ in a multiroom building. he model treats each room as a well-mixed chamber that contains pollution sources and sinks. he model a...

A Reduced Form Model for Ozone Based on Two Decades of CMAQ Simulations for the Continental United States

EPA Science Inventory

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 con...

77 FR 69399 - Approval and Promulgation of Air Quality Implementation Plans; Delaware; Attainment Plan for the...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-19

... results of speciation data analyses, air quality modeling studies, chemical tracer studies, emission... Demonstration 1. Pollutants Addressed 2. Emission Inventory Requirements 3. Modeling 4. Reasonably Available... modeling (40 CFR 51.1007) that is performed in accordance with EPA modeling guidance (EPA-454/B-07-002...

Seasonal ozone vertical profiles over North America using the AQMEII group of air quality models: model inter-comparison and stratospheric intrusion

EPA Science Inventory

This study utilizes simulations for the North American domain from four modeling groups that participated in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) to evaluate seasonal ozone vertical profiles simulated for the year 2010 against ozo...

APPLICATION OF THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODEL SYSTEM TO SOS/NASHVILLE 1999

EPA Science Inventory

The Models-3 Community Multi-scale Air Quality (CMAQ) model, first released by the USEPA in 1999 (Byun and Ching. 1999), continues to be developed and evaluated. The principal components of the CMAQ system include a comprehensive emission processor known as the Sparse Matrix O...

A database and tool for boundary conditions for regional air quality modeling: description and evaluation

NASA Astrophysics Data System (ADS)

Henderson, B. H.; Akhtar, F.; Pye, H. O. T.; Napelenok, S. L.; Hutzell, W. T.

2013-09-01

Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying Lateral Boundary Conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2000-2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complimented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone vertical profiles. The results show performance is largely within uncertainty estimates for the Tropospheric Emission Spectrometer (TES) with some exceptions. The major difference shows a high bias in the upper troposphere along the southern boundary in January. This publication documents the global simulation database, the tool for conversion to LBC, and the fidelity of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants.

Modeling green infrastructure land use changes on future air ...

EPA Pesticide Factsheets

Green infrastructure can be a cost-effective approach for reducing stormwater runoff and improving water quality as a result, but it could also bring co-benefits for air quality: less impervious surfaces and more vegetation can decrease the urban heat island effect, and also result in more removal of air pollutants via dry deposition with increased vegetative surfaces. Cooler surface temperatures can also decrease ozone formation through the increases of NOx titration; however, cooler surface temperatures also lower the height of the boundary layer resulting in more concentrated pollutants within the same volume of air, especially for primary emitted pollutants (e.g. NOx, CO, primary particulate matter). To better understand how green infrastructure impacts air quality, the interactions between all of these processes must be considered collectively. In this study, we use a comprehensive coupled meteorology-air quality model (WRF-CMAQ) to simulate the influence of planned land use changes that include green infrastructure in Kansas City (KC) on regional meteorology and air quality. Current and future land use data was provided by the Mid-America Regional Council for 2012 and 2040 (projected land use due to population growth, city planning and green infrastructure implementation). These land use datasets were incorporated into the WRF-CMAQ modeling system allowing the modeling system to propagate the changes in vegetation and impervious surface coverage on meteoro

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

NASA Technical Reports Server (NTRS)

Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

1996-01-01

Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

Variation in Estimated Ozone-Related Health Impacts of Climate Change due to Modeling Choices and Assumptions

PubMed Central

Post, Ellen S.; Grambsch, Anne; Weaver, Chris; Morefield, Philip; Leung, Lai-Yung; Nolte, Christopher G.; Adams, Peter; Liang, Xin-Zhong; Zhu, Jin-Hong; Mahoney, Hardee

2012-01-01

Background: Future climate change may cause air quality degradation via climate-induced changes in meteorology, atmospheric chemistry, and emissions into the air. Few studies have explicitly modeled the potential relationships between climate change, air quality, and human health, and fewer still have investigated the sensitivity of estimates to the underlying modeling choices. Objectives: Our goal was to assess the sensitivity of estimated ozone-related human health impacts of climate change to key modeling choices. Methods: Our analysis included seven modeling systems in which a climate change model is linked to an air quality model, five population projections, and multiple concentration–response functions. Using the U.S. Environmental Protection Agency’s (EPA’s) Environmental Benefits Mapping and Analysis Program (BenMAP), we estimated future ozone (O3)-related health effects in the United States attributable to simulated climate change between the years 2000 and approximately 2050, given each combination of modeling choices. Health effects and concentration–response functions were chosen to match those used in the U.S. EPA’s 2008 Regulatory Impact Analysis of the National Ambient Air Quality Standards for O3. Results: Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2,500 deaths attributable to climate change (although the large majority produced increases in mortality). The choice of the climate change and the air quality model reflected the greatest source of uncertainty, with the other modeling choices having lesser but still substantial effects. Conclusions: Our results highlight the need to use an ensemble approach, instead of relying on any one set of modeling choices, to assess the potential risks associated with O3-related human health effects resulting from climate change. PMID:22796531

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

Influence of Boundary Conditions on Simulated U.S. Air Quality

EPA Science Inventory

One of the key inputs to regional-scale photochemical models frequently used in air quality planning and forecasting applications are chemical boundary conditions representing background pollutant concentrations originating outside the regional modeling domain. A number of studie...

Notification: Evaluation of EPA’s Approval Process for Air Quality Dispersion Models

EPA Pesticide Factsheets

Project #OPE-FY17-0016, June 5, 2017. The EPA OIG plans to begin preliminary research to assess the effectiveness of EPA's process for reviewing and approving air quality dispersion models it recommends for use.

Fragmentation of urban forms and the environmental consequences: results from a high-spatial resolution model system

NASA Astrophysics Data System (ADS)

Tang, U. W.; Wang, Z. S.

2008-10-01

Each city has its unique urban form. The importance of urban form on sustainable development has been recognized in recent years. Traditionally, air quality modelling in a city is in a mesoscale with grid resolution of kilometers, regardless of its urban form. This paper introduces a GIS-based air quality and noise model system developed to study the built environment of highly compact urban forms. Compared with traditional mesoscale air quality model system, the present model system has a higher spatial resolution down to individual buildings along both sides of the street. Applying the developed model system in the Macao Peninsula with highly compact urban forms, the average spatial resolution of input and output data is as high as 174 receptor points per km2. Based on this input/output dataset with a high spatial resolution, this study shows that even the highly compact urban forms can be fragmented into a very small geographic scale of less than 3 km2. This is due to the significant temporal variation of urban development. The variation of urban form in each fragment in turn affects air dispersion, traffic condition, and thus air quality and noise in a measurable scale.

Source Apportionment of Final Particulate Matterin North China Plain based on Air Quality Modeling

NASA Astrophysics Data System (ADS)

Xing, J.; Wu, W.; Chang, X.; Wang, S.; Hao, J.

2016-12-01

Most Chinese cities in North China Plain are suffering from serious air pollution. To develop the regional air pollution control policies, we need to identify the major source contributions to such pollution and to design the control policy which is accurate, efficient and effective. This study used the air quality model with serval advanced technologies including ISAM and ERSM, to assess the source contributions from individual pollutants (incl. SO2, NOx, VOC, NH3, primary PM), sectors (incl. power plants, industry, transportation and domestic), and regions (Beijing, Hebei, Tianjing and surrounding provinces). The modeling period is two months in 2012 as January and July which represent winter and summer respectively. The non-linear relationship between air pollutant emissions and air quality will be addressed, and the integrated control of multi-pollutants and multi-regions in China will be suggested.

Assessment of the emissions and air quality impacts of biomass and biogas use in California.

PubMed

Carreras-Sospedra, Marc; Williams, Robert; Dabdub, Donald

2016-02-01

It is estimated that there is sufficient in-state "technically" recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NOx) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality. This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.

Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2

EPA Science Inventory

Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional...

DIAGNOSTIC MODEL EVALUATION FOR CARBONACEOUS PM 2.5 USING ORGANIC MARKERS MEASURED IN THE SOUTHEASTERN U.S.

EPA Science Inventory

Summertime concentrations of fine particulate carbon in the southeastern United States are consistently underestimated by air quality models. In an effort to understand the cause of this error, the Community Multiscale Air Quality (CMAQ) model is instrumented to track primary org...

Characterizing CO and NOy Sources and Relative Ambient Ratios in the Baltimore Area Using Ambient Measurements and Source Attribution Modeling

EPA Science Inventory

Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess ...

Incremental Testing of the Community Multiscale Air Quality (CMAQ) Modeling System Version 4.7

EPA Science Inventory

This paper describes the scientific and structural updates to the latest release of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 (v4.7) and points the reader to additional resources for further details. The model updates were evaluated relative to obse...

INDOOR AIR QUALITY MODEL VERSION 1.0 DOCUMENTATION

EPA Science Inventory

The report presents a multiroom model for estimating the impact of various sources on indoor air quality (IAQ). The model is written for use on IBM-PC and compatible microcomputers. It is easy to use with a menu-driven user interface. Data are entered using a fill-in-a-form inter...

Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modeled processes were examined and enhanced to suitably represent the extended space and timesca...

Progress on Implementing Additional Physics Schemes into MPAS-A v5.1 for Next Generation Air Quality Modeling

EPA Science Inventory

The U.S. Environmental Protection Agency (USEPA) has a team of scientists developing a next generation air quality modeling system employing the Model for Prediction Across Scales – Atmosphere (MPAS-A) as its meteorological foundation. Several preferred physics schemes and ...

A description and evaluation of an air quality model nested within global and regional composition-climate models using MetUM

NASA Astrophysics Data System (ADS)

Neal, Lucy S.; Dalvi, Mohit; Folberth, Gerd; McInnes, Rachel N.; Agnew, Paul; O'Connor, Fiona M.; Savage, Nicholas H.; Tilbee, Marie

2017-11-01

There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues simultaneously. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the meteorological modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a 5-year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher-resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations, effectively producing an analysis of annual mean surface pollutant concentrations. We show that using a high-resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulfur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher-resolution nested model are more limited and reasons for this are discussed. This study highlights the point that the resolution of models is not the only factor in determining model performance - consistency between nested models is also important.

Chapter 4: Assessing the Air Pollution, Greenhouse Gas, Air Quality, and Health Benefits of Clean Energy Initiatives

EPA Pesticide Factsheets

Chapter 4 of Assessing the Multiple Benefits of Clean Energy helps state states understand the methods, models, opportunities, and issues associated with assessing the GHG, air pollution, air quality, and human health benefits of clean energy options.

Urban airshed modeling of air quality impacts of alternative transportation fuel use in Los Angeles and Atlanta

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

NONE

1997-12-01

The main objective of NREL in supporting this study is to determine the relative air quality impact of the use of compressed natural gas (CNG) as an alternative transportation fuel when compared to low Reid vapor pressure (RVP) gasoline and reformulated gasoline (RFG). A table lists the criteria, air toxic, and greenhouse gas pollutants for which emissions were estimated for the alternative fuel scenarios. Air quality impacts were then estimated by performing photochemical modeling of the alternative fuel scenarios using the Urban Airshed Model Version 6.21 and the Carbon Bond Mechanism Version IV (CBM-IV) (Geary et al., 1988) Using thismore » model, the authors examined the formation and transport of ozone under alternative fuel strategies for motor vehicle transportation sources for the year 2007. Photochemical modeling was performed for modeling domains in Los Angeles, California, and Atlanta, Georgia.« less

Evaluation of air quality zone classification methods based on ambient air concentration exposure.

PubMed

Freeman, Brian; McBean, Ed; Gharabaghi, Bahram; Thé, Jesse

2017-05-01

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO 2 and 8-hr O 3 within a zone that meets the compliance requirements of each method. The first method, the "3 Strike" method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method. A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.

A changing climate: impacts on human exposures to O3 using ...

EPA Pesticide Factsheets

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposures due to these impacts was developed by linking climate, air quality, land-use, and human exposure models. This methodology was then applied to characterize changes in predicted human exposures to O3 under multiple future scenarios. Regional climate projections for the U.S. were developed by downscaling global circulation model (GCM) scenarios for three of the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCPs) using the Weather Research and Forecasting (WRF) model. The regional climate results were in turn used to generate air quality (concentration) projections using the Community Multiscale Air Quality (CMAQ) model. For each of the climate change scenarios, future U.S. census-tract level population distributions from the Integrated Climate and Land Use Scenarios (ICLUS) model for four future scenarios based on the IPCC’s Special Report on Emissions Scenarios (SRES) storylines were used. These climate, air quality, and population projections were used as inputs to EPA’s Air Pollutants Exposure (APEX) model for 12 U.S. cities. Probability density functions show changes in the population distribution of 8 h maximum daily O3 exposur

Modeling study of biomass burning plumes and their impact on urban air quality; a case study of Santiago de Chile

NASA Astrophysics Data System (ADS)

Cuchiara, Gustavo C.; Rappenglück, Bernhard; Angelica Rubio, Maria; Lissi, Eduardo; Gramsch, Ernesto; Garreaud, Rene D.

2017-04-01

Wildfires are a significant direct source of atmospheric pollutants; on a global scale biomass burning is believed to be the largest source of primary fine particles in the atmosphere and the second largest source of trace gases after anthropogenic emission sources. During the summer of 2014, an intense forest and dry pasture wildfire occurred nearby the city of Santiago de Chile. The biomass-burning plume was transported towards the metropolitan area of Santiago and exacerbated the air quality in this region. In this study, we investigated this wildfire event using a forward plume-rise and a chemistry (WRF/Chem) simulation. These data sets provided an opportunity to validate a regional air-quality simulation over Santiago, and a unique case to assess the performance of biomass burning plume modeling in complex topography and validated against an established air quality network. The results from both meteorological and air quality models provide insights about the transport of biomass-burning plumes from the wildfire region towards the metropolitan region of Santiago de Chile. We studied a seven-day period between January 01-07, 2014, and the impact of biomass burning plume emissions estimated by Fire Inventory from NCAR version 1 (FINNv1) on the air quality of Santiago de Chile.

Changes in O3 and NO2 due to emissions from Fracking in the UK.

NASA Astrophysics Data System (ADS)

Archibald, Alexander; Ordonez, Carlos

2016-04-01

Poor air quality is a problem that affects millions of people around the world. Understanding the driving forces behind air pollution is complicated as the precursor gases which combine to produce air pollutants react in a highly non-linear manner and are subject to a range of atmospheric transport mechanisms compounded by the weather. A great deal of money has been spent on mitigating air pollution and so it's important to assess the impacts that new technologies that emit air pollutant precursors may have on local and regional air pollution. One of the most highly discussed new technologies that could impact air quality is the adoption of wide-scale hydraulic fracturing or "fracking" for natural gas. Indeed in regions of the USA where fracking is commonplace large levels of ozone (O3 - a key air pollutant) have been observed and attributed directly to the fracking process. In this study, a numerical modelling framework was used to assess possible impacts of fracking in the UK where at present no large scale fracking facilities are in operation. A number of emissions scenarios were developed for the principle gas phase air pollution precursors: the oxides of nitrogen (NOx) and volatile organic compounds (VOCs). These emissions scenarios were then used in a state-of-the-art numerical air quality model (the UK Met Office operational air quality forecasting model AQUM) to determine potential impacts related to fracking on UK air quality. Comparison of base model results and observations for the year 2013 of NOx, O3 and VOCs from the UK Automatic Urban and Rural Network (AURN) showed that AQUM has good skill at simulating these gas phase air pollutants (O3 r=0.64, NMGE=0.3; NO2 r=0.62, NMGE=0.51). Analysis of the simulations with fracking emissions demonstrate that there are large changes in 1hr max NO2 (11.6±6.6 ppb) with modest increases in monthly mean NO2, throughout the British Isles (150±100 ppt). These results highlight that stringent measures should be applied to prevent deleterious impacts on air quality from emissions related to fracking in the UK.

78 FR 34903 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; 1997 8-Hour Ozone...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-11

... Promulgation of Air Quality Implementation Plans; Ohio; 1997 8-Hour Ozone Maintenance Plan Revision; Motor... request by Ohio to revise the 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP) to... area with budgets developed using EPA's Motor Vehicle Emissions Simulator (MOVES) emissions model. Ohio...

78 FR 28497 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Canton-Massillon 1997 8-Hour...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

... Promulgation of Air Quality Implementation Plans; Ohio; Canton-Massillon 1997 8-Hour Ozone Maintenance Plan... the Canton-Massillon, Ohio 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP... using EPA's Motor Vehicle Emissions Simulator (MOVES) emissions model. Ohio submitted the SIP revision...

The Air Quality Model Evaluation International Initiative (AQMEII)

EPA Science Inventory

This presentation provides an overview of the Air Quality Model Evaluation International Initiative (AQMEII). It contains a synopsis of the three phases of AQMEII, including objectives, logistics, and timelines. It also provides a number of examples of analyses conducted through ...

Scale Issues in Air Quality Modeling Policy Support

EPA Science Inventory

This study examines the issues relating to the use of regional photochemical air quality models for evaluating their performance in reproducing the spatio-temporal features embedded in the observations and for designing emission control strategies needed to achieve compliance wit...

Community Multiscale Air Quality Model

EPA Science Inventory

The U.S. EPA developed the Community Multiscale Air Quality (CMAQ) system to apply a “one atmosphere” multiscale and multi-pollutant modeling approach based mainly on the “first principles” description of the atmosphere. The multiscale capability is supported by the governing di...

Incorporating principal component analysis into air quality model evaluation

EPA Science Inventory

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Princi...

76 FR 81371 - Approval and Disapproval and Promulgation of Implementation Plans; Texas; Infrastructure and...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-28

... Source Ozone Modeling K. Comments That Address Cumulative Air Quality Impacts IV. Final Action V... (section 110(a)(2)(J)); air quality modeling/data (section 110(a)(2)(K)); permitting fees (section 110(a)(2...

Methodologies for Evaluating Environmental Benefits of Intelligent Transportation Systems

DOT National Transportation Integrated Search

2001-05-01

This report provides an overview of the current state of practice in evaluation of air quality impacts and also in emissions modeling. This report also describes the recent developments in emissions modeling. The air quality impacts of various ITS st...

Innovations in projecting emissions for air quality modeling ...

EPA Pesticide Factsheets

Air quality modeling is used in setting air quality standards and in evaluating their costs and benefits. Historically, modeling applications have projected emissions and the resulting air quality only 5 to 10 years into the future. Recognition that the choice of air quality management strategy has climate change implications is encouraging longer modeling time horizons. However, for multi-decadal time horizons, many questions about future conditions arise. For example, will current population, economic, and land use trends continue, or will we see shifts that may alter the spatial and temporal pattern of emissions? Similarly, will technologies such as building-integrated solar photovoltaics, battery storage, electric vehicles, and CO2 capture emerge as disruptive technologies - shifting how we produce and use energy - or will these technologies achieve only niche markets and have little impact? These are some of the questions that are being evaluated by researchers within the U.S. EPA’s Office of Research and Development. In this presentation, Dr. Loughlin will describe a range of analytical approaches that are being explored. These include: (i) the development of alternative scenarios of the future that can be used to evaluate candidate management strategies over wide-ranging conditions, (ii) the application of energy system models to project emissions decades into the future and to assess the environmental implications of new technologies, (iii) and methodo

The influence of ocean halogen and sulfur emissions in the air quality of a coastal megacity: The case of Los Angeles.

PubMed

Muñiz-Unamunzaga, Maria; Borge, Rafael; Sarwar, Golam; Gantt, Brett; de la Paz, David; Cuevas, Carlos A; Saiz-Lopez, Alfonso

2018-01-01

The oceans are the main source of natural halogen and sulfur compounds, which have a significant influence on the oxidizing capacity of the marine atmosphere; however, their impact on the air quality of coastal cities is currently unknown. We explore the effect of marine halogens (Cl, Br and I) and dimethyl sulfide (DMS) on the air quality of a large coastal city through a set of high-resolution (4-km) air quality simulations for the urban area of Los Angeles, US, using the Community Multiscale Air Quality (CMAQ model). The results indicate that marine halogen emissions decrease ozone and nitrogen dioxide levels up to 5ppbv and 2.5ppbv, respectively, in the city of Los Angeles. Previous studies suggested that the inclusion of chlorine in air quality models leads to the generation of ozone in urban areas through photolysis of nitryl chloride (ClNO 2 ). However, we find that when considering the chemistry of Cl, Br and I together the net effect is a reduction of surface ozone concentrations. Furthermore, combined ocean emissions of halogens and DMS cause substantial changes in the levels of key urban atmospheric oxidants such as OH, HO 2 and NO 3 , and in the composition and mass of fine particles. Although the levels of ozone, NO 3 and HO x are reduced, we find a 10% increase in secondary organic aerosol (SOA) mean concentration, attributed to the increase in aerosol acidity and sulfate aerosol formation when combining DMS and bromine. Therefore, this new pathway for enhanced SOA formation may potentially help with current model under predictions of urban SOA. Although further observations and research are needed to establish these preliminary conclusions, this first city-scale investigation suggests that the inclusion of oceanic halogens and DMS in air quality models may improve regional air quality predictions over coastal cities around the world. Copyright © 2017 Elsevier B.V. All rights reserved.

DEVELOPMENT OF MESOSCALE AIR QUALITY SIMULATION MODELS. VOLUME 1. COMPARATIVE SENSITIVITY STUDIES OF PUFF, PLUME, AND GRID MODELS FOR LONG DISTANCE DISPERSION

EPA Science Inventory

This report provides detailed comparisons and sensitivity analyses of three candidate models, MESOPLUME, MESOPUFF, and MESOGRID. This was not a validation study; there was no suitable regional air quality data base for the Four Corners area. Rather, the models have been evaluated...

FIRST RESULTS FROM OPERATIONAL TESTING OF THE U.S. EPA MODELS-3 COMMUNITY MULTISCALE MODEL FOR AIR QUALITY (CMAQ)

EPA Science Inventory

The Models 3 / Community Multiscale Model for Air Quality (CMAQ) has been designed for one-atmosphere assessments for multiple pollutants including ozone (O3), particulate matter (PM10, PM2.5), and acid / nutrient deposition. In this paper we report initial results of our evalu...

Dynamic evaluation of the CMAQv5.0 modeling system: Assessing the model’s ability to simulate ozone changes due to NOx emission reductions

EPA Science Inventory

Regional air quality models are frequently used for regulatory applications to predict changes in air quality due to changes in emissions or changes in meteorology. Dynamic model evaluation is thus an important step in establishing credibility in the model predicted pollutant re...

EVALUATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODEL VERSION 4.5: UNCERTAINTIES AND SENSITIVITIES IMPACTING MODEL PERFORMANCE: PART I - OZONE

EPA Science Inventory

This study examines ozone (O₃) predictions from the Community Multiscale Air Quality (CMAQ) model version 4.5 and discusses potential factors influencing the model results. Daily maximum 8-hr average O₃ levels are largely underpredicted when observed O_...

DIAGNOSTIC EVALUATION OF NUMBERICAL AIR QUALITY MODELS WITH SPECIALIZED AMBIENT OBSERVATIONS: TESTING THE COMMUNITY MULTISCALE AIR QUALITY MODELING SYSTEM (CMAQ) AT SELECTED SOS 95 GROUND SITES

EPA Science Inventory

Three probes for diagnosing photochemical dynamics are presented and applied to specialized ambient surface-level observations and to a numerical photochemical model to better understand rates of production and other process information in the atmosphere and in the model. Howeve...

Overview and Evaluation of the Community Multiscale Air Quality (CMAQ) Modeling System Version 5.2

EPA Science Inventory

A new version of the Community Multiscale Air Quality (CMAQ) model, version 5.2 (CMAQv5.2), is currently being developed, with a planned release date in 2017. The new model includes numerous updates from the previous version of the model (CMAQv5.1). Specific updates include a new...

AIR QUALITY ASSESSMENT IN USA - TECHNICAL TOOLS AND LINKAGE TO HUMAN HEALTH

EPA Science Inventory

This is an invited presentation to the Air4EU Final Conference to held in Prague, Czech Republic, on 10 November 2006. Air4EU is a jointly-sponsored, three-year European effort to provide recommendations on air quality assessment by monitoring and modeling for regulated pollutan...

The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.

The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

PubMed Central

Soyiri, Ireneous N.; Reidpath, Daniel D.

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal / temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept. PMID:24147122

The Impact of a Potential Shale Gas Development in Germany and the United Kingdom on Local and Regional Air Quality

NASA Astrophysics Data System (ADS)

Weger, L.; Lupascu, A.; Cremonese, L.; Butler, T. M.

2017-12-01

Numerous countries in Europe that possess domestic shale gas reserves are considering exploiting this unconventional gas resource as part of their energy transition agenda. While natural gas generates less CO2 emissions upon combustion compared to coal or oil, making it attractive as a bridge in the transition from fossil fuels to renewables, production of shale gas leads to emissions of CH4 and air pollutants such as NOx, VOCs and PM. These gases in turn influence the climate as well as air quality. In this study, we investigate the impact of a potential shale gas development in Germany and the United Kingdom on local and regional air quality. This work builds on our previous study in which we constructed emissions scenarios based on shale gas utilization in these counties. In order to explore the influence of shale gas production on air quality, we investigate emissions predicted from our shale gas scenarios with the Weather Research and Forecasting model with chemistry (WRF-Chem) model. In order to do this, we first design a model set-up over Europe and evaluate its performance for the meteorological and chemical parameters. Subsequently we add shale gas emissions fluxes based on the scenarios over the area of the grid in which the shale gas activities are predicted to occur. Finally, we model these emissions and analyze the impact on air quality on both a local and regional scale. The aims of this work are to predict the range of adverse effects on air quality, highlight the importance of emissions control strategies in reducing air pollution, to promote further discussion, and to provide policy makers with information for decision making on a potential shale gas development in the two study countries.

Study on the Influence of Building Materials on Indoor Pollutants and Pollution Sources

NASA Astrophysics Data System (ADS)

Wang, Yao

2018-01-01

The paper summarizes the achievements and problems of indoor air quality research at home and abroad. The pollutants and pollution sources in the room are analyzed systematically. The types of building materials and pollutants are also discussed. The physical and chemical properties and health effects of main pollutants were analyzed and studied. According to the principle of mass balance, the basic mathematical model of indoor air quality is established. Considering the release rate of pollutants and indoor ventilation, a mathematical model for predicting the concentration of indoor air pollutants is derived. The model can be used to analyze and describe the variation of pollutant concentration in indoor air, and to predict and calculate the concentration of pollutants in indoor air at a certain time. The results show that the mathematical model established in this study can be used to analyze and predict the variation law of pollutant concentration in indoor air. The evaluation model can be used to evaluate the impact of indoor air quality and evaluation of current situation. Especially in the process of building and interior decoration, through pre-evaluation, it can provide reliable design parameters for selecting building materials and determining ventilation volume.

High Electricity Demand in the Northeast U.S.: PJM Reliability Network and Peaking Unit Impacts on Air Quality.

PubMed

Farkas, Caroline M; Moeller, Michael D; Felder, Frank A; Henderson, Barron H; Carlton, Annmarie G

2016-08-02

On high electricity demand days, when air quality is often poor, regional transmission organizations (RTOs), such as PJM Interconnection, ensure reliability of the grid by employing peak-use electric generating units (EGUs). These "peaking units" are exempt from some federal and state air quality rules. We identify RTO assignment and peaking unit classification for EGUs in the Eastern U.S. and estimate air quality for four emission scenarios with the Community Multiscale Air Quality (CMAQ) model during the July 2006 heat wave. Further, we population-weight ambient values as a surrogate for potential population exposure. Emissions from electricity reliability networks negatively impact air quality in their own region and in neighboring geographic areas. Monitored and controlled PJM peaking units are generally located in economically depressed areas and can contribute up to 87% of hourly maximum PM2.5 mass locally. Potential population exposure to peaking unit PM2.5 mass is highest in the model domain's most populated cities. Average daily temperature and national gross domestic product steer peaking unit heat input. Air quality planning that capitalizes on a priori knowledge of local electricity demand and economics may provide a more holistic approach to protect human health within the context of growing energy needs in a changing world.

A spectral method for spatial downscaling

EPA Science Inventory

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrat...

A FRAMEWORK FOR FINE-SCALE COMPUTATIONAL FLUID DYNAMICS AIR QUALITY MODELING AND ANALYSIS

EPA Science Inventory

Fine-scale Computational Fluid Dynamics (CFD) simulation of pollutant concentrations within roadway and building microenvironments is feasible using high performance computing. Unlike currently used regulatory air quality models, fine-scale CFD simulations are able to account rig...

INTERDEPENDENCIES OF MULTI-POLLUTANT CONTROL SIMULATIONS IN AN AIR QUALITY MODEL

EPA Science Inventory

In this work, we use the Community Multi-Scale Air Quality (CMAQ) modeling system to examine the effect of several control strategies on simultaneous concentrations of ozone, PM_2.5, and three important HAPs: formaldehyde, acetaldehyde and benzene.

A FEDERATED PARTNERSHIP FOR URBAN METEOROLOGICAL AND AIR QUALITY MODELING

EPA Science Inventory

Recently, applications of urban meteorological and air quality models have been performed at resolutions on the order of km grid sizes. This necessitated development and incorporation of high resolution landcover data and additional boundary layer parameters that serve to descri...

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

Preface: Special issue of Atmospheric Environment for AQMEII

EPA Science Inventory

In December 2008, a handful of European and North American scientists got together to discuss a possible collaboration on the evaluation of regional-scale air quality models. This led to the development of the Air Quality Model Evaluation International Initiative (AQMEII) with th...

A cost-efficiency and health benefit approach to improve urban air quality.

PubMed

Miranda, A I; Ferreira, J; Silveira, C; Relvas, H; Duque, L; Roebeling, P; Lopes, M; Costa, S; Monteiro, A; Gama, C; Sá, E; Borrego, C; Teixeira, J P

2016-11-01

When ambient air quality standards established in the EU Directive 2008/50/EC are exceeded, Member States are obliged to develop and implement Air Quality Plans (AQP) to improve air quality and health. Notwithstanding the achievements in emission reductions and air quality improvement, additional efforts need to be undertaken to improve air quality in a sustainable way - i.e. through a cost-efficiency approach. This work was developed in the scope of the recently concluded MAPLIA project "Moving from Air Pollution to Local Integrated Assessment", and focuses on the definition and assessment of emission abatement measures and their associated costs, air quality and health impacts and benefits by means of air quality modelling tools, health impact functions and cost-efficiency analysis. The MAPLIA system was applied to the Grande Porto urban area (Portugal), addressing PM10 and NOx as the most important pollutants in the region. Four different measures to reduce PM10 and NOx emissions were defined and characterized in terms of emissions and implementation costs, and combined into 15 emission scenarios, simulated by the TAPM air quality modelling tool. Air pollutant concentration fields were then used to estimate health benefits in terms of avoided costs (external costs), using dose-response health impact functions. Results revealed that, among the 15 scenarios analysed, the scenario including all 4 measures lead to a total net benefit of 0.3M€·y(-1). The largest net benefit is obtained for the scenario considering the conversion of 50% of open fire places into heat recovery wood stoves. Although the implementation costs of this measure are high, the benefits outweigh the costs. Research outcomes confirm that the MAPLIA system is useful for policy decision support on air quality improvement strategies, and could be applied to other urban areas where AQP need to be implemented and monitored. Copyright © 2016. Published by Elsevier B.V.

Project ATLANTA (Atlanta Land use Analysis: Temperature and Air Quality): Use of Remote Sensing and Modeling to Analyze How Urban Land Use Change Affects Meteorology and Air Quality Through Time

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.; Estes, Maurice G., Jr.

1999-01-01

This paper presents an overview of Project ATLANTA (ATlanta Land use ANalysis: Temperature and Air-quality) which is an investigation that seeks to observe, measure, model, and analyze how the rapid growth of the Atlanta, Georgia metropolitan area since the early 1970's has impacted the region's climate and air quality. The primary objectives for this research effort are: (1) To investigate and model the relationships between land cover change in the Atlanta metropolitan, and the development of the urban heat island phenomenon through time; (2) To investigate and model the temporal relationships between Atlanta urban growth and land cover change on air quality; and (3) To model the overall effects of urban development on surface energy budget characteristics across the Atlanta urban landscape through time. Our key goal is to derive a better scientific understanding of how land cover changes associated with urbanization in the Atlanta area, principally in transforming forest lands to urban land covers through time, has, and will, effect local and regional climate, surface energy flux, and air quality characteristics. Allied with this goal is the prospect that the results from this research can be applied by urban planners, environmental managers and other decision-makers, for determining how urbanization has impacted the climate and overall environment of the Atlanta area. Multiscaled remote sensing data, particularly high resolution thermal infrared data, are integral to this study for the analysis of thermal energy fluxes across the Atlanta urban landscape.

Air Quality Science and Regulatory Efforts Require Geostationary Satellite Measurements

NASA Technical Reports Server (NTRS)

Pickering, Kenneth E.; Allen, D. J.; Stehr, J. W.

2006-01-01

Air quality scientists and regulatory agencies would benefit from the high spatial and temporal resolution trace gas and aerosol data that could be provided by instruments on a geostationary platform. More detailed time-resolved data from a geostationary platform could be used in tracking regional transport and in evaluating mesoscale air quality model performance in terms of photochemical evolution throughout the day. The diurnal cycle of photochemical pollutants is currently missing from the data provided by the current generation of atmospheric chemistry satellites which provide only one measurement per day. Often peak surface ozone mixing ratios are reached much earlier in the day during major regional pollution episodes than during local episodes due to downward mixing of ozone that had been transported above the boundary layer overnight. The regional air quality models often do not simulate this downward mixing well enough and underestimate surface ozone in regional episodes. Having high time-resolution geostationary data will make it possible to determine the magnitude of this lower-and mid-tropospheric transport that contributes to peak eight-hour average ozone and 24-hour average PM2.5 concentrations. We will show ozone and PM(sub 2.5) episodes from the CMAQ model and suggest ways in which geostationary satellite data would improve air quality forecasting. Current regulatory modeling is typically being performed at 12 km horizontal resolution. State and regional air quality regulators in regions with complex topography and/or land-sea breezes are anxious to move to 4-km or finer resolution simulations. Geostationary data at these or finer resolutions will be useful in evaluating such models.

Improvements to the Noah Land Surface Model in WRF-CMAQ, and its Application to Future Changes in the Chesapeake Bay Region

EPA Science Inventory

Regional, state, and local environmental regulatory agencies often use Eulerian meteorological and air quality models to investigate the potential impacts of climate, emissions, and land use changes on nutrient loading and air quality. The Noah land surface model in WRF could be...

Operational Model Evaluation for Particulate Matter in Europe and North America in the Context of the AQMEII Project

EPA Science Inventory

Ten state-of-the-science regional air quality (AQ) modeling systems have been applied to continental scale domains in North America and Europe for full-year simulations of 2006 in the context of Air Quality Model Evaluation International Initiative (AQMEII), whose main goals are ...

Two reduced form air quality modeling techniques for rapidly calculating pollutant mitigation potential across many sources, locations and precursor emission types

EPA Science Inventory

Due to the computational cost of running regional-scale numerical air quality models, reduced form models (RFM) have been proposed as computationally efficient simulation tools for characterizing the pollutant response to many different types of emission reductions. The U.S. Envi...

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...

An Evaluation of carbon monoxide emissions models and mobile source dispersion models applicable to Alaskan cities : final report

DOT National Transportation Integrated Search

1986-01-01

This report describes an investigation of state-of-the-art models for predicting the impact on air quality of additions or changes to a highway system identified by the U.S. Environmental Protection Agency as a "non-attainment area" for air quality s...

Evaluation of the Community Multiscale Air Quality Model for Simulating Winter Ozone Formation in the Uinta Basin.

EPA Science Inventory

The Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models were used to simulate a 10 day high‐ozone episode observed during the 2013 Uinta Basin Winter Ozone Study (UBWOS). The baseline model had a large negative bias when compared to ozo...

Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

2010-05-01

The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

Assessing air quality in Aksaray with time series analysis

NASA Astrophysics Data System (ADS)

Kadilar, Gamze Özel; Kadilar, Cem

2017-04-01

Sulphur dioxide (SO2) is a major air pollutant caused by the dominant usage of diesel, petrol and fuels by vehicles and industries. One of the most air-polluted city in Turkey is Aksaray. Hence, in this study, the level of SO2 is analyzed in Aksaray based on the database monitored at air quality monitoring station of Turkey. Seasonal Autoregressive Integrated Moving Average (SARIMA) approach is used to forecast the level of SO2 air quality parameter. The results indicate that the seasonal ARIMA model provides reliable and satisfactory predictions for the air quality parameters and expected to be an alternative tool for practical assessment and justification.

Evaluation of CALGRID using two different ozone episodes and comparison to UAM results

NASA Astrophysics Data System (ADS)

Kumar, Naresh; Russell, Armistead G.; Tesche, Thomas W.; McNally, Dennis E.

Air quality models serve as the foundation for policy decisions regarding programs designed to improve air quality. The California Air Resources Board Airshed Model (CALGRID) is one of the latest photochemical air quality models developed for assessing ozone control strategies. CALGRID was modified to include the lates CBIV chemical mechanism in place of the original SAPRC mechanism. After modification, a detailed evaluation of CALGRID was carried out using two different ozone episodes. The first evaluation used data obtained during the Southern California Air Quality Study (SCAQS). The second evaluation used data obtained for the September, 1984 SCCCAMP episodes in the South Central Coast Air Basin (SCCAB). Model results were compared against observations of O 3, NO, NO 2, and different organic compounds. For the SCCAB episode, the results were also compared with those obtained from the Urban Airshed Model (UAM). Similar to other studies, the ozone predictions from the SCAQS application were biased low, as were various ROG components. The reason for this can be linked to the under-representation of ROG and CO in the emissions inventory. For the SCCAB episode, both the UAM and CALGRID models significantly underestimated NO and NO 2 concentrations. The two models slightly underestimated ozone concentrations above approximately 9 pphm on the third and last day of the simulation. Sensitivity experiments were performed for both the studies. It was found that both CALGRID and UAM are strongly sensitive to the boundary conditions and moderately sensitive to the emissions for the episodes modeled.

Meteorological and air pollution modeling for an urban airport

NASA Technical Reports Server (NTRS)

Swan, P. R.; Lee, I. Y.

1980-01-01

Results are presented of numerical experiments modeling meteorology, multiple pollutant sources, and nonlinear photochemical reactions for the case of an airport in a large urban area with complex terrain. A planetary boundary-layer model which predicts the mixing depth and generates wind, moisture, and temperature fields was used; it utilizes only surface and synoptic boundary conditions as input data. A version of the Hecht-Seinfeld-Dodge chemical kinetics model is integrated with a new, rapid numerical technique; both the San Francisco Bay Area Air Quality Management District source inventory and the San Jose Airport aircraft inventory are utilized. The air quality model results are presented in contour plots; the combined results illustrate that the highly nonlinear interactions which are present require that the chemistry and meteorology be considered simultaneously to make a valid assessment of the effects of individual sources on regional air quality.

TESTS OF INDOOR AIR QUALITY SINKS

EPA Science Inventory

Experiments were conducted in a room-size test chamber to determine the sink effects of selected materials on indoor air concentrations of p-dichlorobenzene (PDCB). hese effects might alter pollutant behavior from that predicted using similar indoor air quality models, by reducin...

A Database and Tool for Boundary Conditions for Regional Air Quality Modeling: Description and Evaluation

EPA Science Inventory

Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available ob...

77 FR 38239 - Partial Approval and Disapproval of Air Quality Implementation Plans; Arizona; Infrastructure...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-27

... anonymous access system, and EPA will not know your identity or contact information unless you provide it in... control measures. Section 110(a)(2)(B): Ambient air quality monitoring/data system. Section 110(a)(2)(C... significant deterioration (PSD) and visibility protection. Section 110(a)(2)(K): Air quality modeling and...

78 FR 34965 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Lima 1997 8-Hour Ozone...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-11

... Promulgation of Air Quality Implementation Plans; Ohio; Lima 1997 8-Hour Ozone Maintenance Plan Revision; Motor... 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP) to replace the previously... Simulator (MOVES) emissions model. Ohio submitted the SIP revision request to EPA on January 11, 2013. DATES...

78 FR 34965 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; 1997 8-Hour Ozone...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-11

... Promulgation of Air Quality Implementation Plans; Ohio; 1997 8-Hour Ozone Maintenance Plan Revision; Motor... request by Ohio to revise the 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP...) emissions model. Ohio submitted the SIP revision request to EPA on December 7, 2012. DATES: Comments must be...

78 FR 34906 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Lima 1997 8-Hour Ozone...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-11

... Promulgation of Air Quality Implementation Plans; Ohio; Lima 1997 8-Hour Ozone Maintenance Plan Revision to... Lima, Ohio 1997 8-hour ozone maintenance air quality State Implementation Plan (SIP) to replace motor... (MOVES) emissions model. Ohio submitted the SIP revision request to EPA on January 11, 2013. DATES: This...

Investigation of the emissions and profiles of a wide range of VOCs during the Clean air for London project

NASA Astrophysics Data System (ADS)

Holmes, Rachel; Lidster, Richard; Hamilton, Jacqueline; Lee, James; Hopkins, James; Whalley, Lisa; Lewis, Alistair

2014-05-01

The majority of the World's population live in polluted urbanized areas. Poor air quality is shortening life expectancy of people in the UK by an average 7-8 months and costs society around £20 billion per year.[1] Despite this, our understanding of atmospheric processing in urban environments and its effect on air quality is incomplete. Air quality models are used to predict how air quality changes given different concentrations of pollution precursors, such as volatile organic compounds (VOCs). The urban environment of megacities pose a unique challenge for air quality measurements and modelling, due to high population densities, pollution levels and complex infrastructure. For over 60 years the air quality in London has been monitored, however the existing measurements are limited to a small group of compounds. In order to fully understand the chemical and physical processes that occur in London, more intensive and comprehensive measurements should be made. The Clean air for London (ClearfLo) project was conducted to investigate the air quality, in particular the boundary layer pollution, of London. A relatively new technique, comprehensive two dimensional gas chromatography (GC×GC) [2] was combined with a well-established dual channel GC (DC-GC) [3] system to provide a more comprehensive measurement of VOCs. A total of 78 individual VOCs (36 aliphatics, 19 monoaromatics, 21 oxygenated and 2 halogenated) and 10 groups of VOCs (8 aliphatic, 1 monoaromatic and 1 monoterpene) from C1-C13+ were quantified. Seasonal and diurnal profiles of these VOCs have been found which show the influence of emission source and chemical processing. Including these extra VOCs should enhance the prediction capability of air quality models thus informing policy makers on how to potentially improve air quality in megacities. References 1. House of Commons Environmental Audit Committee, Air Quality: A follow-up report, Ninth Report of session 2012-12. 2. Lidster, R.T., J.F. Hamilton, and A.C. Lewis, The application of two total transfer valve modulators for comprehensive two-dimensional gas chromatography of volatile organic compounds. Journal of Separation Science, 2011. 34(7): p. 812-821. 3. Hopkins, J.R., C.E. Jones, and A.C. Lewis, A dual channel gas chromatograph for atmospheric analysis of volatile organic compounds including oxygenated and monoterpene compounds. Journal of Environmental Monitoring, 2011. 13(8): p. 2268-2276.

RESOLVING NEIGHBORHOOD-SCALE AIR TOXICS MODELING: A CASE STUDY IN WILMINGTON, CALIFORNIA

EPA Science Inventory

Air quality modeling is useful for characterizing exposures to air pollutants. While models typically provide results on regional scales, there is a need for refined modeling approaches capable of resolving concentrations on the scale of tens of meters, across modeling domains 1...

Impact of reduced mass of light commercial vehicles on fuel consumption, CO2 emissions, air quality, and socio-economic costs.

PubMed

Cecchel, S; Chindamo, D; Turrini, E; Carnevale, C; Cornacchia, G; Gadola, M; Panvini, A; Volta, M; Ferrario, D; Golimbioschi, R

2018-02-01

This study presents a modelling system to evaluate the impact of weight reduction in light commercial vehicles with diesel engines on air quality and greenhouse gas emissions. The PROPS model assesses the emissions of one vehicle in the aforementioned category and its corresponding reduced-weight version. The results serve as an input to the RIAT+ tool, an air quality integrated assessment modelling system. This paper applies the tools in a case study in the Lombardy region (Italy) and discusses the input data pre-processing, the PROPS-RIAT+ modelling system runs, and the results. Copyright © 2017 Elsevier B.V. All rights reserved.

AQA-PM: Extension of the Air-Quality Model For Austria with Satellite based Particulate Matter Estimates

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Triebnig, Gerhard; Flandorfer, Claudia

2013-04-01

Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The Air Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Life Sciences in Vienna (BOKU) by order of the regional governments since 2005. AQA conducts daily forecasts of gaseous and particulate (PM10) air pollutants over Austria. In the frame of the project AQA-PM (funded by FFG), satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using regression- and assimilation techniques. For the model simulations WRF/Chem is used with a resolution of 3 km over the alpine region. Interfaces have been developed to account for the different measurements as input data. The available local emission inventories provided by the different Austrian regional governments were harmonized and used for the model simulations. An episode in February 2010 is chosen for the model evaluation. During that month exceedances of PM10-thresholds occurred at many measurement stations of the Austrian network. Different model runs (only model/only ground stations assimilated/satellite and ground stations assimilated) are compared to the respective measurements. The goal of this project is to improve the PM10-forecasts for Austria with the integration of satellite based measurements and to provide a comprehensive product-platform.

Mapping air quality zones for coastal urban centers.

PubMed

Freeman, Brian; Gharabaghi, Bahram; Thé, Jesse; Munshed, Mohammad; Faisal, Shah; Abdullah, Meshal; Al Aseed, Athari

2017-05-01

This study presents a new method that incorporates modern air dispersion models allowing local terrain and land-sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year's worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates. A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land-sea breeze effects that can be used to manage local air quality in areas of similar microclimates.

Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health

NASA Astrophysics Data System (ADS)

West, J. Jason; Smith, Steven J.; Silva, Raquel A.; Naik, Vaishali; Zhang, Yuqiang; Adelman, Zachariah; Fry, Meridith M.; Anenberg, Susan; Horowitz, Larry W.; Lamarque, Jean-Francois

2013-10-01

Actions to reduce greenhouse gas (GHG) emissions often reduce co-emitted air pollutants, bringing co-benefits for air quality and human health. Past studies typically evaluated near-term and local co-benefits, neglecting the long-range transport of air pollutants, long-term demographic changes, and the influence of climate change on air quality. Here we simulate the co-benefits of global GHG reductions on air quality and human health using a global atmospheric model and consistent future scenarios, via two mechanisms: reducing co-emitted air pollutants, and slowing climate change and its effect on air quality. We use new relationships between chronic mortality and exposure to fine particulate matter and ozone, global modelling methods and new future scenarios. Relative to a reference scenario, global GHG mitigation avoids 0.5+/-0.2, 1.3+/-0.5 and 2.2+/-0.8 million premature deaths in 2030, 2050 and 2100. Global average marginal co-benefits of avoided mortality are US$50-380 per tonne of CO2, which exceed previous estimates, exceed marginal abatement costs in 2030 and 2050, and are within the low range of costs in 2100. East Asian co-benefits are 10-70 times the marginal cost in 2030. Air quality and health co-benefits, especially as they are mainly local and near-term, provide strong additional motivation for transitioning to a low-carbon future.

Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern United States to MM5 and WRF Meteorological Drivers

EPA Science Inventory

This paper presents a comparison of the operational performance of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th generation Mesoscale Model MM5 and the Weather Research and Forecasting (WRF) meteorological models.

40 CFR 51.852 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... manager means the Federal agency or the Federal official charged with direct responsibility for management... requirements of the Act. Areawide air quality modeling analysis means an assessment on a scale that includes the entire nonattainment or maintenance area which uses an air quality dispersion model to determine...

AQMEII: A New International Initiative on Air Quality Model Evaluation

EPA Science Inventory

We provide a conceptual view of the process of evaluating regional-scale three-dimensional numerical photochemical air quality modeling system, based on an examination of existing approached to the evaluation of such systems as they are currently used in a variety of application....

Recent Enhancements to the Community Multiscale Air Quality Modeling System (CMAQ)

EPA Science Inventory

EPA’s Office of Research and Development, Computational Exposure Division held a webinar on January 31, 2017 to present the recent scientific and computational updates made by EPA to the Community Multi-Scale Air Quality Model (CMAQ). Topics covered included: (1) Improveme...

OBJECTIVE REDUCTION OF THE SPACE-TIME DOMAIN DIMENSIONALITY FOR EVALUATING MODEL PERFORMANCE

EPA Science Inventory

In the United States, photochemical air quality models are the principal tools used by governmental agencies to develop emission reduction strategies aimed at achieving National Ambient Air Quality Standards (NAAQS). Before they can be applied with confidence in a regulatory sett...

CHOOSING A CHEMICAL MECHANISM FOR REGULATORY AND RESEARCH AIR QUALITY MODELING APPLICATIONS

EPA Science Inventory

There are numerous, different chemical mechanisms currently available for use in air quality models, and new mechanisms and versions of mechanisms are continually being developed. The development of Morphecule-type mechanisms will add a near-infinite number of additional mecha...

Linking Meteorology, Air Quality Models and Observations to Characterize Human Exposures in Support of the Environmental Health Studies

EPA Science Inventory

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air polluta...

Air quality early-warning system for cities in China

NASA Astrophysics Data System (ADS)

Xu, Yunzhen; Yang, Wendong; Wang, Jianzhou

2017-01-01

Air pollution has become a serious issue in many developing countries, especially in China, and could generate adverse effects on human beings. Air quality early-warning systems play an increasingly significant role in regulatory plans that reduce and control emissions of air pollutants and inform the public in advance when harmful air pollution is foreseen. However, building a robust early-warning system that will improve the ability of early-warning is not only a challenge but also a critical issue for the entire society. Relevant research is still poor in China and cannot always satisfy the growing requirements of regulatory planning, despite the issue's significance. Therefore, in this paper, a hybrid air quality early-warning system was successfully developed, composed of forecasting and evaluation. First, a hybrid forecasting model was proposed as an important part of this system based on the theory of "decomposition and ensemble" and combined with the advanced data processing technique, support vector machine, the latest bio-inspired optimization algorithm and the leave-one-out strategy for deciding weights. Afterwards, to intensify the research, fuzzy evaluation was performed, which also plays an indispensable role in the early-warning system. The forecasting model and fuzzy evaluation approaches are complementary. Case studies using daily air pollution concentrations of six air pollutants from three cities in China (i.e., Taiyuan, Harbin and Chongqing) are used as examples to evaluate the efficiency and effectiveness of the developed air quality early-warning system. Experimental results demonstrate that both the accuracy and the effectiveness of the developed system are greatly superior for air quality early warning. Furthermore, the application of forecasting and evaluation enables the informative and effective quantification of future air quality, offering a significant advantage, and can be employed to develop rapid air quality early-warning systems.

Application and evaluation of the WRF-CMAQ modeling system to the 2011 DISCOVER-AQ Baltimore-Washington D.C. study

NASA Astrophysics Data System (ADS)

Appel, W.; Gilliam, R. C.; Pouliot, G. A.; Godowitch, J. M.; Pleim, J.; Hogrefe, C.; Kang, D.; Roselle, S. J.; Mathur, R.

2013-12-01

The DISCOVER-AQ project (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality), is a joint collaboration between NASA, U.S. EPA and a number of other local organizations with the goal of characterizing air quality in urban areas using satellite, aircraft, vertical profiler and ground based measurements (http://discover-aq.larc.nasa.gov). In July 2011, the DISCOVER-AQ project conducted intensive air quality measurements in the Baltimore, MD and Washington, D.C. area in the eastern U.S. To take advantage of these unique data, the Community Multiscale Air Quality (CMAQ) model, coupled with the Weather Research and Forecasting (WRF) model is used to simulate the meteorology and air quality in the same region using 12-km, 4-km and 1-km horizontal grid spacings. The goal of the modeling exercise is to demonstrate the capability of the coupled WRF-CMAQ modeling system to simulate air quality at fine grid spacings in an urban area. Development of new data assimilation techniques and the use of higher resolution input data for the WRF model have been implemented to improve the meteorological results, particularly at the 4-km and 1-km grid resolutions. In addition, a number of updates to the CMAQ model were made to enhance the capability of the modeling system to accurately represent the magnitude and spatial distribution of pollutants at fine model resolutions. Data collected during the 2011 DISCOVER-AQ campaign, which include aircraft transects and spirals, ship measurements in the Chesapeake Bay, ozonesondes, tethered balloon measurements, DRAGON aerosol optical depth measurements, LIDAR measurements, and intensive ground-based site measurements, are used to evaluate results from the WRF-CMAQ modeling system for July 2011 at the three model grid resolutions. The results of the comparisons of the model results to these measurements will be presented, along with results from the various sensitivity simulations examining the impact the various updates to the modeling system have on the model estimates.

Performance and diagnostic evaluation of ozone predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study.

PubMed

Yu, Shaocai; Mathur, Rohit; Kang, Daiwen; Schere, Kenneth; Eder, Brian; Pleim, Jonathan

2006-10-01

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O3) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O3 was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O3 on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O3 concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O3 very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within +/- 20%. The model reproduced 53.3% of the observed hourly O3 within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites. The comparison of modeled and observed lidar O3 vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64-77% of observed NO2 photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O3/nitrogen oxide (NOx) ratios suggests that the site is mainly under strongly NOx-sensitive conditions (>53%). It was found that the modeled lower limits of the O3 production efficiency values (inferred from O3-CO correlation) are close to the observations.

Air pollution exposure prediction approaches used in air pollution epidemiology studies.

PubMed

Özkaynak, Halûk; Baxter, Lisa K; Dionisio, Kathie L; Burke, Janet

2013-01-01

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Predictability Analysis of PM10 Concentrations in Budapest

NASA Astrophysics Data System (ADS)

Ferenczi, Zita

2013-04-01

Climate, weather and air quality may have harmful effects on human health and environment. Over the past few hundred years we had to face the changes in climate in parallel with the changes in air quality. These observed changes in climate, weather and air quality continuously interact with each other: pollutants are changing the climate, thus changing the weather, but climate also has impacts on air quality. The increasing number of extreme weather situations may be a result of climate change, which could create favourable conditions for rising of pollutant concentrations. Air quality in Budapest is determined by domestic and traffic emissions combined with the meteorological conditions. In some cases, the effect of long-range transport could also be essential. While the time variability of the industrial and traffic emissions is not significant, the domestic emissions increase in winter season. In recent years, PM10 episodes have caused the most critical air quality problems in Budapest, especially in winter. In Budapest, an air quality network of 11 stations detects the concentration values of different pollutants hourly. The Hungarian Meteorological Service has developed an air quality prediction model system for the area of Budapest. The system forecasts the concentration of air pollutants (PM10, NO2, SO2 and O3) for two days in advance. In this work we used meteorological parameters and PM10 data detected by the stations of the air quality network, as well as the forecasted PM10 values of the air quality prediction model system. In this work we present the evaluation of PM10 predictions in the last two years and the most important meteorological parameters affecting PM10 concentration. The results of this analysis determine the effect of the meteorological parameters and the emission of aerosol particles on the PM10 concentration values as well as the limits of this prediction system.

Likelihood of achieving air quality targets under model uncertainties.

PubMed

Digar, Antara; Cohan, Daniel S; Cox, Dennis D; Kim, Byeong-Uk; Boylan, James W

2011-01-01

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

NASA Tropospheric Composition Program field campagins as prototypes to advance the Integrated Observing System for Air Quality

NASA Astrophysics Data System (ADS)

Lefer, B. L.; Crawford, J. H.; Pierce, R. B.; Berkoff, T.; Swap, R.; Janz, S. J.; Ahn, J.; Al-Saadi, J. A.

2017-12-01

With the launch over the virtual constellation of earth observing satellites for atmospheric composition (e.g., TROPOMI, GEMS, TEMPO, and Sentinel-4) over the next several years, we have a unique opportunity to develop an Integrated Observing System (IOS) for air quality in the northern hemisphere. Recently, NASA's Tropospheric Composition Program (TCP) has participated in several different air quality related field campaigns as an effort to explore various prototypes of the IOS for Air Quality. The IOS for air quality could be a system were space-based observations of air quality (generally, column abundances of NO2, HCHO, O3, SO2, and AOD) are given added "value" by being integrated with: a) long-term ground-based observations;b) regional and global air quality and chemical transport models; as well as c) measurements from targeted airborne field campaigns. The recent Korea-US Air Quality Study (KORUS-AQ), the Lake Michigan Ozone Study 2017 (LMOS), and the Ozone Water-Land Environmental Transition Study (OWLETS) field campaigns were held in different locations and made measurements over different scale. However, all of these provide an opportunity to learn about how a future integrated air quality observing system can be implemented to serve a variety of air quality related objectives. NASA TCP is also exploring enchancements to our routine observations to strengthen the IOS for air quality in the future.

A Multi-Resolution Assessment of the Community Multiscale Air Quality (CMAQ) Model v4.7 Wet Deposition Estimates for 2002 - 2006

EPA Science Inventory

This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002 - 2006 using both 36-km and 12-km horizontal grid spacing, with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (...

Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model

EPA Science Inventory

Sea spray aerosols (SSA) impact the particle mass concentration and gas-particle partitioning in coastal environments, with implications for human and ecosystem health. In this study, the Community Multiscale Air Quality (CMAQ) model is updated to enhance fine mode SSA emissions,...

USING CMAQ FOR EXPOSURE MODELING AND CHARACTERIZING THE SUB-GRID VARIABILITY FOR EXPOSURE ESTIMATES

EPA Science Inventory

Atmospheric processes and the associated transport and dispersion of atmospheric pollutants are known to be highly variable in time and space. Current air quality models that characterize atmospheric chemistry effects, e.g. the Community Multi-scale Air Quality (CMAQ), provide vo...

Modelling Carpool and Transit Park-and-Ride Lots

DOT National Transportation Integrated Search

1997-01-01

Park-and-Ride (PnR) lots are an increasingly common element of many areas plans for air quality conformity. However, few, if any, travel models estimate the impacts of carpool PnR lots, and it is not at all clear that they always improve air quality....

Urban airshed modeling of air quality impacts of alternative transportation fuel use in Los Angeles and Atlanta

DOT National Transportation Integrated Search

1997-12-01

This report documents a photochemical modeling study of the potential impacts on air quality of future emissions from alternative fuel vehicles (AFVs). The main objective of the National Renewable Energy Laboratory (NREL) in supporting this study is ...

SIMULATING REGIONAL-SCALE AIR QUALITY WITH DYNAMIC CHANGES IN REGIONAL CLIMATE AND CHEMICAL BOUNDARY CONDITIONS

EPA Science Inventory

This poster compares air quality modeling simulations under current climate and a future (approximately 2050) climate scenario. Differences in predicted ozone episodes and daily average PM_2.5 concentrations are presented, along with vertical ozone profiles. Modeling ...

“Estimating Regional Background Air Quality using Space/Time Ordinary Kriging to Support Exposure Studies”

EPA Science Inventory

Local-scale dispersion models are increasingly being used to perform exposure assessments. These types of models, while able to characterize local-scale air quality at increasing spatial scale, however, lack the ability to include background concentration in their overall estimat...

Cloud processing of gases and aerosols in the Community Multiscale Air Quality (CMAQ) model: Impacts of extended chemistry

EPA Science Inventory

Clouds and fogs can significantly impact the concentration and distribution of atmospheric gases and aerosols through chemistry, scavenging, and transport. This presentation summarizes the representation of cloud processes in the Community Multiscale Air Quality (CMAQ) modeling ...

Air quality assessment in the periurban area of Mexico Megacity during dry hot season in 2011 and 2012

NASA Astrophysics Data System (ADS)

Garcia-Reynoso, Agustin; Santos Garcia-Yee, Jose; Barrera-Huertas, Hugo; Gerardo Ruiz-Suárez, Luis

2016-04-01

Air quality is a human health threat not only in urbanized areas, it also affects the surrounding zones. Interaction between urban and rural areas can be evaluated by measurements and using models for regional areas that includes in its domain the peri-urban regions. The use of monitoring sites in remote areas is useful however it is not possible to cover all the region the use of models can provide valuable information about the source and fate of the pollution and its transformation. In order to evaluate the influence of the Mexico Megacity in the air quality of the region, two field campaigns were performed during the dry hot season during 2011 and 2012. Meterological and pollutant measurements were made during February and march 2011, in three sites towards the south east of Mexico Megacity, and from march to April 2012 towards the west after the Popocatepetl-Iztaccihuatl mountain range. Air quality modeling were performed by using the National Emissions Inventory 2008 during the studied periods, a comparison between measurements and the air quality model was performed. This type of studies can offer information about the pollutant distribution, the meteorological conditions and the exactness of emissions inventories. The latest can be useful for emissions inventory developers and policy makers.

Recent Enhancements to the Community Multiscale Air Quality Model (CMAQ)

EPA Science Inventory

This presentation overviews recent updates to the CMAQ modeling system. The presentation will be given as part of the information exchange session on Regional Air Pollution Modeling at the UK-US Collaboration Meeting on Air Pollution Exposure Science.

MEGAPOLI: concept and first results of multi-scale modelling of megacity impacts

NASA Astrophysics Data System (ADS)

Baklanov, A. A.; Lawrence, M.; Pandis, S.

2009-09-01

The European FP7 project MEGAPOLI: ‘Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation' (http://megapoli.info), started in October 2008, brings together 27 leading European research groups from 11 countries, state-of-the-art scientific tools and key players from countries outside Europe to investigate the interactions among megacities, air quality and climate. MEGAPOLI bridges the spatial and temporal scales that connect local emissions, air quality and weather with global atmospheric chemistry and climate. The main MEGAPOLI objectives are: 1. to assess impacts of megacities and large air-pollution hot-spots on local, regional and global air quality, 2. to quantify feedbacks among megacity air quality, local and regional climate, and global climate change, 3. to develop improved integrated tools for prediction of air pollution in megacities. In order to achieve these objectives the following tasks are realizing: • Develop and evaluate integrated methods to improve megacity emission data, • Investigate physical and chemical processes starting from the megacity street level, continuing to the city, regional and global scales, • Assess regional and global air quality impacts of megacity plumes, • Determine the main mechanisms of regional meteorology/climate forcing due to megacity plumes, • Assess global megacity pollutant forcing on climate, • Examine feedback mechanisms including effects of climate change on megacity air quality, • Develop integrated tools for prediction of megacity air quality, • Evaluate these integrated tools and use them in case studies, • Develop a methodology to estimate the impacts of different scenarios of megacity development on human health and climate change, • Propose and assess mitigation options to reduce the impacts of megacity emissions. We follow a pyramid strategy of undertaking detailed measurements in one European major city, Paris, performing detailed analysis for 12 megacities with existing air quality datasets and investigate the effects of all megacities on climate and global atmospheric chemistry. The project focuses on the multi-scale modelling of interacting meteorology and air quality, spanning the range from emissions to air quality, effects on climate, and feedbacks and mitigation potentials. Our hypothesis is that megacities around the world have an impact on air quality not only locally, but also regionally and globally and therefore can also influence the climate of our planet. Some of the links between megacities, air quality and climate are reasonably well-understood. However, a complete quantitative picture of these interactions is clearly missing. Understanding and quantifying these missing links is the focus of MEGAPOLI. The current status and modeling results after the first project year on examples of Paris and other European megacities are discussed.

Urban-rural variations in air quality and health impacts in northern India

NASA Astrophysics Data System (ADS)

Karambelas, A. N.; Holloway, T.; Fiore, A. M.; Kinney, P.; DeFries, R. S.; Kiesewetter, G.; Heyes, C.

2017-12-01

Ambient air pollution in India is a severe problem, contributing to negative health impacts and early death. Ground-based monitors often used to quantify health impacts are often located in urban regions, however approximately 70% of India's population resides in rural areas. We use high-resolution concentrations from the regional Community Multi-scale Air Quality (CMAQ) model over densely-populated northern India to estimate air quality and health impacts due to anthropogenic emission sectors separately for urban and rural regions. Modeled concentrations inform relative risk calculations and exposure estimates as performed in the Global Burden of Disease. Anthropogenic emissions from the International Institute for Applied Systems Analysis (IIASA) Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project gridding structure are updated to reflect urban- and rural-specific activity information for transportation and residential combustion, and industrial and electrical generating unit location and magnitude information. We estimate 314,000 (95% Confidence Interval: 304,000—323,000) and 58,000 (CI: 39,000—70,000) adults (25 years or older) die prematurely each year from PM2.5 and O3 respectively in northern India, with the greatest impacts along the Indo-Gangetic Plain. Using urban and rural population distributions, we estimate that the majority of premature deaths resulting from PM2.5 and O3 are in rural (292,000) as opposed to urban (79,000) regions. These findings indicate the need for designing monitoring networks and ground-based health studies in rural areas of India to more accurately quantify the true health implications of ambient air pollution, in addition to supporting model evaluation. Using this urban-versus-rural emissions framework, we are assessing anthropogenic contributions to regional air quality and health impacts, and examining mitigation strategies to reduce anthropogenic emissions, improve air quality, and reduce PM2.5 and O3 attributable premature death in the near-term.

APPLICATION OF FINE SCALE AIR TOXICS MODELING WITH CMAQ TO HAPEM5

EPA Science Inventory

This paper provides a preliminary demonstration of the EPA neighborhood scale modeling paradigm for air toxics by linking concentration from the Community Multiscale Air Quality (CMAQ) modeling system to the fifth version of the Hazardous Pollutant Exposure Model (HAPEM5). For t...

Air quality in California forests: current efforts to initiate biomonitoring with lichens.

Treesearch

Sarah Jovan

2002-01-01

The primary objective of the Forest Health Monitoring indicator project is to develop models that use the composition of epiphytic lichen communities to detect and monitor air quality in forests. The designs of existing air quality monitoring networks in California do not provide adequate representation of rural areas to assess impacts to forests. This article is...

Air-quality bioindication in the greater central valley of California, with epiphytic macrolichen communities.

Treesearch

Sarah Jovan; Bruce McCune

2005-01-01

Air-quality monitoring in the United States is typically focused on urban areas even though the detrimental effects of pollution often extend into surrounding ecosystems. The purpose of this study was to construct a model, based upon epiphytic macrolichen community data, to indicate air-quality and climate in forested areas throughout the greater Central Valley of...

A diagnostic model for studying daytime urban air quality trends

NASA Technical Reports Server (NTRS)

Brewer, D. A.; Remsberg, E. E.; Woodbury, G. E.

1981-01-01

A single cell Eulerian photochemical air quality simulation model was developed and validated for selected days of the 1976 St. Louis Regional Air Pollution Study (RAPS) data sets; parameterizations of variables in the model and validation studies using the model are discussed. Good agreement was obtained between measured and modeled concentrations of NO, CO, and NO2 for all days simulated. The maximum concentration of O3 was also predicted well. Predicted species concentrations were relatively insensitive to small variations in CO and NOx emissions and to the concentrations of species which are entrained as the mixed layer rises.

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

CMAQ MODELING FOR AIR TOXICS AT FINE SCALES: A PROTOTYPE STUDY

EPA Science Inventory

Toxic air pollutants (TAPs) or hazardous air pollutants (HAPs) exhibit considerable spatial and temporal variability across urban areas. Therefore, the ability of chemical transport models (CTMs), e.g. Community Multi-scale Air Quality (CMAQ), to reproduce the spatial and tempor...

Measuring the value of air quality: application of the spatial hedonic model.

PubMed

Kim, Seung Gyu; Cho, Seong-Hoon; Lambert, Dayton M; Roberts, Roland K

2010-03-01

This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autocorrelation. SEM mitigates spatial dependency while GWR addresses spatial heterogeneity by allowing response coefficients to vary across observations. Positive amenity values of improved air quality are found in four major clusters: (1) in East Kentucky and most of Georgia around the Southern Appalachian area; (2) in a few counties in Illinois; (3) on the border of Oklahoma and Kansas, on the border of Kansas and Nebraska, and in east Texas; and (4) in a few counties in Montana. Clusters of significant positive amenity values may exist because of a combination of intense air pollution and consumer awareness of diminishing air quality.

Impact of aircraft emissions on air quality in the vicinity of airports. Volume I. Recent airport measurement programs, data analyses, and sub-model development. Final report Jan78-Jul 80

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

Yamartino, R.J.; Smith, D.G.; Bremer, S.A.

1980-07-01

This report documents the results of the Federal Aviation Administration (FAA)/Environmental Protection Agency (EPA) air quality study which has been conducted to assess the impact of aircraft emissions of carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NOx) in the vicinity of airports. This assessment includes the results of recent modeling and monitoring efforts at Washington National (DCA), Los Angeles International (LAX), Dulles International (IAD), and Lakeland, Florida airports and an updated modeling of aircraft generated pollution at LAX, John F. Kennedy (JFK) and Chicago O'Hare (ORD) airports. The Airport Vicinity Air Pollution (AVAP) model which was designed formore » use at civil airports was used in this assessment. In addition the results of the application of the military version of the AVAP model the Air Quality Assessment Model (AQAM), are summarized.« less

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

Sondrup, Andrus Jeffrey

The Department of Energy Idaho Operations Office (DOE-ID) is applying for a synthetic minor, Sitewide, air quality permit to construct (PTC) with a facility emission cap (FEC) component from the Idaho Department of Environmental Quality (DEQ) for Idaho National Laboratory (INL) to limit its potential to emit to less than major facility limits for criteria air pollutants (CAPs) and hazardous air pollutants (HAPs) regulated under the Clean Air Act. This document is supplied as an appendix to the application, Idaho National Laboratory Application for a Synthetic Minor Sitewide Air Quality Permit to Construct with a Facility Emissions Cap Component, hereaftermore » referred to as “permit application” (DOE-ID 2015). Air dispersion modeling was performed as part of the permit application process to demonstrate pollutant emissions from the INL will not cause a violation of any ambient air quality standards. This report documents the modeling methodology and results for the air dispersion impact analysis. All CAPs regulated under Section 109 of the Clean Air Act were modeled with the exception of lead (Pb) and ozone, which are not required to be modeled by DEQ. Modeling was not performed for toxic air pollutants (TAPs) as uncontrolled emissions did not exceed screening emission levels for carcinogenic and non-carcinogenic TAPs. Modeling for CAPs was performed with the EPA approved AERMOD dispersion modeling system (Version 14134) (EPA 2004a) and five years (2000-2004) of meteorological data. The meteorological data set was produced with the companion AERMET model (Version 14134) (EPA 2004b) using surface data from the Idaho Falls airport, and upper-air data from Boise International Airport supplied by DEQ. Onsite meteorological data from the Grid 3 Mesonet tower located near the center of the INL (north of INTEC) and supplied by the local National Oceanic and Atmospheric Administration (NOAA) office was used for surface wind directions and wind speeds. Surface data (i.e., land use data that defines roughness, albedo, Bowen ratio, and other parameters) were processed using the AERSURFACE utility (Version 13016) (EPA 2013). Emission sources were modeled as point sources using actual stack locations and dimensions. Emissions, flow rates and exit temperatures were based on the design operating capacity of each source. All structures close enough to produce an area of wake effect were included for all sources. For multi-tiered structures, the heights of the tiers were included or the entire building height was assumed to be equal to the height of the tallest tier. Concentrations were calculated at 1,352 receptor locations provided by DEQ. All receptors were considered for each pollutant and averaging period. Maximum modeled CAP concentrations summed with average background concentration values were presented and compared to National Ambient Air Quality Standards (NAAQS). The background concentration values used were obtained using the Washington State University’s Laboratory for Atmospheric Research North West Airquest web-based retrieval tool (http://lar.wsu.edu/nw airquest/lookup.html). The air dispersion modeling results show the maximum impacts for CAPs are less than applicable standards and demonstrate the INL will not cause a violation of any ambient air quality standards.« less

Satellite data driven modeling system for predicting air quality and visibility during wildfire and prescribed burn events

NASA Astrophysics Data System (ADS)

Nair, U. S.; Keiser, K.; Wu, Y.; Maskey, M.; Berendes, D.; Glass, P.; Dhakal, A.; Christopher, S. A.

2012-12-01

The Alabama Forestry Commission (AFC) is responsible for wildfire control and also prescribed burn management in the state of Alabama. Visibility and air quality degradation resulting from smoke are two pieces of information that are crucial for this activity. Currently the tools available to AFC are the dispersion index available from the National Weather Service and also surface smoke concentrations. The former provides broad guidance for prescribed burning activities but does not provide specific information regarding smoke transport, areas affected and quantification of air quality and visibility degradation. While the NOAA operational air quality guidance includes surface smoke concentrations from existing fire events, it does not account for contributions from background aerosols, which are important for the southeastern region including Alabama. Also lacking is the quantification of visibility. The University of Alabama in Huntsville has developed a state-of-the-art integrated modeling system to address these concerns. This system based on the Community Air Quality Modeling System (CMAQ) that ingests satellite derived smoke emissions and also assimilates NASA MODIS derived aerosol optical thickness. In addition, this operational modeling system also simulates the impact of potential prescribed burn events based on location information derived from the AFC prescribed burn permit database. A lagrangian model is used to simulate smoke plumes for the prescribed burns requests. The combined air quality and visibility degradation resulting from these smoke plumes and background aerosols is computed and the information is made available through a web based decision support system utilizing open source GIS components. This system provides information regarding intersections between highways and other critical facilities such as old age homes, hospitals and schools. The system also includes satellite detected fire locations and other satellite derived datasets relevant for fire and smoke management.

IMPACT OF AN OZONE GENERATOR AIR CLEANER ON STYRENE CONCENTRATIONS IN AN INDOOR AIR QUALITY RESEARCH CHAMBER

EPA Science Inventory

The paper gives results of an investigation of the impact of an ozone generator air cleaner on vapor-phase styrene concentrations in a full-scale indoor air quality test chamber. The time history of the concentrations of styrene and ozone is well predicted by a simulation model u...

Does air pollution pose a public health problem for New Zealand?

PubMed

Scoggins, Amanda

2004-02-01

Air pollution is increasingly documented as a threat to public health and a major focus of regulatory activity in developed and developing countries. Air quality indicators suggest New Zealand has clean air relative to many other countries. However, media releases such as 'Christchurch wood fires pump out deadly smog' and 'Vehicle pollution major killer' have sparked public health concern regarding exposure to ambient air pollution, especially in anticipation of increasing emissions and population growth. Recent evidence is presented on the effects of air quality on health, which has been aided by the application of urban airshed models and Geographic Information Systems (GIS). Future directions for research into the effects of air quality on health in New Zealand are discussed, including a national ambient air quality management project: HAPINZ--Health and Air Pollution in New Zealand.

ASSESSMENT OF ETA-CMAQ FORECASTS OF PARTICULATE MATTER DISTRIBUTIONS THROUGH COMPARISONS WITH SURFACE NETWORK AND SPECIALIZED MEASUREMENTS

EPA Science Inventory

An air-quality forecasting (AQF) system based on the National Weather Service (NWS) National Centers for Environmental Prediction's (NCEP's) Eta model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System is used to simulate the distributions of tropospheric ...

TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES?

EPA Science Inventory

Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereb...

Modeling crop residue burning experiments and assessing the fire impacts on air quality

EPA Science Inventory

Prescribed burning is a common land management practice that results in ambient emissions of a variety of primary and secondary pollutants with negative health impacts. The community Multiscale Air Quality (CMAQ) model is used to conduct 2 km grid resolution simulations of prescr...

Application and evaluation of high-resolution WRF-CMAQ with simple urban parameterization.

EPA Science Inventory

The 2-way coupled WRF-CMAQ meteorology and air quality modeling system is evaluated for high-resolution applications by comparing to a regional air quality field study (Discover-AQ). The model was modified to better account for the effects of urban environments. High-resolution...

Extending the Applicability of the Community Multiscale Air Quality Model to Hemispheric Scales: Motivation, Challenges, and Progress

EPA Science Inventory

The adaptation of the Community Multiscale Air Quality (CMAQ) modeling system to simulate O₃, particulate matter, and related precursor distributions over the northern hemisphere is presented. Hemispheric simulations with CMAQ and the Weather Research and Forecasting (...

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]....

Influence of Boundary Conditions on Regional Air Quality Simulations—Analysis of AQMEII Phase 3 Results

EPA Science Inventory

Chemical boundary conditions are a key input to regional-scale photochemical models. In this study, performed during the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3), we perform annual simulations over North America with chemical boundary con...

Application and evaluation of high-resolution WRF-CMAQ with simple urban parameterization

EPA Science Inventory

The 2-way coupled WRF-CMAQ meteorology and air quality modeling system is evaluated for high-resolution applications by comparing to a regional air quality field study (Discover-AQ). The model was modified to better account for the effects of urban environments. High-resolution...

The Satellite based Monitoring Initiative for Regional Air quality (SAMIRA): Project summary and first results

NASA Astrophysics Data System (ADS)

Schneider, Philipp; Stebel, Kerstin; Ajtai, Nicolae; Diamandi, Andrei; Horalek, Jan; Nemuc, Anca; Stachlewska, Iwona; Zehner, Claus

2017-04-01

We present a summary and some first results of a new ESA-funded project entitled Satellite based Monitoring Initiative for Regional Air quality (SAMIRA), which aims at improving regional and local air quality monitoring through synergetic use of data from present and upcoming satellite instruments, traditionally used in situ air quality monitoring networks and output from chemical transport models. Through collaborative efforts in four countries, namely Romania, Poland, the Czech Republic and Norway, all with existing air quality problems, SAMIRA intends to support the involved institutions and associated users in their national monitoring and reporting mandates as well as to generate novel research in this area. The primary goal of SAMIRA is to demonstrate the usefulness of existing and future satellite products of air quality for improving monitoring and mapping of air pollution at the regional scale. A total of six core activities are being carried out in order to achieve this goal: Firstly, the project is developing and optimizing algorithms for the retrieval of hourly aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard of Meteosat Second Generation. As a second activity, SAMIRA aims to derive particulate matter (PM2.5) estimates from AOD data by developing robust algorithms for AOD-to-PM conversion with the support from model- and Lidar data. In a third activity, we evaluate the added value of satellite products of atmospheric composition for operational European-scale air quality mapping using geostatistics and auxiliary datasets. The additional benefit of satellite-based monitoring over existing monitoring techniques (in situ, models) is tested by combining these datasets using geostatistical methods and demonstrated for nitrogen dioxide (NO2), sulphur dioxide (SO2), and aerosol optical depth/particulate matter. As a fourth activity, the project is developing novel algorithms for downscaling coarse-resolution satellite products of air quality with the help of high-resolution model information. This will add value to existing earth observation products of air quality by bringing them to spatial scales that are more in line with what is generally required for studying urban and regional scale air quality. In a fifth activity, we implement robust and independent validation schemes for evaluating the quality of the generated products. Finally, in a sixth activity the consortium is working towards a pre-operational system for improved PM forecasts using observational (in situ and satellite) data assimilation. SAMIRA aims to maximize project benefits by liaison with national and regional environmental protection agencies and health institutions, as well as related ESA and European initiatives such as the Copernicus Atmosphere Monitoring Service (CAMS).

Predicting indoor pollutant concentrations, and applications to air quality management

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

Lorenzetti, David M.

Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptomsmore » such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.« less

A site-specific screening comparison of modeled and monitored air dispersion and deposition for perfluorooctanoate.

PubMed

Barton, Catherine A; Zarzecki, Charles J; Russell, Mark H

2010-04-01

This work assessed the usefulness of a current air quality model (American Meteorological Society/Environmental Protection Agency Regulatory Model [AERMOD]) for predicting air concentrations and deposition of perfluorooctanoate (PFO) near a manufacturing facility. Air quality models play an important role in providing information for verifying permitting conditions and for exposure assessment purposes. It is important to ensure traditional modeling approaches are applicable to perfluorinated compounds, which are known to have unusual properties. Measured field data were compared with modeling predictions to show that AERMOD adequately located the maximum air concentration in the study area, provided representative or conservative air concentration estimates, and demonstrated bias and scatter not significantly different than that reported for other compounds. Surface soil/grass concentrations resulting from modeled deposition flux also showed acceptable bias and scatter compared with measured concentrations of PFO in soil/grass samples. Errors in predictions of air concentrations or deposition may be best explained by meteorological input uncertainty and conservatism in the PRIME algorithm used to account for building downwash. In general, AERMOD was found to be a useful screening tool for modeling the dispersion and deposition of PFO in air near a manufacturing facility.

Assessing the impacts of seasonal and vertical atmospheric conditions on air quality over the Pearl River Delta region

NASA Astrophysics Data System (ADS)

Tong, Cheuk Hei Marcus; Yim, Steve Hung Lam; Rothenberg, Daniel; Wang, Chien; Lin, Chuan-Yao; Chen, Yongqin David; Lau, Ngar Cheung

2018-05-01

Air pollution is an increasingly concerning problem in many metropolitan areas due to its adverse public health and environmental impacts. Vertical atmospheric conditions have strong effects on vertical mixing of air pollutants, which directly affects surface air quality. The characteristics and magnitude of how vertical atmospheric conditions affect surface air quality, which are critical to future air quality projections, have not yet been fully understood. This study aims to enhance understanding of the annual and seasonal sensitivities of air pollution to both surface and vertical atmospheric conditions. Based on both surface and vertical meteorological characteristics provided by 1994-2003 monthly dynamic downscaling data from the Weather and Research Forecast Model, we develop generalized linear models (GLMs) to study the relationships between surface air pollutants (ozone, respirable suspended particulates, and sulfur dioxide) and atmospheric conditions in the Pearl River Delta (PRD) region. Applying Principal Component Regression (PCR) to address multi-collinearity, we study the contributions of various meteorological variables to pollutants' concentration levels based on the loading and model coefficient of major principal components. Our results show that relatively high pollutant concentration occurs under relatively low mid-level troposphere temperature gradients, low relative humidity, weak southerly wind (or strong northerly wind) and weak westerly wind (or strong easterly wind). Moreover, the correlations vary among pollutant species, seasons, and meteorological variables at various altitudes. In general, pollutant sensitivity to meteorological variables is found to be greater in winter than in other seasons, and the sensitivity of ozone to meteorology differs from that of the other two pollutants. Applying our GLMs to anomalous air pollution episodes, we find that meteorological variables up to mid troposphere (∼700 mb) play an important role in influencing surface air quality, pinpointing the significant and unique associations between meteorological variables at higher altitudes and surface air quality.

AQA-PM: Extension of the Air-Quality model for Austria with satellite based Particulate Matter estimates

NASA Astrophysics Data System (ADS)

Hirtl, M.; Mantovani, S.; Krüger, B. C.; Triebnig, G.

2012-04-01

Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The Air Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Applied Life Sciences in Vienna (BOKU) by order of the regional governments since 2005. AQA conducts daily forecasts of gaseous and particulate (PM10) air pollutants over Austria. In the frame of the project AQA-PM (funded by FFG), satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using assimilation techniques. It is expected that the assimilation of satellite measurements will significantly improve the quality of AQA. Currently no observations are considered in the modeling system. At the current stage of the project, different datasets have been collected (ground measurements, satellite measurements, fine resolved regional emission inventories) and are analyzed and prepared for further processing. This contribution gives an overview of the project working plan and the upcoming developments. The goal of this project is to improve the PM10-forecasts for Austria with the integration of satellite based measurements and to provide a comprehensive product-platform.

Air quality modeling for effective environmental management in the mining region.

PubMed

Asif, Zunaira; Chen, Zhi; Han, Yi

2018-04-18

Air quality in the mining sector is a serious environmental concern and associated with many health issues. The air quality management in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. A modeling approach called mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model is taken into account through the planet's boundary conditions and assuming that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values as the predicted concentrations of PM 10 , PM 2.5 , TSP, NO 2 and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening of summer, and minimum mixing height (380 m) is attained during the evening of winter. The dust fall (PM coarse) deposition flux is maximum during February and March with the deposition velocity of 4.67 cm/s. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM 2.5 ; 0.71 to 0.82 for PM 10 and from 0.71 to 0.89 for NO 2 . The analyses illustrate that presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. The comparison of MADM and CALPUFF modeling values are made for four different pollutants (PM 2.5 , PM 10 , TSP, and NO 2 ) under three different atmospheric stability classes (stable, neutral and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

New Methods for Air Quality Model Evaluation with Satellite Data

NASA Astrophysics Data System (ADS)

Holloway, T.; Harkey, M.

2015-12-01

Despite major advances in the ability of satellites to detect gases and aerosols in the atmosphere, there remains significant, untapped potential to apply space-based data to air quality regulatory applications. Here, we showcase research findings geared toward increasing the relevance of satellite data to support operational air quality management, focused on model evaluation. Particular emphasis is given to nitrogen dioxide (NO2) and formaldehyde (HCHO) from the Ozone Monitoring Instrument aboard the NASA Aura satellite, and evaluation of simulations from the EPA Community Multiscale Air Quality (CMAQ) model. This work is part of the NASA Air Quality Applied Sciences Team (AQAST), and is motivated by ongoing dialog with state and federal air quality management agencies. We present the response of satellite-derived NO2 to meteorological conditions, satellite-derived HCHO:NO2 ratios as an indicator of ozone production regime, and the ability of models to capture these sensitivities over the continental U.S. In the case of NO2-weather sensitivities, we find boundary layer height, wind speed, temperature, and relative humidity to be the most important variables in determining near-surface NO2 variability. CMAQ agreed with relationships observed in satellite data, as well as in ground-based data, over most regions. However, we find that the southwest U.S. is a problem area for CMAQ, where modeled NO2 responses to insolation, boundary layer height, and other variables are at odds with the observations. Our analyses utilize a software developed by our team, the Wisconsin Horizontal Interpolation Program for Satellites (WHIPS): a free, open-source program designed to make satellite-derived air quality data more usable. WHIPS interpolates level 2 satellite retrievals onto a user-defined fixed grid, in effect creating custom-gridded level 3 satellite product. Currently, WHIPS can process the following data products: OMI NO2 (NASA retrieval); OMI NO2 (KNMI retrieval); OMI HCHO (NASA retrieval); MOPITT CO (NASA retrieval); MODIS AOD (NASA retrieval). More information at http://nelson.wisc.edu/sage/data-and-models/software.php.

The Improvement of Spatial-Temporal PM2.5 Resolution in Taiwan by Using Data Assimilation Method

NASA Astrophysics Data System (ADS)

Lin, Yong-Qing; Lin, Yuan-Chien

2017-04-01

Forecasting air pollution concentration, e.g., the concentration of PM2.5, is of great significance to protect human health and the environment. Accurate prediction of PM2.5 concentrations is limited in number and the data quality of air quality monitoring stations. The spatial and temporal variations of PM2.5 concentrations are measured by 76 National Air Quality Monitoring Stations (built by the TW-EPA) in Taiwan. The National Air Quality Monitoring Stations are costly and scarce because of the highly precise instrument and their size. Therefore, many places still out of the range of National Air Quality Monitoring Stations. Recently, there are an enormous number of portable air quality sensors called "AirBox" developed jointly by the Taiwan government and a private company. By virtue of its price and portative, the AirBox can provide higher resolution of space-time PM2.5 measurement. However, the spatiotemporal distribution and data quality are different between AirBox and National Air Quality Monitoring Stations. To integrate the heterogeneous PM2.5 data, the data assimilation method should be performed before further analysis. In this study, we propose a data assimilation method based on Ensemble Kalman Filter (EnKF), which is a variant of classic Kalman Filter, can be used to combine additional heterogeneous data from different source while modeling to improve the estimation of spatial-temporal PM2.5 concentration. The assimilation procedure uses the advantages of the two kinds of heterogeneous data and merges them to produce the final estimation. The results have shown that by combining AirBox PM2.5 data as additional information in our model based EnKF can bring the better estimation of spatial-temporal PM2.5 concentration and improve the it's space-time resolution. Under the approach proposed in this study, higher spatial-temporal resoultion could provide a very useful information for a better spatial-temporal data analysis and further environmental management, such as air pollution source localization and micro-scale air pollution analysis. Keywords: PM2.5, Data Assimilation, Ensemble Kalman Filter, Air Quality

A database and tool for boundary conditions for regional air quality modeling: description and evaluation

NASA Astrophysics Data System (ADS)

Henderson, B. H.; Akhtar, F.; Pye, H. O. T.; Napelenok, S. L.; Hutzell, W. T.

2014-02-01

Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying lateral boundary conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2001-2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complemented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone and carbon monoxide vertical profiles. The results show performance is largely within uncertainty estimates for ozone from the Ozone Monitoring Instrument and carbon monoxide from the Measurements Of Pollution In The Troposphere (MOPITT), but there were some notable biases compared with Tropospheric Emission Spectrometer (TES) ozone. Compared with TES, our ozone predictions are high-biased in the upper troposphere, particularly in the south during January. This publication documents the global simulation database, the tool for conversion to LBC, and the evaluation of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants.

Temporalization of Electric Generation Emissions for Improved Representation of Peak Air Quality Episodes

NASA Astrophysics Data System (ADS)

Farkas, C. M.; Moeller, M.; Carlton, A. G.

2013-12-01

Photochemical transport models routinely under predict peak air quality events. This deficiency may be due, in part, to inadequate temporalization of emissions from the electric generating sector. The National Emissions Inventory (NEI) reports emissions from Electric Generating Units (EGUs) by either Continuous Emission Monitors (CEMs) that report hourly values or as an annual total. The Sparse Matrix Operator Kernel Emissions preprocessor (SMOKE), used to prepare emissions data for modeling with the CMAQ air quality model, allocates annual emission totals throughout the year using specific monthly, weekly, and hourly weights according to standard classification code (SCC) and location. This approach represents average diurnal and seasonal patterns of electricity generation but does not capture spikes in emissions due to episodic use as with peaking units or due to extreme weather events. In this project we use a combination of state air quality permits, CEM data, and EPA emission factors to more accurately temporalize emissions of NOx, SO2 and particulate matter (PM) during the extensive heat wave of July and August 2006. Two CMAQ simulations are conducted; the first with the base NEI emissions and the second with improved temporalization, more representative of actual emissions during the heat wave. Predictions from both simulations are evaluated with O3 and PM measurement data from EPA's National Air Monitoring Stations (NAMS) and State and Local Air Monitoring Stations (SLAMS) during the heat wave, for which ambient concentrations of criteria pollutants were often above NAAQS. During periods of increased photochemistry and high pollutant concentrations, it is critical that emissions are most accurately represented in air quality models.

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2010 CFR

2010-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2014 CFR

2014-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2013 CFR

2013-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2011 CFR

2011-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

40 CFR 52.1164 - Localized high concentrations-carbon monoxide.

Code of Federal Regulations, 2012 CFR

2012-07-01

... meteorological modeling, traffic flow monitoring, air quality monitoring and other measures necessary to... reviewing all available traffic data, physical site data and air quality and meteorological data for all... containing measures to regulate traffic and parking so as to reduce carbon monoxide emissions to achieve air...

Computational fluid dynamics modeling to assess the impact of roadside barriers on near-road air quality

EPA Science Inventory

Near-road air quality is an issue of emerging concern, with field studies consistently showing elevated air pollutant concentrations adjacent to major roads, usually decreasing to background levels within several hundred meters. Roadside barriers, both vegetative and structural, ...

A Hybrid Approach for Estimating Total Deposition in the ...

EPA Pesticide Factsheets

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Networ

A Novel Hybrid Approach for Estimating Total Deposition in ...

EPA Pesticide Factsheets

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Networ

A Comparison between 2010 and 2006 Air Quality and Meteorological Conditions, andEmissions and Boundary Conditions used in Simulations of the AQMEII-2 North American Domain

EPA Science Inventory

Several participants in Phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2) who are applying coupled models to the North American domain are comparing model results for two years: 2006 and 2010. While a key difference of interest between these two yea...

NEIGHBORHOOD SCALE AIR QUALITY MODELING IN HOUSTON USING URBAN CANOPY PARAMETERS IN MM5 AND CMAQ WITH IMPROVED CHARACTERIZATION OF MESOSCALE LAKE-LAND BREEZE CIRCULATION

EPA Science Inventory

Advanced capability of air quality simulation models towards accurate performance at finer scales will be needed for such models to serve as tools for performing exposure and risk assessments in urban areas. It is recognized that the impact of urban features such as street and t...

Performance Summary of the 2006 Community Multiscale Air Quality (CMAQ) Simulation for the AQMEII Project: North American Application

EPA Science Inventory

The CMAQ modeling system has been used to simulate the CONUS using 12-km by 12-km horizontal grid spacing for the entire year of 2006 as part of the Air Quality Model Evaluation International initiative (AQMEII). The operational model performance for O₃ and PM_2.5<...

Visual air quality simulation techniques

NASA Astrophysics Data System (ADS)

Molenar, John V.; Malm, William C.; Johnson, Christopher E.

Visual air quality is primarily a human perceptual phenomenon beginning with the transfer of image-forming information through an illuminated, scattering and absorbing atmosphere. Visibility, especially the visual appearance of industrial emissions or the degradation of a scenic view, is the principal atmospheric characteristic through which humans perceive air pollution, and is more sensitive to changing pollution levels than any other air pollution effect. Every attempt to quantify economic costs and benefits of air pollution has indicated that good visibility is a highly valued and desired environmental condition. Measurement programs can at best approximate the state of the ambient atmosphere at a few points in a scenic vista viewed by an observer. To fully understand the visual effect of various changes in the concentration and distribution of optically important atmospheric pollutants requires the use of aerosol and radiative transfer models. Communication of the output of these models to scientists, decision makers and the public is best done by applying modern image-processing systems to generate synthetic images representing the modeled air quality conditions. This combination of modeling techniques has been under development for the past 15 yr. Initially, visual air quality simulations were limited by a lack of computational power to simplified models depicting Gaussian plumes or uniform haze conditions. Recent explosive growth in low cost, high powered computer technology has allowed the development of sophisticated aerosol and radiative transfer models that incorporate realistic terrain, multiple scattering, non-uniform illumination, varying spatial distribution, concentration and optical properties of atmospheric constituents, and relative humidity effects on aerosol scattering properties. This paper discusses these improved models and image-processing techniques in detail. Results addressing uniform and non-uniform layered haze conditions in both urban and remote pristine areas will be presented.

InMAP: A model for air pollution interventions

DOE PAGES

Tessum, Christopher W.; Hill, Jason D.; Marshall, Julian D.; ...

2017-04-19

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. We present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical informationmore » from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons we run, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.« less

InMAP: A model for air pollution interventions

PubMed Central

Hill, Jason D.; Marshall, Julian D.

2017-01-01

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of −17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license. PMID:28423049

InMAP: A model for air pollution interventions

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

Tessum, Christopher W.; Hill, Jason D.; Marshall, Julian D.

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. We present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical informationmore » from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons we run, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.« less

STOCHASTIC DESCRIPTION OF SUBGRID POLLUTANT VARIABILITY IN CMAQ

EPA Science Inventory

This paper describes a tool for investigating and describing fine scale spatial variability in model concentration fields with the goal of improving the use of air quality models for driving exposure modeling to assess human risk to hazardous air pollutants or air toxics. Region...

An Evaluation of Real-time Air Quality Forecasts and their Urban Emissions over Eastern Texas During the Summer of 2006 Second Texas Air Quality Study Field Study

EPA Science Inventory

Forecasts of ozone (O₃) and particulate matter (diameter less than 2.5 µm, PM_2.5) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the AIRNow netwo...

What Air Quality Models Tell Us About Sources and Sinks of Atmospheric Aldehydes

NASA Astrophysics Data System (ADS)

Luecken, D.; Hutzell, W. T.; Phillips, S.

2010-12-01

Atmospheric aldehydes play important roles in several aspects of air quality: they are critical radical sources that drive ozone formation, they are hazardous air pollutants that are national drivers for cancer risk, they participate in aqueous chemistry and potentially aerosol formation, and are key species for evaluating the accuracy of isoprene emissions. For these reasons, it is important to accurately understand their sources and sinks, and the sensitivity of their concentrations to emission controls. While both compounds have been included in air quality modeling for many years, current, state-of-the-science chemical mechanisms have difficulty reproducing measured values of aldehydes, which calls into question the robustness of ozone, HAPs and aerosol predictions. In the past, we have attributed discrepancies to measurement errors, inventory errors, or the focus on high-NOx urban regimes. Despite improvements in all of these areas, the measurements still diverge from model predictions, with formaldehyde often underpredicted by 50% and acetaldehyde showing a large degree of scatter - from 20% overprediction to 50% underprediction. To better examine the sources of aldehydes, we implemented the new SAPRC07T mechanism in the Community Multi-Scale Air Quality (CMAQ) model. This mechanism incorporates current recommendations for kinetic data and has the most detailed representation of product formation under a wide variety of conditions of any mechanism used in regional air quality models. We use model simulations to pinpoint where and when aldehyde concentrations tend to deviate from measurements. We demonstrate the role of secondary production versus primary emissions in aldehdye concentrations and find that secondary sources produce the largest deviations from measurements. We identify which VOCs are most responsible for aldehyde secondary production in the areas of the U.S. where the largest health effects are seen, and discuss how this affects consideration of control strategies.

Case study of odor and indoor air quality assessment in the dewatering building at the Stickney Water Reclamation Plant.

PubMed

Sharma, Manju; O'Connell, Susan; Garelli, Brett; Sattayatewa, Chakkrid; Moschandreas, Demetrios; Pagilla, Krishna

2012-01-01

Indoor air quality (IAQ) and odors were determined using sampling/monitoring, measurement, and modeling methods in a large dewatering building at a very large water reclamation plant. The ultimate goal was to determine control strategies to reduce the sensory impacts on the workforce and achieve odor reduction within the building. Study approaches included: (1) investigation of air mixing by using CO(2) as an indicator, (2) measurement of airflow capacity of ventilation fans, (3) measurement of odors and odorants, (4) development of statistical and IAQ models, and (5) recommendation of control strategies. The results showed that air quality in the building complies with occupational safety and health guidelines; however, nuisance odors that can increase stress and productivity loss still persist. Excess roof fan capacity induced odor dispersion to the upper levels. Lack of a local air exhaust system of sufficient capacity and optimum design was found to be the contributor to occasional less than adequate indoor air quality and odors. Overall, air ventilation rate in the building has less effect on persistence of odors in the building. Odor/odorant emission rates from centrifuge drops were approximately 100 times higher than those from the open conveyors. Based on measurements and modeling, the key control strategies recommended include increasing local air exhaust system capacity and relocation of exhaust hoods closer to the centrifuge drops.

Assessing the influence of land use land cover pattern, socio economic factors and air quality status to predict morbidity on the basis of logistic based regression model

NASA Astrophysics Data System (ADS)

Dixit, A.; Singh, V. K.

2017-12-01

Recent studies conducted by World Health Organisation (WHO) estimated that 92 % of the total world population are living in places where the air quality level has exceeded the WHO standard limit for air quality. This is due to the change in Land Use Land Cover (LULC) pattern, socio economic drivers and anthropogenic heat emission caused by manmade activity. Thereby, many prevalent human respiratory diseases such as lung cancer, chronic obstructive pulmonary disease and emphysema have increased in recent times. In this study, a quantitative relationship is developed between land use (built-up land, water bodies, and vegetation), socio economic drivers and air quality parameters using logistic based regression model over 7 different cities of India for the winter season of 2012 to 2016. Different LULC, socio economic, industrial emission sources, meteorological condition and air quality level from the monitoring stations are taken to estimate the influence on morbidity of each city. Results of correlation are analyzed between land use variables and monthly concentration of pollutants. These values range from 0.63 to 0.76. Similarly, the correlation value between land use variable with socio economic and morbidity ranges from 0.57 to 0.73. The performance of model is improved from 67 % to 79 % in estimating morbidity for the year 2015 and 2016 due to the better availability of observed data.The study highlights the growing importance of incorporating socio-economic drivers with air quality data for evaluating morbidity rate for each city in comparison to just change in quantitative analysis of air quality.

Urban compaction or dispersion? An air quality modelling study

NASA Astrophysics Data System (ADS)

Martins, Helena

2012-07-01

Urban sprawl is altering the landscape, with current trends pointing to further changes in land use that will, in turn, lead to changes in population, energy consumption, atmospheric emissions and air quality. Urban planners have debated on the most sustainable urban structure, with arguments in favour and against urban compaction and dispersion. However, it is clear that other areas of expertise have to be involved. Urban air quality and human exposure to atmospheric pollutants as indicators of urban sustainability can contribute to the discussion, namely through the study of the relation between urban structure and air quality. This paper addresses the issue by analysing the impacts of alternative urban growth patterns on the air quality of Porto urban region in Portugal, through a 1-year simulation with the MM5-CAMx modelling system. This region has been experiencing one of the highest European rates of urban sprawl, and at the same time presents a poor air quality. As part of the modelling system setup, a sensitivity study was conducted regarding different land use datasets and spatial distribution of emissions. Two urban development scenarios were defined, SPRAWL and COMPACT, together with their new land use and emission datasets; then meteorological and air quality simulations were performed. Results reveal that SPRAWL land use changes resulted in an average temperature increase of 0.4 °C, with local increases reaching as high as 1.5 °C. SPRAWL results also show an aggravation of PM10 annual average values and an increase in the exceedances to the daily limit value. For ozone, differences between scenarios were smaller, with SPRAWL presenting larger concentration differences than COMPACT. Finally, despite the higher concentrations found in SPRAWL, population exposure to the pollutants is higher for COMPACT because more inhabitants are found in areas of highest concentration levels.

A novel air quality analysis and prediction system for São Paulo, Brazil to support decision-making

NASA Astrophysics Data System (ADS)

Hoshyaripour, Gholam Ali; Brasseur, Guy; Andrade, Maria Fatima; Gavidia-Calderón, Mario; Bouarar, Idir

2016-04-01

The extensive economic development and urbanization in southeastern Brazil (SEB) in recent decades have notably degraded the air quality with adverse impacts on human health. Since the Metropolitan Area of São Paulo (MASP) accommodates the majority of the economic growth in SEB, it overwhelmingly suffers from the air pollution. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality and to assist the decision makers to mitigate the air pollution in MASP. Here we present the results of an air quality modeling system designed for SEB with focuses on MASP. The Weather Research and Forecast model with Chemistry (WRF-Chem) is used considering the anthropogenic, biomass-burning and biogenic emissions within a 1000×1500 km domain with resolution of 10 km. FINN and MEGAN are used for the biomass-burning and biogenic emissions, respectively. For the anthropogenic emissions we use a local bottom-up inventory for the transport sector and the HTAPv2 global inventory for all other sectors. The bottom-up inventory accounts for the traffic patterns, vehicle types and their emission factors in the area and thus could be used to evaluate the effect of changes in these parameters on air quality in MASP. The model outputs are compered to the satellite and ground-based observations for O3 and NOx. The results show that using the bottom-up or top-down inventories individually can result in a huge deviation between the predictions and observations. On the other hand, combining the inventories significantly enhances the forecast accuracy. It also provides a powerful tool to quantify the effects of traffic and vehicle emission policies on air quality in MASP.

An Observational and modeling strategy to investigate the impact of remote sources on local air quality: A Houston, Texas case study from the Second Texas Air Quality Study (TEXAQS II)

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

McMillan, W. W.; Pierce, R.; Sparling, L. C.

2010-01-05

Quantifying the impacts of remote sources on individual air quality exceedances remains a significant challenge for air quality forecasting. One goal of the 2006 Texas Air Quality Study (TEXAQS II) was to assess the impact of distant sources on air quality in east Texas. From 23-30 August 2006, retrievals of tropospheric carbon monoxide (CO) from NASA’s Atmospheric InfraRed Sounder (AIRS) reveal the transport of CO from fires in the United States Pacific Northwest to Houston, Texas. This transport occurred behind a cold front and contributed to the worst ozone exceedance period of the summer in the Houston area. We presentmore » supporting satellite observations from the NASA A-Train constellation of the vertical distribution of smoke aerosols and CO. Ground-based in situ CO measurements in Oklahoma and Texas track the CO plume as it moves south and indicate mixing of the aloft plume to the surface by turbulence in the nocturnal boundary layer and convection during the day. Ground-based aerosol speciation and lidar observations do not find appreciable smoke aerosol transport for this case. However, MODIS aerosol optical depths and model simulations indicate some smoke aerosols were transported from the Pacific Northwest through Texas to the Gulf of Mexico. Chemical transport and forward trajectory models confirm the three major observations: (1) the AIRS envisioned CO transport, (2) the satellite determined smoke plume height, and (3) the timing of the observed surface CO increases. Further, the forward trajectory simulations find two of the largest Pacific Northwest fires likely had the most significant impact.« less

2005 v4.3 Technical Support Document

EPA Pesticide Factsheets

Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document describes how updated 2005 NEI, version 2 emissions were processed for air quality modeling in support of the final Mercury and Air Toxics Standards (MATS).

A Multi-Model Assessment for the 2006 and 2010 Simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of Column Variable Predictions Using Satellite Data

EPA Science Inventory

Within the context of the Air Quality Model Evaluation International Initiative phase 2 (AQMEII2) project, this part II paper performs a multi-model assessment of major column abundances of gases, radiation, aerosol, and cloud variables for 2006 and 2010 simulations with three on...

Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period.

PubMed

Gokhale, Sharad; Raokhande, Namita

2008-05-01

There are several models that can be used to evaluate roadside air quality. The comparison of the operational performance of different models pertinent to local conditions is desirable so that the model that performs best can be identified. Three air quality models, namely the 'modified General Finite Line Source Model' (M-GFLSM) of particulates, the 'California Line Source' (CALINE3) model, and the 'California Line Source for Queuing & Hot Spot Calculations' (CAL3QHC) model have been identified for evaluating the air quality at one of the busiest traffic intersections in the city of Guwahati. These models have been evaluated statistically with the vehicle-derived airborne particulate mass emissions in two sizes, i.e. PM10 and PM2.5, the prevailing meteorology and the temporal distribution of the measured daily average PM10 and PM2.5 concentrations in wintertime. The study has shown that the CAL3QHC model would make better predictions compared to other models for varied meteorology and traffic conditions. The detailed study reveals that the agreements between the measured and the modeled PM10 and PM2.5 concentrations have been reasonably good for CALINE3 and CAL3QHC models. Further detailed analysis shows that the CAL3QHC model performed well compared to the CALINE3. The monthly performance measures have also led to the similar results. These two models have also outperformed for a class of wind speed velocities except for low winds (<1 m s(-1)), for which, the M-GFLSM model has shown the tendency of better performance for PM10. Nevertheless, the CAL3QHC model has outperformed for both the particulate sizes and for all the wind classes, which therefore can be optional for air quality assessment at urban traffic intersections.

A PRELIMINARY EVALUATION OF MODELS-3 CMAQ USING PARTICULATE MATTER DATA FROM THE IMPROVE NETWORK

EPA Science Inventory

The Clean Air Act and its Amendments require the United States Environmental Protection Agency (EPA) to establish National Ambient Air Quality Standards for Particulate Matter (PM) and to assess current and future air quality regulations designed to protect human health and wel...

30 CFR 250.302 - Definitions concerning air quality.

Code of Federal Regulations, 2011 CFR

2011-07-01

... and 250.304 of this part: Air pollutant means any combination of agents for which the Environmental... shown by monitored data or which is calculated by air quality modeling (or other methods determined by... standards established by EPA. Best available control technology (BACT) means an emission limitation based on...

Integrating PM2.5 Observations, Model Estimates and Satellite Signals for the Eastern United States by Projection onto Latent Structures

EPA Science Inventory

Detailed, time-varying spatial fields of air contaminant concentrations are valuable to public health professionals seeking to identify relationships between human health and ambient air quality, and policy makers interested in assessing compliance with air quality regulations. ...

A Community-Scale Modeling System to Assess Port-Related Air Quality Impacts

EPA Science Inventory

Near-port air pollution has been identified by numerous organizations as a potential public health concern. Based upon multiple near-road and near-source monitoring studies, both busy roadways and large emission sources at the ports may impact local air quality within several hun...

IMPACTS OF CLIMATE-INDUCED CHANGES IN EXTREME EVENTS ON OZONE AND PARTICULATE MATTER AIR QUALITY

EPA Science Inventory

Historical data records of air pollution meteorology from multiple datasets will be compiled and analyzed to identify possible trends in extreme events. Changes in climate and air quality between 2010 and 2050 will be simulated with a suite of models. The consequential effe...

75 FR 9410 - Science Advisory Board Staff Office Notification of a Public Teleconference of the Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-02

... advisory report on EPA's air quality modeling work for the Second Section 812 Prospective Benefit-Cost... Section 812 Benefit-Cost Analysis of the Clean Air Act. The Council was established in 1991 pursuant to... assess benefits and costs of the EPA's regulatory actions under the Clean Air Act. The Council has...

Impact of aircraft emissions on air quality in the vicinity of airports. Volume II. An updated model assessment of aircraft generated air pollution at LAX, JFK, and ORD. Final report Jan 1978-Jul 1980

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

Yamartino, R.J.; Smith, D.G.; Bremer, S.A.

1980-07-01

This report documents the results of the Federal Aviation Administration (FAA)/Environmental Protection Agency (EPA) air quality study which has been conducted to assess the impact of aircraft emissions of carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NOx) in the vicinity of airports. This assessment includes the results of recent modeling and monitoring efforts at Washington National (DCA), Los Angeles International (LAX), Dulles International (IAD), and Lakeland, Florida airports and an updated modeling of aircraft generated pollution at LAX, John F. Kennedy (JFK) and Chicago O'Hare (ORD) airports. The Airport Vicinity Air Pollution (AVAP) model which was designed formore » use at civil airports was used in this assessment. In addition the results of the application of the military version of the AVAP model the Air Quality Assessment Model (AQAM), are summarized. Both the results of the pollution monitoring analyses in Volume I and the modeling studies in Volume II suggest that: maximum hourly average CO concentrations from aircraft are unlikely to exceed 5 parts per million (ppm) in areas of public exposure and are thus small in comparison to the National Ambient Air Quality Standard of 35 ppm; maximum hourly HC concentrations from aircraft can exceed 0.25 ppm over an area several times the size of the airport; and annual average NO2 concentrations from aircraft are estimated to contribute only 10 to 20 percent of the NAAQS limit level.« less

Evaluating and improving the results of air quality models in Texas using TES, AIRS and other satellite data

NASA Astrophysics Data System (ADS)

Osterman, G.; Harper, C.; Estes, M.; Zhao, W.; Bowman, K.; Pierce, B.; Irion, B.; Kahn, B.; Al-Saadi, J.

2008-05-01

The Houston/Galveston/Brazoria (HGB) area of Texas has been classified as in moderate nonattainment of the Environmental Protection Agency (EPA) 8-hour standard for ground level ozone since April 30, 2004. The Texas Commission on Environmental Quality uses photochemical model results as one of its primary tools to develop strategies to bring the HGB area into attainment with the EPA standard. The state of Texas then includes the strategies into a revised version of its State Implementation Plan (SIP). We will discuss efforts that have been or soon will be underway to use satellite data to evaluate and improve the meteorological and photochemical modeling efforts at TCEQ. In particular we will show the use of GOES, AIRS and TES data to improve the ability to model, using the MM5 model, the meteorological conditions over Texas and the Gulf of Mexico. The meteorological fields are then used as one of the inputs to the CAMx air quality model used at TCEQ. We will discuss the use of chemical transport model results as initial and boundary conditions which are a key uncertainty in the modeling of the air above Houston. We will also discuss the use of TES data to assist in the evaluation of preliminary model results generated by TCEQ for time periods in 2005. The satellite data will provide key information on ozone and carbon monoxide concentrations away from surface monitors in the troposphere. We will show how satellite data is becoming a key tool in the effort to improve air quality in the HGB area and one that can easily applied for use in other regions of the country.

Indoor Air Quality and Energy Efficiency

EPA Pesticide Factsheets

EPA completed an extensive modeling study to assess the compatibilities and trade-offs between energy, indoor air quality, and thermal comfort objectives for HVAC systems and to formulate strategies for superior performance across all areas.

Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

PubMed

Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

2016-08-15

In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their environment are needed to improve human well-being. Copyright © 2016 Elsevier B.V. All rights reserved.

A framework for expanding aqueous chemistry in the Community Multiscale Air Quality (CMAQ) model version 5.1

EPA Science Inventory

This paper describes the development and implementation of an extendable aqueous-phase chemistry option (AQCHEM − KMT(I)) for the Community Multiscale Air Quality (CMAQ) modeling system, version 5.1. Here, the Kinetic PreProcessor (KPP), version 2.2.3, is used t...

TESTING PHYSICS AND CHEMISTRY SENSITIVITIES IN THE U.S. EPA COMMUNITY MULTISCALE AIR QUALITY MODELING SYSTEM (CMAQ)

EPA Science Inventory

Uncertainties in key elements of emissions and meteorology inputs to air quality models (AQMs) can range from 50 to 100% with some areas of emissions uncertainty even higher (Russell and Dennis, 2000). Uncertainties in the chemical mechanisms are thought to be smaller (Russell an...

40 CFR 93.159 - Procedures for conformity determinations of general Federal actions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... based on the applicable air quality models, data bases, and other requirements specified in the most... applicable air quality models, data bases, and other requirements specified in the most recent version of the... data are available, such as actual stack test data from stationary sources which are part of the...

Evaluation of the Community Multiscale Air Quality (CMAQ) modeling system against size-resolved measurements of inorganic particle composition across sites in North America

EPA Science Inventory

This work evaluates particle size-composition distributions simulated by the Community Multiscale Air Quality (CMAQ) model using Micro-Orifice Uniform Deposit Impactor (MOUDI) measurements at 18 sites across North America. Size-resolved measurements of particulate SO₄<...

FACILITATING ADVANCED URBAN METEOROLOGY AND AIR QUALITY MODELING CAPABILITIES WITH HIGH RESOLUTION URBAN DATABASE AND ACCESS PORTAL TOOLS

EPA Science Inventory

Information of urban morphological features at high resolution is needed to properly model and characterize the meteorological and air quality fields in urban areas. We describe a new project called National Urban Database with Access Portal Tool, (NUDAPT) that addresses this nee...

Dynamic Evaluation of Two Decades of WRF-CMAQ Ozone Simulations over the Contiguous United States (2017 CMAS)

EPA Science Inventory

Weather Research and Forecasting (WRF)–Community Multi-scale Air Quality (CMAQ) model over the contiguous United States is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and...

A study of the influence of regional environmental expenditure on air quality in China: the effectiveness of environmental policy.

PubMed

He, Lingyun; Wu, Meng; Wang, Deqing; Zhong, Zhangqi

2018-03-01

Based on the panel data model, data on environmental expenditures, the air quality index, economic aggregates, industrial structures, etc., of seven seriously polluted cities in China, from the period 2007-2015, were collected, and this paper estimates the general relationship between environmental expenditures and the air quality index. Besides, the impact of the fuel tax policy on air quality as well as on the relationship between environmental expenditure and the air quality index is tested using the method of regression discontinuity. We find that there is a long-term equilibrium relationship between environmental expenditure and air quality index as well as a 0.0507% positive effect of the former on the latter. Second, for Beijing, Taiyuan, Chongqing, and Lanzhou, a 1% increase in environmental expenditure leads to 0.0773, 0.0125, 0.0965, and 0.0912% decreases in the air quality index, respectively; however, for Shijiazhuang, Ji'nan, and Urumqi, effect of environmental expenditure on air quality is insignificant. Third, both economic growth and optimization of the industrial structure can lead to an improvement of air quality. Fourth, since the implementation of the fuel tax policy in 2009, the air quality of the sample cities has improved, and the pulling effect of environmental expenditure on the air quality index has decreased from 0.0507 to 0.0048%. Our findings cannot only clarify the effect of environmental expenditures on air quality but can also objectively judge the effectiveness of environmental policies of China to a certain extent. It may benefit Chinese government to effectively govern air pollution with fiscal tools in conjunction with economic and environmental characteristics.

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

NASA Astrophysics Data System (ADS)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Sensor-Based Optimization Model for Air Quality Improvement in Home IoT

PubMed Central

Kim, Jonghyuk

2018-01-01

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market. PMID:29570684

Sensor-Based Optimization Model for Air Quality Improvement in Home IoT.

PubMed

Kim, Jonghyuk; Hwangbo, Hyunwoo

2018-03-23

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market.

Regional Modelling of Air Quality in the Canadian Arctic: Impact of marine shipping and North American wild fire emissions

NASA Astrophysics Data System (ADS)

Gong, W.; Beagley, S. R.; Zhang, J.; Cousineau, S.; Sassi, M.; Munoz-Alpizar, R.; Racine, J.; Menard, S.; Chen, J.

2015-12-01

Arctic atmospheric composition is strongly influenced by long-range transport from mid-latitudes as well as processes occurring in the Arctic locally. Using an on-line air quality prediction model GEM-MACH, simulations were carried out for the 2010 northern shipping season (April - October) over a regional Arctic domain. North American wildfire emissions and Arctic shipping emissions were represented, along with other anthropogenic and biogenic emissions. Sensitivity studies were carried out to investigate the principal sources and processes affecting air quality in the Canadian Northern and Arctic regions. In this paper, we present an analysis of sources, transport, and removal processes on the ambient concentrations and atmospheric loading of various pollutants with air quality and climate implications, such as, O3, NOx, SO2, CO, and aerosols (sulfate, black carbon, and organic carbon components). Preliminary results from a model simulation of a recent summertime Arctic field campaign will also be presented.

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.

LINKING AIR TOXIC CONCENTRATIONS FROM CMAQ TO THE HAPEM5 EXPOSURE MODEL AT NEIGHORHOOD SCALES FOR THE PHILADELPHIA AREA

EPA Science Inventory

This paper provides a preliminary demonstration of the EPA neighborhood scale modeling paradigm for air toxics by linking concentration from the Community Multi-scale Air Quality (CMAQ) modeling system to the fifth version of the Hazardous Pollutant Exposure Model (HAPEM5). For ...

Development of risk-based air quality management strategies under impacts of climate change.

PubMed

Liao, Kuo-Jen; Amar, Praveen; Tagaris, Efthimios; Russell, Armistead G

2012-05-01

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies. Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.

A Multiplatform Observations of Air Quality in Korea as the Pre-campaign of Korea and US Air Quality (KORUS-AQ) Study.

NASA Astrophysics Data System (ADS)

Park, J. H.; Hong, J.; Hong, Y.; Song, C. K.; Kim, S. K.; Chang, L. S.; Lim, J.; Ahn, J.; Kim, J. Y.; Han, Y. J.; Kim, J.; Park, R.; Lee, G.; Park, J.

2015-12-01

Despite the Korea government's efforts to regulate air pollutant emission for attaining the national air quality standard, current serious dust events and high ozone episodes in summer time remain important societal issues in Korea. In order to make effective policy for air quality attainment, it is contingent upon a thorough understanding of chemical production/loss mechanism of air pollutants and their precursors which drive air quality such as nitrogen oxides (NOX), volatile organic compounds (VOCs), and oxidants (e.g. OH, HO2, RO, RO2, etc.). At present, policy development is constrained by a lack of data for broad suite of chemical species which significantly affect on air quality.During 4 weeks between May and June 2013, the pre-campaign for the Korea and U.S. Air Quality (KORUS-AQ) study took place in multiplatform including fifteen ground sites, one mobile laboratory, and one small air crafts. An integrated research activity covering field observations, chemical transport models, and remote sensing has been intensively conducted. This study was focused on studying photochemistry and nighttime chemistry in urban area and transboundary transport of air pollutants from upwind. Scientific overview and outcomes from the campaign will be presented.

Evaluating Air-Quality Models: Review and Outlook.

NASA Astrophysics Data System (ADS)

Weil, J. C.; Sykes, R. I.; Venkatram, A.

1992-10-01

Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (c about the mean concentration (C) over an ensemble-a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For cC large residuals exist between predicted and observed concentrations, which confuse model evaluations.This paper addresses ways of evaluating model physics in light of the large c the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C-from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.In general, c models and predictions of the probability distribution of the fluctuating concentration (c), (c, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that (c approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model-ARAP-that predicts C, c, and (c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications- the uncertainty in the very high and infrequent concentrations. The uncertainty is large and is needed in evaluating operational performance and in predicting the attainment of air-quality standards.

Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

NASA Astrophysics Data System (ADS)

Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

2018-02-01

A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

Air quality co-benefits and costs under state, regional, or national cooperation to regulate CO2 from existing power plants

NASA Astrophysics Data System (ADS)

Saari, R.; Selin, N. E.

2015-12-01

We examine the effect of state, regional, and national cooperation on the costs and air quality co-benefits of a policy to limit the carbon intensity of existing electricity generation. Electricity generation is a significant source of both greenhouse gases and air pollutant emissions that harm human health. Previous studies have shown that air quality co-benefits can be substantial compared to the costs of limiting carbon emissions in the energy system. The EPA's proposed Clean Power Plan seeks to impose carbon intensity limits for each state, but allows states to cooperate in order to meet combined limits. We explore how such cooperation might produce trade-offs between lower costs, widespread pollution reductions, and local reductions. We employ a new state-level model of the US energy system and economy to examine the costs and emissions as states reduce demand or deploy cleaner generation. We use an advanced air quality impacts modeling system, including SMOKE, CAMx, and BenMAP, to estimate health-related air quality co-benefits and compare these to costs under different levels of cooperation. We draw conclusions about the potential impacts of cooperation on economic welfare at various scales.

Air quality assessment in Portugal and the special case of the Tâmega e Sousa region

NASA Astrophysics Data System (ADS)

de Almeida, Fátima; Correia, Aldina; Silva, Eliana Costa e.

2017-06-01

Air pollution is a major environmental problem which can present a significant risk for human health. This paper, presents the evaluation of the air quality in several region of Portugal. Special focus is given to the region of Tâmega e Sousa where ESTG/P. Porto is located. ANOVA and MANOVA techniques are applied to study the differences between air quality in the period between 2009 and 2012 in several regions of Portugal. The data includes altitude, area, expenditure of environmental measures on protection of air quality and climate, expenditure on protection of biodiversity and landscape, burned area, number of forest fires, extractive and manufacturing industries, per municipality and per year. Using information gathered by the project QualAr about concentrations of the pollutants: CO, NO2, O3, PM10 and SO2, an air quality indicator with five levels is consider. The results point to significant differences in the air quality for the regions and the years considered. Additionally, for identifying the factors that influence the air quality in 2012 a multivariate regression model was used. The results show statistical evidence that air quality in 2011, number of forest fires in 2012 and 2010, number of manufacturing industries per km2 in 2012 and number of forest fires in 2010 are the variables that present a larger contribution to the quality of the air in 2012.

Modeling to Evaluate Contribution of Oil and Gas Emissions to Air Pollution.

PubMed

Thompson, Tammy M; Shepherd, Donald; Stacy, Andrea; Barna, Michael G; Schichtel, Bret A

2017-04-01

Oil and gas production in the Western United States has increased considerably over the past 10 years. While many of the still limited oil and gas impact assessments have focused on potential human health impacts, the typically remote locations of production in the Intermountain West suggests that the impacts of oil and gas production on national parks and wilderness areas (Class I and II areas) could also be important. To evaluate this, we utilize the Comprehensive Air quality Model with Extensions (CAMx) with a year-long modeling episode representing the best available representation of 2011 meteorology and emissions for the Western United States. The model inputs for the 2011 episodes were generated as part of the Three State Air Quality Study (3SAQS). The study includes a detailed assessment of oil and gas (O&G) emissions in Western States. The year-long modeling episode was run both with and without emissions from O&G production. The difference between these two runs provides an estimate of the contribution of the O&G production to air quality. These data were used to assess the contribution of O&G to the 8 hour average ozone concentrations, daily and annual fine particulate concentrations, annual nitrogen deposition totals and visibility in the modeling domain. We present the results for the Class I and II areas in the Western United States. Modeling results suggest that emissions from O&G activity are having a negative impact on air quality and ecosystem health in our National Parks and Class I areas. In this research, we use a modeling framework developed for oil and gas evaluation in the western United States to determine the modeled impacts of emissions associated with oil and gas production on air pollution metrics. We show that oil and gas production may have a significant negative impact on air quality and ecosystem health in some national parks and other Class I areas in the western United States. Our findings are of particular interest to federal land managers as well as regulators in states heavy in oil and gas production as they consider control strategies to reduce the impact of development.

Support Center for Regulatory Atmospheric Modeling (SCRAM)

EPA Pesticide Factsheets

This technical site provides access to air quality models (including computer code, input data, and model processors) and other mathematical simulation techniques used in assessing air emissions control strategies and source impacts.

Downscaler Model for predicting daily air pollution

EPA Pesticide Factsheets

This model combines daily ozone and particulate matter monitoring and modeling data from across the U.S. to provide improved fine-scale estimates of air quality in communities and other specific locales.