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.

Forecasting human exposure to atmospheric pollutants in Portugal - A modelling approach

NASA Astrophysics Data System (ADS)

Borrego, C.; Sá, E.; Monteiro, A.; Ferreira, J.; Miranda, A. I.

2009-12-01

Air pollution has become one main environmental concern because of its known impact on human health. Aiming to inform the population about the air they are breathing, several air quality modelling systems have been developed and tested allowing the assessment and forecast of air pollution ambient levels in many countries. However, every day, an individual is exposed to different concentrations of atmospheric pollutants as he/she moves from and to different outdoor and indoor places (the so-called microenvironments). Therefore, a more efficient way to prevent the population from the health risks caused by air pollution should be based on exposure rather than air concentrations estimations. The objective of the present study is to develop a methodology to forecast the human exposure of the Portuguese population based on the air quality forecasting system available and validated for Portugal since 2005. Besides that, a long-term evaluation of human exposure estimates aims to be obtained using one-year of this forecasting system application. Additionally, a hypothetical 50% emission reduction scenario has been designed and studied as a contribution to study emission reduction strategies impact on human exposure. To estimate the population exposure the forecasting results of the air quality modelling system MM5-CHIMERE have been combined with the population spatial distribution over Portugal and their time-activity patterns, i.e. the fraction of the day time spent in specific indoor and outdoor places. The population characterization concerning age, work, type of occupation and related time spent was obtained from national census and available enquiries performed by the National Institute of Statistics. A daily exposure estimation module has been developed gathering all these data and considering empirical indoor/outdoor relations from literature to calculate the indoor concentrations in each one of the microenvironments considered, namely home, office/school, and other indoors (leisure activities like shopping areas, gym, theatre/cinema and restaurants). The results show how this developed modelling system can be useful to anticipate air pollution episodes and to estimate their effects on human health on a long-term basis. The two metropolitan areas of Porto and Lisbon are identified as the most critical ones in terms of air pollution effects on human health over Portugal in a long-term as well as in a short-term perspective. The coexistence of high concentration values and high population density is the key factor for these stressed areas. Regarding the 50% emission reduction scenario, the model results are significantly different for both pollutants: there is a small overall reduction in the individual exposure values of PM 10 (<10 μg m -3 h), but for O 3, in contrast, there is an extended area where exposure values increase with emission reduction. This detailed knowledge is a prerequisite for the development of effective policies to reduce the foreseen adverse impact of air pollution on human health and to act on time.

Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere.

PubMed

Genc, D Deniz; Yesilyurt, Canan; Tuncel, Gurdal

2010-07-01

Spatial and temporal variations in concentrations of CO, NO, NO(2), SO(2), and PM(10), measured between 1999 and 2000, at traffic-impacted and residential stations in Ankara were investigated. Air quality in residential areas was found to be influenced by traffic activities in the city. Pollutant ratios were proven to be reliable tracers to differentiate between different sources. Air pollution index (API) of the whole city was calculated to evaluate the level of air quality in Ankara. Multiple linear regression model was developed for forecasting API in Ankara. The correlation coefficients were found to be 0.79 and 0.63 for different time periods. The assimilative capacity of Ankara atmosphere was calculated in terms of ventilation coefficient (VC). The relation between API and VC was investigated and found that the air quality in Ankara was determined by meteorology rather than emissions.

PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

NASA Astrophysics Data System (ADS)

Zhu, Suling; Lian, Xiuyuan; Wei, Lin; Che, Jinxing; Shen, Xiping; Yang, Ling; Qiu, Xuanlin; Liu, Xiaoning; Gao, Wenlong; Ren, Xiaowei; Li, Juansheng

2018-06-01

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Towards an operational high-resolution air quality forecasting system at ECCC

NASA Astrophysics Data System (ADS)

Munoz-Alpizar, Rodrigo; Stroud, Craig; Ren, Shuzhan; Belair, Stephane; Leroyer, Sylvie; Souvanlasy, Vanh; Spacek, Lubos; Pavlovic, Radenko; Davignon, Didier; Moran, Moran

2017-04-01

Urban environments are particularly sensitive to weather, air quality (AQ), and climatic conditions. Despite the efforts made in Canada to reduce pollution in urban areas, AQ continues to be a concern for the population, especially during short-term episodes that could lead to exceedances of daily air quality standards. Furthermore, urban air pollution has long been associated with significant adverse health effects. In Canada, the large percentage of the population living in urban areas ( 81%, according to the Canada's 2011 census) is exposed to elevated air pollution due to local emissions sources. Thus, in order to improve the services offered to the Canadian public, Environment and Climate Change Canada has launched an initiative to develop a high-resolution air quality prediction capacity for urban areas in Canada. This presentation will show observed pollution trends (2010-2016) for Canadian mega-cities along with some preliminary high-resolution air quality modelling results. Short-term and long-term plans for urban AQ forecasting in Canada will also be described.

"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.

Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China.

PubMed

Xu, Yunzhen; Du, Pei; Wang, Jianzhou

2017-04-01

As the atmospheric environment pollution has been becoming more and more serious in China, it is highly desirable to develop a scientific and effective early warning system that plays a great significant role in analyzing and monitoring air quality. However, establishing a robust early warning system for warning the public in advance and ameliorating air quality is not only an extremely challenging task but also a public concerned problem for human health. Most previous studies are focused on improving the prediction accuracy, which usually ignore the significance of uncertainty information and comprehensive evaluation concerning air pollutants. Therefore, in this paper a novel robust early warning system was successfully developed, which consists of three modules: evaluation module, forecasting module and characteristics estimating module. In this system, a new dynamic fuzzy synthetic evaluation is proposed and applied to determine air quality levels and primary pollutants, which can be regarded as the research objectives; Moreover, to further mine and analyze the characteristics of air pollutants, four different distribution functions and interval forecasting method are also employed that can not only provide predictive range, confidence level and the other uncertain information of the pollutants future values, but also assist decision-makers in reducing and controlling the emissions of atmospheric pollutants. Case studies utilizing hourly PM 2.5 , PM 10 and SO 2 data collected from Tianjin and Shanghai in China are applied as illustrative examples to estimate the effectiveness and efficiency of the proposed system. Experimental results obviously indicated that the developed novel early warning system is much suitable for analyzing and monitoring air pollution, which can also add a novel viable option for decision-makers. Copyright © 2017 Elsevier Ltd. All rights reserved.

The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

NASA Astrophysics Data System (ADS)

Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

2015-10-01

The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

OPERATIONAL AND DIAGNOSTIC EVALUATION OF THE OZONE FORECASTS BY THE ETA-CMAQ MODEL SUITE DURING THE 2002 NEW ENGLAND AIR QUALITY STUDY (NEAQS)

EPA Science Inventory

Ozone (O₃), a secondary pollutant, is created in part by emissions from anthropogenic and biogenic sources. It is necessary for local air quality agencies to accurately forecast ozone concentrations to warn the public of unhealthy air and to encourage people to volunta...

Regression trees modeling and forecasting of PM10 air pollution in urban areas

NASA Astrophysics Data System (ADS)

Stoimenova, M.; Voynikova, D.; Ivanov, A.; Gocheva-Ilieva, S.; Iliev, I.

2017-10-01

Fine particulate matter (PM10) air pollution is a serious problem affecting the health of the population in many Bulgarian cities. As an example, the object of this study is the pollution with PM10 of the town of Pleven, Northern Bulgaria. The measured concentrations of this air pollutant for this city consistently exceeded the permissible limits set by European and national legislation. Based on data for the last 6 years (2011-2016), the analysis shows that this applies both to the daily limit of 50 micrograms per cubic meter and the allowable number of daily concentration exceedances to 35 per year. Also, the average annual concentration of PM10 exceeded the prescribed norm of no more than 40 micrograms per cubic meter. The aim of this work is to build high performance mathematical models for effective prediction and forecasting the level of PM10 pollution. The study was conducted with the powerful flexible data mining technique Classification and Regression Trees (CART). The values of PM10 were fitted with respect to meteorological data such as maximum and minimum air temperature, relative humidity, wind speed and direction and others, as well as with time and autoregressive variables. As a result the obtained CART models demonstrate high predictive ability and fit the actual data with up to 80%. The best models were applied for forecasting the level pollution for 3 to 7 days ahead. An interpretation of the modeling results is presented.

New Developments in Wildfire Pollution Forecasting at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Chen, Jack; Munoz-Alpizar, Rodrigo; Davignon, Didier; Beaulieu, Paul-Andre; Landry, Hugo; Menard, Sylvain; Gravel, Sylvie; Moran, Michael

2017-04-01

Environment and Climate Change Canada's air quality forecast system with near-real-time wildfire emissions, named FireWork, was developed in 2012 and has been run by the Canadian Meteorological Centre Operations division (CMCO) since 2013. In June 2016 this system was upgraded to operational status and wildfire smoke forecasts for North America are now available to the general public. FireWork's ability to model the transport and diffusion of wildfire smoke plumes has proved to be valuable to regional air quality forecasters and emergency first responders. Some of the most challenging issues with wildfire pollution modelling concern the production of wildfire emission estimates and near-source dispersion within the air quality model. As a consequence, FireWork is undergoing constant development. During the massive Fort McMurray wildfire event in western Canada in May 2016, for example, different wildfire emissions processing approaches and wildfire emissions injection and dispersion schemes were tested within the air quality model. Work on various FireWork components will continue in order to deliver a new operational version of the forecasting system for the 2017 wildfire season. Some of the proposed improvements will be shown in this presentation along with current and planned FireWork post-processing products.

Air Quality Forecasts Using the NASA GEOS Model

NASA Technical Reports Server (NTRS)

Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua;

Chemical weather forecasting for the Yangtze River Delta

NASA Astrophysics Data System (ADS)

Xie, Y.; Xu, J.; Zhou, G.; Chang, L.; Chen, B.

2016-12-01

Shanghai is one of the largest megacities in the world. With rapid economic growth of the city and its surrounding areas in recent years, air pollution has posed adverse effects on public health and ecosystem. In winter heavy pollution episodes are often associated with PM exceedances under stagnant conditions or transport events, whereas in summer the region frequently experiences elevated O3 levels. Chemical weather prediction systems with the WRF-Chem and CMAQ models are being developed to support air quality and haze forecasting for Shanghai and the Yangtze River Delta region. We will present main components of the modeling system, forecasting products, as well as evaluation results. Evaluation of the WRF-Chem forecasts show the model has generally good ability to capture the temporal variations of O3 and PM2.5. Substantial regional differences exist, with the best performance in Shanghai. Meanwhile, the forecasts tend to degrade during highly polluted episodes and transitional time periods, which highlights the need to improve model representation of key process (e.g. meteorological fields and formation of secondary pollutants). Recent work includes using the ECMWF global model forecasts as chemical boundary conditions for our regional model. We investigate the impact of chemical downscaling, and also compare the results from different models participated in the PANDA (PArtnership with chiNa on space Data) project. Results from ongoing efforts (e.g. chemical weather forecasting driven by SMS regional high resolution NWP) will also be presented.

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.

PubMed

Voukantsis, Dimitris; Karatzas, Kostas; Kukkonen, Jaakko; Räsänen, Teemu; Karppinen, Ari; Kolehmainen, Mikko

2011-03-01

In this paper we propose a methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants). We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities. Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks (multi-layer perceptron) models. The latter ones were used for the forecasting of the daily mean concentrations of PM₁₀ and PM₂.₅ for the next day. Results demonstrated an index of agreement between measured and modelled daily averaged PM₁₀ concentrations, between 0.80 and 0.85, while the kappa index for the forecasting of the daily averaged PM₁₀ concentrations reached 60% for both cities. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting. It was also found that the performance of the models for the forecasting of the daily mean concentrations of PM₁₀ was not substantially different for both cities, despite the major differences of the two urban environments under consideration. Copyright © 2011 Elsevier B.V. All rights reserved.

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).

Ozone - Current Air Quality Index

MedlinePlus

GO! Local Air Quality Conditions Zip Code: State : My Current Location Current AQI Forecast AQI Loop More Maps AQI: Good (0 - 50) ... resources for Hawaii residents and visitors more announcements Air Quality Basics Air Quality Index | Ozone | Particle Pollution | Smoke ...

Multi-Scale Enviro-HIRLAM Forecasting of Weather and Atmospheric Composition over China and its Megacities

NASA Astrophysics Data System (ADS)

Mahura, Alexander; Amstrup, Bjarne; Nuterman, Roman; Yang, Xiaohua; Baklanov, Alexander

2017-04-01

Air pollution is a serious problem in different regions of China and its continuously growing megacities. Information on air quality, and especially, in urbanized areas is important for decision making, emergency response and population. In particular, the metropolitan areas of Shanghai, Beijing, and Pearl River Delta are well known as main regions having serious air pollution problems. The on-line integrated meteorology-chemistry-aerosols Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) model adapted for China and selected megacities is applied for forecasting of weather and atmospheric composition (with focus on aerosols). The model system is running in downscaling chain from regional to urban scales at subsequent horizontal resolutions of 15-5-2.5 km. The model setup includes also the urban Building Effects Parameterization module, describing different types of urban districts (industrial commercial, city center, high density and residential) with its own morphological and aerodynamical characteristics. The effects of urbanization are important for atmospheric transport, dispersion, deposition, and chemical transformations, in addition to better quality emission inventories for China and selected urban areas. The Enviro-HIRLAM system provides meteorology and air quality forecasts at regional-subregional-urban scales (China - East China - selected megacities). In particular, such forecasting is important for metropolitan areas, where formation and development of meteorological and chemical/aerosol patterns are especially complex. It also provides information for evaluation impact on selected megacities of China as well as for investigation relationship between air pollution and meteorology.

THE USE OF AIR QUALITY FORECASTS TO ASSESS IMPACTS OF AIR POLLUTION ON CROPS: METHODOLOGY AND CASE STUDY

EPA Science Inventory

It has been reported that ambient ozone (O₃), either alone or in concurrence with acid rain precursors, accounts for up to 90% of U.S. crop losses resulting from exposure to all major air pollutants. Crop damage due to O₃ exposure is of particular concern as...

Air Quality Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

NASA Technical Reports Server (NTRS)

Knowland, E. Emma; Keller, Christoph; Nielsen, J. Eric; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Cook, Melanie; Liu, Junhua;

"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.

"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.

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

A latent process model for forecasting multiple time series in environmental public health surveillance.

PubMed

Morrison, Kathryn T; Shaddick, Gavin; Henderson, Sarah B; Buckeridge, David L

2016-08-15

This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003-2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Summary Report for the Workshop on Integrating Climate Change Adaption into Air Quality Decision Making

EPA Science Inventory

Over the past few decades, air quality planners have forecasted future air pollution levels based on information about changing emissions from stationary and mobile sources, population trends, transportation demand, natural sources of emissions, and other pressures on air quality...

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.

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.

Impact of Trans-Boundary Emissions on Modelled Air Pollution in Canada

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Moran, Mike; Zhang, Junhua; Zheng, Qiong; Menard, Sylvain; Anselmo, David; Davignon, Didier

2014-05-01

The operational air quality model GEM-MACH is run twice daily at the Canadian Meteorological Centre in Montreal, Quebec to produce 48-hour forecasts of hourly O3, NO2, and PM2.5 fields over a North American domain. The hourly gridded anthropogenic emissions fields needed by GEM-MACH are currently based on the 2006 Canadian emissions inventory, a 2012 projected U.S. inventory, and the 1999 Mexican inventory. The Sparse Matrix Operator Kernel Emissions (SMOKE) processing package was used to process these three national emissions inventories to create the GEM-MACH emissions fields. While Canada is the second-largest country in the world by total area, its population and its emissions of criteria contaminants are both only about one-tenth of U.S. values and roughly 80% of the Canadian population lives within 150 km of the international border with the U.S. As a consequence, transboundary transport of air pollution has a major impact on air quality in Canada. To quantify the impact of non-Canadian emissions on forecasted pollutant levels in Canada, the following two tests were performed: (a) all U.S. and Mexican anthropogenic emissions were switched off; and (b) anthropogenic emissions from the southernmost tier of U.S. states and Mexico were switched off. These sensitivity tests were performed for the summer and winter periods of 2012 or 2011. The results obtained show that the impact of non-Canadian sources on forecasted pollution is generally larger in summer than in winter, especially in south-eastern parts of Canada. For the three pollutants considered in the Canadian national Air Quality Health Index, PM2.5 is impacted the most (up to 80%) and NO2 the least (<10%). Emissions from the southern U.S. and Mexico do impact Canadian air quality, but the sign may change depending on the season (i.e., increase vs. decrease), reflecting chemical processing en route.

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

NASA Technical Reports Server (NTRS)

Kleb, Mary M.; AlSaadi, Jassim A.; Neil, Doreen O.; Pierce, Robert B.; Pippin, Margartet R.; Roell, Marilee M.; Kittaka, Chieko; Szykman, James J.

2004-01-01

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece.

PubMed

Moustris, Kostas P; Douros, Konstantinos; Nastos, Panagiotis T; Larissi, Ioanna K; Anthracopoulos, Michael B; Paliatsos, Athanasios G; Priftis, Kostas N

2012-01-01

Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001-2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children's Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0-4, 5-14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA.

The air quality forecast in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) System: model evaluation and improvement

NASA Astrophysics Data System (ADS)

Wu, Q.

2013-12-01

The MM5-SMOKE-CMAQ model system, which is developed by the United States Environmental Protection Agency(U.S. EPA) as the Models-3 system, has been used for the daily air quality forecast in the Beijing Municipal Environmental Monitoring Center(Beijing MEMC), as a part of the Ensemble Air Quality Forecast System for Beijing(EMS-Beijing) since the Olympic Games year 2008. In this study, we collect the daily forecast results of the CMAQ model in the whole year 2010 for the model evaluation. The results show that the model play a good model performance in most days but underestimate obviously in some air pollution episode. A typical air pollution episode from 11st - 20th January 2010 was chosen, which the air pollution index(API) of particulate matter (PM10) observed by Beijing MEMC reaches to 180 while the prediction of PM10-API is about 100. Taking in account all stations in Beijing, including urban and suburban stations, three numerical methods are used for model improvement: firstly, enhance the inner domain with 4km grids, the coverage from only Beijing to the area including its surrounding cities; secondly, update the Beijing stationary area emission inventory, from statistical county-level to village-town level, that would provide more detail spatial informance for area emissions; thirdly, add some industrial points emission in Beijing's surrounding cities, the latter two are both the improvement of emission. As the result, the peak of the nine national standard stations averaged PM10-API, which is simulated by CMAQ as daily hindcast PM10-API, reach to 160 and much near to the observation. The new results show better model performance, which the correlation coefficent is 0.93 in national standard stations average and 0.84 in all stations, the relative error is 15.7% in national standard stations averaged and 27% in all stations. The time series of 9 national standard in Beijing urban The scatter diagram of all stations in Beijing, the red is the forecast and the blue is new result.

Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Davignon, Didier; Ménard, Sylvain; Munoz-Alpizar, Rodrigo; Landry, Hugo; Beaulieu, Paul-André; Gilbert, Samuel; Moran, Michael; Chen, Jack

2017-04-01

ECCC's Canadian Meteorological Centre Operations (CMCO) division runs a number of operational air quality (AQ)-related systems that revolve around the Regional Air Quality Deterministic Prediction System (RAQDPS). The RAQDPS generates 48-hour AQ forecasts and outputs hourly concentration fields of O3, PM2.5, NO2, and other pollutants twice daily on a North-American domain with 10-km horizontal grid spacing and 80 vertical levels. A closely related AQ forecast system with near-real-time wildfire emissions, known as FireWork, has been run by CMCO during the Canadian wildfire season (April to October) since 2014. This system became operational in June 2016. The CMCO`s operational AQ forecast systems also benefit from several support systems, such as a statistical post-processing model called UMOS-AQ that is applied to enhance forecast reliability at point locations with AQ monitors. The Regional Deterministic Air Quality Analysis (RDAQA) system has also been connected to the RAQDPS since February 2013, and hourly surface objective analyses are now available for O3, PM2.5, NO2, PM10, SO2 and, indirectly, the Canadian Air Quality Health Index. As of June 2015, another version of the RDAQA has been connected to FireWork (RDAQA-FW). For verification purposes, CMCO developed a third support system called Verification for Air QUality Models (VAQUM), which has a geospatial relational database core and which enables continuous monitoring of the AQ forecast systems' performance. Urban environments are particularly subject to AQ pollution. In order to improve the services offered, ECCC has recently been investing efforts to develop a high resolution air quality prediction capability for urban areas in Canada. In this presentation, a comprehensive description of the ECCC AQ systems will be provided, along with a discussion on AQ systems performance. Recent improvements, current challenges, and future directions of the Canadian operational AQ program will also be discussed.

“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.

THE NOAA - EPA NATIONAL AIR QUALITY FORECASTING SYSTEM

EPA Science Inventory

Building upon decades of collaboration in air pollution meteorology research, in 2003 the National Oceanic and Atmospheric Administration (NOAA) and the United States Environmental Protection Agency (EPA) signed formal partnership agreements to develop and implement an operationa...

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...

Utilizing Operational and Improved Remote Sensing Measurements to Assess Air Quality Monitoring Model Forecasts

NASA Astrophysics Data System (ADS)

Gan, Chuen-Meei

Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.

“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

AQA - Air Quality model for Austria - Evaluation and Developments

NASA Astrophysics Data System (ADS)

Hirtl, M.; Krüger, B. C.; Baumann-Stanzer, K.; Skomorowski, P.

2009-04-01

The regional weather forecast model ALADIN of the Central Institute for Meteorology and Geodynamics (ZAMG) is used in combination with the chemical transport model CAMx (www.camx.com) to conduct forecasts of gaseous and particulate air pollution over Europe. The forecasts which are done in cooperation with the University of Natural Resources and Applied Life Sciences in Vienna (BOKU) are supported by the regional governments since 2005 with the main interest on the prediction of tropospheric ozone. The daily ozone forecasts are evaluated for the summer 2008 with the observations of about 150 air quality stations in Austria. In 2008 the emission-model SMOKE was integrated into the modelling system to calculate the biogenic emissions. The anthropogenic emissions are based on the newest EMEP data set as well as on regional inventories for the core domain. The performance of SMOKE is shown for a summer period in 2007. In the frame of the COST-action 728 „Enhancing mesoscale meteorological modelling capabilities for air pollution and dispersion applications", multi-model ensembles are used to conduct an international model evaluation. The model calculations of meteorological- and concentration fields are compared to measurements on the ensemble platform at the Joint Research Centre (JRC) in Ispra. The results for 2 episodes in 2006 show the performance of the different models as well as of the model ensemble.

Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform.

PubMed

Su, Yana; Sha, Yongzhong; Zhai, Guangyu; Zong, Shengliang; Jia, Jiehua

2017-04-21

For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and impact factor of the air pollution time series. The analysis showed that (1) air pollution in the two cities was non-stationary and nonlinear, had multiple timescales, and exhibited the characteristics of high in winter and spring and low in summer and autumn. (2) The monthly variation in air pollution in Shanghai was not significant, whereas the seasonal variation of air pollution in Lanzhou was obvious. (3) Air pollution in Shanghai showed an ascending tendency, whereas that in Lanzhou presented a descending tendency. Overall, air pollution in Lanzhou was higher than that in Shanghai, but the situation has reversed since 2015. (4) The primary cycles of air pollution in these two cities were close, but the secondary cycles were significantly different. The aforementioned differences were mainly due to the impact of topographical and meteorological factors in Lanzhou, the weather process and the surrounding environment in Shanghai. These conclusions have reference significance for Shanghai and Lanzhou to control air pollution. The multi-timescale variation and local features of the wavelet analysis method used in this study can be applied to varied aspects of air pollution analysis. The identification of cycle characteristics and the monitoring, forecasting, and controlling of air pollution can yield valuable reference.

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.

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.

Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling

NASA Astrophysics Data System (ADS)

Zhu, Wenjin; Wang, Jianzhou; Zhang, Wenyu; Sun, Donghuai

2012-05-01

Risk of lower respiratory diseases was significantly correlated with levels of monthly average concentration of SO2; NO2 and association rules have high lifts. In view of Lanzhou's special geographical location, taking into account the impact of different seasons, especially for the winter, the relations between air pollutants and the respiratory disease deserve further study. In this study the monthly average concentration of SO2, NO2, PM10 and the monthly number of people who in hospital because of lower respiratory disease from January 2001 to December 2005 are grouped equidistant and considered as the terms of transactions. Then based on the relational algebraic theory we employed the optimization relation association rule to mine the association rules of the transactions. Based on the association rules revealing the effects of air pollutants on the lower respiratory disease, we forecast the number of person who suffered from lower respiratory disease by the group method of data handling (GMDH) to reveal the risk and give a consultation to the hospital in Xigu District, the most seriously polluted district in Lanzhou. The data and analysis indicate that individuals may be susceptible to the short-term effects of pollution and thus suffer from lower respiratory diseases and this effect presents seasonal.

Improving High-Resolution Weather Forecasts Using the Weather Research and Forecasting (WRF) Model with an Updated Kain–Fritsch Scheme

EPA Science Inventory

Efforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at ...

Lagrangian Aerosol and Ozone Precursor Forecasts Utilizing NASA Aura OMI NO2 and NOAA GOES-GASP AOD Observations

EPA Science Inventory

Over the past decade, the remote sensing of trace gases and aerosols from space has dramatically improved. The emergence and application of these measurements adds a new dimension to air quality Management and forecasting by enabling consistent observations of pollutants over l...

Quantifying automobile refinishing VOC air emissions - a methodology with estimates and forecasts

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

Anderson, S.P.; Rubick, C.

1996-12-31

Automobile refinishing coatings (referred to as paints), paint thinners, reducers, hardeners, catalysts, and cleanup solvents used during their application, contain volatile organic compounds (VOCs) which are precursors to ground level ozone formation. Some of these painting compounds create hazardous air pollutants (HAPs) which are toxic. This paper documents the methodology, data sets, and the results of surveys (conducted in the fall of 1995) used to develop revised per capita emissions factors for estimating and forecasting the VOC air emissions from the area source category of automobile refinishing. Emissions estimates, forecasts, trends, and reasons for these trends are presented. Future emissionsmore » inventory (EI) challenges are addressed in light of data availability and information networks.« less

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.

You Can Help Keep the Air Cleaner -- Every Day

MedlinePlus

... to be high: Conserve electricity and set your air conditioner at a higher temperature. Choose a cleaner commute—share a ride to work or use public transportation. Bicycle or walk to errands when ... quality is forecast. Pesticides Days when particle pollution ...

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 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...

Assessment of planetary boundary layer and residual layer heights in the Northeastern U.S. using Lidar, a network of surface observations, and the WRF-STILT model

NASA Astrophysics Data System (ADS)

Barrera, Y.; Nehrkorn, T.; Hegarty, J. D.; Wofsy, S. C.; Gottlieb, E.; Sargent, M. R.; Decola, P.; Jones, T.

2015-12-01

Simulation of the planetary boundary layer (PBL) and residual layer (RL) are key requirements for forecasting air quality in cities and detecting transboundary air pollution events. This study combines information from a network of Mini Micropulse Lidar (MPL) instruments, the CALIOP satellite, meteorological and air pollution measuring sensors, and a particle-transport model to critically test mesoscale transport models at the regional level. Aerosol backscattering measurements were continuously taken with MPL units in various locations within the Northeastern U.S., between September 2012 to August 2015. Data is analyzed using wavelet covariance transforms and image processing techniques. Initial results for the city of Boston show a PBL growth rate between approx. 150 and 300 meters per hour, in the morning to early afternoon (~12-19 UTC). The RL was present throughout the night and day at approx. 1.3 to 2.0 km. Transboundary air pollution events were detected and quantified, and variations in concentrations of greenhouse gases and aerosols were also evaluated. Results were compared to information retrieved from Weather and Research Forecasting (WRF) model and the Stochastic Time-Inverted Lagrangian Transport (STILT) model.

A statistical inference for concentrations of benzo[a]pyrene partially measured in the ambient air of an industrial city in Korea

NASA Astrophysics Data System (ADS)

Kim, Yongku; Seo, Young-Kyo; Baek, Sung-Ok

2013-12-01

Although large quantities of air pollutants are released into the atmosphere, they are partially monitored and routinely assessed for their health implications. This paper proposes a statistical model describing the temporal behavior of hazardous air pollutants (HAPs), which can have negative effects on human health. Benzo[a]pyrene (BaP) is selected for statistical modeling. The proposed model incorporates the linkage between BaP and meteorology and is specifically formulated to identify meteorological effects and allow for seasonal trends. The model is used to estimate and forecast temporal fields of BaP conditional on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing health indicators, namely the cancer risk and the hazard quotient. The model provides useful information for the optimal monitoring period and projection of future BaP concentrations for both industrial and residential areas in Korea.

Stochastic univariate and multivariate time series analysis of PM2.5 and PM10 air pollution: A comparative case study for Plovdiv and Asenovgrad, Bulgaria

NASA Astrophysics Data System (ADS)

Gocheva-Ilieva, S.; Stoimenova, M.; Ivanov, A.; Voynikova, D.; Iliev, I.

2016-10-01

Fine particulate matter PM2.5 and PM10 air pollutants are a serious problem in many urban areas affecting both the health of the population and the environment as a whole. The availability of large data arrays for the levels of these pollutants makes it possible to perform statistical analysis, to obtain relevant information, and to find patterns within the data. Research in this field is particularly topical for a number of Bulgarian cities, European country, where in recent years regulatory air pollution health limits are constantly being exceeded. This paper examines average daily data for air pollution with PM2.5 and PM10, collected by 3 monitoring stations in the cities of Plovdiv and Asenovgrad between 2011 and 2016. The goal is to find and analyze actual relationships in data time series, to build adequate mathematical models, and to develop short-term forecasts. Modeling is carried out by stochastic univariate and multivariate time series analysis, based on Box-Jenkins methodology. The best models are selected following initial transformation of the data and using a set of standard and robust statistical criteria. The Mathematica and SPSS software were used to perform calculations. This examination showed measured concentrations of PM2.5 and PM10 in the region of Plovdiv and Asenovgrad regularly exceed permissible European and national health and safety thresholds. We obtained adequate stochastic models with high statistical fit with the data and good quality forecasting when compared against actual measurements. The mathematical approach applied provides an independent alternative to standard official monitoring and control means for air pollution in urban areas.

An Overview of the 3C-STAR project

NASA Astrophysics Data System (ADS)

Zhang, Y.

2009-04-01

Over the past three decades, city clusters have played a leading role in the economic growth of China, owing to their collective economic capacity and interdependency. However, pollution prevention lags behind the economic boom, led to a general decline in air quality in city clusters. As a result, industrial emissions and traffic exhausts together contribute to high levels of ozone (O3) and fine particulate matter (PM2.5) pollution problems ranging from urban to regional scale. Such high levels of both primary and secondary airborne pollutants lead to the development of a (perhaps typically Chinese) "air pollution complex" concept. Air pollution complex is particularly true and significant in Beijing-Tianjin area, Pearl River Delta (PRD) and Yangtze River Delta. The concurrent high concentrations of O3 and PM2.5 in PRD as well as in other China city clusters have led to rather unique pollution characteristics due to interactions between primary emissions and photochemical processes, between gaseous compounds and aerosol phase species, and between local and regional scale processes. The knowledge and experience needed to find solutions to the unique pollution complex in China are still lacking. Starting from 2007, we launch a major project "Synthesized Prevention Techniques for Air Pollution Complex and Integrated Demonstration in Key City-Cluster Region" (3C-STAR) to address those problems scientifically and technically. The purpose of the project is to build up the capacity of regional air pollution control and to establish regional coordination mechanism for joint implementation of pollution control. The project includes a number of key components technically: regional air quality monitoring network and super-sites, regional dynamic emission inventory of multi-pollutants, regional ensemble air quality forecasting model system, and regional management system supported by decision making platform. The 3C-STAR project selected PRD as a core area to have technical demonstration, and thus provide opportunities as well as challenges for PRD to improve its regional air quality. An integrated field measurement campaign 3C-STAR2008 was organized during October 15-November 19, 2008, including 3-D regional air quality monitoring network, two super-sites, and in-site meteorological and air quality forecasting. With the efforts of more than 100 scientists and students from 12 research institutes, the 3C-STAR2008 was conducted with great success. A great amount of data with rigorous QA/QC procedures has been obtained and data analysis is underway. In this talk, an overview of the 3C-STAR project will be presented, together with major findings from previous PRD campaigns (PRD2004 and PRD2006).

Solutions Network Formulation Report. NASA's Potential Contributions for Using Solar Ultraviolet Radiation in Conjunction with Photocatalysis for Urban Air Pollution Mitigation and Increasing Air Quality

NASA Technical Reports Server (NTRS)

Underwood, Lauren; Ryan, Robert E.

2007-01-01

This Candidate Solution is based on using NASA Earth science research on atmospheric ozone and aerosols data as a means to predict and evaluate the effectiveness of photocatalytically created surfaces (building materials like glass, tile and cement) for air pollution mitigation purposes. When these surfaces are exposed to near UV light, organic molecules, like air pollutants and smog precursors, will degrade into environmentally friendly compounds. U.S. EPA (Environmental Protection Agency) is responsible for forecasting daily air quality by using the Air Quality Index (AQI) that is provided by AIRNow. EPA is partnered with AIRNow and is responsible for calculating the AQI for five major air pollutants that are regulated by the Clean Air Act. In this Solution, UV irradiance data acquired from the satellite mission Aura and the OMI Surface UV algorithm will be used to help understand both the efficacy and efficiency of the photocatalytic decomposition process these surfaces facilitate, and their ability to reduce air pollutants. Prediction models that estimate photocatalytic function do not exist. NASA UV irradiance data will enable this capability, so that air quality agencies that are run by state and local officials can develop and implement programs that utilize photocatalysis for urban air pollution control and, enable them to make effective decisions about air pollution protection programs.

The role of a peri-urban forest on air quality improvement in the Mexico City megalopolis.

PubMed

Baumgardner, Darrel; Varela, Sebastian; Escobedo, Francisco J; Chacalo, Alicia; Ochoa, Carlos

2012-04-01

Air quality improvement by a forested, peri-urban national park was quantified by combining the Urban Forest Effects (UFORE) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. We estimated the ecosystem-level annual pollution removal function of the park's trees, shrub and grasses using pollution concentration data for carbon monoxide (CO), ozone (O(3)), and particulate matter less than 10 microns in diameter (PM(10)), modeled meteorological and pollution variables, and measured forest structure data. Ecosystem-level O(3) and CO removal and formation were also analyzed for a representative month. Total annual air quality improvement of the park's vegetation was approximately 0.02% for CO, 1% for O(3,) and 2% for PM(10), of the annual concentrations for these three pollutants. Results can be used to understand the air quality regulation ecosystem services of peri-urban forests and regional dynamics of air pollution emissions from major urban areas. Copyright © 2011 Elsevier Ltd. All rights reserved.

Interactions Between Asian Air Pollution and Monsoon System: South Asia (ROSES-2014 ACMAP)

NASA Technical Reports Server (NTRS)

Pan, Xiaohua; Chin, Mian; Tao, Zhining; Kim, Dongchul; Bian, Huisheng; Kucsera, Tom

2018-01-01

Asia's rapid economic growth over the past several decades has brought a remarkable increase in air pollution levels in that region. High concentrations of aerosols (also known as particulate matter or PM) from pollution sources pose major health hazards to half of the world population in Asia including South Asia. How do pollution and dust aerosols regulate the monsoon circulation and rainfall via scattering and absorbing solar radiation, changing the atmospheric heating rates, and modifying the cloud properties? We conducted a series of regional model experiments with NASA-Unified Weather Research and Forecast (NUWRF) regional model with coupled aerosol-chemistry-radiation-microphysics processes over South Asia for winter, pre-monsoon, and monsoon seasons to address this question. This study investigates the worsening air quality problem in South Asia by focusing on the interactions between pollution and South Asian monsoon, not merely focusing on the increase of pollutant emissions.

Representing the Effects of Long-Range Transport and Lateral Boundary Conditions in Regional Air Pollution Models

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) modeling system was applied to a domain covering the northern hemisphere; meteorological information was derived from the Weather Research and Forecasting (WRF) model run on identical grid and projection configuration, while the emissio...

PolEASIA Project: Pollution in Eastern Asia - towards better Air Quality Prevision and Impacts' Evaluation

NASA Astrophysics Data System (ADS)

Dufour, Gaëlle; Albergel, Armand; Balkanski, Yves; Beekmann, Matthias; Cai, Zhaonan; Fortems-Cheiney, Audrey; Cuesta, Juan; Derognat, Claude; Eremenko, Maxim; Foret, Gilles; Hauglustaine, Didier; Lachatre, Matthieu; Laurent, Benoit; Liu, Yi; Meng, Fan; Siour, Guillaume; Tao, Shu; Velay-Lasry, Fanny; Zhang, Qijie; Zhang, Yuli

2017-04-01

The rapid economic development and urbanization of China during the last decades resulted in rising pollutant emissions leading to amongst the largest pollutant concentrations in the world for the major pollutants (ozone, PM2.5, and PM10). Robust monitoring and forecasting systems associated with downstream services providing comprehensive risk indicators are highly needed to establish efficient pollution mitigation strategies. In addition, a precise evaluation of the present and future impacts of Chinese pollutant emissions is of importance to quantify: first, the consequences of pollutants export on atmospheric composition and air quality all over the globe; second, the additional radiative forcing induced by the emitted and produced short-lived climate forcers (ozone and aerosols); third, the long-term health consequences of pollution exposure. To achieve this, a detailed understanding of East Asian pollution is necessary. The French PolEASIA project aims at addressing these different issues by providing a better quantification of major pollutants sources and distributions as well as of their recent and future evolution. The main objectives, methodologies and tools of this starting 4-year project will be presented. An ambitious synergistic and multi-scale approach coupling innovative satellite observations, in situ measurements and chemical transport model simulations will be developed to characterize the spatial distribution, the interannual to daily variability and the trends of the major pollutants (ozone and aerosols) and their sources over East Asia, and to quantify the role of the different processes (emissions, transport, chemical transformation) driving the observed pollutant distributions. A particular attention will be paid to assess the natural and anthropogenic contributions to East Asian pollution. Progress made with the understanding of pollutant sources, especially in terms of modeling of pollution over East Asia and advanced numerical approaches such as inverse modeling will serve the development of an efficient and marketable forecasting system for regional outdoor air pollution. The performances of this upgraded forecasting system will be evaluated and promoted to ensure a good visibility of the French technology. In addition, the contribution of Chinese pollution to the regional and global atmospheric composition, as well as the resulting radiative forcing of short-lived species will be determined using both satellite observations and model simulations. Health Impact Assessment (HIA) methods coupled with model simulations will be used to estimate the long-term impacts of exposure to pollutants (PM2.5 and ozone) on cardiovascular and respiratory mortality. First results obtained in this framework will be presented.

Operational source receptor calculations for large agglomerations

NASA Astrophysics Data System (ADS)

Gauss, Michael; Shamsudheen, Semeena V.; Valdebenito, Alvaro; Pommier, Matthieu; Schulz, Michael

2016-04-01

For Air quality policy an important question is how much of the air pollution within an urbanized region can be attributed to local sources and how much of it is imported through long-range transport. This is critical information for a correct assessment of the effectiveness of potential emission measures. The ratio between indigenous and long-range transported air pollution for a given region depends on its geographic location, the size of its area, the strength and spatial distribution of emission sources, the time of the year, but also - very strongly - on the current meteorological conditions, which change from day to day and thus make it important to provide such calculations in near-real-time to support short-term legislation. Similarly, long-term analysis over longer periods (e.g. one year), or of specific air quality episodes in the past, can help to scientifically underpin multi-regional agreements and long-term legislation. Within the European MACC projects (Monitoring Atmospheric Composition and Climate) and the transition to the operational CAMS service (Copernicus Atmosphere Monitoring Service) the computationally efficient EMEP MSC-W air quality model has been applied with detailed emission data, comprehensive calculations of chemistry and microphysics, driven by high quality meteorological forecast data (up to 96-hour forecasts), to provide source-receptor calculations on a regular basis in forecast mode. In its current state, the product allows the user to choose among different regions and regulatory pollutants (e.g. ozone and PM) to assess the effectiveness of fictive emission reductions in air pollutant emissions that are implemented immediately, either within the agglomeration or outside. The effects are visualized as bar charts, showing resulting changes in air pollution levels within the agglomeration as a function of time (hourly resolution, 0 to 4 days into the future). The bar charts not only allow assessing the effects of emission reduction measures but they also indicate the relative importance of indigenous versus imported air pollution. The calculations are currently performed weekly by MET Norway for the Paris, London, Berlin, Oslo, Po Valley and Rhine-Ruhr regions and the results are provided free of charge at the MACC website (http://www.gmes-atmosphere.eu/services/aqac/policy_interface/regional_sr/). A proposal to extend this service to all EU capitals on a daily basis within the Copernicus Atmosphere Monitoring Service is currently under review. The tool is an important example illustrating the increased application of scientific tools to operational services that support Air Quality policy. This paper will describe this tool in more detail, focusing on the experimental setup, underlying assumptions, uncertainties, computational demand, and the usefulness for air quality for policy. Options to apply the tool for agglomerations outside the EU will also be discussed (making reference to, e.g., PANDA, which is a European-Chinese collaboration project).

Status of Air Quality in Central California and Needs for Further Study

NASA Astrophysics Data System (ADS)

Tanrikulu, S.; Beaver, S.; Soong, S.; Tran, C.; Jia, Y.; Matsuoka, J.; McNider, R. T.; Biazar, A. P.; Palazoglu, A.; Lee, P.; Wang, J.; Kang, D.; Aneja, V. P.

2012-12-01

Ozone and PM2.5 levels frequently exceed NAAQS in central California (CC). Additional emission reductions are needed to attain and maintain the standards there. Agencies are developing cost-effective emission control strategies along with complementary incentive programs to reduce emissions when exceedances are forecasted. These approaches require accurate modeling and forecasting capabilities. A variety of models have been rigorously applied (MM5, WRF, CMAQ, CAMx) over CC. Despite the vast amount of land-based measurements from special field programs and significant effort, models have historically exhibited marginal performance. Satellite data may improve model performance by: establishing IC/BC over outlying areas of the modeling domain having unknown conditions; enabling FDDA over the Pacific Ocean to characterize important marine inflows and pollutant outflows; and filling in the gaps of the land-based monitoring network. BAAQMD, in collaboration with the NASA AQAST, plans to conduct four studies that include satellite-based data in CC air quality analysis and modeling: The first project enhances and refines weather patterns, especially aloft, impacting summer ozone formation. Surface analyses were unable to characterize the strong attenuating effect of the complex terrain to steer marine winds impinging on the continent. The dense summer clouds and fog over the Pacific Ocean form spatial patterns that can be related to the downstream air flows through polluted areas. The goal of this project is to explore, characterize, and quantify these relationships using cloud cover data. Specifically, cloud agreement statistics will be developed using satellite data and model clouds. Model skin temperature predictions will be compared to both MODIS and GOES skin temperatures. The second project evaluates and improves the initial and simulated fields of meteorological models that provide inputs to air quality models. The study will attempt to determine whether a cloud dynamical adjustment developed by UAHuntsville can improve model performance for maritime stratus and whether a moisture adjustment scheme in the Pleim-Xiu boundary layer scheme can use satellite data in place of coarse surface air temperature measurements. The goal is to improve meteorological model performance that leads to improved air quality model performance. The third project evaluates and improves forecasting skills of the National Air Quality Forecasting Model in CC by using land-based routine measurements as well as satellite data. Local forecasts are mostly based on surface meteorological and air quality measurements and weather charts provided by NWS. The goal is to improve the average accuracy in forecasting exceedances, which is around 60%. The fourth project uses satellite data for monitoring trends in fine particulate matter (PM2.5) in the San Francisco Bay Area. It evaluates the effectiveness of a rule adopted in 2008 that restricts household wood burning on days forecasted to have high PM2.5 levels. The goal is to complement current analyses based on surface data covering the largest sub-regions and population centers. The overall goal is to use satellite data to overcome limitations of land-based measurements. The outcomes will be further conceptual understanding of pollutant formation, improved regulatory model performance, and better optimized forecasting programs.

Morbidity Forecast in Cities: A Study of Urban Air Pollution and Respiratory Diseases in the Metropolitan Region of Curitiba, Brazil.

PubMed

de Souza, Fabio Teodoro

2018-05-29

In the last two decades, urbanization has intensified, and in Brazil, about 90% of the population now lives in urban centers. Atmospheric patterns have changed owing to the high growth rate of cities, with negative consequences for public health. This research aims to elucidate the spatial patterns of air pollution and respiratory diseases. A data-based model to aid local urban management to improve public health policies concerning air pollution is described. An example of data preparation and multivariate analysis with inventories from different cities in the Metropolitan Region of Curitiba was studied. A predictive model with outstanding accuracy in prediction of outbreaks was developed. Preliminary results describe relevant relations among morbidity scales, air pollution levels, and atmospheric seasonal patterns. The knowledge gathered here contributes to the debate on social issues and public policies. Moreover, the results of this smaller scale study can be extended to megacities.

Improving short-term air quality predictions over the U.S. using chemical data assimilation

NASA Astrophysics Data System (ADS)

Kumar, R.; Delle Monache, L.; Alessandrini, S.; Saide, P.; Lin, H. C.; Liu, Z.; Pfister, G.; Edwards, D. P.; Baker, B.; Tang, Y.; Lee, P.; Djalalova, I.; Wilczak, J. M.

2017-12-01

State and local air quality forecasters across the United States use air quality forecasts from the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) as one of the key tools to protect the public from adverse air pollution related health effects by dispensing timely information about air pollution episodes. This project funded by the National Aeronautics and Space Administration (NASA) aims to enhance the decision-making process by improving the accuracy of NAQFC short-term predictions of ground-level particulate matter of less than 2.5 µm in diameter (PM2.5) by exploiting NASA Earth Science Data with chemical data assimilation. The NAQFC is based on the Community Multiscale Air Quality (CMAQ) model. To improve the initialization of PM2.5 in CMAQ, we developed a new capability in the community Gridpoint Statistical Interpolation (GSI) system to assimilate Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals in CMAQ. Specifically, we developed new capabilities within GSI to read/write CMAQ data, a forward operator that calculates AOD at 550 nm from CMAQ aerosol chemical composition and an adjoint of the forward operator that translates the changes in AOD to aerosol chemical composition. A generalized background error covariance program called "GEN_BE" has been extended to calculate background error covariance using CMAQ output. The background error variances are generated using a combination of both emissions and meteorological perturbations to better capture sources of uncertainties in PM2.5 simulations. The newly developed CMAQ-GSI system is used to perform daily 24-h PM2.5 forecasts with and without data assimilation from 15 July to 14 August 2014, and the resulting forecasts are compared against AirNOW PM2.5 measurements at 550 stations across the U. S. We find that the assimilation of MODIS AOD retrievals improves initialization of the CMAQ model in terms of improved correlation coefficient and reduced bias. However, we notice a large bias in nighttime PM2.5 simulations which is primarily associated with very shallow boundary layer in the model. The developments and results will be discussed in detail during the presentation.

Development of a Statistical Model for Forecasting Episodes of Visibility Degradation in the Denver Metropolitan Area.

NASA Astrophysics Data System (ADS)

Reddy, P. J.; Barbarick, D. E.; Osterburg, R. D.

1995-03-01

In 1990, the State of Colorado implemented a visibility standard of 0.076 km1 of beta extinction for the Denver metropolitan area. Meteorologists with Colorado's Air Pollution Control Division forecast high pollution days associated with visibility impairment as well as those due to high levels of the federal criteria pollutants. Visibility forecasts are made from a few hours up to about 26 h in advance of the period of interest. Here we discuss the key microscale, mesoscale, and synoptic-scale features associated with episodes of visibility impairment. Data from special studies, case studies, and the 22 NOAA Program for Regional Observing and Forecasting Services mesonet sites have been invaluable in identifying patterns associated with extremes in visibility conditions. A preliminary statistical forecast model has been developed using variables that represent many of these patterns. Six variables were selected from an initial pool of 27 to be used in a model based on linear logistic regression. These six variables include forecast measures of snow cover, surface pressures and a surface pressure gradient in eastern Colorado, relative humidity, and 500-mb ridge position. The initial testing of the model has been encouraging. The model correctly predicted 76% of the good visibility days and 67% of the poor visibility days for a test set of 171 days.

Space-Time Urban Air Pollution Forecasts

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.; Soares, A.

2012-04-01

Air pollution, like other natural phenomena, may be considered a space-time process. However, the simultaneous integration of time and space is not an easy task to perform, due to the existence of different uncertainties levels and data characteristics. In this work we propose a hybrid method that combines geostatistical and neural models to analyze PM10 time series recorded in the urban area of Lisbon (Portugal) for the 2002-2006 period and to produce forecasts. Geostatistical models have been widely used to characterize air pollution in urban areas, where the pollutant sources are considered diffuse, and also to industrial areas with localized emission sources. It should be stressed however that most geostatistical models correspond basically to an interpolation methodology (estimation, simulation) of a set of variables in a spatial or space-time domain. The temporal prediction of a pollutant usually requires knowledge of the main trends and complex patterns of physical dispersion phenomenon. To deal with low resolution problems and to enhance reliability of predictions, an approach based on neural network short term predictions in the monitoring stations which behave as a local conditioner to a fine grid stochastic simulation model is presented here. After the pollutant concentration is predicted for a given time period at the monitoring stations, we can use the local conditional distributions of observed values, given the predicted value for that period, to perform the spatial simulations for the entire area and consequently evaluate the spatial uncertainty of pollutant concentration. To attain this objective, we propose the use of direct sequential simulations with local distributions. With this approach one succeed to predict the space-time distribution of pollutant concentration that accounts for the time prediction uncertainty (reflecting the neural networks efficiency at each local monitoring station) and the spatial uncertainty as revealed by the spatial variograms. The dataset used consists of PM10 concentrations recorded hourly by 12 monitoring stations within the Lisbon's area, for the period 2002-2006. In addition, meteorological data recorded at 3 monitoring stations and boundary layer height (BLH) daily values from the ECMWF (European Centre for Medium Weather Forecast), ERA Interim, were also used. Based on the large-scale standard pressure fields from the ERA40/ECMWF, prevailing circulation patterns at regional scale where determined and used on the construction of the models. After the daily forecasts were produced, the difference between the average maps based on real observations and predicted values were determined and the model's performance was assessed. Based on the analysis of the results, we conclude that the proposed approach shows to be a very promising alternative for urban air quality characterization because of its good results and simplicity of application.

Case study of PM pollution in playgrounds in Istanbul

NASA Astrophysics Data System (ADS)

Ozdemir, Huseyin; Mertoglu, Bulent; Demir, Goksel; Deniz, Ali; Toros, Hüseyin

2012-05-01

In a world where at least 50% of the population is living in urban environments, air pollution and specifically particulate matter (PM) have become one of the most critical issues for human health. Children are more susceptible than adults to air pollution and its adverse effects because they inhale and retain larger amounts of air pollutants per unit of body weight. In this study, PM pollution, particularly PM10 and PM2.5, at selected playgrounds were investigated in Istanbul city. Istanbul is a megacity of over 15 million inhabitants, and on-road traffic is increasing rapidly (over 3 million vehicles on the road). To estimate the effect of traffic emissions on children, the location of the playgrounds were selected according to traffic density. Measurements were carried out at five different playgrounds throughout the city in 2009. Field results show that the values of PM10 and PM2.5 have reached critical limits at the playgrounds close to the main roads, especially at P-1. Thus, we focused on this location and investigated a source other than traffic emissions. One of the episode days has been observed on 5-7 March 2009. Evaluations of meteorological events are very important to determine air pollution sources and their long-range transport. Therefore, the Weather Research and Forecasting model (WRF) was used to simulate and forecast meteorological parameters and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) applied to investigate long-range transport. According to the WRF model outputs, there was a low-pressure system over Geneva gulf on the 500-hPa level, and its core had been located over Britain on 5 March 2009 00UTC. The system had been sweeping dust from the Sahara Desert and carrying the air particles over Istanbul. Similarly, backward HYSPLIT analysis showed that air particles had moved through Istanbul from Northern Africa.

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.

Experimental Forecasts of Wildfire Pollution at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Beaulieu, Paul-Andre; Chen, Jack; Landry, Hugo; Cousineau, Sophie; Moran, Michael

2016-04-01

Environment and Climate Change Canada's Canadian Meteorological Centre Operations division (CMCO) has been running an experimental North American air quality forecast system with near-real-time wildfire emissions since 2014. This system, named FireWork, also takes anthropogenic and other natural emission sources into account. FireWork 48-hour forecasts are provided to CMCO forecasters and external partners in Canada and the U.S. twice daily during the wildfire season. This system has proven to be very useful in capturing short- and long-range smoke transport from wildfires over North America. Several upgrades to the FireWork system have been made since 2014 to accommodate the needs of operational AQ forecasters and to improve system performance. In this talk we will present performance statistics and some case studies for the 2014 and 2015 wildfire seasons. We will also describe current limitations of the FireWork system and ongoing and future work planned for this air quality forecast system.

Simple statistical bias correction techniques greatly improve moderate resolution air quality forecast at station level

NASA Astrophysics Data System (ADS)

Curci, Gabriele; Falasca, Serena

2017-04-01

Deterministic air quality forecast is routinely carried out at many local Environmental Agencies in Europe and throughout the world by means of eulerian chemistry-transport models. The skill of these models in predicting the ground-level concentrations of relevant pollutants (ozone, nitrogen dioxide, particulate matter) a few days ahead has greatly improved in recent years, but it is not yet always compliant with the required quality level for decision making (e.g. the European Commission has set a maximum uncertainty of 50% on daily values of relevant pollutants). Post-processing of deterministic model output is thus still regarded as a useful tool to make the forecast more reliable. In this work, we test several bias correction techniques applied to a long-term dataset of air quality forecasts over Europe and Italy. We used the WRF-CHIMERE modelling system, which provides operational experimental chemical weather forecast at CETEMPS (http://pumpkin.aquila.infn.it/forechem/), to simulate the years 2008-2012 at low resolution over Europe (0.5° x 0.5°) and moderate resolution over Italy (0.15° x 0.15°). We compared the simulated dataset with available observation from the European Environmental Agency database (AirBase) and characterized model skill and compliance with EU legislation using the Delta tool from FAIRMODE project (http://fairmode.jrc.ec.europa.eu/). The bias correction techniques adopted are, in order of complexity: (1) application of multiplicative factors calculated as the ratio of model-to-observed concentrations averaged over the previous days; (2) correction of the statistical distribution of model forecasts, in order to make it similar to that of the observations; (3) development and application of Model Output Statistics (MOS) regression equations. We illustrate differences and advantages/disadvantages of the three approaches. All the methods are relatively easy to implement for other modelling systems.

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

Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011.

PubMed

Li, Li; Qian, Jun; Ou, Chun-Quan; Zhou, Ying-Xue; Guo, Cui; Guo, Yuming

2014-07-01

There is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001-2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia

NASA Astrophysics Data System (ADS)

Lee, Hsiang-He; Iraqui, Oussama; Gu, Yefu; Hung-Lam Yim, Steve; Chulakadabba, Apisada; Yiu-Ming Tonks, Adam; Yang, Zhengyu; Wang, Chien

2018-05-01

Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chemistry component (WRF-Chem) to quantitatively examine the contributions of aerosols emitted from fire (i.e., biomass burning) versus non-fire (including fossil fuel combustion, and road dust, etc.) sources to the degradation of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and are driven by emissions from (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. The model results reveal that 39 % of observed low-visibility days (LVDs) can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. Analysis of an 24 h PM2.5 air quality index (AQI) indicates that the case with coexisting fire and non-fire PM2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 to 34 %. The premature mortality in major Southeast Asian cities due to degradation of air quality by particulate pollutants is estimated to increase from ˜ 4110 per year in 2002 to ˜ 6540 per year in 2008. In addition, we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degradation. An experiment of using machine learning algorithms to forecast the occurrence of haze events in Singapore is also explored in this study. All of these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources.

Research on PM2.5 time series characteristics based on data mining technology

NASA Astrophysics Data System (ADS)

Zhao, Lifang; Jia, Jin

2018-02-01

With the development of data mining technology and the establishment of environmental air quality database, it is necessary to discover the potential correlations and rules by digging the massive environmental air quality information and analyzing the air pollution process. In this paper, we have presented a sequential pattern mining method based on the air quality data and pattern association technology to analyze the PM2.5 time series characteristics. Utilizing the real-time monitoring data of urban air quality in China, the time series rule and variation properties of PM2.5 under different pollution levels are extracted and analyzed. The analysis results show that the time sequence features of the PM2.5 concentration is directly affected by the alteration of the pollution degree. The longest time that PM2.5 remained stable is about 24 hours. As the pollution degree gets severer, the instability time and step ascending time gradually changes from 12-24 hours to 3 hours. The presented method is helpful for the controlling and forecasting of the air quality while saving the measuring costs, which is of great significance for the government regulation and public prevention of the air pollution.

Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: a numerical study with the WRF-Chem model

NASA Astrophysics Data System (ADS)

Abou Rafee, Sameh A.; Martins, Leila D.; Kawashima, Ana B.; Almeida, Daniela S.; Morais, Marcos V. B.; Souza, Rita V. A.; Oliveira, Maria B. L.; Souza, Rodrigo A. F.; Medeiros, Adan S. S.; Urbina, Viviana; Freitas, Edmilson D.; Martin, Scot T.; Martins, Jorge A.

2017-06-01

This paper evaluates the contributions of the emissions from mobile, stationary and biogenic sources on air pollution in the Amazon rainforest by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. The analyzed air pollutants were CO, NOx, SO2, O3, PM2. 5, PM10 and volatile organic compounds (VOCs). Five scenarios were defined in order to evaluate the emissions by biogenic, mobile and stationary sources, as well as a future scenario to assess the potential air quality impact of doubled anthropogenic emissions. The stationary sources explain the highest concentrations for all air pollutants evaluated, except for CO, for which the mobile sources are predominant. The anthropogenic sources considered resulted an increasing in the spatial peak-temporal average concentrations of pollutants in 3 to 2780 times in relation to those with only biogenic sources. The future scenario showed an increase in the range of 3 to 62 % in average concentrations and 45 to 109 % in peak concentrations depending on the pollutant. In addition, the spatial distributions of the scenarios has shown that the air pollution plume from the city of Manaus is predominantly transported west and southwest, and it can reach hundreds of kilometers in length.

THE USE OF AIR QUALITY FORECASTS TO ASSESS IMPACTS OF AIR POLLUTION ON CROPS

EPA Science Inventory

Assessing O₃ damage to crops is challenging due to the difficulties in determining the reduction in crop yield that results from exposure to surface O₃, for which monitors are limited and deployed mostly in non-rural areas. This work explores the potential b...

Application, evaluation and sensitivity analysis of the coupled WRF-CMAQ system from regional to urban scales

EPA Science Inventory

The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science chemical transport model (CTM) capable of simulating the emission, transport and fate of numerous air pollutants. Similarly, the Weather Research and Forecasting (WRF) model is a state-of-the-science mete...

Vertical Profiling of Air Pollution at RAPCD

NASA Technical Reports Server (NTRS)

Newchurch, Michael J.; Fuller, Kirk A.; Bowdle, David A.; Johnson, Steven; Knupp, Kevin; Gillani, Noor; Biazar, Arastoo; Mcnider, Richard T.; Burris, John

2004-01-01

The interaction between local and regional pollution levels occurs at the interface of the Planetary Boundary Layer and the Free Troposphere. Measuring the vertical distribution of ozone, aerosols, and winds with high temporal and vertical resolution is essential to diagnose the nature of this interchange and ultimately for accurately forecasting ozone and aerosol pollution levels. The Regional Atmospheric Profiling Center for Discovery, RAPCD, was built and instrumented to address this critical issue. The ozone W DIAL lidar, Nd:YAG aerosol lidar, and 2.1 micron Doppler wind lidar, along with balloon- borne ECC ozonesondes form the core of the W C D instrumentation for addressing this problem. Instrumentation in the associated Mobile Integrated Profiling (MIPS) laboratory includes 91 5Mhz profiler, sodar, and ceilometer. The collocated Applied particle Optics and Radiometry (ApOR) laboratory hosts an FTIR along with MOUDI and optical particle counters. With MODELS-3 analysis by colleagues in the National Space Science and Technology Center on the UAH campus and the co- located National Weather Service Forecasting Office in Huntsville, AL we are developing a unique facility for advancing the state of the science of pollution forecasting.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.

PubMed

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

NASA Astrophysics Data System (ADS)

Ma, Chaoqun; Wang, Tijian; Zang, Zengliang; Li, Zhijin

2018-07-01

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PubMed Central

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. PMID:28708836

The analysis and application of a new hybrid pollutants forecasting model using modified Kolmogorov-Zurbenko filter.

PubMed

Li, Peizhi; Wang, Yong; Dong, Qingli

2017-04-01

Cities in China suffer from severe smog and haze, and a forecasting system with high accuracy is of great importance to foresee the concentrations of the airborne particles. Compared with chemical transport models, the growing artificial intelligence models can simulate nonlinearities and interactive relationships and getting more accurate results. In this paper, the Kolmogorov-Zurbenko (KZ) filter is modified and firstly applied to construct the model using an artificial intelligence method. The concentration of inhalable particles and fine particulate matter in Dalian are used to analyze the filtered components and test the forecasting accuracy. Besides, an extended experiment is made by implementing a comprehensive comparison and a stability test using data in three other cities in China. Results testify the excellent performance of the developed hybrid models, which can be utilized to better understand the temporal features of pollutants and to perform a better air pollution control and management. Copyright © 2017 Elsevier B.V. All rights reserved.

A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

PubMed

Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

2015-02-01

Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising. Copyright © 2014 Elsevier B.V. All rights reserved.

An analysis of effects of San Diego wildfire on ambient air quality.

PubMed

Viswanathan, Shekar; Eria, Luis; Diunugala, Nimal; Johnson, Jeffrey; McClean, Christopher

2006-01-01

The impact of major gaseous and particulate pollutants emitted by the wildfire of October 2003 on ambient air quality and health of San Diego residents before, during, and after the fire are analyzed using data available from the San Diego County Air Pollution Control District and California Air Resources Board. It was found that fine particulate matter (PM) levels exceeded the federal daily 24-hr average standard during the fire. There was a slight increase in some of the gaseous pollutants, such as carbon monoxide, which exceeded federal standards. Ozone (O3) precursors, such as total hydrocarbons and methane gases, experienced elevated concentration during the fire. Fortunately, the absence of sunlight because of the cloud of thick smoke that covered most of the county during the fire appears to have prevented the photochemical conversion of the precursor gases to harmful concentrations of O3. Statistical analysis of the compiled medical surveillance data has been used to establish correlations between pollutant levels in the region and the resultant health problems experienced by the county citizens. The study shows that the increased PM concentration above the federal standard resulted in a significant increase in hospital emergency room visits for asthma, respiratory problems, eye irritation, and smoke inhalation. On the basis of the findings, it is recommended that hospitals and emergency medical facilities engage in pre-event planning that would ensure a rapid response to an impact on the healthcare system as a result of a large wildfire and appropriate agencies engage in the use of all available meteorological forecasting resources, including real-time satellite imaging assets, to accurately forecast air quality and assist firefighting efforts.

Chemical fields during Southeast Nexus (SENEX) field experiment and design of verification metrics for efficacy of capturing wild fire emissions

NASA Astrophysics Data System (ADS)

Lee, P.

2016-12-01

Wildfires are commonplace in North America. Air pollution resulted from wildfires pose a significant risk for human health and crop damage. The pollutants alter the vertical distribution of many atmospheric constituents including O3 and many fine particulate (PM) species. Compared to anthropogenic emissions of air pollutants, emissions from wildfires are largely uncontrolled and unpredictable. Therefore, quantitatively describing wildfire emissions and their contributions to air pollution remains a substantial challenge for atmospheric modeler and air quality forecasters. In this study, we investigated the modification and redistribution of atmospheric composition within the Conterminous U.S (CONUS) by wild fire plumes originated within and outside of the CONUS. We used the National Air Quality Forecasting Capability (NAQFC) to conduct the investigation. NAQFC uses dynamic lateral chemical boundary conditions derived from the National Weather Service experimental global aerosol tracer model accounting for intrusion of fire-associated aerosol species. Within CONUS, the NAQFC derives both gaseous and aerosol wildfire associated species from the National Environmental Satellite, Data, and Information Service (NESDIS) hazard mapping system (HMS) hot-spot detection, and US Forestry Service Blue-sky protocol for quantifying fire characteristics, and the US EPA Sparse Matrix Object Kernel Emission (SMOKE) calculation for plume rise. Attributions of both of these wildfire influences inherently reflect the aged plumes intruded into the CONUS through the model boundaries as well as the fresher emissions from sources within the CONUS. Both emission sources contribute significantly to the vertical structure modification of the atmosphere. We conducted case studies within the fire active seasons to demonstrate some possible impacts on the vertical structures of O3 and PM species by the wildfire activities.

The Atmospheric Boundary Layer

ERIC Educational Resources Information Center

Tennekes, Hendrik

1974-01-01

Discusses some important parameters of the boundary layer and effects of turbulence on the circulation and energy dissipation of the atmosphere. Indicates that boundary-layer research plays an important role in long-term forecasting and the study of air-pollution meteorology. (CC)

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

PubMed Central

Mathur, Rohit; Xing, Jia; Gilliam, Robert; Sarwar, Golam; Hogrefe, Christian; Pleim, Jonathan; Pouliot, George; Roselle, Shawn; Spero, Tanya L.; Wong, David C.; Young, Jeffrey

2018-01-01

The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modelled processes were examined and enhanced to suitably represent the extended space and time scales for such applications. Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting (WRF) model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. The expansion of CMAQ to simulate the hemispheric scales provides a framework to examine interactions between atmospheric processes occurring at various spatial and temporal scales with physical, chemical, and dynamical consistency. PMID:29681922

Simulation of Smoke-Haze Dispersion from Wildfires in South East Asia with a Lagrangian Particle Model

NASA Astrophysics Data System (ADS)

Hertwig, D.; Burgin, L.; Gan, C.; Hort, M.; Jones, A. R.; Shaw, F.; Witham, C. S.; Zhang, K.

2014-12-01

Biomass burning, often related to agricultural deforestation, not only affects local pollution levels but periodically deteriorates air quality in many South East Asian megacities due to the transboundary transport of smoke-haze. In June 2013, Singapore experienced the worst wildfire related air-pollution event on record following from the escalation of peatland fires in Sumatra. An extended dry period together with anomalous westerly winds resulted in severe and unhealthy pollution levels in Singapore that lasted for more than two weeks. Reacting to this event, the Met Office and the Meteorological Service Singapore have explored how to adequately simulate haze-pollution dispersion, with the aim to provide a reliable operational forecast for Singapore. Simulations with the Lagrangian particle model NAME (Numerical Atmospheric-dispersion Modelling Environment), running on numerical weather prediction data from the Met Office and Meteorological Service Singapore and emission data derived from satellite observations of the fire radiative power, are validated against PM10 observations in South East Asia. Comparisons of simulated concentrations with hourly averages of PM10 measurements in Singapore show that the model captures well the severe smoke-haze event in June 2013 and a minor episode in March 2014. Different quantitative satellite-derived emissions have been tested, with one source demonstrating a consistent factor of two under-prediction for Singapore. Confidence in the skill of the model system has been substantiated by further comparisons with data from monitoring sites in Malaysia, Brunei and Thailand. Following the validation study, operational smoke-haze pollution forecasts with NAME were launched in Singapore, in time for the 2014 fire season. Real-time bias correction and verification of this forecast will be discussed.

Modeling urban air pollution in Budapest using WRF-Chem model

NASA Astrophysics Data System (ADS)

Kovács, Attila; Leelőssy, Ádám; Lagzi, István; Mészáros, Róbert

2017-04-01

Air pollution is a major problem for urban areas since the industrial revolution, including Budapest, the capital and largest city of Hungary. The main anthropogenic sources of air pollutants are industry, traffic and residential heating. In this study, we investigated the contribution of major industrial point sources to the urban air pollution in Budapest. We used the WRF (Weather Research and Forecasting) nonhydrostatic mesoscale numerical weather prediction system online coupled with chemistry (WRF-Chem, version 3.6).The model was configured with three nested domains with grid spacings of 15, 5 and 1 km, representing Central Europe, the Carpathian Basin and Budapest with its surrounding area. Emission data was obtained from the National Environmental Information System. The point source emissions were summed in their respective cells in the second nested domain according to latitude-longitude coordinates. The main examined air pollutants were carbon monoxide (CO) and nitrogen oxides (NOx), from which the secondary compound, ozone (O3) forms through chemical reactions. Simulations were performed under different weather conditions and compared to observations from the automatic monitoring site of the Hungarian Air Quality Network. Our results show that the industrial emissions have a relatively weak role in the urban background air pollution, confirming the effect of industrial developments and regulations in the recent decades. However, a few significant industrial sources and their impact area has been demonstrated.

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-user Resources, Answers to Faqs, and Common Mistakes to Avoid

NASA Technical Reports Server (NTRS)

Duncan, Bryan Neal; Prados, Ana; Lamsal, Lok N.; Liu, Yang; Streets, David G.; Gupta, Pawan; Hilsenrath, Ernest; Kahn, Ralph A.; Nielsen, J. Eric; Beyersdorf, Andreas J.;

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid

NASA Technical Reports Server (NTRS)

Duncan, Bryan; Prados, Ana I.; Lamsal, Lok; Liu, Yang; Streets, David G.; Gupta, Pawan; Hilsenrath, Ernest; Kahn, Ralph A.; Nielsen, J. Eric; Beyersdorf, Andreas J.;

Assessment of winter air pollution episodes using long-range transport modeling in Hangzhou, China, during World Internet Conference, 2015.

PubMed

Ni, Zhi-Zhen; Luo, Kun; Zhang, Jun-Xi; Feng, Rui; Zheng, He-Xin; Zhu, Hao-Ran; Wang, Jing-Fan; Fan, Jian-Ren; Gao, Xiang; Cen, Ke-Fa

2018-05-01

A winter air pollution episode was observed in Hangzhou, South China, during the Second World Internet Conference, 2015. To study the pollution characteristics and underlying causes, the Weather Research and Forecasting with Chemistry model was used to simulate the spatial and temporal evolution of the pollution episode from December 8 to 19, 2015. In addition to scenario simulations, analysis of the atmospheric trajectory and synoptic weather conditions were also performed. The results demonstrated that control measures implemented during the week preceding the conference reduced the fine particulate matter (PM 2.5 ) pollution level to some extent, with a decline in the total PM 2.5 concentration in Hangzhou of 15% (7%-25% daily). Pollutant long-range transport, which occurred due to a southward intrusion of strong cold air driven by the Siberia High, led to severe pollution in Hangzhou on December 15, 2015, accounting for 85% of the PM 2.5 concentration. This study provides new insights into the challenge of winter pollution prevention in Hangzhou. For adequate pollution prevention, more regional collaborations should be fostered when creating policies for northern China. Copyright © 2018 Elsevier Ltd. All rights reserved.

Research and application of a novel hybrid air quality early-warning system: A case study in China.

PubMed

Li, Chen; Zhu, Zhijie

2018-06-01

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of "decomposition and ensemble", an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China

NASA Astrophysics Data System (ADS)

Deng, T.; Chen, Y.; Wan, Q.

2017-12-01

The Community Multiscale Air Quality (CMAQ) model was utilized for forecasting air quality over the Pearl River Delta (PRD) region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with the two different meteorological models, the Global/Regional Assimilation and Prediction System (GRAPES) and the 5th-generation Mesoscale Model (MM5), was assessed by combining observational data. The effect of meteorological factors and physical-chemical processes on forecast results was discussed through process analysis. The results showed that both models have similar good performance with better performance by GRAPES-CMAQ. GRAPES was superior in predicting the overall meteorological element variation tendencies but showed large deviations in atmospheric pressure and wind speed. It contributed to higher correlation coefficients of the pollutants with GRAPES-CMAQ, but with greater deviation. The underestimations of nitrate and ammonium salt contributed to the underestimations of Particle Matter (PM) and extinction coefficients. Surface layer SO2, CO and NO source emissions made the sole positive contribution. O3 originated mainly from horizontal and vertical transport and chemical processes were the main consumption item. On the contrary, NO2 derived mainly from chemical production.

Markov Chain-Based Acute Effect Estimation of Air Pollution on Elder Asthma Hospitalization

PubMed Central

Luo, Li; Zhang, Fengyi; Sun, Lin; Li, Chunyang; Huang, Debin; Han, Gao; Wang, Bin

2017-01-01

Background Asthma caused substantial economic and health care burden and is susceptible to air pollution. Particularly, when it comes to elder asthma patient (older than 65), the phenomenon is more significant. The aim of this study is to investigate the Markov-based acute effects of air pollution on elder asthma hospitalizations, in forms of transition probabilities. Methods A retrospective, population-based study design was used to assess temporal patterns in hospitalizations for asthma in a region of Sichuan province, China. Approximately 12 million residents were covered during this period. Relative risk analysis and Markov chain model were employed on daily hospitalization state estimation. Results Among PM2.5, PM10, NO2, and SO2, only SO2 was significant. When air pollution is severe, the transition probability from a low-admission state (previous day) to high-admission state (next day) is 35.46%, while it is 20.08% when air pollution is mild. In particular, for female-cold subgroup, the counterparts are 30.06% and 0.01%, respectively. Conclusions SO2 was a significant risk factor for elder asthma hospitalization. When air pollution worsened, the transition probabilities from each state to high admission states increase dramatically. This phenomenon appeared more evidently, especially in female-cold subgroup (which is in cold season for female admissions). Based on our work, admission amount forecast, asthma intervention, and corresponding healthcare allocation can be done. PMID:29147496

Emissions Models and Other Methods to Produce Emission Inventories

EPA Pesticide Factsheets

An emissions inventory is a summary or forecast of the emissions produced by a group of sources in a given time period. Inventories of air pollution from mobile sources are often produced by models such as the MOtor Vehicle Emission Simulator (MOVES).

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.

Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China.

PubMed

Zhai, Binxu; Chen, Jianguo

2018-04-18

A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of the stacked ensemble model. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

Korea-United States Air Quality (KORUS-AQ) Campaign

NASA Technical Reports Server (NTRS)

Castellanos, Patricia; Da Silva, Arlindo; Longo-De Freitas, Karla

2017-01-01

The Korea-United States Air Quality (KORUS-AQ) campaign was an international cooperative field study based out of Osan Air Base, Songtan, South Korea (about 60 kilometers south of Seoul) in April-June 2016. A comprehensive suite of instruments capable of measuring atmospheric composition was deployed around the Korean peninsula on aircrafts, ships, and at ground sites in order to characterize local and transboundary pollution. The NASA Goddard Earth Observing System, version 5 (GEOS-5) forecast model was used for near real time meteorological and aerosol forecasting and flight planning during the KORUS-AQ campaign. Evaluation of GEOS-5 against observations from the campaign will help to identify inaccuracies in the models physical and chemical processes in this region within East Asia and lead to further developments of the modeling system.

Lagrangian transport model forecasts and a transport climatology for the Intercontinental Transport and Chemical Transformation 2002 (ITCT 2K2) measurement campaign

NASA Astrophysics Data System (ADS)

Forster, Caroline; Cooper, Owen; Stohl, Andreas; Eckhardt, Sabine; James, Paul; Dunlea, Edward; Nicks, Dennis K.; Holloway, John S.; Hübler, Gerd; Parrish, David D.; Ryerson, Tom B.; Trainer, Michael

2004-04-01

On the basis of Lagrangian tracer transport simulations this study presents an intercontinental transport climatology and tracer forecasts for the Intercontinental Transport and Chemical Transformation 2002 (ITCT 2K2) aircraft measurement campaign, which took place at Monterey, California, in April-May 2002 to measure Asian pollution arriving at the North American West Coast. For the climatology the average transport of an Asian CO tracer was calculated over a time period of 15 years using the particle dispersion model FLEXPART. To determine by how much the transport from Asia to North America during ITCT 2K2 deviated from the climatological mean, the 15-year average for April and May was compared with the average for April and May 2002 and that for the ITCT 2K2 period. It was found that 8% less Asian CO tracer arrived at the North American West Coast during the ITCT 2K2 period compared to the climatological mean. Below 8-km altitude, the maximum altitude of the research aircraft, 13% less arrived. Nevertheless, pronounced layers of Asian pollution were measured during 3 of the 13 ITCT 2K2 flights. FLEXPART was also successfully used as a forecasting tool for the flight planning during ITCT 2K2. It provided 3-day forecasts for three different anthropogenic CO tracers originating from Asia, North America, and Europe. In two case studies the forecast abilities of FLEXPART are analyzed and discussed by comparing the forecasts with measurement data and infrared satellite images. The model forecasts underestimated the measured CO enhancements by about a factor of 4, mainly because of an underestimation of the Asian emissions in the emission inventory and because of biomass-burning influence that was not modeled. Nevertheless, the intercontinental transport and dispersion of pollution plumes were qualitatively well predicted, and on the basis of the model results the aircraft could successfully be guided into the polluted air masses.

Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF-Chem CO tracer model

NASA Astrophysics Data System (ADS)

Saide, Pablo E.; Carmichael, Gregory R.; Spak, Scott N.; Gallardo, Laura; Osses, Axel E.; Mena-Carrasco, Marcelo A.; Pagowski, Mariusz

2011-05-01

This study presents a system to predict high pollution events that develop in connection with enhanced subsidence due to coastal lows, particularly in winter over Santiago de Chile. An accurate forecast of these episodes is of interest since the local government is entitled by law to take actions in advance to prevent public exposure to PM10 concentrations in excess of 150 μg m -3 (24 h running averages). The forecasting system is based on accurately simulating carbon monoxide (CO) as a PM10/PM2.5 surrogate, since during episodes and within the city there is a high correlation (over 0.95) among these pollutants. Thus, by accurately forecasting CO, which behaves closely to a tracer on this scale, a PM estimate can be made without involving aerosol-chemistry modeling. Nevertheless, the very stable nocturnal conditions over steep topography associated with maxima in concentrations are hard to represent in models. Here we propose a forecast system based on the WRF-Chem model with optimum settings, determined through extensive testing, that best describe both meteorological and air quality available measurements. Some of the important configurations choices involve the boundary layer (PBL) scheme, model grid resolution (both vertical and horizontal), meteorological initial and boundary conditions and spatial and temporal distribution of the emissions. A forecast for the 2008 winter is performed showing that this forecasting system is able to perform similarly to the authority decision for PM10 and better than persistence when forecasting PM10 and PM2.5 high pollution episodes. Problems regarding false alarm predictions could be related to different uncertainties in the model such as day to day emission variability, inability of the model to completely resolve the complex topography and inaccuracy in meteorological initial and boundary conditions. Finally, according to our simulations, emissions from previous days dominate episode concentrations, which highlights the need for 48 h forecasts that can be achieved by the system presented here. This is in fact the largest advantage of the proposed system.

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

Emissions Inventory of Anthropogenic PM2.5 and PM10 in Mega city, Delhi, India for Air Quality Forecasting during CWG- 2010

NASA Astrophysics Data System (ADS)

Sahu, S.; Beig, G.; Schultz, M.; Parkhi, N.; Stein, O.

2012-04-01

The mega city of Delhi is the second largest urban agglomeration in India with 16.7 mio. inhabitants. Delhi has the highest per capita power consumption of electricity in India and the demand has risen by more than 50% during the last decade. Emissions from commercial, power, domestic and industrial sectors have strongly increased causing more and more environmental problems due to air pollution and its adverse impacts on human health. Particulate matter (PM) of size less than 2.5-micron (PM2.5) and 10 micron (PM10) have emerged as primary pollutants of concern due to their adverse impact on human health. As part of the System of Air quality Forecasting and Research (SAFAR) project developed for air quality forecasting during the Commonwealth Games (CWG) - 2010, a high resolution Emission Inventory (EI) of PM10 and PM2.5 has been developed for the metropolitan city Delhi for the year 2010. The comprehensive inventory involves detailed activity data and has been developed for a domain of 70km×65km with a 1.67km×1.67km resolution covering Delhi and its surrounding region (i.e. National Capital Region (NCR)). In creating this inventory, Geographical Information System (GIS) based techniques were used for the first time in India. The major sectors considered are, transport, thermal power plants, industries, residential and commercial cooking along with windblown road dust which is found to play a major role for the megacity environment. Extensive surveys were conducted among the Delhi slum dwellers (Jhuggi) in order to obtain more robust estimates for the activity data related to domestic cooking and heating. Total emissions of PM10 and PM2.5 including wind blown dust over the study area are found to be 236 Gg/yr and 94 Gg/yr respectively. About half of the PM10 emissions stem from windblown road dust. The new emission inventory has been used for regional air quality forecasts in the Delhi region during the Commonwealth games (SAFAR project), and they will soon be tested in simulations of the global atmospheric composition in the framework of the European MACC project which provided the chemical boundary conditions to the regional air quality forecasts in 2010.

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.

The relationship between birch pollen, air pollution and weather types and their effect on antihistamine purchase in two Swedish cities.

PubMed

Grundström, Maria; Dahl, Åslög; Ou, Tinghai; Chen, Deliang; Pleijel, Håkan

2017-01-01

Exposure to elevated air pollution levels can aggravate pollen allergy symptoms. The aim of this study was to investigate the relationships between airborne birch ( Betula ) pollen, urban air pollutants NO 2 , O 3 and PM 10 and their effects on antihistamine demand in Gothenburg and Malmö, Sweden, 2006-2012. Further, the influence of large-scale weather pattern on pollen-/pollution-related risk, using Lamb weather types (LWTs), was analysed. Daily LWTs were obtained by comparing the atmospheric pressure over a 16-point grid system over southern Sweden (scale ~3000 km). They include two non-directional types, cyclonic (C) and anticyclonic (A) and eight directional types depending on the wind direction (N, NE, E…). Birch pollen levels were exceptionally high under LWTs E and SE in both cities. Furthermore, LWTs with dry and moderately calm meteorological character (A, NE, E, SE) were associated with strongly elevated air pollution (NO 2 and PM 10 ) in Gothenburg. For most weather situations in both cities, simultaneously high birch pollen together with high air pollution had larger over-the-counter (OTC) sales of antihistamines than situations with high birch pollen alone. LWTs NE, E, SE and S had the highest OTC sales in both cities. In Gothenburg, the city with a higher load of both birch pollen and air pollution, the higher OTC sales were especially obvious and indicate an increased effect on allergic symptoms from air pollution. Furthermore, Gothenburg LWTs A, NE, E and SE were associated with high pollen and air pollution levels and thus classified as high-risk weather types. In Malmö, corresponding high-risk LWTs were NE, E, SE and S. Furthermore, occurrence of high pollen and air pollutants as well as OTC sales correlated strongly with vapour pressure deficit and temperature in Gothenburg (much less so in Malmö). This provides evidence that the combination of meteorological properties associated with LWTs can explain high levels of birch pollen and air pollution. Our study shows that LWTs represent a useful tool for integrated daily air quality forecasting/warning.

Air quality real-time forecast before and during the G-20 ...

EPA Pesticide Factsheets

The 2016 G-20 Hangzhou summit, the eleventh annual meeting of the G-20 heads of government, will be held during September 3-5, 2016 in Hangzhou, China. For a successful summit, it is important to ensure good air quality. To achieve this goal, governments of Hangzhou and its surrounding provinces will enforce a series of emission reductions, such as a forced closure of major highly-polluting industries and also limiting car and construction emissions in the cities and surroundings during the 2016 G-20 Hangzhou summit. Air quality forecast systems consisting of the two-way coupled WRF-CMAQ and online-coupled WRF-Chem have been applied to forecast air quality in Hangzhou regularly. This study will present the results of real-time forecasts of air quality over eastern China using 12-km grid spacing and for Hangzhou area using 4-km grid spacing with these two modeling systems using emission inventories for base and 2016 G-20 scenarios before and during the 2016 G-20 Hangzhou summit. Evaluations of models’ performance for both cases for PM2.5, PM10, O3, SO2, NO2, CO, air quality index (AQI), and aerosol optical depth (AOD) are carried out by comparing them with observations obtained from satellites, such as MODIS, and surface monitoring networks. The effects of the emission reduction efforts on expected air quality improvements during the2016 G-20 Hangzhou summit will be studied in depth. This study provides insights on how air quality will be improved by a plan

Contrail Tracking and ARM Data Product Development

NASA Technical Reports Server (NTRS)

Duda, David P.; Russell, James, III

2005-01-01

A contrail tracking system was developed to help in the assessment of the effect of commercial jet contrails on the Earth's radiative budget. The tracking system was built by combining meteorological data from the Rapid Update Cycle (RUC) numerical weather prediction model with commercial air traffic flight track data and satellite imagery. A statistical contrail-forecasting model was created a combination of surface-based contrail observations and numerical weather analyses and forecasts. This model allows predictions of widespread contrail occurrences for contrail research on either a real-time basis or for long-term time scales. Satellite-derived cirrus cloud properties in polluted and unpolluted regions were compared to determine the impact of air traffic on cirrus.

Lagrangian Transport Model Forecasts as Useful Support of the Flight Planning During the Intercontinental Transport and Chemical Transformation 2002 (ITCT 2k2) Measurement Campaign

NASA Astrophysics Data System (ADS)

Forster, C.; Cooper, O.; Stohl, A.; Eckhardt, S.; James, P.; Dunlea, E.; Nicks, D. K.; Holloway, J. S.; Hübler, G.; Parrish, D. D.; Ryerson, T. B.; Trainer, M.

2002-12-01

In this study, the Lagrangian tracer transport model FLEXPART is shown to be a useful forecasting tool for the flight planning during the ITCT 2k2 (Intercontinental Transport and Chemical Transformation 2002) aircraft measurement campaign. The advantages of this model are that it requires only a short computation time, has a finer spatial resolution and does not suffer numerical diffusion compared to chemistry transport models (CTMs). It is a compromise between simple trajectory calculations and complex CTMs that makes best use of available computer hardware. During the campaign FLEXPART provided three-day forecasts for four different anthropogenic CO tracers: Asian, North American, Japanese, and European. The forecasts were based on data from the Aviation model (AVN) of the National Center for Environmental Prediction (NCEP) and relied on the EDGAR emission inventory for the base year 1990. In two case studies, the forecast abilities of FLEXPART are analysed and discussed by comparing the forecasts with measurement data, results from the post analysis modelling, infrared satellite images, and backward trajectories calculated with two different Lagrangian trajectory models. It is shown that intercontinental transport and dispersion of pollution plumes were qualitatively well predicted, and the aircraft could successfully be directed into the polluted air masses.

Resilience of urban ambulance services under future climate, meteorology and air pollution scenarios

NASA Astrophysics Data System (ADS)

Pope, Francis; Chapman, Lee; Fisher, Paul; Mahmood, Marliyyah; Sangkharat, Kamolrat; Thomas, Neil; Thornes, John

2017-04-01

Ambulances are an integral part of a country's infrastructure ensuring its citizens and visitors are kept healthy. The impact of weather, climate and climate change on ambulance services around the world has received increasing attention in recent years but most studies have been area specific and there is a need to establish basic relationships between ambulance data (both response and illness data) and meteorological parameters. In this presentation, the effects of temperature, other meteorological and air pollution variables on ambulance call out rates for different medical categories will be investigated. We use ambulance call out obtained from various ambulance services worldwide which have significantly different meteorologies, climatologies and pollution conditions. A time-series analysis is utilized to understand the relation between meteorological conditions, air pollutants and different call out categories. We will present findings that support the opinion that ambulance attendance call outs records are an effective and well-timed source of data and can be used for health early warning systems. Furthermore the presented results can much improve our understanding of the relationships between meteorology, climate, air pollution and human health thereby allowing for better prediction of ambulance use through the application of long and short-term weather, climate and pollution forecasts.

Measurement of air pollutant emissions from Lome, Cotonou and Accra

NASA Astrophysics Data System (ADS)

Lee, James; Vaughan, Adam; Nelson, Bethany; Young, Stuart; Evans, Mathew; Morris, Eleanor; Ladkin, Russel

2017-04-01

High concentrations of airborne pollutants (e.g. the oxides of nitrogen, sulphur dioxide and carbon monoxide) in existing and evolving cities along the Guinea Coast cause respiratory diseases with potentially large costs to human health and the economic capacity of the local workforce. It is important to understand the rate of emission of such pollutants in order to model current and future air quality and provide guidance to the potential outcomes of air pollution abatement strategies. Often dated technologies and poor emission control strategies lead to substantial uncertainties in emission estimates calculated from vehicle and population number density statistics. The unreliable electrical supply in cities in the area has led to an increased reliance on small-scale diesel powered generators and these potentially present a significant source of emissions. The uncontrolled open incineration of waste adds a further very poorly constrained emission source within the cities. The DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) project involved a field campaign which used highly instrumented aircraft capable of in situ measurements of a range of air pollutants. Seven flights using the UK British Antarctic Survey's Twin Otter aircraft specifically targeted air pollution emissions from cities in West Africa (4 x Accra, Ghana; 2 x Lome, Togo and 1 x Cotonou, Benin). Measurements of NO, NO2, SO2, CO, CH4 and CO2 were made at multiple altitudes upwind and downwind of the cities, with the mass balance technique used to calculate emission rates. These are then compared to the Emissions Database for Global Atmospheric Research (EDGAR) estimates. Ultimately the data will be used to inform on and potentially improve the emission estimates, which in turn should lead to better forecasting of air pollution in West African cities and help guide future air pollution abatement strategy.

Development and demonstration of a Lagrangian dispersion modeling system for real-time prediction of smoke haze pollution from biomass burning in Southeast Asia

NASA Astrophysics Data System (ADS)

Hertwig, Denise; Burgin, Laura; Gan, Christopher; Hort, Matthew; Jones, Andrew; Shaw, Felicia; Witham, Claire; Zhang, Kathy

2015-12-01

Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satellite-based active-fire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including up-to-date representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a real-time bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors.

Meteorological Modeling of Wintertime Cold Air Pool Stagnation Episodes in the Uintah and Salt Lake Basins

NASA Astrophysics Data System (ADS)

Crosman, E.; Horel, J.; Blaylock, B. K.; Foster, C.

2014-12-01

High wintertime ozone concentrations in rural areas associated with oil and gas development and high particulate concentrations in urban areas have become topics of increasing concern in the Western United States, as both primary and secondary pollutants become trapped within stable wintertime boundary layers. While persistent cold air pools that enable such poor wintertime air quality are typically associated with high pressure aloft and light winds, the complex physical processes that contribute to the formation, maintenance, and decay of persistent wintertime temperature inversions are only partially understood. In addition, obtaining sufficiently accurate numerical weather forecasts and meteorological simulations of cold air pools for input into chemical models remains a challenge. This study examines the meteorological processes associated with several wintertime pollution episodes in Utah's Uintah and Salt Lake Basins using numerical Weather Research and Forecasting model simulations and observations collected from the Persistent Cold Air Pool and Uintah Basin Ozone Studies. The temperature, vertical structure, and winds within these cold air pools was found to vary as a function of snow cover, snow albedo, land use, cloud cover, large-scale synoptic flow, and episode duration. We evaluate the sensitivity of key atmospheric features such as stability, planetary boundary layer depth, local wind flow patterns and transport mechanisms to variations in surface forcing, clouds, and synoptic flow. Finally, noted deficiencies in the meteorological models of cold air pools and modifications to the model snow and microphysics treatment that have resulted in improved cold pool simulations will be presented.

The influence from synoptic weather on the variation of air pollution and pollen exposure

NASA Astrophysics Data System (ADS)

Grundström, Maria; Dahl, Åslög; Chen, Deliang; Pleijel, Håkan

2014-05-01

Exposure to elevated air pollution levels can make people more susceptible to allergies or result in more severe allergic reactions for people with an already pronounced sensitivity to pollen. The aim of this study was to investigate the relationships between urban air pollution (nitrogen oxides, ozone and particles) and airborne Betula pollen in Gothenburg, Sweden, during the pollen seasons for the years 2001-2012. Further, the influence from atmospheric weather pattern on pollen/pollution related risk, using Lamb Weather Types (LWT), was also considered. Daily LWTs were obtained by comparing the variation in atmospheric pressure from a 16 point grid over a given region on earth (scale ~1000km) and essentially describe the air mass movement for the region. They include two non-directional types, cyclonic (C) and anticyclonic (A) and eight directional types depending on the wind direction (N, NE, E... etc.). LWTs with dry and calm meteorological character e.g. limited precipitation and low to moderate wind speeds (A, NE, E, SE) were associated with strongly elevated air pollution and pollen levels where Betula was exceptionally high in LWTs NE and E. The co-variation between Betula pollen and ozone was strong and significant during situations with LWTs A, NE, E and SE. The most important conclusion from this study was that LWTs A, NE, E and SE were associated with high pollen and air pollution levels and can therefore be classified as high risk weather situations for combined air pollution and pollen exposure. Our study shows that LWTs have the potential to be developed into an objective tool for integrated air quality forecasting and a warning system for risk of high exposure situations.

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi, China.

PubMed

Wu, Hao; Zhang, Yan; Yu, Qi; Ma, Weichun

2018-04-01

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM 10 ], sulfur dioxide [SO 2 ], and nitrogen oxides [NO x ]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants. The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality. Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.

Physical Activity, a Critical Exposure Factor of Environmental Pollution in Children and Adolescents Health Risk Assessment.

PubMed

Dong, Jingmei; Zhang, Su; Xia, Li; Yu, Yi; Hu, Shuangshuang; Sun, Jingyu; Zhou, Ping; Chen, Peijie

2018-01-23

It is an extremely urgent problem that physical fitness promotion must face not only the increasing air pollution but also the decline of physical activity level of children and adolescents worldwide at present, which is the major reason that forms an inactive lifestyle and does harm to adolescents' health. Thus, it is necessary to focus on the exposure factor in environmental health risk assessment (EHRA) which conducts supervision of environmental pollution and survey of adolescents' activity patterns according to the harmful characteristics of air pollutant and relationship between dose and response. Some countries, such as USA, Canada and Australia, regard both respiratory rate and physical activity pattern as main exposure factors for adolescents in both air pollution health risk assessment and exercise risk assessment to forecast a safe exposing condition of pollutant for adolescents while they are doing exercise outdoors. In addition, it suggests that the testing indexes and testing methods of these two exposure factors, such as investigating the time of daily physical activity, strength, and characteristic of frequency, help to set up the quantitative relationship between environmental pollution index and the time, strength, frequency of daily activities, and formulate children's and adolescents' activity instructions under different levels of environmental pollutions. As smog becomes increasingly serious at present, it is meaningful to take physical activity as a critical composition of exposure factor and establish physical activity guideline, so as to reduce the risk of air pollution, and promote physical health of children and adolescents effectively.

4 km Forecasting System to Support DISCOVER-AQ Campaigns: Model Configuration, Testing and Evaluation

NASA Astrophysics Data System (ADS)

Lee, P.; Pan, L.; Kim, H. C.; Chai, T.; Hu, Y.; Tong, D.; Ngan, F.; Wong, D.; Dornblaser, B.; Tanrikulu, S.; Pickering, K. E.

2012-12-01

This work presents the development and evaluation of a high-resolution air quality forecasting system to support two NASA Earth Venture campaigns (DISCOVER-AQ) in 2013. These campaigns aim to further understanding of column-integrated and vertically resolved observations in determining air pollution conditions near the surface (http://science.nasa.gov/missions/discover-aq/). The first one will be carried out in San Joaquin Valley (SJV) in winter and the second one in Houston (HOU) area in late summer. Accurate forecast of pollution plumes is critical for on-site deployment and co-ordination of the various observation platforms. We develop of a fine resolution forecasting system to provide dynamic prediction of the chemical fields over these regions. This system utilizes meteorology fields from the US National Centers for Environmental Prediction North American Model (NAM) that is equipped with an elaborative NAM Data Assimilation System (NDAS) for its Land Surface Model (LSM) and initialization processes. NAM output is used to drive the US EPA Community Multi-scale Air Quality Model (CMAQ) with identical horizontal resolution. The SJV campaign is believed to be subjected to rather high particulate matter loading and possible frequent occurrence of multiple-day fog. NDAS provides advanced methodology to constrain atmospheric stability and soil moisture characteristics. These meteorological parameters are critical for the winter campaign. Special attention is paid to emission modeling for agricultural dust aerosols, which were found important for the SJV area. In contrast to the winter campaign where strong atmospheric stability will likely be a challenge, the HOU campaign in September of 2013 will be challenged with strong atmospheric convection and rather rapid growth of and a sustained deep Planetary Boundary Layer (PBL) during mid-morning and afternoon, respectively. Convection often results in lightning. Wild-fires can contribute significantly to pollution. Therefore both climatology lightning NOx and real-time fires observed by the Hazardous Mapping System by National Environmental Satellite, Data and Information Service (NESDIS) will be included as emission sources. We have performed a real-time test for the HOU area. We will show model evaluation and post-campaign sensitivity modeling results to shed additional insight on processes responsible for the characteristics of the pollutant concentrations.

[Exploring the Severe Haze in Beijing During December, 2015: Pollution Process and Emissions Variation].

PubMed

Xue, Yi-feng; Zhou, Zhen; Nie, Teng; Pan, Tao; Qi, Jun; Nie, Lei; Wang, Zhan-shan; Li, Yun-ting; Li, Xue-feng; Tian, He-zhong

2016-05-15

Severe haze episodes shrouded Beijing and its surrounding regions again during December, 2015, causing major environmental and health problems. Beijing authorities had launched two red alerts for atmospheric heavy pollution in this period, adopted a series of emergency control measures to reduce the emissions from major pollution sources. To better understand the pollution process and emissions variation during these extreme pollution events, we performed a model-assisted analysis of the hourly observation data of PM₂.₅, and meteorological parameters combined with the emissions variation of pollution sources. The synthetic analysis indicated that: (1) Compared with the same period of last year, the emissions of atmospheric pollution sources decreased in December 2015. However, the emission levels of primary pollutants were still rather high, which were the main intrinsic causes for haze episodes, and the unfavorable diffusion conditions represented the important external factor. High source emissions and meteorological factors together led to this heavy air pollution process. (2) Emergency control measures taken by the red alert for heavy air pollution could decrease the pollutants emission by about 36% and the PM₂.₅ concentrations by 11% to 21%. Though the implementation of red alert could not reverse the evolution trend of heavier pollution, it indeed played an active role in mitigation of PM₂.₅ pollution aggravating. (3) Under the heavy pollution weather conditions, air pollutants continued to accumulate in the atmosphere, and the maximum effect by taking emergency measures occurred 48-72 hours after starting the implementation; therefore, the best time for executing emergency measures should be 36-48 hours before the rapid rise of PM₂.₅ concentration, which requires a more powerful demand on the accuracy of air quality forecast.

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia.

PubMed

Crippa, P; Castruccio, S; Archer-Nicholls, S; Lebron, G B; Kuwata, M; Thota, A; Sumin, S; Butt, E; Wiedinmyer, C; Spracklen, D V

2016-11-16

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153-17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia

PubMed Central

Crippa, P.; Castruccio, S.; Archer-Nicholls, S.; Lebron, G. B.; Kuwata, M.; Thota, A.; Sumin, S.; Butt, E.; Wiedinmyer, C.; Spracklen, D. V.

2016-01-01

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153–17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality. PMID:27848989

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.

Impact of particulate air pollution on quality-adjusted life expectancy in Canada.

PubMed

Coyle, Douglas; Stieb, Dave; Burnett, Richard T; DeCivita, Paul; Krewski, Daniel; Chen, Yue; Thun, Michael J

Air pollution and premature death are important public health concerns. Analyses have repeatedly demonstrated that airborne particles are associated with increased mortality and estimates have been used to forecast the impact on life expectancy. In this analysis, we draw upon data from the American Cancer Society (ACS) cohort and literature on utility-based measures of quality of life in relation to health status to more fully quantify the effects of air pollution on mortality in terms of quality-adjusted life expectancy. The analysis was conducted within a decision analytic model using Monte Carlo simulation techniques. Outcomes were estimated based on projections of the Canadian population. A one-unit reduction in sulfate air pollution would yield a mean annual increase in Quality-Adjusted Life Years (QALYs) of 20,960, with gains being greater for individuals with lower educational status and for males compared to females. This suggests that the impact of reductions in sulfate air pollution on quality-adjusted life expectancy is substantial. Interpretation of the results is unclear. However, the potential gains in QALYs from reduced air pollutants can be contrasted to the costs of policies to bring about such reductions. Based on a tentative threshold for the value of health benefits, analysis suggests that an investment in Canada of over 1 billion dollars per annum would be an efficient use of resources if it could be demonstrated that this would reduce sulfate concentrations in ambient air by 1 microg/m(3). Further analysis can assess the efficiency of targeting such initiatives to communities that are most likely to benefit.

Air quality impact assessment of multiple open pit coal mines in northern Colombia.

PubMed

Huertas, José I; Huertas, María E; Izquierdo, Sebastián; González, Enrique D

2012-01-01

The coal mining region in northern Colombia is one of the largest open pit mining regions of the world. In 2009, there were 8 mining companies in operation with an approximate coal production of ∼70 Mtons/year. Since 2007, the Colombian air quality monitoring network has reported readings that exceed the daily and annual air quality standards for total suspended particulate (TSP) matter and particles with an equivalent aerodynamic diameter smaller than 10 μm (PM₁₀) in nearby villages. This paper describes work carried out in order to establish an appropriate clean air program for this region, based on the Colombian national environmental authority requirement for modeling of TSP and PM(10) dispersion. A TSP and PM₁₀ emission inventory was initially developed, and topographic and meteorological information for the region was collected and analyzed. Using this information, the dispersion of TSP was modeled in ISC3 and AERMOD using meteorological data collected by 3 local stations during 2008 and 2009. The results obtained were compared to actual values measured by the air quality monitoring network. High correlation coefficients (>0.73) were obtained, indicating that the models accurately described the main factors affecting particle dispersion in the region. The model was then used to forecast concentrations of particulate matter for 2010. Based on results from the model, areas within the modeling region were identified as highly, fairly, moderately and marginally polluted according to local regulations. Additionally, the contribution particulate matter to the pollution at each village was estimated. Using these predicted values, the Colombian environmental authority imposed new decontamination measures on the mining companies operating in the region. These measures included the relocation of three villages financed by the mine companies based on forecasted pollution levels. Copyright © 2011. Published by Elsevier Ltd.

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.

Co-Mitigation of Ozone and PM2.5 Pollution over the Beijing-Tianjin-Hebei Region

NASA Astrophysics Data System (ADS)

Liu, J.; Xiang, S.; Yi, K.; Tao, W.

2017-12-01

With the rapid industrialization and urbanization, emissions of air pollutants in China were increasing rapidly during the past few decades, causing severe particulate matter and ozone pollution in many megacities. Facing these knotty environmental problems, China has released a series of pollution control policies to mitigate air pollution emissions and optimize energy supplement structure. Consequently, fine particulate matters (PM2.5) decrease recently. However, the concentrations of ambient ozone have been increasing, especially during summer time and over megacities. In this study, we focus on the opposite trends of ozone and PM2.5 over the Beijing-Tianjin-Hebei region. We use the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) to simulate and analyze the best emission reduction strategies, and adopt the Empirical Kinetics Modeling Approach (EKMA) to depict the influences of mitigating NOx and VOCs. We also incorporate the abatement costs for NOx and VOCs in our analysis to explore the most cost-effective mitigation strategies for both ozone and PM2.5.

A systematic review of data mining and machine learning for air pollution epidemiology.

PubMed

Bellinger, Colin; Mohomed Jabbar, Mohomed Shazan; Zaïane, Osmar; Osornio-Vargas, Alvaro

2017-11-28

Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology. We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology. The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

Improving Air Pollution Modeling Over The Po Valley Using Saharan Dust Transport Forecasts

NASA Astrophysics Data System (ADS)

Kishcha, P.; Carnevale, C.; Finzi, G.; Pisoni, E.; Volta, M.; Nickovic, S.; Alpert, P.

2012-04-01

Our study shows that Saharan dust can contribute significantly to PM10 concentrations in the Po Valley. This dust contribution should be taken into account when estimating the exceedance of pollution limits. The DREAM dust model has been used for several years for producing operational dust forecasts at Tel-Aviv University, Israel. DREAM has been producing daily forecasts of 3-D distribution of dust concentrations over the Mediterranean region, Middle East, Europe, and over the Atlantic Ocean (http://wind.tau.ac.il/dust8/dust.html). In the current study, DREAM dust forecasts were used to give better model estimates of the contribution of Saharan dust to PM10 concentration over the Po Valley, in Northern Italy. This was carried out by the integration of daily Saharan dust forecasts into a mesoscale Transport Chemical Aerosol Model (TCAM). The Po Valley in Northern Italy is frequently affected by high PM10 concentrations, where both natural and anthropogenic sources play a significant role. Our study of TCAM and DREAM integration was carried out for the period May 15 - June 30, 2007, when four significant dust events were observed. The integrated TCAM-DREAM model performance was evaluated by comparing PM10 measurements with modeled PM10 concentrations. First, Saharan dust impact on TCAM performance was analyzed at eleven remote PM10 sites which had the lowest level of air pollution (PM10 ≤ 14 μg/m3) over the period under consideration. For those remote sites, the observed high PM10 concentrations during dust events stood prominently on the background of low PM10 concentrations. At the remote sites, such a strong deviation from the background level can not be attributed to anthropogenic aerosol emissions because of their distance from anthropogenic sources. The observed maxima in PM10 concentration during dust events is evidence of dust aerosol near the surface in Northern Italy. During all dust events under consideration, the integrated TCAM-DREAM model produced more accurate PM10 concentrations than the base TCAM model. Then, a comparison between modeled concentrations and PM10 measurements was carried out at 230 PM10 monitoring sites, distributed within the model domain. This model-vs.-measurement comparison showed that the integrated TCAM -DREAM model more accurately reproduced PM10 concentrations than the base TCAM model, both in term of correlation and mean error. Our results are of importance to countries which have to pay a penalty for exceeding the pollution limit. By extracting dust contribution from PM10 measurements, these countries could show lower rates of man-made pollution.

Air pollution and public health: emerging hazards and improved understanding of risk.

PubMed

Kelly, Frank J; Fussell, Julia C

2015-08-01

Despite past improvements in air quality, very large parts of the population in urban areas breathe air that does not meet European standards let alone the health-based World Health Organisation Air Quality Guidelines. Over the last 10 years, there has been a substantial increase in findings that particulate matter (PM) air pollution is not only exerting a greater impact on established health endpoints, but is also associated with a broader number of disease outcomes. Data strongly suggest that effects have no threshold within the studied range of ambient concentrations, can occur at levels close to PM2.5 background concentrations and that they follow a mostly linear concentration-response function. Having firmly established this significant public health problem, there has been an enormous effort to identify what it is in ambient PM that affects health and to understand the underlying biological basis of toxicity by identifying mechanistic pathways-information that in turn will inform policy makers how best to legislate for cleaner air. Another intervention in moving towards a healthier environment depends upon the achieving the right public attitude and behaviour by the use of optimal air pollution monitoring, forecasting and reporting that exploits increasingly sophisticated information systems. Improving air quality is a considerable but not an intractable challenge. Translating the correct scientific evidence into bold, realistic and effective policies undisputedly has the potential to reduce air pollution so that it no longer poses a damaging and costly toll on public health.

The CHRONOS mission: capability for sub-hourly synoptic observations of carbon monoxide and methane to quantify emissions and transport of air pollution

NASA Astrophysics Data System (ADS)

Edwards, David P.; Worden, Helen M.; Neil, Doreen; Francis, Gene; Valle, Tim; Arellano, Avelino F., Jr.

2018-02-01

The CHRONOS space mission concept provides time-resolved abundance for emissions and transport studies of the highly variable and highly uncertain air pollutants carbon monoxide and methane, with sub-hourly revisit rate at fine (˜ 4 km) horizontal spatial resolution across a North American domain. CHRONOS can provide complete synoptic air pollution maps (snapshots) of the continental domain with less than 10 min of observations. This rapid mapping enables visualization of air pollution transport simultaneously across the entire continent and enables a sentinel-like capability for monitoring evolving, or unanticipated, air pollution sources in multiple locations at the same time with high temporal resolution. CHRONOS uses a compact imaging gas filter correlation radiometer for these observations, with heritage from more than 17 years of scientific data and algorithm advances by the science teams for the Measurements of Pollution in the Troposphere (MOPITT) instrument on NASA's Terra spacecraft in low Earth orbit. To achieve continental-scale sub-hourly sampling, the CHRONOS mission would be conducted from geostationary orbit, with the instrument hosted on a communications or meteorological platform. CHRONOS observations would contribute to an integrated observing system for atmospheric composition using surface, suborbital and satellite data with atmospheric chemistry models, as defined by the Committee on Earth Observing Satellites. Addressing the U.S. National Academy's 2007 decadal survey direction to characterize diurnal changes in tropospheric composition, CHRONOS observations would find direct societal applications for air quality management and forecasting to protect public health.

Physical Activity, a Critical Exposure Factor of Environmental Pollution in Children and Adolescents Health Risk Assessment

PubMed Central

Zhang, Su; Xia, Li; Yu, Yi; Hu, Shuangshuang; Sun, Jingyu; Zhou, Ping; Chen, Peijie

2018-01-01

It is an extremely urgent problem that physical fitness promotion must face not only the increasing air pollution but also the decline of physical activity level of children and adolescents worldwide at present, which is the major reason that forms an inactive lifestyle and does harm to adolescents’ health. Thus, it is necessary to focus on the exposure factor in environmental health risk assessment (EHRA) which conducts supervision of environmental pollution and survey of adolescents’ activity patterns according to the harmful characteristics of air pollutant and relationship between dose and response. Some countries, such as USA, Canada and Australia, regard both respiratory rate and physical activity pattern as main exposure factors for adolescents in both air pollution health risk assessment and exercise risk assessment to forecast a safe exposing condition of pollutant for adolescents while they are doing exercise outdoors. In addition, it suggests that the testing indexes and testing methods of these two exposure factors, such as investigating the time of daily physical activity, strength, and characteristic of frequency, help to set up the quantitative relationship between environmental pollution index and the time, strength, frequency of daily activities, and formulate children’s and adolescents’ activity instructions under different levels of environmental pollutions. As smog becomes increasingly serious at present, it is meaningful to take physical activity as a critical composition of exposure factor and establish physical activity guideline, so as to reduce the risk of air pollution, and promote physical health of children and adolescents effectively. PMID:29360730

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.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of chemical simulations from EMEP4ASIA

NASA Astrophysics Data System (ADS)

Pommier, M.; Gauss, M.; Fagerli, H.; Benedictow, A.; Nyiri, A.; Valdebenito, Á.; Wind, P.

2016-12-01

The EMEP/MSC-W chemistry transport model (CTM) has been used for decades to simulate concentrations of surface air pollutants over Europe and to calculate source-receptor relationships between European countries. Within the framework of the operational air pollution forecasts for East Asia, being offered by the EU project PANDA, this study aims to evaluate the EMEP/MSC-W CTM in simulating high pollution events over Asian cities. This work is the first attempt to use this CTM with a fine horizontal resolution (0.1°×0.1°) over Asia and to simulate the pollution over urban regions. The main part of the work has been to focus on the evaluation of the EMEP/MSC-W CTM with measurements from different platforms (satellite, ground-based, in situ) and to identify the biases or the errors in the simulation. This evaluation is important in order to establish the capabilities of the model to identify air pollution sources. Regional distributions and temporal variation of main pollutants are thus discussed. For example, the daily variation in Ox is well captured while the NOx is under-predicted and the O3 is overestimated, especially in winter. The CTM performs also very well on day-to-day variation in PM25 or on the regional distribution in CO total column as over Beijing.

Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

NASA Astrophysics Data System (ADS)

Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

2017-08-01

Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

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.

Operational air quality forecasting system for Spain: CALIOPE

NASA Astrophysics Data System (ADS)

Baldasano, J. M.; Piot, M.; Jorba, O.; Goncalves, M.; Pay, M.; Pirez, C.; Lopez, E.; Gasso, S.; Martin, F.; García-Vivanco, M.; Palomino, I.; Querol, X.; Pandolfi, M.; Dieguez, J. J.; Padilla, L.

2009-12-01

The European Commission (EC) and the United States Environmental Protection Agency (US-EPA) have shown great concerns to understand the transport and dynamics of pollutants in the atmosphere. According to the European directives (1996/62/EC, 2002/3/EC, 2008/50/EC), air quality modeling, if accurately applied, is a useful tool to understand the dynamics of air pollutants, to analyze and forecast the air quality, and to develop programs reducing emissions and alert the population when health-related issues occur. The CALIOPE project, funded by the Spanish Ministry of the Environment, has the main objective to establish an air quality forecasting system for Spain. A partnership of four research institutions composes the CALIOPE project: the Barcelona Supercomputing Center (BSC), the center of investigation CIEMAT, the Earth Sciences Institute ‘Jaume Almera’ (IJA-CSIC) and the CEAM Foundation. CALIOPE will become the official Spanish air quality operational system. This contribution focuses on the recent developments and implementation of the integrated modelling system for the Iberian Peninsula (IP) and Canary Islands (CI) with a high spatial and temporal resolution (4x4 sq. km for IP and 2x2 sq. km for CI, 1 hour), namely WRF-ARW/HERMES04/CMAQ/BSC-DREAM. The HERMES04 emission model has been specifically developed as a high-resolution (1x1 sq. km, 1 hour) emission model for Spain. It includes biogenic and anthropogenic emissions such as on-road and paved-road resuspension production, power plant generation, ship and plane traffic, airports and ports activities, industrial and agricultural sectors as well as domestic and commercial emissions. The qualitative and quantitative evaluation of the model was performed for a reference year (2004) using data from ground-based measurement networks. The products of the CALIOPE system will provide 24h and 48h forecasts for O3, NO2, SO2, CO, PM10 and PM2.5 at surface level. An operational evaluation system has been developed to provide near real-time evaluation products for the Spanish territory. For this purpose, more than 130 surface stations, 2 ozonesondes and the OMI satellite retrieval information are introduced to the system on a daily basis. A web-based visualization system allows a straightforward access to all the evaluation products. The present contribution will describe the main characteristics of the operational system and results of the operational evaluation.

Fast and optimized methodology to generate road traffic emission inventories and their uncertainties

NASA Astrophysics Data System (ADS)

Blond, N.; Ho, B. Q.; Clappier, A.

2012-04-01

Road traffic emissions are one of the main sources of air pollution in the cities. They are also the main sources of uncertainties in the air quality numerical models used to forecast and define abatement strategies. Until now, the available models for generating road traffic emission always required a big effort, money and time. This inhibits decisions to preserve air quality, especially in developing countries where road traffic emissions are changing very fast. In this research, we developed a new model designed to fast produce road traffic emission inventories. This model, called EMISENS, combines the well-known top-down and bottom-up approaches to force them to be coherent. A Monte Carlo methodology is included for computing emission uncertainties and the uncertainty rate due to each input parameters. This paper presents the EMISENS model and a demonstration of its capabilities through an application over Strasbourg region (Alsace), France. Same input data as collected for Circul'air model (using bottom-up approach) which has been applied for many years to forecast and study air pollution by the Alsatian air quality agency, ASPA, are used to evaluate the impact of several simplifications that a user could operate . These experiments give the possibility to review older methodologies and evaluate EMISENS results when few input data are available to produce emission inventories, as in developing countries and assumptions need to be done. We show that same average fraction of mileage driven with a cold engine can be used for all the cells of the study domain and one emission factor could replace both cold and hot emission factors.

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.

Simulation of West African air pollution during the DACCIWA experiment with the GEOS-Chem West African regional model.

NASA Astrophysics Data System (ADS)

Morris, Eleanor; Evans, Mathew

2017-04-01

Pollutant emissions from West African cities are forecast to increase rapidly in future years because of extensive economic and population growth, together with poorly regulated industrialisation and urbanisation. Observational constraints in this region are few, leading to poor understanding of present-day air pollution in this region. To increase our understanding of the processes controlling air pollutants over the region, airborne observations were made from three research aircraft based out of Lomé, Togo during the DACCIWA field campaign in June-July 2016. A new 0.25x0.3125 degree West Africa regional version of the GEOS-Chem offline chemical transport model has also been developed to explore the processes controlling pollutants over the region. We evaluate the model using the aircraft data and focus on primary (CO, SO2, NOx, VOCs) and secondary pollutants (O3, aerosol). We find significant differences between the model and the measurements for certain primary compounds which is indicative of significant uncertainties in the base (EDGAR) emissions. For CO (a general tracer of pollution) we evaluate the role of different emissions sources (transport, low temperature combustion, power generation) in determining its concentration in the region. We conclude that the leading cause of uncertainty in our simulation is associated with the emissions datasets and explore the impact of using differing datasets.

Comment on "Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics"

EPA Science Inventory

A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most se...

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

Treesearch

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

2018-01-01

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment...

Physical and mathematical modeling of pollutant emissions when burning peat

NASA Astrophysics Data System (ADS)

Vasilyev, A.; Lozhkin, V.; Tarkhov, D.; Lozhkina, O.; Timofeev, V.

2017-11-01

The article presents an original neural network model of CO dispersion around the experimentally simulated peat fire. It is a self-learning model considering both the measured CO concentrations in the smoke cloud and the refined coefficients of the main equation. The method is recommended for the development of air quality control and forecasting systems.

Environment and air pollution like gun and bullet for low-income countries: war for better health and wealth.

PubMed

Zou, Xiang; Azam, Muhammad; Islam, Talat; Zaman, Khalid

2016-02-01

The objective of the study is to examine the impact of environmental indicators and air pollution on "health" and "wealth" for the low-income countries. The study used a number of promising variables including arable land, fossil fuel energy consumption, population density, and carbon dioxide emissions that simultaneously affect the health (i.e., health expenditures per capita) and wealth (i.e., GDP per capita) of the low-income countries. The general representation for low-income countries has shown by aggregate data that consist of 39 observations from the period of 1975-2013. The study decomposes the data set from different econometric tests for managing robust inferences. The study uses temporal forecasting for the health and wealth model by a vector error correction model (VECM) and an innovation accounting technique. The results show that environment and air pollution is the menace for low-income countries' health and wealth. Among environmental indicators, arable land has the largest variance to affect health and wealth for the next 10-year period, while air pollution exerts the least contribution to change health and wealth of low-income countries. These results indicate the prevalence of war situation, where environment and air pollution become visible like "gun" and "bullet" for low-income countries. There are required sound and effective macroeconomic policies to combat with the environmental evils that affect the health and wealth of the low-income countries.

Mortality and emergency hospitalizations associated with atmospheric particulate matter episodes across the UK in spring 2014.

PubMed

Macintyre, Helen L; Heaviside, Clare; Neal, Lucy S; Agnew, Paul; Thornes, John; Vardoulakis, Sotiris

2016-12-01

Exposure to particulate air pollution is known to have negative impacts on human health. Long-term exposure to anthropogenic particulate matter is associated with the equivalent of around 29,000 deaths a year in the UK. However, short-lived air pollution episodes on the order of a few days are also associated with increased daily mortality and emergency hospital admissions for respiratory and cardiovascular conditions. The UK experienced widespread high levels of particulate air pollution in March-April 2014; observations of hourly mean PM 2.5 concentrations reached up to 83μgm -3 at urban background sites. We performed an exposure and health impact assessment of the spring air pollution, focusing on two episodes with the highest concentrations of PM 2.5 (12-14 March and 28 March-3 April 2014). Across these two episodes of elevated air pollution, totalling 10days, around 600 deaths were brought forward from short-term exposure to PM 2.5 , representing 3.9% of total all-cause (excluding external) mortality during these days. Using observed levels of PM 2.5 from other years, we estimate that this is 2.0 to 2.7 times the mortality burden associated with typical urban background levels of PM 2.5 at this time of year. Our results highlight the potential public health impacts and may aid planning for health care resources when such an episode is forecast. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

The effect of long-range air mass transport pathways on PM10 and NO2 concentrations at urban and rural background sites in Ireland: Quantification using clustering techniques.

PubMed

Donnelly, Aoife A; Broderick, Brian M; Misstear, Bruce D

2015-01-01

The specific aims of this paper are to: (i) quantify the effects of various long range transport pathways nitrogen dioxide (NO2) and particulate matter with diameter less than 10μm (PM10) concentrations in Ireland and identify air mass movement corridors which may lead to incidences poor air quality for application in forecasting; (ii) compare the effects of such pathways at various sites; (iii) assess pathways associated with a period of decreased air quality in Ireland. The origin of and the regions traversed by an air mass 96h prior to reaching a receptor is modelled and k-means clustering is applied to create air-mass groups. Significant differences in air pollution levels were found between air mass cluster types at urban and rural sites. It was found that easterly or recirculated air masses lead to higher NO2 and PM10 levels with average NO2 levels varying between 124% and 239% of the seasonal mean and average PM10 levels varying between 103% and 199% of the seasonal mean at urban and rural sites. Easterly air masses are more frequent during winter months leading to higher overall concentrations. The span in relative concentrations between air mass clusters is highest at the rural site indicating that regional factors are controlling concentration levels. The methods used in this paper could be applied to assist in modelling and forecasting air quality based on long range transport pathways and forecast meteorology without the requirement for detailed emissions data over a large regional domain or the use of computationally demanding modelling techniques.

An anticipatory integrated assessment of regional acidification: The RAINS-Asia model

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

Amann, M.; Carmichael, G.R.; Foell, W.

1996-12-31

Across large parts of Asia, air pollution problems are becoming more and more evident. Rainfall in some areas, including China, Japan, and Thailand, has been measured to be 10 times more acidic than unpolluted rain. Increasing evidence of acidification damage to ecosystems such as surface waters, soils, and economically important crops, is beginning to appear. In addition, urban air quality in many areas of the region continues to decrease. Current economic forecasts predict continued rapid economic growth in the region, which will bring with it increasing emissions of air pollutants, especially sulfur. The total primary energy demand in Asia currentlymore » doubles every twelve years (as compared to a world average of every 28 years). Coal is expected to continue to be the dominant energy source, with coal demand projected to increase by 65 percent per year, a rate that outpaces regional economic growth. If current trends in economic development and energy use in Asia continue, emissions of sulfur dioxide, one of the key components in acid rain, will more than triple within the next 30 years. Many ecosystems will be unable to continue to absorb these increased levels of pollution without harmful effects, thus creating a potential danger for irreversible environmental damage in many areas. In view of the potential environmental consequences of projected growth in Asian energy consumption, emissions, and air pollution, the World Bank, together with the Asian Development Bank, have funded a project to develop and implement an integrated assessment model for the acid deposition phenomenon in Asia. The Regional Air Pollution INformation and Simulation model for Asia (RAINS-Asia) is a software tool to help decision makers assess and project future trends in emissions, transport, and deposition of air pollutants, and their potential environmental effects.« less

Simulating Urban Tree Effects on Air, Water, and Heat Pollution Mitigation: iTree-Hydro Model

NASA Astrophysics Data System (ADS)

Yang, Y.; Endreny, T. A.; Nowak, D.

2011-12-01

Urban and suburban development changes land surface thermal, radiative, porous, and roughness properties and pollutant loading rates, with the combined effect leading to increased air, water, and heat pollution (e.g., urban heat islands). In this research we present the USDA Forest Service urban forest ecosystem and hydrology model, iTree Eco and Hydro, used to analyze how tree cover can deliver valuable ecosystem services to mitigate air, water, and heat pollution. Air pollution mitigation is simulated by dry deposition processes based on detected pollutant levels for CO, NO2, SO2, O3 and atmospheric stability and leaf area indices. Water quality mitigation is simulated with event mean concentration loading algorithms for N, P, metals, and TSS, and by green infrastructure pollutant filtering algorithms that consider flow path dispersal areas. Urban cooling considers direct shading and indirect evapotranspiration. Spatially distributed estimates of hourly tree evapotranspiration during the growing season are used to estimate human thermal comfort. Two main factors regulating evapotranspiration are soil moisture and canopy radiation. Spatial variation of soil moisture is represented by a modified urban topographic index and radiation for each tree is modified by considering aspect, slope and shade from surrounding buildings or hills. We compare the urban cooling algorithms used in iTree-Hydro with the urban canopy and land surface physics schemes used in the Weather Research and Forecasting model. We conclude by identifying biophysical feedbacks between tree-modulated air and water quality environmental services and how these may respond to urban heating and cooling. Improvements to this iTree model are intended to assist managers identify valuable tree services for urban living.

Spatio-temporal modelling for assessing air pollution in Santiago de Chile

NASA Astrophysics Data System (ADS)

Nicolis, Orietta; Camaño, Christian; Mařın, Julio C.; Sahu, Sujit K.

2017-01-01

In this work, we propose a space-time approach for studying the PM2.5 concentration in the city of Santiago de Chile. In particular, we apply the autoregressive hierarchical model proposed by [1] using the PM2.5 observations collected by a monitoring network as a response variable and numerical weather forecasts from the Weather Research and Forecasting (WRF) model as covariate together with spatial and temporal (periodic) components. The approach is able to provide short-term spatio-temporal predictions of PM2.5 concentrations on a fine spatial grid (at 1km × 1km horizontal resolution.)

[On the development of a system of medical weather forecast for the Caucasian Mineral Waters spa-and-resort complex].

PubMed

Povolotskaia, N P; Efimova, N V; Zherlitsina, L I; Kirilenko, A A; Kortunova, Z V; Golitsin, G S; Senik, I A; Rubinshteĭn, K G

2010-01-01

A system of medical weather forecast for the Caucasian Mineral Waters spa-and-resort complex has been modified and updated based on the results of long-term observations of weather conditions in the region of interest with special reference to the bioclimatic regime, atmospheric circulation, aerosol pollution of the near-ground air, ultraviolet radiation, heliomagnetic activity, and meteopathic effects. This system provides a basis for the timely emergency meteopreventive treatment of meteodependent patients and therefore can be instrumental in enhancing efficiency of spa-and-resort rehabilitative therapy.

WRF modeling of PM2.5 remediation by SALSCS and its clean air flow over Beijing terrain.

PubMed

Cao, Qingfeng; Shen, Lian; Chen, Sheng-Chieh; Pui, David Y H

2018-06-01

Atmospheric simulations were carried out over the terrain of entire Beijing, China, to investigate the effectiveness of an air-pollution cleaning system named Solar-Assisted Large-Scale Cleaning System (SALSCS) for PM 2.5 mitigation by using the Weather Research and Forecasting (WRF) model. SALSCS was proposed to utilize solar energy to generate airflow therefrom the airborne particulate pollution of atmosphere was separated by filtration elements. Our model used a derived tendency term in the potential temperature equation to simulate the buoyancy effect of SALSCS created with solar radiation on its nearby atmosphere. PM 2.5 pollutant and SALSCS clean air were simulated in the model domain by passive tracer scalars. Simulation conditions with two system flow rates of 2.64 × 10 5  m 3 /s and 3.80 × 10 5  m 3 /s were tested for seven air pollution episodes of Beijing during the winters of 2015-2017. The numerical results showed that with eight SALSCSs installed along the 6 th Ring Road of the city, 11.2% and 14.6% of PM 2.5 concentrations were reduced under the two flow-rate simulation conditions, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season.

PubMed

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-09-01

Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

Integrating Saharan dust forecasts into a regional chemical transport model: a case study over Northern Italy.

PubMed

Carnevale, C; Finzi, G; Pisoni, E; Volta, M; Kishcha, P; Alpert, P

2012-02-15

The Po Valley in Northern Italy is frequently affected by high PM10 concentrations, where both natural and anthropogenic sources play a significant role. To improve air pollution modeling, 3D dust fields, produced by means of the DREAM dust forecasts, were integrated as boundary conditions into the mesoscale 3D deterministic Transport Chemical Aerosol Model (TCAM). A case study of the TCAM and DREAM integration was implemented over Northern Italy for the period May 15-June 30, 2007. First, the Saharan dust impact on PM10 concentration was analyzed for eleven remote PM10 sites with the lowest level of air pollution. These remote sites are the most sensitive to Saharan dust intrusions into Northern Italy, because of the absence of intensive industrial pollution. At these remote sites, the observed maxima in PM10 concentration during dust events is evidence of dust aerosol near the surface in Northern Italy. Comparisons between modeled PM10 concentrations and measurements at 230 PM10 sites in Northern Italy, showed that the integrated TCAM-DREAM model more accurately reproduced PM10 concentration than the base TCAM model, both in terms of correlation and mean error. Specifically, the correlation median increased from 0.40 to 0.65, while the normalized mean absolute error median dropped from 0.5 to 0.4. Copyright © 2011 Elsevier B.V. All rights reserved.

Operational forecast products and applications based on WRF/Chem

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Flandorfer, Claudia; Langer, Matthias; Mantovani, Simone; Olefs, Marc; Schellander-Gorgas, Theresa

2015-04-01

The responsibilities of the national weather service of Austria (ZAMG) 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 ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. The mother domain expands over Europe, North Africa and parts of Russia. The nested domain includes the alpine region and has a horizontal resolution of 4 km. Local emissions (Austria) are used in combination with European inventories (TNO and EMEP) for the simulations. The modeling system is presented and the results from the evaluation of the assimilation of pollutants using the 3D-VAR software GSI is shown. Currently observational data (PM10 and O3) from the Austrian Air-Quality network and from European stations (EEA) are assimilated into the model on an operational basis. In addition PM maps are produced using Aerosol Optical Thickness (AOT) observations from MODIS in combination with model data using machine learning techniques. The modeling system is operationally evaluated with different data sets. The emphasis of the application is on the forecast of pollutants which are compared to the hourly values (PM10, O3 and NO2) of the Austrian Air-Quality network. As the meteorological conditions are important for transport and chemical processes, some parameters like wind and precipitation are automatically evaluated (SAL diagrams, maps, …) with other models (e.g. ECMWF, AROME, …) and ground stations via web interface. The prediction of the AOT is also important for operators of solar power plants. In the past Numerical Weather Prediction (NWP) models were used to predict the AOT based on cloud forecasts at the ZAMG. These models do not consider the spatial and temporal variation of the aerosol distribution in the atmosphere with a consequent impact on the accuracy of forecasts especially during clear-sky days when the influence of the aerosols can have a strong impact on the AOT. WRF/Chem forecasts of the atmospheric optical properties are used to add information on the incoming radiation during these days. The evaluation of the model with satellite data for different episodes with clear-sky conditions is presented.

Chemical characterization of PM2.5 from a southern coastal city of China: applications of modeling and chemical tracers in demonstration of regional transport.

PubMed

Zhou, Jiamao; Ho, Steven Sai Hang; Cao, Junji; Zhao, Zhuzi; Zhao, Shuyu; Zhu, Chongshu; Wang, Qiyuan; Liu, Suixin; Zhang, Ting; Zhao, Youzhi; Wang, Ping; Tie, Xuexi

2018-05-11

An intensive sampling campaign of airborne fine particles (PM 2.5 ) was conducted at Sanya, a coastal city in Southern China, from January to February 2012. Chemical analyses and mass reconstruction were used identify potential pollution sources and investigate atmospheric reaction mechanisms. A thermodynamic model indicated that low ammonia and high relative humidity caused the aerosols be acidic and that drove heterogeneous reactions which led to the formation of secondary inorganic aerosol. Relationships among neutralization ratios, free acidity, and air-mass trajectories suggest that the atmosphere at Sanya was impacted by both local and regional emissions. Three major transport pathways were identified, and flow from the northeast (from South China) typically brought the most polluted air to Sanya. A case study confirmed strong impact from South China (e.g., Pearl River Delta region) (contributed 76.8% to EC, and then this result can be extended to primary pollutants) when the northeast winds were dominant. The Weather Research Forecasting Black carbon model and trace organic markers were used to apportion local pollution versus regional contributions. Results of the study offer new insights into the atmospheric conditions and air pollution at this coastal city.

Impact of Model Resolution and Snow Cover Modification on the Performance of Weather Forecasting and Research (WRF) Models of Winter Conditions that Contribute to Ozone Pollution in the Uintah Basin, Eastern Utah, Winter 2013. Trang T. Tran, Marc Mansfield and Seth Lyman Bingham Research Center, Utah State University

NASA Astrophysics Data System (ADS)

Tran, T. T.; Mansfield, M. L.; Lyman, S.

2013-12-01

The Uintah Basin of Eastern Utah, USA, has experienced winter ozone pollution events with ozone concentrations exceeding the National Ambient Air Quality Standard of 75 ppb. With a total of four winter seasons of ozone sampling, winter 2013 is the worst on record for ozone pollution in the basin. Emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx) from oil and gas industries and other activities provide the precursors for ozone formation. The chemical mechanism of ozone formation is non-linear and complicated depending on the availability of VOCs and NOx. Moreover, meteorological conditions also play an important role in triggering ozone pollution. In the Uintah Basin, high albedo due to snow cover, a 'bowl-shaped' terrain, and strong inversions that trap precursors within the boundary layer are important factors contributing to ozone pollution. However, these local meteorological phenomena have been misrepresented by recent numerical modeling studies, probably due to misrepresenting the snow cover and complex terrain of the basin. In this study, Weather Research and Forecasting (WRF) simulations are performed on a model domain covering the entire Uintah Basin for winter 2013 (Dec 2012 - Mar 2013) to test the impacts of several grid resolutions (e.g., 4000, 1300 and 800m) and snow cover modification on performance of models of the local weather conditions of the basin. These sensitivity tests help to determine the best model configurations to produce appropriate meteorological input for air-quality simulations.

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

A comprehensive emission inventory of multiple air pollutants from iron and steel industry in China: Temporal trends and spatial variation characteristics.

PubMed

Wang, Kun; Tian, Hezhong; Hua, Shenbing; Zhu, Chuanyong; Gao, Jiajia; Xue, Yifeng; Hao, Jiming; Wang, Yong; Zhou, Junrui

2016-07-15

China has become the largest producer of iron and steel throughout the world since 1996. However, as an energy-and-pollution intensive manufacturing sector, a detailed comprehensive emission inventory of air pollutants for iron and steel industry of China is still not available. To obtain and better understand the temporal trends and spatial variation characteristics of typical hazardous air pollutants (HAPs) emissions from iron and steel production in China, a comprehensive emission inventory of multiple air pollutants, including size segregated particulate matter (TSP/PM10/PM2.5), gaseous pollutants (SO2, NOx, CO), heavy metals (Pb, Cd, Hg, As, Cr, Ni etc.), as well as the more dangerous PCDD/Fs, is established with the unit-based annual activity, specific dynamic emission factors for the historical period of 1978-2011, and the future potential trends till to 2050 are forecasted by using scenario analysis. Our results show that emissions of gaseous pollutants and particulate matter have experienced a gradual increase tendency since 2000, while emissions of priority-controlled heavy metals (Hg, Pb, As, Cd, Cr, and Ni) have exhibited a short-term fluctuation during the period of 1990 to 2005. With regard to the spatial distribution of HAPs emissions in base year 2011, Bohai economic circle is identified as the top emission intensity region where iron and steel smelting plants are densely built; within iron and steel industry, blast furnaces contribute the majority of PM emissions, sinter plants account for most of gaseous pollutants and the majority of PCDD/Fs, whereas steel making processes are responsible for the majority of heavy metal emissions. Moreover, comparisons of future emission trends under three scenarios indicate that advanced technologies and integrated whole process management strategies are in great need to further diminish various hazardous air pollutants from iron and steel industry in the future. Copyright © 2016 Elsevier B.V. All rights reserved.

PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization.

PubMed

Antanasijević, Davor Z; Pocajt, Viktor V; Povrenović, Dragan S; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A

2013-01-15

This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables. Copyright © 2012 Elsevier B.V. All rights reserved.

Forecasting volcanic air pollution in Hawaii: Tests of time series models

NASA Astrophysics Data System (ADS)

Reikard, Gordon

2012-12-01

Volcanic air pollution, known as vog (volcanic smog) has recently become a major issue in the Hawaiian islands. Vog is caused when volcanic gases react with oxygen and water vapor. It consists of a mixture of gases and aerosols, which include sulfur dioxide and other sulfates. The source of the volcanic gases is the continuing eruption of Mount Kilauea. This paper studies predicting vog using statistical methods. The data sets include time series for SO2 and SO4, over locations spanning the west, south and southeast coasts of Hawaii, and the city of Hilo. The forecasting models include regressions and neural networks, and a frequency domain algorithm. The most typical pattern for the SO2 data is for the frequency domain method to yield the most accurate forecasts over the first few hours, and at the 24 h horizon. The neural net places second. For the SO4 data, the results are less consistent. At two sites, the neural net generally yields the most accurate forecasts, except at the 1 and 24 h horizons, where the frequency domain technique wins narrowly. At one site, the neural net and the frequency domain algorithm yield comparable errors over the first 5 h, after which the neural net dominates. At the remaining site, the frequency domain method is more accurate over the first 4 h, after which the neural net achieves smaller errors. For all the series, the average errors are well within one standard deviation of the actual data at all the horizons. However, the errors also show irregular outliers. In essence, the models capture the central tendency of the data, but are less effective in predicting the extreme events.

Use of Wildfire Smoke Forcasting Model to Mitigate Burden on a Population's Health and Wellbeing Presentation for International Society of Environmental Epidemiology meeting

EPA Science Inventory

Use of Wildfire Smoke Forecasting Model to Mitigate Burden on a Population’s Health and Wellbeing Ana G. Rappold, Neal Fann, Wayne E. Cascio, Robert B. Devlin, David Diaz-Sanchez Background Wildfires are a major source of fine particular matter and other air pollutants as...

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

PubMed Central

Liu, Dong-jun; Li, Li

2015-01-01

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.

PubMed

Liu, Dong-jun; Li, Li

2015-06-23

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.

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.

Long-path measurements of pollutants and micrometeorology over Highway 401 in Toronto

NASA Astrophysics Data System (ADS)

You, Yuan; Staebler, Ralf M.; Moussa, Samar G.; Su, Yushan; Munoz, Tony; Stroud, Craig; Zhang, Junhua; Moran, Michael D.

2017-11-01

Traffic emissions contribute significantly to urban air pollution. Measurements were conducted over Highway 401 in Toronto, Canada, with a long-path Fourier transform infrared (FTIR) spectrometer combined with a suite of micrometeorological instruments to identify and quantify a range of air pollutants. Results were compared with simultaneous in situ observations at a roadside monitoring station, and with output from a special version of the operational Canadian air quality forecast model (GEM-MACH). Elevated mixing ratios of ammonia (0-23 ppb) were observed, of which 76 % were associated with traffic emissions. Hydrogen cyanide was identified at mixing ratios between 0 and 4 ppb. Using a simple dispersion model, an integrated emission factor of on average 2.6 g km-1 carbon monoxide was calculated for this defined section of Highway 401, which agreed well with estimates based on vehicular emission factors and observed traffic volumes. Based on the same dispersion calculations, vehicular average emission factors of 0.04, 0.36, and 0.15 g km-1 were calculated for ammonia, nitrogen oxide, and methanol, respectively.

Enabling Mobile Air Quality App Development with an AirNow API

NASA Astrophysics Data System (ADS)

Dye, T.; White, J. E.; Ludewig, S. A.; Dickerson, P.; Healy, A. N.; West, J. W.; Prince, L. A.

2013-12-01

The U.S. Environmental Protection Agency's (EPA) AirNow program works with over 130 participating state, local, and federal air quality agencies to obtain, quality control, and store real-time air quality observations and forecasts. From these data, the AirNow system generates thousands of maps and products each hour. Each day, information from AirNow is published online and in other media to assist the public in making health-based decisions related to air quality. However, an increasing number of people use mobile devices as their primary tool for obtaining information, and AirNow has responded to this trend by publishing an easy-to-use Web API that is useful for mobile app developers. This presentation will describe the various features of the AirNow application programming interface (API), including Representational State Transfer (REST)-type web services, file outputs, and RSS feeds. In addition, a web portal for the AirNow API will be shown, including documentation on use of the system, a query tool for configuring and running web services, and general information about the air quality data and forecasts available. Data published via the AirNow API includes corresponding Air Quality Index (AQI) levels for each pollutant. We will highlight examples of mobile apps that are using the AirNow API to provide location-based, real-time air quality information. Examples will include mobile apps developed for Minnesota ('Minnesota Air') and Washington, D.C. ('Clean Air Partners Air Quality'), and an app developed by EPA ('EPA AirNow').

“Modeling Trends in Air Pollutant Concentrations over the ...

EPA Pesticide Factsheets

Regional model calculations over annual cycles have pointed to the need for accurately representing impacts of long-range transport. Linking regional and global scale models have met with mixed success as biases in the global model can propagate and influence regional calculations and often confound interpretation of model results. Since transport is efficient in the free-troposphere and since simulations over Continental scales and annual cycles provide sufficient opportunity for “atmospheric turn-over”, i.e., exchange between the free-troposphere and the boundary-layer, a conceptual framework is needed wherein interactions between processes occurring at various spatial and temporal scales can be consistently examined. The coupled WRF-CMAQ model is expanded to hemispheric scales and model simulations over period spanning 1990-current are analyzed to examine changes in hemispheric air pollution resulting from changes in emissions over this period. 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 pr

Trends of multiple air pollutants emissions from residential coal combustion in Beijing and its implication on improving air quality for control measures

NASA Astrophysics Data System (ADS)

Xue, Yifeng; Zhou, Zhen; Nie, Teng; Wang, Kun; Nie, Lei; Pan, Tao; Wu, Xiaoqing; Tian, Hezhong; Zhong, Lianhong; Li, Jing; Liu, Huanjia; Liu, Shuhan; Shao, Panyang

2016-10-01

Residential coal combustion is considered to be an important source of air pollution in Beijing. However, knowledge regarding the emission characteristics of residential coal combustion and the related impacts on the air quality is very limited. In this study, we have developed an emission inventory for multiple hazardous air pollutants (HAPs) associated with residential coal combustion in Beijing for the period of 2000-2012. Furthermore, a widely used regional air quality model, the Community Multi-Scale Air Quality model (CMAQ), is applied to analyze the impact of residential coal combustion on the air quality in Beijing in 2012. The results show that the emissions of primary air pollutants from residential coal combustion have basically remained the same levels during the past decade, however, along with the strict emission control imposed on major industrial sources, the contribution of residential coal combustion emissions to the overall emissions from anthropogenic sources have increased obviously. In particular, the contributions of residential coal combustion to the total air pollutants concentrations of PM10, SO2, NOX, and CO represent approximately 11.6%, 27.5%, 2.8% and 7.3%, respectively, during the winter heating season. In terms of impact on the spatial variation patterns, the distributions of the pollutants concentrations are similar to the distribution of the associated primary HAPs emissions, which are highly concentrated in the rural-urban fringe zones and rural suburb areas. In addition, emissions of primary pollutants from residential coal combustion are forecasted by using a scenario analysis. Generally, comprehensive measures must be taken to control residential coal combustion in Beijing. The best way to reduce the associated emissions from residential coal combustion is to use economic incentive means to promote the conversion to clean energy sources for residential heating and cooking. In areas with reliable energy supplies, the coal used for residential heating can be replaced with gas-burning wall-heaters, ground-source heat pumps, solar energy and electricity. In areas with inadequate clean energy sources, low-sulfur coal should be used instead of the traditional raw coal with high sulfur and ash content, thereby slightly reducing the emissions of PM, SO2, CO and other toxic pollutants.

Improving biomass burning pollution predictions in Singapore using AERONET and Lidar observations.

NASA Astrophysics Data System (ADS)

Hardacre, Catherine; Chew, Boon Ning; Gan, Christopher; Burgin, Laura; Hort, Matthew; Lee, Shao Yi; Shaw, Felicia; Witham, Claire

2016-04-01

Every year millions of people are affected by poor air quality from trans-boundary smoke haze emitted from large scale biomass burning in Asia. These fires are a particular problem in the Indonesian regions of Sumatra and Kalimantan where peat fires, lit to clear land for oil palm plantations and agriculture, typically result in high levels of particulate matter (PM) emissions. In June 2013 and from August-October 2015 the combination of widespread burning, meteorological and climatological conditions resulted in severe air pollution throughout Southeast Asia. The Met Office of the United Kingdom (UKMO) and the Hazard and Risk Impact Assessment Unit of the Meteorological Service of Singapore (MSS) have developed a quantitative haze forecast to provide a reliable, routine warning of haze events in the Singapore region. The forecast system uses the UKMO's Lagrangian particle dispersion model NAME (Numerical Atmosphere-dispersion Modelling Environment) in combination with high resolution, satellite based emission data from the Global Fire Emissions System (GFAS). The buoyancy of biomass burning smoke and it's rise through the atmosphere has a large impact on the amount of air pollution at the Earth's surface. This is important in Singapore, which is affected by pollution that has travelled long distances and that will have a vertical distribution influenced by meteorology. The vertical distribution of atmospheric aerosol can be observed by Lidar which provides information about haze plume structure. NAME output from two severe haze periods that occurred in June 2013 and from August-October 2015 was compared with observations of total column aerosol optical depth (AOD) from AERONET stations in Singapore and the surrounding region, as well as vertically resolved Lidar data from a station maintained by MSS and from MPLNET. Comparing total column and vertically resolved AOD observations with NAME output indicates that the model underestimates PM concentrations throughout the column. This discrepancy may arise from i) too low emissions of PM, ii) uncertainties in the long range transport of PM or iii) the role of the boundary layer in NWP, all of which are being explored at UKMO and MSS. This study gives a more comprehensive evaluation of the model's performance and indicates that vertically resolved AOD data may be useful as a model input for the haze forecast system.

Emissions of CO2 and criteria air pollutants from mobile sources: Insights from integrating real-time traffic data into local air quality models

NASA Astrophysics Data System (ADS)

Gately, Conor; Hutyra, Lucy

2016-04-01

In 2013, on-road mobile sources were responsible for over 26% of U.S. fossil fuel carbon dioxide (ffCO2) emissions, and over 34% of both CO and NOx emissions. However, accurate representations of these emissions at the scale of urban areas remains a difficult challenge. Quantifying emissions at the scale of local streets and highways is critical to provide policymakers with the information needed to develop appropriate mitigation strategies and to guide research into the underlying process that drive mobile emissions. Quantification of vehicle ffCO2 emissions at high spatial and temporal resolutions requires a detailed synthesis of data on traffic activity, roadway attributes, fleet characteristics and vehicle speeds. To accurately characterize criteria air pollutant emissions, information on local meteorology is also critical, as the temperature and relative humidity can affect emissions rates of these pollutants by as much as 400%. As the health impacts of air pollutants are more severe for residents living in close proximity (<500m) to road sources, it is critical that inventories of these emissions rely on highly resolved source data to locate potential hot-spots of exposure. In this study we utilize real-time GPS estimates of vehicle speeds to estimate ffCO2 and criteria air pollutant emissions at multiple spatial and temporal scales across a large metropolitan area. We observe large variations in emissions associated with diurnal activity patterns, congestion, sporting and civic events, and weather anomalies. We discuss the advantages and challenges of using highly-resolved source data to quantify emissions at a roadway scale, and the potential of this methodology for forecasting the air quality impacts of changes in infrastructure, urban planning policies, and regional climate.

Emissions of CO2 and criteria air pollutants from mobile sources: Insights from integrating real-time traffic data into local air quality models

NASA Astrophysics Data System (ADS)

Gately, C.; Hutyra, L.; Sue Wing, I.; Peterson, S.; Janetos, A.

2015-12-01

In 2013, on-road mobile sources were responsible for over 26% of U.S. fossil fuel carbon dioxide (ffCO2) emissions, and over 34% of both CO and NOx emissions. However, accurate representations of these emissions at the scale of urban areas remains a difficult challenge. Quantifying emissions at the scale of local streets and highways is critical to provide policymakers with the information needed to develop appropriate mitigation strategies and to guide research into the underlying process that drive mobile emissions. Quantification of vehicle ffCO2 emissions at high spatial and temporal resolutions requires a detailed synthesis of data on traffic activity, roadway attributes, fleet characteristics and vehicle speeds. To accurately characterize criteria air pollutant emissions, information on local meteorology is also critical, as the temperature and relative humidity can affect emissions rates of these pollutants by as much as 400%. As the health impacts of air pollutants are more severe for residents living in close proximity (<500m) to road sources, it is critical that inventories of these emissions rely on highly resolved source data to locate potential hot-spots of exposure. In this study we utilize real-time GPS estimates of vehicle speeds to estimate ffCO2 and criteria air pollutant emissions at multiple spatial and temporal scales across a large metropolitan area. We observe large variations in emissions associated with diurnal activity patterns, congestion, sporting and civic events, and weather anomalies. We discuss the advantages and challenges of using highly-resolved source data to quantify emissions at a roadway scale, and the potential of this methodology for forecasting the air quality impacts of changes in infrastructure, urban planning policies, and regional climate.

Air quality in Delhi during the Commonwealth Games

NASA Astrophysics Data System (ADS)

Marrapu, P.; Cheng, Y.; Beig, G.; Sahu, S.; Srinivas, R.; Carmichael, G. R.

2014-10-01

Air quality during the Commonwealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m-3, respectively. Black carbon (BC) accounted for ~ 10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ~ 25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5, and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change. The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ~ 25% for BC and PM to ~ 60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

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

Weather and climate

NASA Technical Reports Server (NTRS)

1975-01-01

Recommendations for using space observations of weather and climate to aid in solving earth based problems are given. Special attention was given to: (1) extending useful forecasting capability of space systems, (2) reducing social, economic, and human losses caused by weather, (3) development of space system capability to manage and control air pollutant concentrations, and (4) establish mechanisms for the national examination of deliberate and inadvertent means for modifying weather and climate.

Refinement of pollutant gas emissions in the state of Rio de Janeiro for applications in modeling air quality on a local scale

NASA Astrophysics Data System (ADS)

Domínguez Chovert, Angel; Félix Alonso, Marcelo; Frassoni, Ariane; José Ferreira, Valter; Eiras, Denis; Longo, Karla; Freitas, Saulo

2017-04-01

Numerical modeling is a fundamental tool for studying the earth system components along with weather and climate forecast. In fact, the development of on-line models allows to simulate conditions of the atmosphere, for example, to evaluate certain chemicals in weather events with the purpose of improving a region's quality of air. For this determined purpose, the on-line models employ information from a broad range of sources in order to generate its variables forecasts. But beyond vast information sources, for a region's quality of air study, the data concerning the amount and distribution of emissions of polluting gases must be representative, as well as, it's required complete georeferenced emissions for simulations made with high resolution. Consequently, the modifications made in this work to the PREP-CHEM-SRC (Preprocessor of trace gas and aerosol emission fields for regional and a global atmospheric chemistry models) tool are presented to meliorate the initialization files for BRAMS models, 5.2 version (Brazilian Developments on the Regional Atmospheric Modeling System) and WRF (Weather Research and Forecasting Model) with vehicle emissions in the state of Rio de Janeiro, Brazil. It was determined the annual vehicle emission, until the year 2030, of the nitrogen oxides species (NOx) and carbon monoxide (CO) for each city and using different scenarios. For Rio de Janeiro city, a process of distribution by emissions of the main pollutant gases was implemented. In total, five different types of routes were used and the emission percentage for each one was calculated using the most current traffic information in them. For to check the industrial contributions to the emissions were used the global datasets RETRO (REanalysis of TROpospheric chemical composition) and EDGAR-HTAP (Emission Database for Global Atmospheric Research). On the other hand, for the biogenic contributions was used information from the MEGAN model (Model of Emissions of Gases and Aerosols from Nature). For all the analyzed species it was possible to observe the strong influence of the vehicular activity on the emission distribution.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

PubMed Central

Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

2016-01-01

ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders. PMID:26934496

The behaviour of PM10 and ozone in Malaysia through non-linear dynamical systems

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

Sapini, Muhamad Luqman; Rahim, Nurul Zahirah binti Abd; Noorani, Mohd Salmi Md.

Prediction of ozone (O3) and PM10 is very important as both these air pollutants affect human health, human activities and more. Short-term forecasting of air quality is needed as preventive measures and effective action can be taken. Therefore, if it is detected that the ozone data is of a chaotic dynamical systems, a model using the nonlinear dynamic from chaos theory data can be made and thus forecasts for the short term would be more accurate. This study uses two methods, namely the 0-1 Test and Lyapunov Exponent. In addition, the effect of noise reduction on the analysis of timemore » series data will be seen by using two smoothing methods: Rectangular methods and Triangle methods. At the end of the study, recommendations were made to get better results in the future.« less

Boundary layer height determination from Lidar for improving air pollution episode modelling: development of new algorithm and evaluation

NASA Astrophysics Data System (ADS)

Yang, T.; Wang, Z.; Zhang, W.; Gbaguidi, A.; Sugimoto, N.; Matsui, I.; Wang, X.; Yele, S.

2017-12-01

Predicting air pollution events in low atmosphere over megacities requires thorough understanding of the tropospheric dynamic and chemical processes, involving notably, continuous and accurate determination of the boundary layer height (BLH). Through intensive observations experimented over Beijing (China), and an exhaustive evaluation existing algorithms applied to the BLH determination, persistent critical limitations are noticed, in particular over polluted episodes. Basically, under weak thermal convection with high aerosol loading, none of the retrieval algorithms is able to fully capture the diurnal cycle of the BLH due to pollutant insufficient vertical mixing in the boundary layer associated with the impact of gravity waves on the tropospheric structure. Subsequently, a new approach based on gravity wave theory (the cubic root gradient method: CRGM), is developed to overcome such weakness and accurately reproduce the fluctuations of the BLH under various atmospheric pollution conditions. Comprehensive evaluation of CRGM highlights its high performance in determining BLH from Lidar. In comparison with the existing retrieval algorithms, the CRGM potentially reduces related computational uncertainties and errors from BLH determination (strong increase of correlation coefficient from 0.44 to 0.91 and significant decreases of the root mean square error from 643 m to 142 m). Such newly developed technique is undoubtedly expected to contribute to improve the accuracy of air quality modelling and forecasting systems.

A study on the uncertainty based on Meteorological fields on Source-receptor Relationships for Total Nitrate in the Northeast Asia

NASA Astrophysics Data System (ADS)

Sunwoo, Y.; Park, J.; Kim, S.; Ma, Y.; Chang, I.

2010-12-01

Northeast Asia hosts more than one third of world population and the emission of pollutants trends to increase rapidly, because of economic growth and the increase of the consumption in high energy intensity. In case of air pollutants, especially, its characteristics of emissions and transportation become issued nationally, in terms of not only environmental aspects, but also long-range transboundary transportation. In meteorological characteristics, westerlies area means what air pollutants that emitted from China can be delivered to South Korea. Therefore, considering meteorological factors can be important to understand air pollution phenomena. In this study, we used MM5(Fifth-Generation Mesoscale Model) and WRF(Weather Research and Forecasting Model) to produce the meteorological fields. We analyzed the feature of physics option in each model and the difference due to characteristic of WRF and MM5. We are trying to analyze the uncertainty of source-receptor relationships for total nitrate according to meteorological fields in the Northeast Asia. We produced the each meteorological fields that apply the same domain, same initial and boundary conditions, the best similar physics option. S-R relationships in terms of amount and fractional number for total nitrate (sum of N from HNO3, nitrate and PAN) were calculated by EMEP method 3.

Operational on-line coupled chemical weather forecasts for Europe with WRF/Chem

NASA Astrophysics Data System (ADS)

Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Flandorfer, Claudia; Langer, Matthias

2014-05-01

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 the 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. ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. Meteorology is simulated simultaneously with the emissions, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. The emphasis of the application is on predicting pollutants over Austria. Two domains are used for the simulations: the mother domain covers Europe with a resolution of 12 km, the inner domain includes the alpine region with a horizontal resolution of 4 km; 45 model levels are used in the vertical direction. The model runs 2 times per day for a period of 72 hours and is initialized with ECMWF forecasts. On-line coupled models allow considering two-way interactions between different atmospheric processes including chemistry (both gases and aerosols), clouds, radiation, boundary layer, emissions, meteorology and climate. In the operational set-up direct-, indirect and semi-direct effects between meteorology and air chemistry are enabled. The model is running on the HPCF (High Performance Computing Facility) of the ZAMG. In the current set-up 1248 CPUs are used. As the simulations need a big amount of computing resources, a method to safe I/O-time was implemented. Every MPI task writes all its output into the shared memory filesystem of the compute nodes. Once the WRF/Chem integration is finished, all split NetCDF-files are merged and saved on the global file system. The merge-routine is based on parallel-NetCDF. With this method the model runs about 30% faster on the SGI-ICEX. Different additional external data sources can be used to improve the forecasts. 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. The available local emission inventories provided by the different Austrian regional governments were harmonized and are used for the model simulations. A model evaluation for a selected episode in February 2010 is presented with respect to PM10 forecasts. 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.

Impact of the Loess Plateau on the atmospheric boundary layer structure and air quality in the North China Plain: a case study.

PubMed

Hu, Xiao-Ming; Ma, ZhiQiang; Lin, Weili; Zhang, Hongliang; Hu, Jianlin; Wang, Ying; Xu, Xiaobin; Fuentes, Jose D; Xue, Ming

2014-11-15

The North China Plain (NCP), to the east of the Loess Plateau, experiences severe regional air pollution. During the daytime in the summer, the Loess Plateau acts as an elevated heat source. The impacts of such a thermal effect on meteorological phenomena (e.g., waves, precipitation) in this region have been discussed. However, its impacts on the atmospheric boundary layer structure and air quality have not been reported. It is hypothesized that the thermal effect of the Plateau likely modulates the boundary layer structure and ambient concentrations of pollutants over the NCP under certain meteorological conditions. Thus, this study investigates such effect and its impacts using measurements and three-dimensional model simulations. It is found that in the presence of daytime westerly wind in the lower troposphere (~1 km above the NCP), warmer air above the Loess Plateau was transported over the NCP and imposed a thermal inversion above the mixed boundary layer, which acted as a lid and suppressed the mixed layer growth. As a result, pollutants accumulated in the shallow mixed layer and ozone was efficiently produced. The downward branch of the thermally-induced Mountain-Plains Solenoid circulation over the NCP contributed to enhancing the capping inversion and exacerbating air pollution. Previous studies have reported that low mixed layer, a factor for elevated pollution in the NCP, may be caused by aerosol scattering and absorption of solar radiation, frontal inversion, and large scale subsidence. The present study revealed a different mechanism (i.e., westerly warm advection) for the suppression of the mixed layer in summer NCP, which caused severe O3 pollution. This study has important implications for understanding the essential meteorological factors for pollution episodes in this region and forecasting these severe events. Copyright © 2014 Elsevier B.V. All rights reserved.

Uncertainty characterization and quantification in air pollution models. Application to the CHIMERE model

NASA Astrophysics Data System (ADS)

Debry, Edouard; Mallet, Vivien; Garaud, Damien; Malherbe, Laure; Bessagnet, Bertrand; Rouïl, Laurence

2010-05-01

Prev'Air is the French operational system for air pollution forecasting. It is developed and maintained by INERIS with financial support from the French Ministry for Environment. On a daily basis it delivers forecasts up to three days ahead for ozone, nitrogene dioxide and particles over France and Europe. Maps of concentration peaks and daily averages are freely available to the general public. More accurate data can be provided to customers and modelers. Prev'Air forecasts are based on the Chemical Transport Model CHIMERE. French authorities rely more and more on this platform to alert the general public in case of high pollution events and to assess the efficiency of regulation measures when such events occur. For example the road speed limit may be reduced in given areas when the ozone level exceeds one regulatory threshold. These operational applications require INERIS to assess the quality of its forecasts and to sensitize end users about the confidence level. Indeed concentrations always remain an approximation of the true concentrations because of the high uncertainty on input data, such as meteorological fields and emissions, because of incomplete or inaccurate representation of physical processes, and because of efficiencies in numerical integration [1]. We would like to present in this communication the uncertainty analysis of the CHIMERE model led in the framework of an INERIS research project aiming, on the one hand, to assess the uncertainty of several deterministic models and, on the other hand, to propose relevant indicators describing air quality forecast and their uncertainty. There exist several methods to assess the uncertainty of one model. Under given assumptions the model may be differentiated into an adjoint model which directly provides the concentrations sensitivity to given parameters. But so far Monte Carlo methods seem to be the most widely and oftenly used [2,3] as they are relatively easy to implement. In this framework one probability density function (PDF) is associated with an input parameter, according to its assumed uncertainty. Then the combined PDFs are propagated into the model, by means of several simulations with randomly perturbed input parameters. One may then obtain an approximation of the PDF of modeled concentrations, provided the Monte Carlo process has reasonably converged. The uncertainty analysis with CHIMERE has been led with a Monte Carlo method on the French domain and on two periods : 13 days during January 2009, with a focus on particles, and 28 days during August 2009, with a focus on ozone. The results show that for the summer period and 500 simulations, the time and space averaged standard deviation for ozone is 16 µg/m3, to be compared with an averaged concentration of 89 µg/m3. It is noteworthy that the space averaged standard deviation for ozone is relatively constant over time (the standard deviation of the timeseries itself is 1.6 µg/m3). The space variation of the ozone standard deviation seems to indicate that emissions have a significant impact, followed by western boundary conditions. Monte Carlo simulations are then post-processed by both ensemble [4] and Bayesian [5] methods in order to assess the quality of the uncertainty estimation. (1) Rao, K.S. Uncertainty Analysis in Atmospheric Dispersion Modeling, Pure and Applied Geophysics, 2005, 162, 1893-1917. (2) Beekmann, M. and Derognat, C. Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign, Journal of Geophysical Research, 2003, 108, 8559-8576. (3) Hanna, S.R. and Lu, Z. and Frey, H.C. and Wheeler, N. and Vukovich, J. and Arunachalam, S. and Fernau, M. and Hansen, D.A. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain, Atmospheric Environment, 2001, 35, 891-903. (4) Mallet, V., and B. Sportisse (2006), Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling, J. Geophys. Res., 111, D01302, doi:10.1029/2005JD006149. (5) Romanowicz, R. and Higson, H. and Teasdale, I. Bayesian uncertainty estimation methodology applied to air pollution modelling, Environmetrics, 2000, 11, 351-371.

Air pollutant emissions from Chinese households: A major and underappreciated ambient pollution source.

PubMed

Liu, Jun; Mauzerall, Denise L; Chen, Qi; Zhang, Qiang; Song, Yu; Peng, Wei; Klimont, Zbigniew; Qiu, Xinghua; Zhang, Shiqiu; Hu, Min; Lin, Weili; Smith, Kirk R; Zhu, Tong

2016-07-12

As part of the 12th Five-Year Plan, the Chinese government has developed air pollution prevention and control plans for key regions with a focus on the power, transport, and industrial sectors. Here, we investigate the contribution of residential emissions to regional air pollution in highly polluted eastern China during the heating season, and find that dramatic improvements in air quality would also result from reduction in residential emissions. We use the Weather Research and Forecasting model coupled with Chemistry to evaluate potential residential emission controls in Beijing and in the Beijing, Tianjin, and Hebei (BTH) region. In January and February 2010, relative to the base case, eliminating residential emissions in Beijing reduced daily average surface PM2.5 (particulate mater with aerodynamic diameter equal or smaller than 2.5 micrometer) concentrations by 14 ± 7 μg⋅m(-3) (22 ± 6% of a baseline concentration of 67 ± 41 μg⋅m(-3); mean ± SD). Eliminating residential emissions in the BTH region reduced concentrations by 28 ± 19 μg⋅m(-3) (40 ± 9% of 67 ± 41 μg⋅m(-3)), 44 ± 27 μg⋅m(-3) (43 ± 10% of 99 ± 54 μg⋅m(-3)), and 25 ± 14 μg⋅m(-3) (35 ± 8% of 70 ± 35 μg⋅m(-3)) in Beijing, Tianjin, and Hebei provinces, respectively. Annually, elimination of residential sources in the BTH region reduced emissions of primary PM2.5 by 32%, compared with 5%, 6%, and 58% achieved by eliminating emissions from the transportation, power, and industry sectors, respectively. We also find air quality in Beijing would benefit substantially from reductions in residential emissions from regional controls in Tianjin and Hebei, indicating the value of policies at the regional level.

Measuring HRQoL by comparing the perception of air quality among residents in Selangor

NASA Astrophysics Data System (ADS)

Mohammad, Nor Hazlina; Abdullah, Mohammad Nasir; Razi, Nor Faezah Mohd; Ismail, Adriana

2017-05-01

Most studies regarding to air pollution were focused on forecasting Air Pollutant Index (API). Yet, there were no studies that conducted in Malaysia focused on Health-Related Quality of Life (HRQoL). The aim of this study was to investigate the HRQoL in two urban cities, which are Shah Alam and Putrajaya with different air pollution index. In doing so, SF36v2 questionnaire has been utilized to elicit data on HRQoL domains measured using eight domains (Physical Functioning, Role-Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role-Emotional and Mental Health). A cross-sectional study was conducted and residents were selected using simple random sampling from Shah Alam and Putrajaya. The SF36v2 questionnaire with socio demographic information was distributed to the residents. A total of 266 participated in the study, of which 133 samples per groups. The statistical methods employed were descriptive analyses, independent samples t-test and MANOVA to analyze the HRQoL data. There was no difference in perceptions on HRQoL for Role Physical, Vitality, Social Functioning and Mental Health between residents in Shah Alam and residents in Putrajaya. However, there was difference in perception on HRQoL for Physical Functioning, Bodily Pain, General Health and Role Emotional.

New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

2009-12-01

Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product was developed for routine near-real time verification of these predictions. The footprint of the predicted smoke from identified fires is verified with satellite observations of the spatial extent of smoke aerosols (5km resolution). Based on geostationary aerosol optical depth measurements that provide good time resolution of the horizontal spatial extent of the plumes, these observations do not yield quantitative concentrations of smoke particles at the surface. Predicted surface smoke concentrations are consistent with the limited number of in situ observations of total fine particle mass from all sources; however they are much higher than predicted for most CONUS fires. To assess uncertainty associated with fire emissions estimates, sensitivity analyses are in progress.

Recognize PM2.5 sources and emission patterns via high-density sensor network: An application case in Beijing

NASA Astrophysics Data System (ADS)

Ba, Yu tao; xian Liu, Bao; Sun, Feng; Wang, Li hua; Zhang, Da wei; Yin, Wen jun

2017-04-01

Beijing suffered severe air pollution during wintertime, 2016, with the unprecedented high level pollutants monitored. As the most dominant pollutant, fine particulate matter (PM2.5) was measured via high-density sensor network (>1000 fixed monitors across 16000 km2 area). This campaign provided precise observations (spatial resolution ≈ 3 km, temporal resolution = 10 min, error of measure < 5 ug/m3) to track potential emission sources. In addition, these observations coupled with WRF-Chem model (Weather Research and Forecasting model coupled with Chemistry) were analyzed to elucidate the effects of atmospheric transportations across regions, both horizontal and vertical, on emission patterns during this haze period. The results quantified the main cause of regional transport and local emission, and highlighted the importance of cross-region cooperation in anti-pollution campaigns.

Applicability of internet search index for asthma admission forecast using machine learning.

PubMed

Luo, Li; Liao, Chengcheng; Zhang, Fengyi; Zhang, Wei; Li, Chunyang; Qiu, Zhixin; Huang, Debin

2018-04-15

This study aimed to determine whether a search index could provide insight into trends in asthma admission in China. An Internet search index is a powerful tool to monitor and predict epidemic outbreaks. However, whether using an internet search index can significantly improve asthma admissions forecasts remains unknown. The long-term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma health care resource shortages in advance. In this study, we used a search index combined with air pollution data, weather data, and historical admissions data to forecast asthma admissions using machine learning. Results demonstrated that the best area under the curve in the test set that can be achieved is 0.832, using all predictors mentioned earlier. A search index is a powerful predictor in asthma admissions forecast, and a recent search index can reflect current asthma admissions with a lag-effect to a certain extent. The addition of a real-time, easily accessible search index improves forecasting capabilities and demonstrates the predictive potential of search index. Copyright © 2018 John Wiley & Sons, Ltd.

Assessing the Value of Enhancing AirNow Data with NASA Satellite Data

NASA Astrophysics Data System (ADS)

Pasch, A. N.; Burke, B.; Huang, S.; Dye, T.; Dawes, S. S.; DeWinter, J. L.; Zahn, P. H.; Haderman, M.; Szykman, J.; White, J. E.; Dickerson, P.; van Donkelaar, A.; Martin, R.

2013-12-01

We will describe the methodology and findings from a study that addressed how satellite-enhanced air quality information provided through the U.S. Environmental Protection Agency's (EPA) AirNow Satellite Data Processor (ASDP) program could contribute to greater socioeconomic benefits. This study was funded by the National Aeronautics and Space Administration (NASA) and conducted, in partnership with the EPA, by the Center for Technology in Government at the University at Albany (CTG) and Sonoma Technology, Inc. (STI). AirNow is the national repository of real-time air quality data and forecasts for the United States. While mainly a public outreach and awareness tool, AirNow relies on the same network of ground-based air quality monitors that is used by federal, state, local, and tribal governments throughout the United States. Extensive as the monitoring network is, considerable gaps exist in certain parts of the United States. Even areas with monitors considered adequate for regulatory purposes can lack information needed to resolve localized air quality issues or give forecasters sufficient confidence about the potential air quality impact of specific events. Monitors are expensive to deploy and maintain; thus, EPA is seeking other ways to improve coverage and detail. Satellite-estimated data can provide information for many places where ground monitors do not exist, and supplement ground monitors, providing additional information for use in analysis and forecasting. ASDP uses satellite-derived estimates for fine-particle pollution (PM2.5) and provides coverage for a small window of time during the day. As satellite capabilities improve in terms of different types of sensors and increased coverage throughout the day, the ASDP program is prepared to extend its scope to additional pollutants and provide greater enhancements to the ground-based networks. In this study, CTG assessed the socioeconomic benefits of air quality data at a community level through three case studies in the Denver, Atlanta, and Kansas City regions by interviewing people at EPA regional offices, state environmental and public health agencies, local public health authorities, regional planning and non-profit outreach organizations, and universities. The interviews focused on the existing uses of air quality information and the potential value of incorporating NASA satellite-enhanced AirNow data to support and enhance the missions of the organizations interviewed. STI analyzed the economic benefit of using satellite data to fill in gaps in the current air quality monitoring network used to provide information to the public. This presentation will discuss how the findings can be used to improve estimation of the socioeconomic benefits derived from Earth observation science in policy and management decisions.

Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Luna, A. S.; Paredes, M. L. L.; de Oliveira, G. C. G.; Corrêa, S. M.

2014-12-01

It is well known that air quality is a complex function of emissions, meteorology and topography, and statistical tools provide a sound framework for relating these variables. The observed data were contents of nitrogen dioxide (NO2), nitrogen monoxide (NO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), scalar wind speed (SWS), global solar radiation (GSR), temperature (TEM), moisture content in the air (HUM), collected by a mobile automatic monitoring station at Rio de Janeiro City in two places of the metropolitan area during 2011 and 2012. The aims of this study were: (1) to analyze the behavior of the variables, using the method of PCA for exploratory data analysis; (2) to propose forecasts of O3 levels from primary pollutants and meteorological factors, using nonlinear regression methods like ANN and SVM, from primary pollutants and meteorological factors. The PCA technique showed that for first dataset, variables NO, NOx and SWS have a greater impact on the concentration of O3 and the other data set had the TEM and GSR as the most influential variables. The obtained results from the nonlinear regression techniques ANN and SVM were remarkably closely and acceptable to one dataset presenting coefficient of determination for validation respectively 0.9122 and 0.9152, and root mean square error of 7.66 and 7.85, respectively. For these datasets, the PCA, SVM and ANN had demonstrated their robustness as useful tools for evaluation, and forecast scenarios for air quality.

Efficacy of an outdoor air pollution education program in a community at risk for asthma morbidity.

PubMed

Dorevitch, Samuel; Karandikar, Abhijay; Washington, Gregory F; Walton, Geraldine Penny; Anderson, Renate; Nickels, Leslie

2008-11-01

Asthma management guidelines recommend avoiding exposure to indoor and outdoor air pollutants. A limitation of such recommendations is that they do not provide information about how the public should obtain and act on air quality information. Although the Air Quality Index (AQI) provides simplified outdoor air quality forecasts, communities with high rates of asthma morbidity tend to have low rates of internet access due to factors such as low socioeconomic status. Assessments of knowledge about air quality among low-income minority communities are lacking, as are community-based programs to educate the public about using the AQI. An air quality education program and system for disseminating air quality information were developed to promote pollutant avoidance during the reconstruction of a major highway in a low-income minority community on Chicago's South Side. The program, which centered on workshops run by community asthma educators, was evaluated using a pre-test, post-test, and 1-year follow-up questionnaire. A total of 120 community workshop participants completed at least a portion of the evaluation process. At baseline, knowledge about air quality was limited. Following the workshops, substantial increases were noted in rates of correct answers to questions about health effects of air pollution, the availability of air quality information, and the color code for an AQI category. Approximately 1 year after the workshops were held, few participants could recall elements of the training. Few participants have internet access, and alternative means of distributing air quality information were suggested by study participants. Baseline knowledge of air quality information was limited in the community studied. Air quality education workshops conducted by community educators can increase knowledge about outdoor air quality and its impact on health over the short term. Refresher workshops or other efforts to sustain the knowledge increase may be useful. Given the known short-term and long-term effects of air quality on morbidity and mortality, air quality education efforts should be further developed, evaluated, and promoted for the general public, for people with underlying cardiopulmonary disease, and given the documented health disparities within the general population, for low-income and minority communities.

Environmental noise forecasting based on support vector machine

NASA Astrophysics Data System (ADS)

Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

2018-01-01

As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

Air Quality and Population Exposure in Urban Areas: Potential Co-Benefits of Alternative Strategies

NASA Astrophysics Data System (ADS)

Mikolajczyk, U.; Suppan, P.; Forkel, R.; Williams, M.

2014-12-01

Even though much progress has been achieved through dedicated approaches to improving air quality in many European cities, there are various threats which still remain unchanged. According to the World Health Organization, outdoor air pollution was linked to 3.7 million deaths in year 2012. As climate changes, the frequency of days with harmful levels of air pollutants may significantly increase causing exacerbation of cardiovascular and respiratory diseases. The aim of this study is to conduct health impact assessment by utilizing regionally and spatially specific data in order to assess the influence of alternative emission strategies on human health. In the first stage of this investigation, a modeling study was carried out using the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem; Grell et al., 2005) to estimate ambient concentrations of air pollutants. The model set-up included a nesting approach, where three domains with horizontal resolution of 18 km, 6 km and 2 km were defined. The investigation area included the city of Munich (1.5 million inhabitants). The model performance has been evaluated against available air quality observations from the monitoring database "AirBase". The chemical species including O3, NO, NO2 and PM10 simulated by WRF/Chem compare favorably with the observations. The model performs especially well in resolving the observed O3 concentrations. In the ongoing study, different emission reduction scenarios are compared to a baseline 2009 scenario based on Germany's National Emissions Inventory. To investigate health effects associated with air pollution concentrations a local-scale health impact assessment (HIA) will be conducted. Concentration-response functions (CRFs) link the change in mortality rates to the change in concentrations of air pollutants. CRFs are applied to population-weighted mean concentrations to estimate relative risks and hence estimate numbers of attributable deaths and associated life-years lost. The health benefits that we assume with introducing alternative air quality strategies can be used to provide options for future policy decisions to achieve the reduction of emissions and thereby premature deaths.

Simulating the dispersion of NOx and CO2 in the city of Zurich at building resolving scale

NASA Astrophysics Data System (ADS)

Brunner, Dominik; Berchet, Antoine; Emmenegger, Lukas; Henne, Stephan; Müller, Michael

2017-04-01

Cities are emission hotspots for both greenhouse gases and air pollutants. They contribute about 70% of global greenhouse gas emissions and are home to a growing number of people potentially suffering from poor air quality in the urban environment. High-resolution atmospheric transport modelling of greenhouse gases and air pollutants at the city scale has, therefore, several important applications such as air pollutant exposure assessment, air quality forecasting, or urban planning and management. When combined with observations, it also has the potential to quantify emissions and monitor their long-term trends, which is the main motivation for the deployment of urban greenhouse gas monitoring networks. We have developed a comprehensive atmospheric modeling model system for the city of Zurich, Switzerland ( 600,000 inhabitants including suburbs), which is composed of the mesoscale model GRAMM simulating the flow in a larger domain around Zurich at 100 m resolution, and the nested high-resolution model GRAL simulating the flow and air pollutant dispersion in the city at building resolving (5-10 m) scale. Based on an extremely detailed emission inventory provided by the municipality of Zurich, we have simulated two years of hourly NOx and CO2 concentration fields across the entire city. Here, we present a detailed evaluation of the simulations against a comprehensive network of continuous monitoring sites and passive samplers for NOx and analyze the sensitivity of the results to the temporal variability of the emissions. Furthermore, we present first simulations of CO2 and investigate the challenges associated with CO2 sources not covered by the inventory such as human respiration and exchange fluxes with urban vegetation.

Meteorological controls on atmospheric particulate pollution during hazard reduction burns

NASA Astrophysics Data System (ADS)

Di Virgilio, Giovanni; Hart, Melissa Anne; Jiang, Ningbo

2018-05-01

Internationally, severe wildfires are an escalating problem likely to worsen given projected changes to climate. Hazard reduction burns (HRBs) are used to suppress wildfire occurrences, but they generate considerable emissions of atmospheric fine particulate matter, which depend upon prevailing atmospheric conditions, and can degrade air quality. Our objectives are to improve understanding of the relationships between meteorological conditions and air quality during HRBs in Sydney, Australia. We identify the primary meteorological covariates linked to high PM2.5 pollution (particulates < 2.5 µm in diameter) and quantify differences in their behaviours between HRB days when PM2.5 remained low versus HRB days when PM2.5 was high. Generalised additive mixed models were applied to continuous meteorological and PM2.5 observations for 2011-2016 at four sites across Sydney. The results show that planetary boundary layer height (PBLH) and total cloud cover were the most consistent predictors of elevated PM2.5 during HRBs. During HRB days with low pollution, the PBLH between 00:00 and 07:00 LT (local time) was 100-200 m higher than days with high pollution. The PBLH was similar during 10:00-17:00 LT for both low and high pollution days, but higher after 18:00 LT for HRB days with low pollution. Cloud cover, temperature and wind speed reflected the above pattern, e.g. mean temperatures and wind speeds were 2 °C cooler and 0.5 m s-1 lower during mornings and evenings of HRB days when air quality was poor. These cooler, more stable morning and evening conditions coincide with nocturnal westerly cold air drainage flows in Sydney, which are associated with reduced mixing height and vertical dispersion, leading to the build-up of PM2.5. These findings indicate that air pollution impacts may be reduced by altering the timing of HRBs by conducting them later in the morning (by a matter of hours). Our findings support location-specific forecasts of the air quality impacts of HRBs in Sydney and similar regions elsewhere.

Impacts of emission reduction and meteorological conditions on air quality improvement during the 2014 Youth Olympic Games in Nanjing, China

NASA Astrophysics Data System (ADS)

Huang, Qian; Wang, Tijian; Chen, Pulong; Huang, Xiaoxian; Zhu, Jialei; Zhuang, Bingliang

2017-11-01

As the holding city of the 2nd Youth Olympic Games (YOG), Nanjing is highly industrialized and urbanized, and faces several air pollution issues. In order to ensure better air quality during the event, the local government took great efforts to control the emissions from pollutant sources. However, air quality can still be affected by synoptic weather, not only emission. In this paper, the influences of meteorological factors and emission reductions were investigated using observational data and numerical simulations with WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality). During the month in which the YOG were held (August 2014), the observed hourly mean concentrations of SO2, NO2, PM10, PM2.5, CO and O3 were 11.6 µg m-3, 34.0 µg m-3, 57.8 µg m-3, 39.4 µg m-3, 0.9 mg m-3 and 38.8 µg m-3, respectively, which were below China National Ambient Air Quality Standard (level 2). However, model simulation showed that the weather conditions, such as weaker winds during the YOG, were adverse for better air quality and could increase SO2, NO2, PM10, PM2.5 and CO by 17.5, 16.9, 18.5, 18.8, 7.8 and 0.8 %. Taking account of local emission abatement only, the simulated SO2, NO2, PM10, PM2.5 and CO decreased by 24.6, 12.1, 15.1, 8.1 and 7.2 %. Consequently, stringent emission control measures can reduce the concentrations of air pollutants in the short term, and emission reduction is very important for air quality improvement during the YOG. A good example has been set for air quality protection for important social events.

Predicting vehicle fuel consumption patterns using floating vehicle data.

PubMed

Du, Yiman; Wu, Jianping; Yang, Senyan; Zhou, Liutong

2017-09-01

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model. Copyright © 2017. Published by Elsevier B.V.

Microscale anthropogenic pollution modelling in a small tropical island during weak trade winds: Lagrangian particle dispersion simulations using real nested LES meteorological fields

NASA Astrophysics Data System (ADS)

Cécé, Raphaël; Bernard, Didier; Brioude, Jérome; Zahibo, Narcisse

2016-08-01

Tropical islands are characterized by thermal and orographical forcings which may generate microscale air mass circulations. The Lesser Antilles Arc includes small tropical islands (width lower than 50 km) where a total of one-and-a-half million people live. Air quality over this region is affected by anthropogenic and volcanic emissions, or saharan dust. To reduce risks for the population health, the atmospheric dispersion of emitted pollutants must be predicted. In this study, the dispersion of anthropogenic nitrogen oxides (NOx) is numerically modelled over the densely populated area of the Guadeloupe archipelago under weak trade winds, during a typical case of severe pollution. The main goal is to analyze how microscale resolutions affect air pollution in a small tropical island. Three resolutions of domain grid are selected: 1 km, 333 m and 111 m. The Weather Research and Forecasting model (WRF) is used to produce real nested microscale meteorological fields. Then the weather outputs initialize the Lagrangian Particle Dispersion Model (FLEXPART). The forward simulations of a power plant plume showed good ability to reproduce nocturnal peaks recorded by an urban air quality station. The increase in resolution resulted in an improvement of model sensitivity. The nesting to subkilometer grids helped to reduce an overestimation bias mainly because the LES domains better simulate the turbulent motions governing nocturnal flows. For peaks observed at two air quality stations, the backward sensitivity outputs identified realistic sources of NOx in the area. The increase in resolution produced a sharper inverse plume with a more accurate source area. This study showed the first application of the FLEXPART-WRF model to microscale resolutions. Overall, the coupling model WRF-LES-FLEXPART is useful to simulate the pollutant dispersion during a real case of calm wind regime over a complex terrain area. The forward and backward simulation results showed clearly that the subkilometer resolution of 333 m is necessary to reproduce realistic air pollution patterns in this case of short-range transport over a complex terrain area. Globally, this work contributes to enrich the sparsely documented domain of real nested microscale air pollution modelling. This study dealing with the determination of the proper resolution grid and proper turbulence scheme, is of significant interest to the near-source and complex terrain air quality research community.

The association of air pollution and greenness with mortality and life expectancy in Spain: A small-area study.

PubMed

de Keijzer, Carmen; Agis, David; Ambrós, Albert; Arévalo, Gustavo; Baldasano, Jose M; Bande, Stefano; Barrera-Gómez, Jose; Benach, Joan; Cirach, Marta; Dadvand, Payam; Ghigo, Stefania; Martinez-Solanas, Èrica; Nieuwenhuijsen, Mark; Cadum, Ennio; Basagaña, Xavier

2017-02-01

Air pollution exposure has been associated with an increase in mortality rates, but few studies have focused on life expectancy, and most studies had restricted spatial coverage. A limited body of evidence is also suggestive for a beneficial association between residential exposure to greenness and mortality, but the evidence for such an association with life expectancy is still very scarce. To investigate the association of exposure to air pollution and greenness with mortality and life expectancy in Spain. Mortality data from 2148 small areas (average population of 20,750 inhabitants, and median population of 7672 inhabitants) covering Spain for years 2009-2013 were obtained. Average annual levels of PM 10 , PM 2.5 , NO 2 and O 3 were derived from an air quality forecasting system at 4×4km resolution. The normalized difference vegetation index (NDVI) was used to assess greenness in each small area. Air pollution and greenness were linked to standardized mortality rates (SMRs) using Poisson regression and to life expectancy using linear regression. The models were adjusted for socioeconomic status and lung cancer mortality rates (as a proxy for smoking), and accounted for spatial autocorrelation. The increase of 5μg/m 3 in PM 10 , NO 2 and O 3 or of 2μg/m 3 in PM 2.5 concentration resulted in a loss of life in years of 0.90 (95% credibility interval CI: 0.83, 0.98), 0.13 (95% CI: 0.09, 0.17), 0.20years (95% CI: 0.16, 0.24) and 0.64 (0.59, 0.70), respectively. Similar associations were found in the SMR analysis, with stronger associations for PM 2.5 and PM 10 , which were associated with an increased mortality risk of 3.7% (95% CI: 3.5%, 4.0%) and 5.7% (95% CI: 5.4%, 6.1%). For greenness, a protective effect on mortality and longer life expectancy was only found in areas with lower socioeconomic status. Air pollution concentrations were associated to important reductions in life expectancy. The reduction of air pollution should be a priority for public health. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

PubMed

Li, Weide; Kong, Demeng; Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China

PubMed Central

Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality. PMID:28932237

Local and regional interactions between air quality and climate in New Delhi- A sector based analysis

NASA Astrophysics Data System (ADS)

Marrapu, Pallavi

Deteriorating air quality is one of the major problems faced worldwide and in particular in Asia. The world's most polluted megacities are located in Asia highlighting the urgent need for efforts to improve the air quality. New Delhi (India), one of the world's most polluted cities, was the host of the Common Wealth Games during the period of 4-14 October 2010. This high profile event provided a good opportunity to accelerate efforts to improve air quality. Computational advances now allow air quality forecast models to fully couple the meteorology with chemical constituents within a unified modeling system that allows two-way interactions. The WRF-Chem model is used to simulate air quality in New Delhi. The thesis focuses on evaluating air quality and meteorology feedbacks. Four nested domains ranging from South Asia, Northern India, NCR Delhi and Delhi city at 45km, 15km, 5km and 1.67km resolution for a period of 20 day (26th Sep--15th Oct, 2010) are used in the study. The predicted mean surface concentrations of various pollutants show similar spatial distributions with peak values in the middle of the domain reflecting the traffic and population patterns in the city. Along with these activities, construction dust and industrial emissions contribute to high levels of criteria pollutants. The study evaluates the WRF-Chem capabilities using a new emission inventory developed over Delhi at a fine resolution of 1.67km and evaluating the results with observational data from 11 monitoring sties placed at various Game venues. The contribution of emission sectors including transportation, power, industry, and domestic to pollutant concentrations at targeted regions are studied and the results show that transportation and domestic sector are the major contributors to the pollution levels in Delhi, followed by industry. Apart from these sectors, emissions outside of Delhi contribute 20-50% to surface concentrations depending on the species. This indicates that pollution control efforts should take a regional perspective. Air quality projections in Delhi for 2030 are investigated. The Greenhouse Gas and Air Pollution I nteractions and Synergies (GAINS) model is used to generate a 2030 future emission scenario for Delhi using projections of air quality control measures and energy demands. Net reductions in CO concentrations by 50%, and increases of 140% and 40% in BC and NOx concentrations, respectively, are predicted. The net changes in concentration are associated with increases in transport and industry sectors. The domestic sector still has a significant contribution to air pollutant levels. The air quality levels show a profound effect under this scenario on the environment and human health. The increase in pollution from 2010 to 2030 is predicted to cause an increase in surface temperature by ˜0.65K. These increasing pollution levels also show effects on the radiative forcing. The high aerosols loading i.e. BC, PM2.5 and PM10 levels show strong influence on the short and longwave fluxes causing strong surface dimming and strong atmosphere heating due to BC. These results indicate transport and domestic sectors should be targeted for air quality and climate mitigations.

Modeling the influence of climate change on the mass balance of polychlorinated biphenyls in the Adriatic Sea.

PubMed

Lamon, Lara; MacLeod, Matthew; Marcomini, Antonio; Hungerbühler, Konrad

2012-05-01

Climate forcing is forecasted to influence the Adriatic Sea region in a variety of ways, including increasing temperature, and affecting wind speeds, marine currents, precipitation and water salinity. The Adriatic Sea is intensively developed with agriculture, industry, and port activities that introduce pollutants to the environment. Here, we developed and applied a Level III fugacity model for the Adriatic Sea to estimate the current mass balance of polychlorinated biphenyls in the Sea, and to examine the effects of a climate change scenario on the distribution of these pollutants. The model's performance was evaluated for three PCB congeners against measured concentrations in the region using environmental parameters estimated from the 20th century climate scenario described in the Special Report on Emission Scenarios (SRES) by the IPCC, and using Monte Carlo uncertainty analysis. We find that modeled fugacities of PCBs in air, water and sediment of the Adriatic are in good agreement with observations. The model indicates that PCBs in the Adriatic Sea are closely coupled with the atmosphere, which acts as a net source to the water column. We used model experiments to assess the influence of changes in temperature, wind speed, precipitation, marine currents, particulate organic carbon and air inflow concentrations forecast in the IPCC A1B climate change scenario on the mass balance of PCBs in the Sea. Assuming an identical PCBs' emission profile (e.g. use pattern, treatment/disposal of stockpiles, mode of entry), modeled fugacities of PCBs in the Adriatic Sea under the A1B climate scenario are higher because higher temperatures reduce the fugacity capacity of air, water and sediments, and because diffusive sources to the air are stronger. Copyright © 2012 Elsevier Ltd. All rights reserved.

Impacts of meteorological conditions on wintertime PM2.5 pollution in Taiyuan, North China.

PubMed

Miao, Yucong; Liu, Shuhua; Guo, Jianping; Yan, Yan; Huang, Shunxiang; Zhang, Gen; Zhang, Yong; Lou, Mengyun

2018-05-23

Taiyuan frequently experiences heavy PM 2.5 pollution in winter under unfavorable meteorological conditions. To understand how the meteorological factors influence the pollution in Taiyuan, this study involved a systematic analysis for a continuous period from November 2016 to January 2017, using near-surface meteorological observations, radiosonde soundings, PM 2.5 measurements, and three-dimension numerical simulation, in combination with backward trajectory calculations. The results show that PM 2.5 concentration positively correlates with surface temperature and relative humidity and anti-correlates with near-surface wind speed and boundary layer height (BLH). The low BLH is often associated with a strong thermal inversion layer capping over. In addition to the high local emissions, it is found that under certain synoptic conditions, the southwesterly and southerly winds could bring pollutants from Linfen to Taiyuan, leading to a near-surface PM 2.5 concentration higher than 200 μg m -3 . Another pollution enhancing issue is due to the semi-closed basin of Taiyuan affecting the planetary boundary layer (PBL): the surrounding mountains favor the formation of a cold air pool in the basin, which inhibits vertical exchanges of heat, flux, and momentum between PBL and the free troposphere, resulting in stagnant conditions and poor air quality in Taiyuan. These findings can be utilized to improve the understanding of PM 2.5 pollution in Taiyuan, to enhance the accuracy of forecasting pollution, and to provide scientific support for policy makers to mitigate the pollution.

The Impact of Future Emissions Changes on Air Pollution Concentrations and Related Human Health Effects

NASA Astrophysics Data System (ADS)

Mikolajczyk, U.; Suppan, P.; Williams, M.

2015-12-01

Quantification of potential health benefits of reductions in air pollution on the local scale is becoming increasingly important. The aim of this study is to conduct health impact assessment (HIA) by utilizing regionally and spatially specific data in order to assess the influence of future emission scenarios on human health. In the first stage of this investigation, a modeling study was carried out using the Weather Research and Forecasting (WRF) model coupled with Chemistry to estimate ambient concentrations of air pollutants for the baseline year 2009, and for the future emission scenarios in southern Germany. Anthropogenic emissions for the baseline year 2009 are derived from the emission inventory provided by the Netherlands Organization of Applied Scientific Research (TNO) (Denier van der Gon et al., 2010). For Germany, the TNO emissions were replaced by gridded emission data with a high spatial resolution of 1/64 x 1/64 degrees. Future air quality simulations are carried out under different emission scenarios, which reflect possible energy and climate measures in year 2030. The model set-up included a nesting approach, where three domains with horizontal resolution of 18 km, 6 km and 2 km were defined. The simulation results for the baseline year 2009 are used to quantify present-day health burdens. Concentration-response functions (CRFs) for PM2.5 and NO2 from the WHO Health risks of air Pollution in Europe (HRAPIE) project were applied to population-weighted mean concentrations to estimate relative risks and hence to determine numbers of attributable deaths and associated life-years lost. In the next step, future health impacts of projected concentrations were calculated taking into account different emissions scenarios. The health benefits that we assume with air pollution reductions can be used to provide options for future policy decisions to protect public health.

Air quality in Delhi during the CommonWealth Games

NASA Astrophysics Data System (ADS)

Marrapu, P.; Cheng, Y.; Beig, G.; Sahu, S.; Srinivas, R.; Carmichael, G. R.

2014-04-01

Air quality during The CommonWealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the Games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m-3, respectively. Black carbon (BC) accounted for ∼10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ∼25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by: 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5 and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change. The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ∼25% for BC and PM to ∼60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

Probabilistic Predictions of PM2.5 Using a Novel Ensemble Design for the NAQFC

NASA Astrophysics Data System (ADS)

Kumar, R.; Lee, J. A.; Delle Monache, L.; Alessandrini, S.; Lee, P.

2017-12-01

Poor air quality (AQ) in the U.S. is estimated to cause about 60,000 premature deaths with costs of 100B-150B annually. To reduce such losses, the National AQ Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of ozone, particulate matter less than 2.5 mm in diameter (PM2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit the exposure and reduce air pollution-caused health problems. The current NAQFC, based on the U.S. Environmental Protection Agency Community Multi-scale AQ (CMAQ) modeling system, provides only deterministic AQ forecasts and does not quantify the uncertainty associated with the predictions, which could be large due to the chaotic nature of atmosphere and nonlinearity in atmospheric chemistry. This project aims to take NAQFC a step further in the direction of probabilistic AQ prediction by exploring and quantifying the potential value of ensemble predictions of PM2.5, and perturbing three key aspects of PM2.5 modeling: the meteorology, emissions, and CMAQ secondary organic aerosol formulation. This presentation focuses on the impact of meteorological variability, which is represented by three members of NOAA's Short-Range Ensemble Forecast (SREF) system that were down-selected by hierarchical cluster analysis. These three SREF members provide the physics configurations and initial/boundary conditions for the Weather Research and Forecasting (WRF) model runs that generate required output variables for driving CMAQ that are missing in operational SREF output. We conducted WRF runs for Jan, Apr, Jul, and Oct 2016 to capture seasonal changes in meteorology. Estimated emissions of trace gases and aerosols via the Sparse Matrix Operator Kernel (SMOKE) system were developed using the WRF output. WRF and SMOKE output drive a 3-member CMAQ mini-ensemble of once-daily, 48-h PM2.5 forecasts for the same four months. The CMAQ mini-ensemble is evaluated against both observations and the current operational deterministic NAQFC products, and analyzed to assess the impact of meteorological biases on PM2.5 variability. Quantification of the PM2.5 prediction uncertainty will prove a key factor to support cost-effective decision-making while protecting public health.

Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

NASA Astrophysics Data System (ADS)

Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

1998-11-01

Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.

Emission Sectoral Contributions of Foreign Emissions to Particulate Matter Concentrations over South Korea

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Kim, H. C.; Kim, B. U.; Cho, J. H.; Woo, J. H.

2017-12-01

In this study, we investigated the contributions of major emission source categories located upwind of South Korea to Particulate Matter (PM) in South Korea. In general, air quality in South Korea is affected by anthropogenic air pollutants emitted from foreign countries including China. Some studies reported that foreign emissions contributed 50 % of annual surface PM total mass concentrations in the Seoul Metropolitan Area, South Korea in 2014. Previous studies examined PM contributions of foreign emissions from all sectors considering meteorological variations. However, little studies conducted to assess contributions of specific foreign source categories. Therefore, we attempted to estimate sectoral contributions of foreign emissions from China to South Korea PM using our air quality forecasting system. We used Model Inter-Comparison Study in Asia 2010 for foreign emissions and Clean Air Policy Support System 2010 emission inventories for domestic emissions. To quantify contributions of major emission sectors to South Korea PM, we applied the Community Multi-scale Air Quality system with brute force method by perturbing emissions from industrial, residential, fossil-fuel power plants, transportation, and agriculture sectors in China. We noted that industrial sector was pre-dominant over the region except during cold season for primary PMs when residential emissions drastically increase due to heating demand. This study will benefit ensemble air quality forecasting and refined control strategy design by providing quantitative assessment on seasonal contributions of foreign emissions from major source categories.

Impacts of Particulate Pollution from Fossil Fuel and Biomass Burnings on the Air Quality and Human Health in Southeast Asia

NASA Astrophysics Data System (ADS)

Lee, H. H.; Iraqui, O.; Gu, Y.; Yim, S. H. L.; Wang, C.

2017-12-01

Severe haze events in Southeast Asia have attracted the attention of governments and the general public in recent years, due to their impact on local economies, air quality and public health. Widespread biomass burning activities are a major source of severe haze events in Southeast Asia. On the other hand, particulate pollutants from human activities other than biomass burning also play an important role in degrading air quality in Southeast Asia. These pollutants can be locally produced or brought in from neighboring regions by long-range transport. A better understanding of the respective contributions of fossil fuel and biomass burning aerosols to air quality degradation becomes an urgent task in forming effective air pollution mitigation policies in Southeast Asia. In this study, to examine and quantify the contributions of fossil fuel and biomass burning aerosols to air quality and visibility degradation over Southeast Asia, we conducted three numerical simulations using the Weather Research and Forecasting (WRF) model coupled with a chemistry component (WRF-Chem). These simulations were driven by different aerosol emissions from: (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. By comparing the simulation results, we examined the corresponding impacts of fossil fuel and biomass burning emissions, separately and combined, on the air quality and visibility of the region. The results also showed that the major contributors to low visibility days (LVDs) among 50 ASEAN cities are fossil fuel burning aerosols (59%), while biomass burning aerosols provided an additional 13% of LVDs in Southeast Asia. In addition, the number of premature mortalities among ASEAN cities has increased from 4110 in 2002 to 6540 in 2008, caused primarily by fossil fuel burning aerosols. This study suggests that reductions in both fossil fuel and biomass burning emissions are necessary to improve the air quality in Southeast Asia.

PLAM - a meteorological pollution index for air quality and its applications in fog-haze forecasts in north China

NASA Astrophysics Data System (ADS)

Yang, Y.; Wang, J.; Gong, S.; Zhang, X.; Wang, H.; Wang, Y.; Wang, J.; Li, D.; Guo, J.

2015-03-01

Using surface meteorological observation and high resolution emission data, this paper discusses the application of PLAM/h Index (Parameter Linking Air-quality to Meteorological conditions/haze) in the prediction of large-scale low visibility and fog-haze events. Based on the two-dimensional probability density function diagnosis model for emissions, the study extends the diagnosis and prediction of the meteorological pollution index PLAM to the regional visibility fog-haze intensity. The results show that combining the influence of regular meteorological conditions and emission factors together in the PLAM/h parameterization scheme is very effective in improving the diagnostic identification ability of the fog-haze weather in North China. The correlation coefficients for four seasons (spring, summer, autumn and winter) between PLAM/h and visibility observation are 0.76, 0.80, 0.96 and 0.86 respectively and all their significance levels exceed 0.001, showing the ability of PLAM/h to predict the seasonal changes and differences of fog-haze weather in the North China region. The high-value correlation zones are respectively located in Jing-Jin-Ji (Beijing, Tianjin, Hebei), Bohai Bay rim and the southern Hebei-northern Henan, indicating that the PLAM/h index has relations with the distribution of frequent heavy fog-haze weather in North China and the distribution of emission high-value zone. Comparatively analyzing the heavy fog-haze events and large-scale fine weather processes in winter and summer, it is found that PLAM/h index 24 h forecast is highly correlated to the visibility observation. Therefore, PLAM/h index has better capability of doing identification, analysis and forecasting.

PLAM - a meteorological pollution index for air quality and its applications in fog-haze forecasts in North China

NASA Astrophysics Data System (ADS)

Yang, Y. Q.; Wang, J. Z.; Gong, S. L.; Zhang, X. Y.; Wang, H.; Wang, Y. Q.; Wang, J.; Li, D.; Guo, J. P.

2016-02-01

Using surface meteorological observation and high-resolution emission data, this paper discusses the application of the PLAM/h index (Parameter Linking Air-quality to Meteorological conditions/haze) in the prediction of large-scale low visibility and fog-haze events. Based on the two-dimensional probability density function diagnosis model for emissions, the study extends the diagnosis and prediction of the meteorological pollution index PLAM to the regional visibility fog-haze intensity. The results show that combining the influence of regular meteorological conditions and emission factors together in the PLAM/h parameterization scheme is very effective in improving the diagnostic identification ability of the fog-haze weather in North China. The determination coefficients for four seasons (spring, summer, autumn, and winter) between PLAM/h and visibility observation are 0.76, 0.80, 0.96, and 0.86, respectively, and all of their significance levels exceed 0.001, showing the ability of PLAM/h to predict the seasonal changes and differences of fog-haze weather in the North China region. The high-value correlation zones are located in Jing-Jin-Ji (Beijing, Tianjin, Hebei), Bohai Bay rim, and southern Hebei-northern Henan, indicating that the PLAM/h index is related to the distribution of frequent heavy fog-haze weather in North China and the distribution of emission high-value zone. Through comparative analysis of the heavy fog-haze events and large-scale clear-weather processes in winter and summer, it is found that PLAM/h index 24 h forecast is highly correlated with the visibility observation. Therefore, the PLAM/h index has good capability in identification, analysis, and forecasting.

Nonlinear data assimilation for the regional modeling of maximum ozone values.

PubMed

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

2017-11-01

We present a new method of data assimilation with the aim of correcting the forecast of the maximum values of ozone in regional photo-chemical models for areas over complex terrain using multilayer perceptron artificial neural networks. Up until now, these types of models have been used as a single model for one location when forecasting concentrations of air pollutants. We propose a method for constructing a more ambitious model: a single model, which can be used at several locations because the model is spatially transferable and is valid for the whole 2D domain. To achieve this goal, we introduce three novel ideas. The new method improves correlation at measurement station locations by 10% on average and improves by approximately 5% elsewhere.

A WRF sensitivity study for summer ozone and winter PM events in California

NASA Astrophysics Data System (ADS)

Zhao, Z.; Chen, J.; Mahmud, A.; Di, P.; Avise, J.; DaMassa, J.; Kaduwela, A. P.

2014-12-01

Elevated summer ozone and winter PM frequently occur in the San Joaquin Valley (SJV) and the South Coast Air Basin (SCAB) in California. Meteorological conditions, such as wind, temperature and planetary boundary layer height (PBLH) play crucial roles in these air pollution events. Therefore, accurate representation of these fields from a meteorological model is necessary to successfully reproduce these air pollution events in subsequent air quality model simulations. California's complex terrain and land-sea interface can make it challenging for meteorological models to replicate the atmospheric conditions over the SJV and SCAB during extreme pollution events. In this study, the performance of the Weather Research and Forecasting Model (WRF) over these two regions for a summer month (July 2012) and a winter month (January 2013) is evaluated with different model configurations and forcing. Different land surface schemes (Pleim-Xiu vs. hybrid scheme), the application of observational and soil nudging, two SST datasets (the Global Ocean Data Assimilation Experiment (GODAE) SST vs. the default SST from North American Regional Reanalysis (NARR) reanalysis), and two land use datasets (the National Land Cover Data (NLCD) 2006 40-category vs. USGS 24-category land use data) have been tested. Model evaluation will focus on both surface and vertical profiles for wind, temperature, relative humidity, as well as PBLH. Sensitivity of the Community Multi-scale Air Quality Model (CMAQ) results to different WRF configurations will also be presented and discussed.

Updating representation of land surface-atmosphere feedbacks in airborne campaign modeling analysis

NASA Astrophysics Data System (ADS)

Huang, M.; Carmichael, G. R.; Crawford, J. H.; Chan, S.; Xu, X.; Fisher, J. A.

2017-12-01

An updated modeling system to support airborne field campaigns is being built at NASA Ames Pleiades, with focus on adjusting the representation of land surface-atmosphere feedbacks. The main updates, referring to previous experiences with ARCTAS-CARB and CalNex in the western US to study air pollution inflows, include: 1) migrating the WRF (Weather Research and Forecasting) coupled land surface model from Noah to improved/more complex models especially Noah-MP and Rapid Update Cycle; 2) enabling the WRF land initialization with suitably spun-up land model output; 3) incorporating satellite land cover, vegetation dynamics, and soil moisture data (i.e., assimilating Soil Moisture Active Passive data using the ensemble Kalman filter approach) into WRF. Examples are given of comparing the model fields with available aircraft observations during spring-summer 2016 field campaigns taken place at the eastern side of continents (KORUS-AQ in South Korea and ACT-America in the eastern US), the air pollution export regions. Under fair weather and stormy conditions, air pollution vertical distributions and column amounts, as well as the impact from land surface, are compared. These help identify challenges and opportunities for LEO/GEO satellite remote sensing and modeling of air quality in the northern hemisphere. Finally, we briefly show applications of this system on simulating Australian conditions, which would explore the needs for further development of the observing system in the southern hemisphere and inform the Clean Air and Urban Landscapes (https://www.nespurban.edu.au) modelers.

Intercontinental transport of pollution from North America to Europe: Airborne trace gas measurements over Central and Northern Europe during CONTRACE

NASA Astrophysics Data System (ADS)

Huntrieser, H.; Schlager, H.; Heland, J.; Forster, C.; Stohl, A.; Lawrence, M.; Arnold, F.; Aufmhoff, H.; Cooper, O.

2003-04-01

The CONTRACE project investigates the uplift of pollution in frontal systems (warm conveyor belts) over North America and the transport of these air masses to Europe. The first airborne field experiment was carried out from Southern Germany in fall 2001. The DLR research aircraft Falcon was equipped with a complex instrumentation to measure NO, NOy, CO, CO2, O3, J(NO2), acetone, SO2, ions, H2O2, formaldehyde, NMHC, J(O1D) and particles. An extensive set of chemical and meteorological forecast products, including trajectory calculations, was developed and used in combination with satellite images to plan the flights. A passive tracer for surface emissions (CO) was included in the forecast models to separate the regional and intercontinental transport of polluted air masses. For the first time it succeeded to guide the Falcon aircraft into pollution plumes transported all the way from North America (NA). On 22nd November a complex chemical weather situation was predicted for Central Europe with lifting of European emissions into the lower troposphere ahead of an approaching cold front and simultaneously, the advection of a pollution plume from Eastern NA in mid tropospheric layers. Similar CO mixing ratios were observed in both plumes making it difficult to distinguish the two plumes without additional trace gas information. The European pollution plume was characterized by large enhancements in the CO (150 ppbv) and NOy (6 ppbv) mixing ratios. The NOy/CO ratio was 0.135 (typical value for fresh emissions). In comparison the estimated NOy/CO ratio for the NA pollution plume was 0.010 which indicate a tracer age of 4 days. The observed CO and NOy mixing ratios in this plume were 160 ppbv and 1 ppbv. The two plumes were also characterized by very different O3/CO relationships. In the plume from NA a positive O3/CO slope was observed indicating photochemical ozone production (O3 mixing ratios up to 50 ppbv were observed). Most likely O3 was produced photochemically in the polluted boundary layer over Southeastern North America and not in transit over the North Atlantic. The European plume showed a strong negative O3/CO relationship with O3 mixing ratios dropping down to 20 ppbv (due to titration by NO emissions in winter in northern latitudes).

Integrating Measurement Based New Knowledge on Wildland Fire Emissions and Chemistry into the AIRPACT Air Quality Forecasting for the Pacific Northwest

NASA Astrophysics Data System (ADS)

Nergui, T.; Lee, Y.; Chung, S. H.; Lamb, B. K.; Yokelson, R. J.; Barsanti, K.

2017-12-01

A number of chamber and field measurements have shown that atmospheric organic aerosols and their precursors produced from wildfires are significantly underestimated in the emission inventories used for air quality models for various applications such as regulatory strategy development, impact assessments of air pollutants, and air quality forecasting for public health. The AIRPACT real-time air quality forecasting system consistently underestimates surface level fine particulate matter (PM2.5) concentrations in the summer at both urban and rural locations in the Pacific Northwest, primarily result of errors in organic particulate matter. In this work, we implement updated chemical speciation and emission factors based on FLAME-IV (Fourth Fire Lab at Missoula Experiment) and other measurements in the Blue-Sky fire emission model and the SMOKE emission preprocessor; and modified parameters for the secondary organic aerosol (SOA) module in CMAQ chemical transport model of the AIRPACT modeling system. Simulation results from CMAQ version 5.2 which has a better treatment for anthropogenic SOA formation (as a base case) and modified parameterization used for fire emissions and chemistry in the model (fire-soa case) are evaluated against airborne measurements downwind of the Big Windy Complex Fire and the Colockum Tarps Fire, both of which occurred in the Pacific Northwest in summer 2013. Using the observed aerosol chemical composition and mass loadings for organics, nitrate, sulfate, ammonium, and chloride from aircraft measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and the Biomass Burning Observation Project (BBOP), we assess how new knowledge gained from wildfire measurements improve model predictions for SOA and its contribution to the total mass of PM2.5 concentrations.

Quantifying the effect of air quality control measures during the 2010 Commonwealth Games at Delhi, India

NASA Astrophysics Data System (ADS)

Beig, Gufran; Chate, Dilip M.; Ghude, Sachin. D.; Mahajan, A. S.; Srinivas, R.; Ali, K.; Sahu, S. K.; Parkhi, N.; Surendran, D.; Trimbake, H. R.

2013-12-01

In 2010, the XIX Commonwealth Games (CWG-2010) were held in India for the first time at Delhi and involved 71 commonwealth nations and dependencies with more than 6000 athletes participating in 272 events. This was the largest international multi-sport event to be staged in India and strict emission controls were imposed during the games in order to ensure improved air quality for the participating athletes as a significant portion of the population in Delhi is regularly exposed to elevated levels of pollution. The air quality control measures ranged from vehicular and traffic controls to relocation of factories and reduction of power plant emissions. In order to understand the effects of these policy induced control measures, a network of air quality and weather monitoring stations was set-up across different areas in Delhi under the Government of India's System of Air quality Forecasting And Research (SAFAR) project. Simultaneous measurements of aerosols, reactive trace gases (e.g. NOx, O3, CO) and meteorological parameters were made before, during and after CWG-2010. Contrary to expectations, the emission controls implemented were not sufficient to reduce the pollutants, instead in some cases, causing an increase. The measured pollutants regularly exceeded the National Ambient Air Quality limits over the games period. The reasons for this increase are attributed to an underestimation of the required control measures, which resulted in inadequate planning. The results indicate that any future air quality control measures need to be well planned and strictly imposed in order to improve the air quality in Delhi, which affects a large population and is deteriorating rapidly. Thus, the presence of systematic high resolution data and realistic emission inventories through networks such as SAFAR will be directly useful for the future.

TEMPO Early Adopters in Air-Quality Forecasting, Planning and Assessment, Pollution Emissions, Health, Agriculture, and Environmental Impacts: Applications and Decision Support

NASA Astrophysics Data System (ADS)

Newchurch, M.; Zavodsky, B.; Chance, K.; Haynes, J.; Lefer, B. L.; Naeger, A.

2016-12-01

The AQ research community has a long legacy of using space-based observations (e.g., Solar Backscatter Ultraviolet Instrument [SBUV], Global Ozone Monitoring Experiment [GOME], Ozone Monitoring Instrument [OMI], and the Ozone Mapping & Profiler Suite [OMPS]) to study atmospheric chemistry. These measurements have been used to observe day-to-day and year-to-year changes in atmospheric constituents. However, they have not been able to capture the diurnal variability of pollution with enough temporal or spatial fidelity and a low enough latency for regular use by operational decision makers. As a result, the operational AQ community has traditionally relied on ground-based (e.g., collection stations, LIDAR) and airborne observing systems to study tropospheric chemistry. In order to maximize its utility for applications and decision support, there is a need to educate the community about the game-changing potential for the geostationary TEMPO mission well ahead of its expected launch date early in the third decade of this millinium. This NASA mission will engage user communities and enable science across the NASA Applied Science Focus Areas of Health and Air Quality, Disasters, Water Resources, and Ecological Forecasting, In addition, topics discussed will provide opportunities for collaborations extending TEMPO applications to future program areas in Agriculture, Weather and Climate (including Numerical Weather Prediction), Energy, and Oceans.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Ambient temperature and air quality in relation to small for gestational age and term low birthweight

PubMed Central

Ha, Sandie; Zhu, Yeyi; Liu, Danping; Sherman, Seth; Mendola, Pauline

2017-01-01

Background Exposures to extreme ambient temperature and air pollution are linked to adverse birth outcomes, but the associations with small for gestational age (SGA) and term low birthweight (tLBW) are unclear. We aimed to investigate exposures to site-specific temperature extremes and selected criteria air pollutants in relation to SGA and tLBW. Methods We linked medical records of 220,572 singleton births (2002–2008) from 12 US sites to local temperature estimated by the Weather Research and Forecasting model, and air pollution estimated by modified Community Multiscale Air Quality models. Exposures to hot (>95th percentile) and cold (<5th percentile) were defined using site-specific distributions of daily temperature over three-month preconception, each trimester, and whole-pregnancy. Average concentrations of five criteria air pollutants and six fine particulate matter constituents were also calculated for these pregnancy windows. Poisson regression with generalized estimating equations calculated the relative risks (RR) and 95% confidence intervals for SGA (weight <10th percentile conditional on gestational age and sex) and tLBW (≥37 weeks and <2,500 grams) associated with an interquartile range increment of air pollutants, and cold or hot compared to mild (5–95th percentile) temperature. Models were adjusted for maternal demographics, lifestyle, and clinical factors, season, and site. Results Compared to mild temperature, cold exposure during trimester 2 [RR: 1.21 (1.05–1.38)], trimester 3 [RR: 1.18 (1.03–1.36)], and whole-pregnancy [RR: 2.57 (2.27–2.91)]; and hot exposure during trimester 3 [RR: 1.31 (1.15–1.50)] and whole-pregnancy [RR: 2.49 (2.20–2.83)] increased tLBW risk. No consistent association was observed between temperature and SGA. Air pollutant analyses were generally null but preconception elemental carbon was associated with a 4% increase in SGA while dust particles increased tLBW by 10%. Particulate matter ≤10 microns in the second trimester and whole pregnancy also appeared related to tLBW. Conclusions: Our findings suggest prenatal exposures to extreme ambient temperature relative to usual environment may increase tLBW risk. Given concerns related to climate change, these findings merit further investigation. PMID:28258738

Survey of air cargo forecasting techniques

NASA Technical Reports Server (NTRS)

Kuhlthan, A. R.; Vermuri, R. S.

1978-01-01

Forecasting techniques currently in use in estimating or predicting the demand for air cargo in various markets are discussed with emphasis on the fundamentals of the different forecasting approaches. References to specific studies are cited when appropriate. The effectiveness of current methods is evaluated and several prospects for future activities or approaches are suggested. Appendices contain summary type analyses of about 50 specific publications on forecasting, and selected bibliographies on air cargo forecasting, air passenger demand forecasting, and general demand and modalsplit modeling.

The particulate-related health benefits of reducing power plant emissions

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

Schneider, C.

The report estimates the adverse human health effects due to exposure to particulate matter from power plants. Power plants are significant emitters of sulfur dioxide and nitrogen oxides. In many parts of the U.S., especially the Midwest, power plants are the largest contributors. These gases are harmful themselves, and they contribute to the formation of acid rain and particulate matter. Particulate matter reduces visibility, often producing a milky haze that blankets wide regions, and it is a serious public health problem. Over the past decade and more, numerous studies have linked particulate matter to a wide range of adverse healthmore » effects in people of all ages. Epidemiologists have consistently linked particulate matter with effects ranging from premature death, hospital admissions and asthma attacks to chronic bronchitis. This study documents the health impacts from power plant air pollution emissions. Using the best available emissions and air quality modeling programs, the stud y forecasts ambient air quality for a business-as-usual baseline scenario for 2007, assuming full implementation of the Acid Rain program and the U.S. Environmental Protection Agency's (EPA) Summer Smog rule (the 1999 NO{sub x} SIP Call). The study then estimates the attributable health impacts from all power plant emissions. Finally, the study estimates air quality for a specific policy alternative: reducing total power plant emissions of SO{sub 2} and NO{sub x} 75 percent form the levels emitted in 1997. The difference between this '75 percent reduction scenario' and the baseline provides an estimate of the health effects that would be avoided by this reduction in power plant emissions. In addition to the policy scenario, the work involved performing sensitivity analyses to examine alternative emission reductions and forecast ambient air quality using a second air quality model. EPA uses both air quality models extensively, and both suggest that power plants make a large contribution to ambient particulate matter levels in the Eastern U.S. To put the power plant results in context, air pollution from all on-road and off-road diesel engine emissions was also examined. The results suggest that both power plants and diesel engines make a large contribution to ambient particulate matter levels and the associated health effects. Chapter 2 describes the development of the emissions inventory. Chapter 3 describes the methods used to estimate changes in particulate matter concentrations. Chapter 4 describes general issues arising in estimating and valuing changes in adverse health effects associated with changes in particulate matter. Chapter 5 describes in some detail the methods used for estimating and valuing adverse health effects, and in Chapter 6, the results of the various analyses are presented. The study includes 6 appendices. Appendix A provides results of this analysis for all metropolitan areas in the U.S. and a list of the counties in each metropolitan area. Appendices B, C and D present a detailed examination of how the pollution emission estimates were derived and then translated into forecasts of ambient particulate matter levels.« less

Transport of Asian Aerosols and Trace Gases to North America During the INTEX-B Field Campaign: A Regional Chemical Transport Model Analysis.

NASA Astrophysics Data System (ADS)

Adhikary, B.; Kulkarni, S.; Carmichael, G. R.; Tang, Y.; Dallura, A.; Mena, M.; Streets, D.; Zhang, Q.

2007-12-01

The Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) was conducted over the Pacific Ocean during the 2006 North American spring season. One of the scientific objectives of the INTEX-B field campaign was to quantify the transport and chemical evolution/aging of Asian air pollution into North America. The field campaign deployed multiple experimental platforms such as satellites, aircrafts and surface measurements stations to study the pollution outflow to North America. Three dimensional chemical transport models were used to provide chemical weather forecasts and assist in flight planning during the mission. The Sulfur Transport and dEposition Model (STEM) is a regional chemical transport model developed at the University of Iowa. The STEM model was involved in providing chemical weather forecasts and assist in flight planning during the INTEX-B intensive field campaign. In this study we will report the STEM model performance of aerosols and trace gases in its ability to capture the pollutant plume with experimental observations obtained from the field campaign. The study will then relate the emissions of trace gases and aerosols to atmospheric composition, sources and sinks using the newly developed emissions inventory for the INTEX-B field campaign.

Impact of ozone observations on the structure of a tropical cyclone using coupled atmosphere-chemistry data assimilation

NASA Astrophysics Data System (ADS)

Lim, S.; Park, S. K.; Zupanski, M.

2015-04-01

Since the air quality forecast is related to both chemistry and meteorology, the coupled atmosphere-chemistry data assimilation (DA) system is essential to air quality forecasting. Ozone (O3) plays an important role in chemical reactions and is usually assimilated in chemical DA. In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting atmospheric as well as chemical variables. To identify the impact of O3 observations on TC structure, including atmospheric and chemical information, we employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with an ensemble-based DA algorithm - the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over the East Asia, our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts atmospheric and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on atmospheric variables was similar in both over China and near the TC. The analysis results are validated using several measures that include the cost function, root-mean-squared error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis - the cost function and root mean square error have decreased by 16.9 and 8.87%, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeast China.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

Impacts of synoptic condition and planetary boundary layer structure on the trans-boundary aerosol transport from Beijing-Tianjin-Hebei region to northeast China

NASA Astrophysics Data System (ADS)

Miao, Yucong; Guo, Jianping; Liu, Shuhua; Zhao, Chun; Li, Xiaolan; Zhang, Gen; Wei, Wei; Ma, Yanjun

2018-05-01

The northeastern China frequently experiences severe aerosol pollution in winter under unfavorable meteorological conditions. How and to what extent the meteorological factors affect the air quality there are not yet clearly understood. Thus, this study investigated the impacts of synoptic patterns on the aerosol transport and planetary boundary layer (PBL) structure in Shenyang from 1 to 3 December 2016, using surface observations, sounding measurements, satellite data, and three-dimensional simulations. Results showed that the aerosol pollution occurred in Shenyang was not only related to the local emissions, but also contributed by trans-boundary transport of aerosols from the Beiijng-Tianjin-Hebei (BTH) region. In the presence of the westerly and southwesterly synoptic winds, the aerosols emitted from BTH could be brought to Shenyang. From December 2 to 3, the aerosols emitted from BTH accounted for ∼20% of near-surface PM2.5 in Shenyang. In addition, the large-scale synoptic forcings could affect the vertical mixing of pollutants through modulating the PBL structure in Shenyang. The westerly and southwesterly synoptic winds not only brought the aerosols but also the warmer air masses from the southwest regions to Shenyang. The strong warm advections above PBL could enhance the already existing thermal inversion layers capping over PBL in Shenyang, leading to the suppressions of PBL. Both the trans-boundary transport of aerosols and the suppressions of PBL caused by the large-scale synoptic forcings should be partly responsible for the poor air quality in Shenyang, in addition to the high pollutant emissions. The present study revealed the physical mechanisms underlying the aerosol pollution in Shenyang, which has important implications for better forecasting and controlling the aerosols pollution.

Forecasting Foreign Currency Exchange Rates for Air Force Budgeting

DTIC Science & Technology

2015-03-26

FORECASTING FOREIGN CURRENCY EXCHANGE RATES FOR AIR FORCE BUDGETING THESIS MARCH 2015...States. AFIT-ENV-MS-15-M-178 FORECASTING FOREIGN CURRENCY EXCHANGE RATES FOR AIR FORCE BUDGETING THESIS Presented to the Faculty...FORECASTING FOREIGN CURRENCY EXCHANGE RATES FOR AIR FORCE BUDGETING Nicholas R. Gardner, BS Captain, USAF Committee Membership: Lt Col Jonathan

Overview of the 2006 MILAGRO Campaign in Mexico City: Transport and Transformation of Emissions from a Megacity

NASA Astrophysics Data System (ADS)

Molina, L.; MILAGRO Science Team

2009-04-01

Megacities (metropolitan areas with population over 10 million) and large urban centers present a major challenge for the global environment. Population growth, increasing motorization and industrialization have resulted in a higher demand for energy, greater use of fossil fuels, and more emission of pollutants into the atmosphere. As a result, air pollution has become not only one of the central environmental problems of the century, but also presents serious consequences to human health and ecosystems and economic costs to society. MILAGRO (Megacity Initiative: Local and Global Research Observations) is the first international effort to study the impact of air pollutants generated and exported by megacity. The Mexico City Metropolitan Area (MCMA) - one of the largest megacities in the world - was selected as the initial case study for MILAGRO. The measurement phase consisted of a month-long series of carefully coordinated observations of the chemistry and physics of the atmosphere in and near Mexico City during March 2006, using a wide range of instruments at ground sites, on aircraft and satellites, complemented by meteorological forecasting and numerical simulations. Together, these research observations have provided the most comprehensive characterization of Mexico City's urban and regional air pollution that will take years to analyze and evaluate fully. Initial analysis of the data is focused on understanding meteorology, emissions, urban and regional photochemistry, aerosol evolution and radiative effects - spanning the urban to regional scale transition. Many interesting aspects of atmospheric chemistry in and near the MCMA are emerging and have already added significantly to our understanding of the chemical and physical properties of the city's reactive atmosphere and the regional impacts. The information can be useful for decision-makers in Mexico in developing air quality management strategies as well as provide insights to air pollution problems in other megacities and large urban centers around the world.

Air pollutant emissions from Chinese households: A major and underappreciated ambient pollution source

PubMed Central

Liu, Jun; Mauzerall, Denise L.; Chen, Qi; Zhang, Qiang; Song, Yu; Peng, Wei; Klimont, Zbigniew; Qiu, Xinghua; Zhang, Shiqiu; Hu, Min; Lin, Weili; Smith, Kirk R.; Zhu, Tong

2016-01-01

As part of the 12th Five-Year Plan, the Chinese government has developed air pollution prevention and control plans for key regions with a focus on the power, transport, and industrial sectors. Here, we investigate the contribution of residential emissions to regional air pollution in highly polluted eastern China during the heating season, and find that dramatic improvements in air quality would also result from reduction in residential emissions. We use the Weather Research and Forecasting model coupled with Chemistry to evaluate potential residential emission controls in Beijing and in the Beijing, Tianjin, and Hebei (BTH) region. In January and February 2010, relative to the base case, eliminating residential emissions in Beijing reduced daily average surface PM2.5 (particulate mater with aerodynamic diameter equal or smaller than 2.5 micrometer) concentrations by 14 ± 7 μg⋅m−3 (22 ± 6% of a baseline concentration of 67 ± 41 μg⋅m−3; mean ± SD). Eliminating residential emissions in the BTH region reduced concentrations by 28 ± 19 μg⋅m−3 (40 ± 9% of 67 ± 41 μg⋅m−3), 44 ± 27 μg⋅m−3 (43 ± 10% of 99 ± 54 μg⋅m−3), and 25 ± 14 μg⋅m−3 (35 ± 8% of 70 ± 35 μg⋅m−3) in Beijing, Tianjin, and Hebei provinces, respectively. Annually, elimination of residential sources in the BTH region reduced emissions of primary PM2.5 by 32%, compared with 5%, 6%, and 58% achieved by eliminating emissions from the transportation, power, and industry sectors, respectively. We also find air quality in Beijing would benefit substantially from reductions in residential emissions from regional controls in Tianjin and Hebei, indicating the value of policies at the regional level. PMID:27354524

“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

A Study on the Potential Applications of Satellite Data in Air Quality Monitoring and Forecasting

NASA Technical Reports Server (NTRS)

Li, Can; Hsu, N. Christina; Tsay, Si-Chee

2011-01-01

In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM(sub 10) concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT-PM(sub 10) correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions, Meteorological factors, particularly relative humidity, were found to influence the AOT-PM(sub 10) relationship. Their inclusion in regression models leads to more accurate assessment of PM(sub 10) from space borne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution, In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM(sub 10) level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM(sub 10) hindcasts for 2008-2009, using ohservations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error less than or equal to 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing, Further studies will be necessa1Y before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.

Merging Air Quality and Public Health Decision Support Systems

NASA Astrophysics Data System (ADS)

Hudspeth, W. B.; Bales, C. L.

2003-12-01

The New Mexico Air Quality Mapper (NMAQM) is a Web-based, open source GIS prototype application that Earth Data Analysis Center is developing under a NASA Cooperative Agreement. NMAQM enhances and extends existing data and imagery delivery systems with an existing Public Health system called the Rapid Syndrome Validation Project (RSVP). RSVP is a decision support system operating in several medical and public health arenas. It is evolving to ingest remote sensing data as input to provide early warning of human health threats, especially those related to anthropogenic atmospheric pollutants and airborne pathogens. The NMAQM project applies measurements of these atmospheric pollutants, derived from both remotely sensed data as well as from in-situ air quality networks, to both forecasting and retrospective analyses that influence human respiratory health. NMAQM provides a user-friendly interface for visualizing and interpreting environmentally-linked epidemiological phenomena. The results, and the systems made to provide the information, will be applicable not only to decision-makers in the public health realm, but also to air quality organizations, demographers, community planners, and other professionals in information technology, and social and engineering sciences. As an accessible and interactive mapping and analysis application, it allows environment and health personnel to study historic data for hypothesis generation and trend analysis, and then, potentially, to predict air quality conditions from daily data acquisitions. Additional spin off benefits to such users include the identification of gaps in the distribution of in-situ monitoring stations, the dissemination of air quality data to the public, and the discrimination of local vs. more regional sources of air pollutants that may bear on decisions relating to public health and public policy.

Mid-latitude storm track variability and its influence on atmospheric composition

NASA Astrophysics Data System (ADS)

Knowland, K. E.; Doherty, R. M.; Hodges, K.

2013-12-01

Using the storm tracking algorithm, TRACK (Hodges, 1994, 1995, 1999), we have studied the behaviour of storm tracks in the North Atlantic basin, using 850-hPa relative vorticity from the ERA-Interim Re-analysis (Dee et al., 2011). We have correlated surface ozone measurements at rural coastal sites in Europe to the storm track data to explore the role mid-latitude cyclones and their transport of pollutants play in determining surface air quality in Western Europe. To further investigate this relationship, we have used the Monitoring Atmospheric Composition Climate (MACC) Re-analysis dataset (Inness et al., 2013) in TRACK. The MACC Re-analysis is a 10-year dataset which couples a chemistry transport model (Mozart-3; Stein 2009, 2012) to an extended version of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS). Storm tracks in the MACC Re-analysis compare well to the storm tracks using the ERA-Interim Re-analysis for the same 10-year period, as both are based on ECMWF IFSs. We also compare surface ozone values from MACC to surface ozone measurements previously studied. Using TRACK, we follow ozone (O3) and carbon monoxide (CO) through the life cycle of storms from North America to Western Europe. Along the storm tracks, we examine the distribution of CO and O3 within 6 degrees of the center of each storm and vertically at different pressure levels in the troposphere. We hope to better understand the mechanisms with which pollution is vented from the boundary layer to the free troposphere, as well as transport of pollutants to rural areas. Our hope is to give policy makers more detailed information on how climate variability associated with storm tracks between 1979-2013 may affect air quality in Northeast USA and Western Europe.

Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

2010-01-01

Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.

PubMed

Abderrahim, Hamza; Chellali, Mohammed Reda; Hamou, Ahmed

2016-01-01

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9% has been reached.

Air Quality Modeling Using the NASA GEOS-5 Multispecies Data Assimilation System

NASA Technical Reports Server (NTRS)

Keller, Christoph A.; Pawson, Steven; Wargan, Krzysztof; Weir, Brad

2018-01-01

The NASA Goddard Earth Observing System (GEOS) data assimilation system (DAS) has been expanded to include chemically reactive tropospheric trace gases including ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). This system combines model analyses from the GEOS-5 model with detailed atmospheric chemistry and observations from MLS (O3), OMI (O3 and NO2), and MOPITT (CO). We show results from a variety of assimilation test experiments, highlighting the improvements in the representation of model species concentrations by up to 50% compared to an assimilation-free control experiment. Taking into account the rapid chemical cycling of NO2 when applying the assimilation increments greatly improves assimilation skills for NO2 and provides large benefits for model concentrations near the surface. Analysis of the geospatial distribution of the assimilation increments suggest that the free-running model overestimates biomass burning emissions but underestimates lightning NOx emissions by 5-20%. We discuss the capability of the chemical data assimilation system to improve atmospheric composition forecasts through improved initial value and boundary condition inputs, particularly during air pollution events. We find that the current assimilation system meaningfully improves short-term forecasts (1-3 day). For longer-term forecasts more emphasis on updating the emissions instead of initial concentration fields is needed.

Ten years of OMI observations: scientific highlights and impacts on the new generation of UV/VIS satellite instrumentation

NASA Astrophysics Data System (ADS)

Levelt, Pieternel; Veefkind, Pepijn; Bhartia, Pawan; Joiner, Joanna; Tamminen, Johanna; OMI Science Team

2014-05-01

On July 15, 2004 Ozone Monitoring Instrument (OMI) was successfully launched from the Vandenberg military air force basis in California, USA, on NASA's EOS-Aura spacecraft. OMI is the first of a new generation of UV/VIS nadir solar backscatter imaging spectrometers, which provides nearly global coverage in one day with an unprecedented spatial resolution of 13 x 24 km2. OMI measures solar irradiance and Earth radiances in the wavelength range of 270 to 500 nm with a spectral resolution of about 0.5 nm. OMI is designed and built by the Netherlands and Finland and is also a third party mission of ESA. The major step that was made in the OMI instrument compared to its predecessors is the use of 2-dimensional detector arrays (CCDs) in a highly innovative small optical design. These innovations enable the combination of a high spatial resolution and a good spectral resolution with daily global coverage. OMI measures a range of trace gases (O3, NO2, SO2, HCHO, BrO, OClO, H2O), clouds and aerosols. Albeit OMI is already 5 years over its design lifetime, the instrument is still fully operational. The successor of OMI is TROPOMI (TROPOspheric Monitoring Instrument) on the Copernicus Sentinel-5 precursor mission, planned for launch in 2015. OMI's unique capabilities rely in measuring tropospheric trace gases with a small footprint and daily global coverage. The unprecedented spatial resolution of the instrument revealed for the first time tropospheric pollution maps on a daily basis with urban scale resolution leading to improved air quality forecasts. The OMI measurements also improve our understanding of air quality and the interaction between air quality and climate change by combining measurements of air pollutants and aerosols. In recent years the data are also used for obtaining high-resolution global emission maps using inverse modelling or related techniques, challenging the bottom-up inventories based emission maps. In addition to scientific research, OMI also contributes to several operational services, including volcanic plume warning systems for aviation, UV forecasts and the air quality forecasts. In this invited talk an overview will be given of unique findings and new scientific results based on OMI data over the last 10 years and which unique OMI instrument features are recurring in the new generation of UV/VIS satellite instrumentation in Europe, USA and Asia.

Application of the Bulgarian emergency response system in case of nuclear accident in environmental assessment study

NASA Astrophysics Data System (ADS)

Syrakov, Dimiter; Veleva, Blagorodka; Georgievs, Emilia; Prodanova, Maria; Slavov, Kiril; Kolarova, Maria

2014-05-01

The development of the Bulgarian Emergency Response System (BERS) for short term forecast in case of accidental radioactive releases to the atmosphere has been started in the mid 1990's [1]. BERS comprises of two main parts - operational and accidental, for two regions 'Europe' and 'Northern Hemisphere'. The operational part runs automatically since 2001 using the 72 hours meteorological forecast from DWD Global model, resolution in space of 1.5o and in time - 12 hours. For specified Nuclear power plants (NPPs), 3 days trajectories are calculated and presented on NIMH's specialized Web-site (http://info.meteo.bg/ews/). The accidental part is applied when radioactive releases are reported or in case of emergency exercises. BERS is based on numerical weather forecast information and long-range dispersion model accounting for the transport, dispersion, and radioactive transformations of pollutants. The core of the accidental part of the system is the Eulerian 3D dispersion model EMAP calculating concentration and deposition fields [2]. The system is upgraded with a 'dose calculation module' for estimation of the prognostic dose fields of 31 important radioactive gaseous and aerosol pollutants. The prognostic doses significant for the early stage of a nuclear accident are calculated as follows: the effective doses from external irradiation (air submersion + ground shinning); effective dose from inhalation; summarized effective dose and absorbed thyroid dose [3]. The output is given as 12, 24, 36, 48, 60 and 72 hours prognostic dose fields according the updated meteorology. The BERS was upgraded to simulate the dispersion of nuclear materials from Fukushima NPP [4], and results were presented in NIMH web-site. In addition BERS took part in the respective ENSEMBLE exercises to model 131I and 137Cs in Fukushima source term. In case of governmental request for expertise BERS was applied for environmental impact assessment of hypothetical accidental transboundary radioactive pollution. The consequences were estimated based on the worst emission scenario for the existing basic reactor type, selection of real meteorological forecast conditions, favoring the direct transport of the contaminated air masses to the territory of the country in consideration. In the present work BERS is used to estimate the worst case accidental scenario impact from a possible new unit of Paks Nuclear Power Plant, Hungary over the territory of Bulgaria. 1. D.Syrakov, M.Prodanova, 1998, Atmospheric Environment, 32 (24), 4367-4375. 2. D. Syrakov, M. Prodanova, K. Slavov, Inernationsal J. Environment and Pollution, 20, 1-6 (2003) 286-296. 3. D. Syrakov, B. Veleva, M. Prodanova, T. Popova, M. Kolarova, Journal of Environmental Radioactivity 100 (2009) 151-156. 4. D.Syrakov, M Prodanova, J. Intern. Sci. Publ.: Ecology & Safety Vol. 6 Part 1 (2011) 94-102. www.scientific-publications.net.

Air quality remote sensing over alpine regions with METEOSAT SEVIRI

NASA Astrophysics Data System (ADS)

Emili, E.; Popp, C.; Petitta, M.; Riffler, M.; Wunderle, S.

2009-04-01

It is well demonstrated that small aerosol particles or particulate matter (PM10 and PM2.5) affect air quality and can have severe effects on human's health. Hence, it is of great interest for public institutions to have an efficient PM monitoring network. In the last decades this data has been provided from ground-based instruments. Moreover, due to the fast development of space-borne remote sensing instruments, we can now be able to take advantage of air pollution measurements from space, which bears the potential to fill up the gap of spatial coverage from ground-based networks. This also improves the capability to assess air pollutants transport properties together with a better implementation in forecasting data assimilation procedures. In this study we examine the possibility of using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI), on-board of the geostationary Meteosat Second Generation (MSG) platform, to provide PM concentrations values over Switzerland. SEVIRI's high temporal resolution (15 minutes) could be very useful in investigating the daily behaviour of air pollutants and therefore be a good complement to measurements from polar orbiting sensors (e.g. MODIS). Switzerland is of particular interest because of its mountainous orography that hampers pollutants dispersion. Further, major transalpine connection routes, often characterised by high traffic load, act as a significant air pollution source. The south of Switzerland is also occasionally influenced by pollutants transported from the highly industrialised Po Valley in northern Italy. We investigate the existence of a linear relation between the SEVIRI retrieved AOD (Aerosol Optical Depth) and the PM concentration obtained from the ground-based air quality network NABEL (Nationales Beobachtungsnetz fuer Luftfremdstoffe). The temporal trend of this two quantities shows a significant relationship over various locations. The correlation coefficient is in some cases higher than 0.6, indicating the possibility of estimating PM concentrations from SEVIRI AOD with a reasonable uncertainty using a statistical empirical linear model. The quality of this approach is highly influenced by the seasonal variability and by the meteorological conditions. We also include meteorological data in order to investigate the observed correlation and to improve the statistical empirical model. Finally, the possible sources of errors for this approach are examined.

Weather and air pollutants have an impact on patients with respiratory diseases and breathing difficulties in Munich, Germany

NASA Astrophysics Data System (ADS)

Wanka, E. R.; Bayerstadler, A.; Heumann, C.; Nowak, D.; Jörres, R. A.; Fischer, R.

2014-03-01

This study determined the influence of various meteorological variables and air pollutants on airway disorders in general, and asthma and/or chronic obstructive pulmonary disease in particular, in Munich, Bavaria, during 2006 and 2007. This was achieved through an evaluation of the daily frequency of calls to medical and emergency call centres, ambulatory medical care visits at general practitioners, and prescriptions of antibiotics for respiratory diseases. Meteorological parameters were extracted from data supplied by the European Centre for Medium Range Weather Forecast. Data on air pollutant levels were extracted from the air quality database of the European Environmental Agency for different measurement sites. In addition to descriptive analyses, a backward elimination procedure was performed to identify variables associated with medical outcome variables. Afterwards, generalised additive models (GAM) were used to verify whether the selected variables had a linear or nonlinear impact on the medical outcomes. The analyses demonstrated associations between environmental parameters and daily frequencies of different medical outcomes, such as visits at GPs and air pressure (-27 % per 10 hPa change) or ozone (-24 % per 10 μg/m3 change). The results of the GAM indicated that the effects of some covariates, such as carbon monoxide on consultations at GPs, or humidity on medical calls in general, were nonlinear, while the type of association varied between medical outcomes. These data suggest that the multiple, complex effect of environmental factors on medical outcomes should not be assumed homogeneous or linear a priori and that different settings might be associated with different types of associations.

On The Development of One-way Nesting of Air-pollution Model Smog Into Numerical Weather Prediction Model Eta

NASA Astrophysics Data System (ADS)

Halenka, T.; Bednar, J.; Brechler, J.

The spatial distribution of air pollution on the regional scale (Bohemian region) is simulated by means of Charles University puff model SMOG. The results are used for the assessment of the concentration fields of ozone, nitrogen oxides and other ozone precursors. Current improved version of the model covers up to 16 groups of basic compounds and it is based on trajectory computation and puff interaction both by means of Gaussian diffusion mixing and chemical reactions of basic species. Gener- ally, the method used for trajectory computation is valuable mainly for episodes sim- ulation, nevertheless, climatological study can be solved as well by means of average wind rose. For the study being presented huge database of real emission sources was incorporated with all kind of sources included. Some problem with the background values of concentrations was removed. The model SMOG has been nested into the forecast model ETA to obtain appropriate meteorological data input. We can estimate air pollution characteristics both for episodes analysis and the prediction of future air quality conditions. Necessary prognostic variables from the numerical weather pre- diction model are taken for the region of the central Bohemia, where the original puff model was tested. We used mainly 850 hPa wind field for computation of prognos- tic trajectories, the influence of surface temperature as a parameter of photochemistry reactions as well as the effect of cloudness has been tested.

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

Aslan, Z.; Topcu, S.

A central objective of micrometeorological research is to establish fluxes from a knowledge of the mean temperature, humidity and wind speed profiles. The effect of time and spatial variations of surface heat and momentum fluxes is studied for various geographic regions. These analysis show the principal boundary conditions for micro and meso-scale analysis, air-sea interactions, weather forecasting air pollution, agrometeorology and climate changing models. The fluxes of heat and momentum can be obtained from observed profiles of wind speed and temperature using the similarity relations for the atmospheric surface layer. In recent years, harmonic analysis is a particularly useful toolmore » in studying annual patterns of some meteorological parameters at the field of micrometeorological studies.« less

Building statistical associations to forecast ethylbenzene levels in European urban-traffic environments.

PubMed

Vlachokostas, Ch; Michailidou, A V; Spyridi, D; Moussiopoulos, N

2013-06-01

Emission from road traffic has become the most important source of local air pollution in numerous European cities. Epidemiological research community has established consistent associations between traffic-related substances and various health outcomes. Nevertheless, the vast majority of urban areas are characterised by infrastructure's absence to routinely monitor chemical health stressors, such as ethylbenzene. This paper aims at developing and presenting a tractable approach to reliably - and inexpensively - predict ethylbenzene trends in EU urban environments. The establishment of empirical relationships between rarely monitored pollutants such as ethylbenzene and more frequently or usually monitored, such as benzene and CO respectively, may cover the infrastructure's absence and support decision-making. Multiple regression analysis is adopted and the resulting statistical associations are applied to EU cities with available data for validation purposes. The results demonstrate that this approach is capable of capturing ethylbenzene concentration trends and should be considered as complementary to air quality monitoring. Copyright © 2013 Elsevier Ltd. All rights reserved.

Impact of WRF model PBL schemes on air quality simulations over Catalonia, Spain.

PubMed

Banks, R F; Baldasano, J M

2016-12-01

Here we analyze the impact of four planetary boundary-layer (PBL) parametrization schemes from the Weather Research and Forecasting (WRF) numerical weather prediction model on simulations of meteorological variables and predicted pollutant concentrations from an air quality forecast system (AQFS). The current setup of the Spanish operational AQFS, CALIOPE, is composed of the WRF-ARW V3.5.1 meteorological model tied to the Yonsei University (YSU) PBL scheme, HERMES v2 emissions model, CMAQ V5.0.2 chemical transport model, and dust outputs from BSC-DREAM8bv2. We test the performance of the YSU scheme against the Assymetric Convective Model Version 2 (ACM2), Mellor-Yamada-Janjic (MYJ), and Bougeault-Lacarrère (BouLac) schemes. The one-day diagnostic case study is selected to represent the most frequent synoptic condition in the northeast Iberian Peninsula during spring 2015; regional recirculations. It is shown that the ACM2 PBL scheme performs well with daytime PBL height, as validated against estimates retrieved using a micro-pulse lidar system (mean bias=-0.11km). In turn, the BouLac scheme showed WRF-simulated air and dew point temperature closer to METAR surface meteorological observations. Results are more ambiguous when simulated pollutant concentrations from CMAQ are validated against network urban, suburban, and rural background stations. The ACM2 scheme showed the lowest mean bias (-0.96μgm -3 ) with respect to surface ozone at urban stations, while the YSU scheme performed best with simulated nitrogen dioxide (-6.48μgm -3 ). The poorest results were with simulated particulate matter, with similar results found with all schemes tested. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Psychosocial and demographic predictors of adherence and non-adherence to health advice accompanying air quality warning systems: a systematic review.

PubMed

D'Antoni, Donatella; Smith, Louise; Auyeung, Vivian; Weinman, John

2017-09-22

Although evidence shows that poor air quality can harm human health, we have a limited understanding about the behavioural impact of air quality forecasts. Our aim was to understand to what extent air quality warning systems influence protective behaviours in the general public, and to identify the demographic and psychosocial factors associated with adherence and non-adherence to the health advice accompanying these warnings. In August 2016 literature was systematically reviewed to find studies assessing intended or actual adherence to health advice accompanying air quality warning systems, and encouraging people to reduce exposure to air pollution. Predictors of adherence to the health advice and/or self-reported reasons for adherence or non-adherence were also systematically reviewed. Studies were included only if they involved participants who were using or were aware of these warning systems. Studies investigating only protective behaviours due to subjective perception of bad air quality alone were excluded. The results were narratively synthesised and discussed within the COM-B theoretical framework. Twenty-one studies were included in the review: seventeen investigated actual adherence; three investigated intended adherence; one assessed both. Actual adherence to the advice to reduce or reschedule outdoor activities during poor air quality episodes ranged from 9.7% to 57% (Median = 31%), whereas adherence to a wider range of protective behaviours (e.g. avoiding busy roads, taking preventative medication) ranged from 17.7% to 98.1% (Median = 46%). Demographic factors did not consistently predict adherence. However, several psychosocial facilitators of adherence were identified. These include knowledge on where to check air quality indices, beliefs that one's symptoms were due to air pollution, perceived severity of air pollution, and receiving advice from health care professionals. Barriers to adherence included: lack of understanding of the indices, being exposed to health messages that reduced both concern about air pollution and perceived susceptibility, as well as perceived lack of self-efficacy/locus of control, reliance on sensory cues and lack of time. We found frequent suboptimal adherence rates to health advice accompanying air quality alerts. Several psychosocial facilitators and barriers of adherence were identified. To maximise their health effects, health advice needs to target these specific psychosocial factors.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

IASI Satellite Observation and Forecast of Pollutants

NASA Astrophysics Data System (ADS)

Clerbaux, C.; Boynard, A.; George, M.; Hadji-Lazaro, J.; Safieddine, S.; Viatte, C.; Clarisse, L.; Pierre-Francois, C.; Hurtmans, D.; van Damme, M.; Wespes, C.; Whitburn, S.

2017-12-01

The IASI family of instruments has been sounding the atmosphere since 2006 onboard the Metop satellite series. Using the radiance data recorded in the thermal infrared spectral range, concentrations for atmospheric pollutants such as carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2) and ammonia (NH3) can be derived. IASI CO and O3 fields are assimilated in regional and global models in order to predict air quality over Europe. Enhanced levels of pollutants are detected in near-real time, and can be followed at city, country and continent levels. This talk will present the findings for an extended time period (2008-2017), and will review the IASI capability to observe exceptional events both at the local and regional scales, as well as seasonal variations due other dynamic patterns (monsoon, ENSO, …). Progresses and current limitations to derive long term trends will also be discussed.

Updated emission inventories of power plants in simulating air quality during haze periods over East China

NASA Astrophysics Data System (ADS)

Zhang, Lei; Zhao, Tianliang; Gong, Sunling; Kong, Shaofei; Tang, Lili; Liu, Duanyang; Wang, Yongwei; Jin, Lianji; Shan, Yunpeng; Tan, Chenghao; Zhang, Yingjie; Guo, Xiaomei

2018-02-01

Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu Province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting model with Chemistry (WRF-Chem), two simulation experiments were executed to assess the atmospheric environment change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that power plant emissions of PM2.5, PM10, SO2, and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs (non-methane volatile organic compounds) were higher in UEIPP relative to those in MEIC, reflecting a large discrepancy in the power plant emissions over East China. In accordance with the changes in UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC, and CO. Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced. This was reflected by increased oxidizing agents, e.g., O3 and OH, thus directly strengthening the chemical production from SO2 and NOx to sulfate and nitrate, respectively, which offset the reduction of primary PM2.5 emissions especially on haze days. This study indicates the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with implications on air quality and environmental changes.

The Impact of Residential Combustion Emissions on Air Quality and Human Health in China

NASA Astrophysics Data System (ADS)

Archer-Nicholls, S.; Wiedinmyer, C.; Baumgartner, J.; Brauer, M.; Cohen, A.; Carter, E.; Frostad, J.; Forouzanfar, M.; Xiao, Q.; Liu, Y.; Yang, X.; Hongjiang, N.; Kun, N.

2015-12-01

Solid fuel cookstoves are used heavily in rural China for both residential cooking and heating purposes. Their use contributes significantly to regional emissions of several key pollutants, including carbon monoxide, volatile organic compounds, oxides of nitrogen, and aerosol particles. The residential sector was responsible for approximately 36%, 46% and 81% of China's total primary PM2.5, BC and OC emissions respectively in 2005 (Lei et al., 2011). These emissions have serious consequences for household air pollution, ambient air quality, tropospheric ozone formation, and the resulting population health and climate impacts. This paper presents initial findings from the modeling component of a multi-disciplinary energy intervention study currently being conducted in Sichuan, China. The purpose of this effort is to quantify the impact of residential cooking and heating emissions on regional air quality and human health. Simulations with varying levels of residential emissions have been carried out for the whole of 2014 using the Weather Research and Forecasting model with Chemistry (WRF-Chem), a fully-coupled, "online" regional chemical transport model. Model output is evaluated against surface air quality measurements across China and compared with seasonal (winter and summer) ambient air pollution measurements conducted at the Sichuan study site in 2014. The model output is applied to available exposure—response relationships between PM2.5 and cardiopulmonary health outcomes. The sensitivity in different regions across China to the different cookstove emission scenarios and seasonality of impacts are presented. By estimating the mortality and disease burden risk attributable to residential emissions we demonstrate the potential benefits from large-scale energy interventions. Lei Y, Zhang Q, He KB, Streets DG. 2011. Primary anthropogenic aerosol emission trends for China, 1990-2005. Atmos. Chem. Phys. 11:931-954.

Development of a Healthy Urban Route Planner for cyclists and pedestrians in Amsterdam

NASA Astrophysics Data System (ADS)

van der Molen, Michiel; Ligtenberg, Arend; Vreugdenhil, Corne; Steeneveld, Gert-Jan

2017-04-01

Cities are hotspots of air pollution and heat stress, the exposure to which results in nuisance, health risks, cost of medication, reduced labour productivity and sick leave for citizens. Yet the air pollution and heat stress are spatially and temporally unevenly distributed over the city, depending on pollutant emissions, street design and atmospheric turbulent mixing and radiation. This spatiotemporal variation allows pedestrians and bikers to choose alternative routes to minimize their exposure, if the distribution is known. In this project, we develop a route planner for bicyclists and pedestrians for Amsterdam (NL), that proposes routes and departure times based on model simulations of weather and air quality. We use the WRF-Chem atmosphere and air quality model at unprecedented grid spacing of 100-m (Ronda et al, 2015, Super et al, 2016), with an underlying urban canopy model and NOx and PM10 emissions. The emissions by traffic are calculated based on observed traffic intensities and emission factors. An urban land use map will characterize urban density and street configuration to estimate urban heat storage (Attema et al, 2015). WRF-Chem runs will be issued daily for a lead time of 48 hours, resulting in forecast maps of temperature and pollutant concentrations that will be uniquely expressed in a metric that combines both threats. The hourly fields of this metric are provided to the route planner based on the open source routing library pgRouting to identify the more healthy routes on the route network of Amsterdam. The objectives of the healthy urban route planner are to raise awareness of heat and air quality issues in Amsterdam, to provide an innovative adaptation tool for citizens and tourists, to locate the most important bottlenecks in (the exposure to) air pollution and heat stress, and ultimately to test the readiness of the travellers to use the information and adapt the route. We expect to particularly target a group of lung- and cardiovascular patients, and elderly people. In the future the planner will be expanded with pollen information and possibly with real-time traffic information.

How wild is your model fire? Constraining WRF-Chem wildfire smoke simulations with satellite observations

NASA Astrophysics Data System (ADS)

Fischer, E. V.; Ford, B.; Lassman, W.; Pierce, J. R.; Pfister, G.; Volckens, J.; Magzamen, S.; Gan, R.

2015-12-01

Exposure to high concentrations of particulate matter (PM) present during acute pollution events is associated with adverse health effects. While many anthropogenic pollution sources are regulated in the United States, emissions from wildfires are difficult to characterize and control. With wildfire frequency and intensity in the western U.S. projected to increase, it is important to more precisely determine the effect that wildfire emissions have on human health, and whether improved forecasts of these air pollution events can mitigate the health risks associated with wildfires. One of the challenges associated with determining health risks associated with wildfire emissions is that the low spatial resolution of surface monitors means that surface measurements may not be representative of a population's exposure, due to steep concentration gradients. To obtain better estimates of ambient exposure levels for health studies, a chemical transport model (CTM) can be used to simulate the evolution of a wildfire plume as it travels over populated regions downwind. Improving the performance of a CTM would allow the development of a new forecasting framework that could better help decision makers estimate and potentially mitigate future health impacts. We use the Weather Research and Forecasting model with online chemistry (WRF-Chem) to simulate wildfire plume evolution. By varying the model resolution, meteorology reanalysis initial conditions, and biomass burning inventories, we are able to explore the sensitivity of model simulations to these various parameters. Satellite observations are used first to evaluate model skill, and then to constrain the model results. These data are then used to estimate population-level exposure, with the aim of better characterizing the effects that wildfire emissions have on human health.

Severe Pollution in China Amplified by Atmospheric Moisture.

PubMed

Tie, Xuexi; Huang, Ru-Jin; Cao, Junji; Zhang, Qiang; Cheng, Yafang; Su, Hang; Chang, Di; Pöschl, Ulrich; Hoffmann, Thorsten; Dusek, Uli; Li, Guohui; Worsnop, Douglas R; O'Dowd, Colin D

2017-11-17

In recent years, severe haze events often occurred in China, causing serious environmental problems. The mechanisms responsible for the haze formation, however, are still not well understood, hindering the forecast and mitigation of haze pollution. Our study of the 2012-13 winter haze events in Beijing shows that atmospheric water vapour plays a critical role in enhancing the heavy haze events. Under weak solar radiation and stagnant moist meteorological conditions in winter, air pollutants and water vapour accumulate in a shallow planetary boundary layer (PBL). A positive feedback cycle is triggered resulting in the formation of heavy haze: (1) the dispersal of water vapour is constrained by the shallow PBL, leading to an increase in relative humidity (RH); (2) the high RH induces an increase of aerosol particle size by enhanced hygroscopic growth and multiphase reactions to increase particle size and mass, which results in (3) further dimming and decrease of PBL height, and thus further depressing of aerosol and water vapour in a very shallow PBL. This positive feedback constitutes a self-amplification mechanism in which water vapour leads to a trapping and massive increase of particulate matter in the near-surface air to which people are exposed with severe health hazards.

Evaluating the Impact of Aerosols on Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Freitas, Saulo; Silva, Arlindo; Benedetti, Angela; Grell, Georg; Members, Wgne; Zarzur, Mauricio

2015-04-01

The Working Group on Numerical Experimentation (WMO, http://www.wmo.int/pages/about/sec/rescrosscut/resdept_wgne.html) has organized an exercise to evaluate the impact of aerosols on NWP. This exercise will involve regional and global models currently used for weather forecast by the operational centers worldwide and aims at addressing the following questions: a) How important are aerosols for predicting the physical system (NWP, seasonal, climate) as distinct from predicting the aerosols themselves? b) How important is atmospheric model quality for air quality forecasting? c) What are the current capabilities of NWP models to simulate aerosol impacts on weather prediction? Toward this goal we have selected 3 strong or persistent events of aerosol pollution worldwide that could be fairly represented in current NWP models and that allowed for an evaluation of the aerosol impact on weather prediction. The selected events includes a strong dust storm that blew off the coast of Libya and over the Mediterranean, an extremely severe episode of air pollution in Beijing and surrounding areas, and an extreme case of biomass burning smoke in Brazil. The experimental design calls for simulations with and without explicitly accounting for aerosol feedbacks in the cloud and radiation parameterizations. In this presentation we will summarize the results of this study focusing on the evaluation of model performance in terms of its ability to faithfully simulate aerosol optical depth, and the assessment of the aerosol impact on the predictions of near surface wind, temperature, humidity, rainfall and the surface energy budget.

GLANCE - calculatinG heaLth impActs of atmospheric pollutioN in a Changing climatE

NASA Astrophysics Data System (ADS)

Vogel, Leif; Faria, Sérgio; Markandya, Anil

2016-04-01

Current annual global estimates of premature deaths from poor air quality are estimated in the range of 2.6-4.4 million, and 2050 projections are expected to double against 2010 levels. In Europe, annual economic burdens are estimated at around 750 bn €. Climate change will further exacerbate air pollution burdens; therefore, a better understanding of the economic impacts on human societies has become an area of intense investigation. European research efforts are being carried out within the MACC project series, which started in 2005. The outcome of this work has been integrated into a European capacity for Earth Observation, the Copernicus Atmospheric Monitoring Service (CAMS). In MACC/CAMS, key pollutant concentrations are computed at the European scale and globally by employing chemically-driven advanced transport models. The project GLANCE (calculatinG heaLth impActs of atmospheric pollutioN in a Changing climatE) aims at developing an integrated assessment model for calculating the health impacts and damage costs of air pollution at different physical scales. It combines MACC/CAMS (assimilated Earth Observations, an ensemble of chemical transport models and state of the art ECWMF weather forecasting) with downscaling based on in-situ network measurements. The strengthening of modelled projections through integration with empirical evidence reduces errors and uncertainties in the health impact projections and subsequent economic cost assessment. In addition, GLANCE will yield improved data accuracy at different time resolutions. This project is a multidisciplinary approach which brings together expertise from natural sciences and socio economic fields. Here, its general approach will be presented together with first results for the years 2007 - 2012 on the European scale. The results on health impacts and economic burdens are compared to existing assessments.

THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

The medical and scientific responsibility of pollen information services.

PubMed

Bastl, Katharina; Berger, Markus; Bergmann, Karl-Christian; Kmenta, Maximilian; Berger, Uwe

2017-01-01

Pollen information as such is highly valuable and was considered so far as a self-evident good free for the public. The foundation for reliable and serious pollen information is the careful, scientific evaluation of pollen content in the air. However, it is essential to state and define now the requirements for pollen data and qualifications needed for institutions working with pollen data in the light of technical developments such as automated pollen counting and various political interests in aerobiology including attempts to finally acknowledge pollen and spores as relevant biological particles in the air worth being considered for pollution and health directives. It has to be emphasized that inadequate pollen forecasts are a considerable health risk for pollen allergy sufferers. Therefore, the responsibility of institutions involved in pollen monitoring and forecasting is high and should be substantiated with respective qualifications and know-how. We suggest here for the first time a portfolio of quality criteria and demand rigorous scientific monitoring and certification of such institutions in the interest and for the protection of persons affected by a pollen allergy.

Simulating air quality in the Netherlands with WRF-Chem 3.8.1 at high resolution

NASA Astrophysics Data System (ADS)

Hilboll, Andreas; Kuenen, Jeroen; Denier van der Gon, Hugo; Vrekoussis, Mihalis

2017-04-01

Air pollution is the single most important environmental hazard for public health. Especially nitrogen dioxide (NO(2)) plays a key role in air quality research, both due to its immediate importance for the production of tropospheric ozone and acid rain, and as a general indicator of fossil fuel burning. To improve the quality and reproducibility of measurements of NO(2) vertical distribution from MAX-DOAS instruments, the CINDI-2 campaign was held in Cabauw (NL) in September 2016, featuring instruments from many of the leading atmospheric research institutions in the world. The measurement site in Cabauw is located in a rather rural region, surrounded by several major pollution centers (Utrecht, Rotterdam, Amsterdam). Since the instruments measure in several azimuthal directions, the measurements are able to provide information about the high spatial and temporal variability in pollutant concentrations, caused by both the spatial heterogeneity of emissions and meteorological conditions. When using air quality models in the analysis of the measured data to identify pollution sources, this mandates high spatial resolution in order to resolve the expected fine spatial structure in NO(2) concentrations. In spite of constant advances in computing power, this remains a challenge, mostly due to the uncertainties and large spatial heterogeneity of emissions and the need to parameterize small-scale processes. In this study, we use the most recent version 3.8.1 of the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to simulate air pollutant concentrations over the Netherlands, to facilitate the analysis of the CINDI-2 NO(2}) measurements. The model setup contains three nested domains with horizontal resolutions of 15, 3, and 1 km. Anthropogenic emissions are taken from the TNO-MACC III inventory and, where available, from the Dutch Pollutant Release and Transfer Register (Emissieregistratie), at a spatial resolution of 7 and 1 km, respectively. We use the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) with the MOSAIC aerosol module. The high spatial resolution of model and emissions will allow us to resolve the strong spatial gradients in the NO(2) concentrations measured during the CINDI-2 campaign, allowing for an unprecedented level of detail in the analysis of individual pollution sources.

Classification of summertime synoptic patterns in Beijing and their associations with boundary layer structure affecting aerosol pollution

NASA Astrophysics Data System (ADS)

Miao, Yucong; Guo, Jianping; Liu, Shuhua; Liu, Huan; Li, Zhanqing; Zhang, Wanchun; Zhai, Panmao

2017-02-01

Meteorological conditions within the planetary boundary layer (PBL) are closely governed by large-scale synoptic patterns and play important roles in air quality by directly and indirectly affecting the emission, transport, formation, and deposition of air pollutants. Partly due to the lack of long-term fine-resolution observations of the PBL, the relationships between synoptic patterns, PBL structure, and aerosol pollution in Beijing have not been well understood. This study applied the obliquely rotated principal component analysis in T-mode to classify the summertime synoptic conditions over Beijing using the National Centers for Environmental Prediction reanalysis from 2011 to 2014, and investigated their relationships with PBL structure and aerosol pollution by combining numerical simulations, measurements of surface meteorological variables, fine-resolution soundings, the concentration of particles with diameters less than or equal to 2.5 µm, total cloud cover (CLD), and reanalysis data. Among the seven identified synoptic patterns, three types accounted for 67 % of the total number of cases studied and were associated with heavy aerosol pollution events. These particular synoptic patterns were characterized by high-pressure systems located to the east or southeast of Beijing at the 925 hPa level, which blocked the air flow seaward, and southerly PBL winds that brought in polluted air from the southern industrial zone. The horizontal transport of pollutants induced by the synoptic forcings may be the most important factor affecting the air quality of Beijing in summer. In the vertical dimension, these three synoptic patterns featured a relatively low boundary layer height (BLH) in the afternoon, accompanied by high CLD and southerly cold advection from the seas within the PBL. The high CLD reduced the solar radiation reaching the surface, and suppressed the thermal turbulence, leading to lower BLH. Besides, the numerical sensitive experiments show that cold advection induced by the large-scale synoptic forcing may have cooled the PBL, leading to an increase in near-surface stability and a decrease in the BLH in the afternoon. Moreover, when warm advection appeared simultaneously above the top level of the PBL, the thermal inversion layer capping the PBL may have been strengthened, resulting in the further suppression of PBL and thus the deterioration of aerosol pollution levels. This study has important implications for understanding the crucial roles that meteorological factors (at both synoptic and local scales) play in modulating and forecasting aerosol pollution in Beijing and its surrounding area.

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

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.

Demonstration of AIRS Total Ozone Products to Operations to Enhance User Readiness

NASA Technical Reports Server (NTRS)

Berndt, Emily; Zavodsky, Bradley; Jedlovec, Gary

2014-01-01

Cyclogenesis is a key forecast challenge at operational forecasting centers such as WPC and OPC, so these centers have a particular interest in unique products that can identify key storm features. In some cases, explosively developing extratropical cyclones can produce hurricane force, non-convective winds along the East Coast and north Atlantic as well as the Pacific Ocean, with the potential to cause significant damage to life and property. Therefore, anticipating cyclogenesis for these types of storms is crucial for furthering the NOAA goal of a "Weather Ready Nation". Over the last few years, multispectral imagery (i.e. RGB) products have gained popularity among forecasters. The GOES-R satellite champion at WPC/OPC has regularly evaluated the Air Mass RGB products from GOES Sounder, MODIS, and SEVIRI to aid in forecasting cyclogenesis as part of ongoing collaborations with SPoRT within the framework of the GOES-R Proving Ground. WPC/OPC has used these products to identify regions of stratospheric air associated with tropopause folds that can lead to cyclogenesis and hurricane force winds. RGB products combine multiple channels or channel differences into multi-color imagery in which different colors represent a particular cloud or air mass type. Initial interaction and feedback from forecasters evaluating the legacy Air Mass RGBs revealed some uncertainty regarding what physical processes the qualitative RGB products represent and color interpretation. To enhance forecaster confidence and interpretation of the Air Mass RGB, NASA SPoRT has transitioned a total column ozone product from AIRS retrievals to the WPC/OPC. The use of legacy AIRS demonstrates future JPSS capabilities possible with CrIS or OMPS. Since stratospheric air can be identified by anomalous potential vorticity and warm, dry, ozone-rich air, hyperspectral infrared sounder ozone products can be used in conjunction with the Air Mass RGB for identifying the role of stratospheric air in explosive cyclogenesis and hurricane force wind events. Currently, forecasters at WPC/OPC are evaluating the Air Mass RGB imagery in conjunction with the AIRS total column ozone to aid forecasting cyclogenesis and high wind forecasts. One of the limitations of the total ozone product is that it is difficult for forecasters to determine whether elevated ozone concentrations are related to stratospheric air or climatologically high values of ozone in certain regions. To address this limitation, SPoRT created an AIRS ozone anomaly product which calculates the percent of normal ozone based on a global stratospheric ozone mean climatology. With the knowledge that ozone values 125 percent of normal and greater typically represent stratospheric air; the anomaly product can be used with the total column ozone product to confirm regions of stratospheric air on the Air Mass RGB. This presentation describes the generation of these products along with forecaster feedback concerning the use of the AIRS ozone products in conjunction with the Air Mass RGB product for the unique forecast challenges WPC/OPC face. Additionally examples of CrIS ozone and anomaly products will be shown to further demonstrate the utility and capability of JPSS in forecasting unique events.

Influence of Ship Emissions on Urban Air Quality: A Comprehensive Study Using Highly Time-Resolved Online Measurements and Numerical Simulation in Shanghai.

PubMed

Liu, Zhanmin; Lu, Xiaohui; Feng, Junlan; Fan, Qianzhu; Zhang, Yan; Yang, Xin

2017-01-03

Shanghai has become an international shipping center in the world. In this study, the multiyear measurements and the high resolution air quality model with hourly ship emission inventory were combined to determine the influence of ship emissions on urban Shanghai. The aerosol time-of-flight mass spectrometer (ATOFMS) measurements were carried out at an urban site from April 2009 to January 2013. During the entire sampling time, most of the half-hourly averaged number fractions of primary ship emitted particles varied between 1.0-10.0%. However, the number fraction could reach up to 50% during the ship plume cases. Ship-plume-influenced periods usually occurred in spring and summer. The simulation of Weather Research and Forecasting/Community Multiscale Air Quality model (WRF/CMAQ) with hourly ship emission inventory provided the highly time-resolved concentrations of ship-related air pollutants during a ship plume case. It showed ships could contribute 20-30% (2-7 μg/m 3 ) of the total PM 2.5 within tens of kilometers of coastal and riverside Shanghai during ship-plume-influenced periods. Our results showed that ship emissions have substantial contribution to the air pollution in urban Shanghai. The control measures of ship emission should be taken considering its negative environment and human 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.

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...

Development and Application of Hyperspectral Infrared Ozone Retrieval Products for Operational Meteorology

NASA Technical Reports Server (NTRS)

Berndt, Emily; Zavodsky, Bradley; Jedlovec, Gary

2015-01-01

Cyclogenesis is a key forecast challenge at operational forecasting centers such as WPC and OPC, so these centers have a particular interest in unique products that can identify key storm features. In some cases, explosively developing extratropical cyclones can produce hurricane force, non-convective winds along the East Coast and north Atlantic as well as the Pacific Ocean, with the potential to cause significant damage to life and property. Therefore, anticipating cyclogenesis for these types of storms is crucial for furthering the NOAA goal of a "Weather Ready Nation". Over the last few years, multispectral imagery (i.e. RGB) products have gained popularity among forecasters. The GOES-R satellite champion at WPC/OPC has regularly evaluated the Air Mass RGB products from GOES Sounder, MODIS, and SEVIRI to aid in forecasting cyclogenesis as part of ongoing collaborations with SPoRT within the framework of the GOES-R Proving Ground. WPC/OPC has used these products to identify regions of stratospheric air associated with tropopause folds that can lead to cyclogenesis and hurricane force winds. RGB products combine multiple channels or channel differences into multi-color imagery in which different colors represent a particular cloud or air mass type. Initial interaction and feedback from forecasters evaluating the legacy Air Mass RGBs revealed some uncertainty regarding what physical processes the qualitative RGB products represent and color interpretation. To enhance forecaster confidence and interpretation of the Air Mass RGB, NASA SPoRT has transitioned a total column ozone product from AIRS retrievals to the WPC/OPC. The use of legacy AIRS demonstrates future JPSS capabilities possible with CrIS or OMPS. Since stratospheric air can be identified by anomalous potential vorticity and warm, dry, ozone-rich air, hyperspectral infrared sounder ozone products can be used in conjunction with the Air Mass RGB for identifying the role of stratospheric air in explosive cyclogenesis and hurricane force wind events. Currently, forecasters at WPC/OPC are evaluating the Air Mass RGB imagery in conjunction with the AIRS total column ozone to aid forecasting cyclogenesis and high wind forecasts. One of the limitations of the total ozone product is that it is difficult for forecasters to determine whether elevated ozone concentrations are related to stratospheric air or climatologically high values of ozone in certain regions. To address this limitation, SPoRT created an AIRS ozone anomaly product which calculates the percent of normal ozone based on a global stratospheric ozone mean climatology. With the knowledge that ozone values 125 percent of normal and greater typically represent stratospheric air; the anomaly product can be used with the total column ozone product to confirm regions of stratospheric air on the Air Mass RGB. This presentation describes the generation of these products along with forecaster feedback concerning the use of the AIRS ozone products in conjunction with the Air Mass RGB product for the unique forecast challenges WPC/OPC face. Additionally examples of CrIS ozone and anomaly products will be shown to further demonstrate the utility and capability of JPSS in forecasting unique events.

Potential impact of climate change on air pollution-related human health effects.

PubMed

Tagaris, Efthimios; Liao, Kuo-Jen; Delucia, Anthony J; Deck, Leland; Amar, Praveen; Russell, Armistead G

2009-07-01

The potential health impact of ambient ozone and PM2.5 concentrations modulated by climate change over the United States is investigated using combined atmospheric and health modeling. Regional air quality modeling for 2001 and 2050 was conducted using CMAQ Modeling System with meteorology from the GISS Global Climate Model, downscaled regionally using MM5,keeping boundary conditions of air pollutants, emission sources, population, activity levels, and pollution controls constant. BenMap was employed to estimate the air pollution health outcomes at the county, state, and national level for 2050 caused by the effect of meteorology on future ozone and PM2.5 concentrations. The changes in calculated annual mean PM2.5 concentrations show a relatively modest change with positive and negative responses (increasing PM2.5 levels across the northeastern U.S.) although average ozone levels slightly decrease across the northern sections of the U.S., and increase across the southern tier. Results suggest that climate change driven air quality-related health effects will be adversely affected in more then 2/3 of the continental U.S. Changes in health effects induced by PM2.5 dominate compared to those caused by ozone. PM2.5-induced premature mortality is about 15 times higher then that due to ozone. Nationally the analysis suggests approximately 4000 additional annual premature deaths due to climate change impacts on PM2.5 vs 300 due to climate change-induced ozone changes. However, the impacts vary spatially. Increased premature mortality due to elevated ozone concentrations will be offset by lower mortality from reductions in PM2.5 in 11 states. Uncertainties related to different emissions projections used to simulate future climate, and the uncertainties forecasting the meteorology, are large although there are potentially important unaddressed uncertainties (e.g., downscaling, speciation, interaction, exposure, and concentration-response function of the human health studies).

Hourly air pollution concentrations and their important predictors over Houston, Texas using deep neural networks: case study of DISCOVER-AQ time period

NASA Astrophysics Data System (ADS)

Eslami, E.; Choi, Y.; Roy, A.

2017-12-01

Air quality forecasting carried out by chemical transport models often show significant error. This study uses a deep-learning approach over the Houston-Galveston-Brazoria (HGB) area to overcome this forecasting challenge, for the DISCOVER-AQ period (September 2013). Two approaches, deep neural network (DNN) using a Multi-Layer Perceptron (MLP) and Restricted Boltzmann Machine (RBM) were utilized. The proposed approaches analyzed input data by identifying features abstracted from its previous layer using a stepwise method. The approaches predicted hourly ozone and PM in September 2013 using several predictors of prior three days, including wind fields, temperature, relative humidity, cloud fraction, precipitation along with PM, ozone, and NOx concentrations. Model-measurement comparisons for available monitoring sites reported Indexes of Agreement (IOA) of around 0.95 for both DNN and RBM. A standard artificial neural network (ANN) (IOA=0.90) with similar architecture showed poorer performance than the deep networks, clearly demonstrating the superiority of the deep approaches. Additionally, each network (both deep and standard) performed significantly better than a previous CMAQ study, which showed an IOA of less than 0.80. The most influential input variables were identified using their associated weights, which represented the sensitivity of ozone to input parameters. The results indicate deep learning approaches can achieve more accurate ozone forecasting and identify the important input variables for ozone predictions in metropolitan areas.

Boundary-Layer & health

NASA Astrophysics Data System (ADS)

Costigliola, V.

2010-09-01

It has long been known that specific atmospheric processes, such as weather and longer-term climatic fluctuations, affect human health. The biometeorological literature refers to this relationship as meteorotropism, defined as a change in an organism that is correlated with a change in atmospheric conditions. Plenty of (patho)physiological functions are affected by those conditions - like the respiratory diseases - and currently it is difficult to put any limits for pathologies developed in reply. Nowadays the importance of atmospheric boundary layer and health is increasingly recognised. A number of epidemiologic studies have reported associations between ambient concentrations of air pollution, specifically particulate pollution, and adverse health effects, even at the relatively low concentrations of pollution found. Since 1995 there have been over twenty-one studies from four continents that have explicitly examined the association between ambient air pollutant mixes and daily mortality. Statistically significant and positive associations have been reported in data from various locations around the world, all with varying air pollutant concentrations, weather conditions, population characteristics and public health policies. Particular role has been given to atmospheric boundary layer processes, the impact of which for specific patient-cohort is, however, not well understood till now. Assessing and monitoring air quality are thus fundamental to improve Europe's welfare. One of current projects run by the "European Medical Association" - PASODOBLE will develop and demonstrate user-driven downstream information services for the regional and local air quality sectors by combining space-based and in-situ data with models in 4 thematic service lines: - Health community support for hospitals, pharmacies, doctors and people at risk - Public information for regions, cities, tourist industry and sporting event organizers - Compliance monitoring support on particulate matter for regional environmental agencies - Local forecast model evaluation support for local authorities and city bodies. Giving value to the above listed aspects, PASODOBLE objectives are following: - Evolution of existing and development of new sustainable air quality services for Europe on regional and local scales - Development and testing of a generic service framework for coordinated input data acquisition and customizable user-friendly access to services - Utilization of multiple cycles of delivery, use and assessment versus requirements and market planning in cooperation with users - Promotion and harmonisation of best practise tools for air quality communities. Further European multidisciplinary projects should be created to better understand the most prevalent atmospheric factors to be impacted in predictive, preventive and personalised medicine considered as the central concept for future medicine.

One multi-media environmental system with linkage between meteorology/ hydrology/ air quality models and water quality model

NASA Astrophysics Data System (ADS)

Tang, C.; Lynch, J. A.; Dennis, R. L.

2016-12-01

The biogeochemical processing of nitrogen and associated pollutants is driven by meteorological and hydrological processes in conjunction with pollutant loading. There are feedbacks between meteorology and hydrology that will be affected by land-use change and climate change. Changes in meteorology will affect pollutant deposition. It is important to account for those feedbacks and produce internally consistent simulations of meteorology, hydrology, and pollutant loading to drive the (watershed/water quality) biogeochemical models. In this study, the ecological response to emission reductions in streams in the Potomac watershed was evaluated. Firstly, we simulated the deposition by using the fully coupled Weather Research & Forecasting (WRF) model and the Community Multiscale Air Quality (CAMQ) model; secondly, we created the hydrological data by the offline linked Variable Infiltration Capacity (VIC) model and the WRF model. Lastly, we investigated the water quality by one comprehensive/environment model, namely the linkage of CMAQ, WRF, VIC and the Model of Acidification of Groundwater In Catchment (MAGIC) model from 2002 to 2010.The simulated results (such as NO3, SO4, and SBC) fit well to the observed values. The linkage provides a generally accurate, well-tested tool for evaluating sensitivities to varying meteorology and environmental changes on acidification and other biogeochemical processes, with capability to comprehensively explore strategic policy and management design.

Development of Future Scenario Emission Inventories for East Asia in Support of Multiple Modeling Studies

NASA Astrophysics Data System (ADS)

Kim, Y.; Woo, J. H.; Choi, K. C.; Lee, J. B.; Song, C. K.; Kim, S. K.; Hong, J.; Hong, S. C.; Zhang, Q.; Hong, C.; Tong, D.

2015-12-01

Future emission scenarios based on up-to-date regional socio-economic and control policy information were developed in support of climate-air quality integrated modeling research over East Asia. Two IPCC-participated Integrated Assessment Models(IAMs) were used to developed those scenario pathways. The two emission processing systems, KU-EPS and SMOKE-Asia, were used to convert these future scenario emissions to comprehensive chemical transport model-ready form. The NIER/KU-CREATE (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment) served as the regional base-year emission inventory. For anthropogenic emissions, it has 54 fuel classes, 201 sub-sectors and 13 pollutants, including CO2, CH4, N2O, SO2, NOx, CO, NMVOC, NH3, OC, BC, PM10, PM2.5, and mercury. Fast energy growth and aggressive penetration of the control measures make emissions projection very active for East Asia. Despite of more stringent air pollution control policies by the governments, however, air quality over the region seems not been improved as much - even worse in many cases. The needs of more scientific understanding of inter-relationship among emissions, transport, chemistry over the region are very high to effectively protect public health and ecosystems against ozone, fine particles, and other toxic pollutants in the air. After developing these long-term future emissions, therefore, we also tried to apply our future scenarios to develop the present emissions inventory for chemical weather forecasting and aircraft field campaign. On site, we will present; 1) the future scenario development framework and process methodologies, 2) initial development results of the future emission pathways, 3) present emission inventories from short-term projection, and 4) air quality modeling performance improvements over the region.

Validation of WRF-Chem air quality simulations in the Netherlands at high resolution

NASA Astrophysics Data System (ADS)

Hilboll, A.; Lowe, D.; Kuenen, J. J. P.; Denier Van Der Gon, H.; Vrekoussis, M.

2017-12-01

Air pollution is the single most important environmental hazard for publichealth, and especially nitrogen dioxide (NO2) plays a key role in air qualityresearch. With the aim of improving the quality and reproducibility ofmeasurements of NO2 vertical distribution from MAX-DOAS instruments, theCINDI-2 campaign was held in Cabauw (NL) in September 2016.The measurement site was rural, but surrounded by several major pollutioncenters. Due to this spatial heterogeneity of emissions, as well as themeteorological conditions, high spatial and temporal variability in NO2 mixingratios were observed.Air quality models used in the analysis of the measured data must have highspatial resolution in order to resolve this fine spatial structure. Thisremains a challenge even today, mostly due to the uncertainties and largespatial heterogeneity of emission data, and the need to parameterize small-scaleprocesses.In this study, we use the state-of-the-art version 3.9 of the Weather Researchand Forecasting Model with Chemistry (WRF-Chem) to simulate air pollutantconcentrations over the Netherlands, to facilitate the analysis of the CINDI-2NO2 measurements. The model setup contains three nested domains withhorizontal resolutions of 15, 3, and 1 km. Anthropogenic emissions are takenfrom the TNO-MACC III inventory and, where available, from the Dutch PollutantRelease and Transfer Register (Emissieregistratie), at a spatial resolution of 7and 1 km, respectively. We use the Common Reactive Intermediates gas-phasechemical mechanism (CRIv2-R5) with the MOSAIC aerosol module.The high spatial resolution of model and emissions will allow us to resolve thestrong spatial gradients in the NO2 concentrations measured during theCINDI-2 campaign, allowing for an unprecedented level of detail in theanalysis of individual pollution sources.

Effect of VOC emissions from vegetation on urban air quality during hot periods

NASA Astrophysics Data System (ADS)

Churkina, Galina; Kuik, Friderike; Bonn, Boris; Lauer, Axel; Grote, Ruediger; Butler, Tim

2016-04-01

Programs to plant millions of trees in cities around the world aim at the reduction of summer temperatures, increase of carbon storage, storm water control, and recreational space, as well as at poverty alleviation. These urban greening programs, however, do not take into account how closely human and natural systems are coupled in urban areas. Compared with the surroundings of cities, elevated temperatures together with high anthropogenic emissions of air and water pollutants are quite typical in urban systems. Urban and sub-urban vegetation respond to changes in meteorology and air quality and can react to pollutants. Neglecting this coupling may lead to unforeseen negative effects on air quality resulting from urban greening programs. The potential of emissions of volatile organic compounds (VOC) from vegetation combined with anthropogenic emissions of air pollutants to produce ozone has long been recognized. This ozone formation potential increases under rising temperatures. Here we investigate how emissions of VOC from urban vegetation affect corresponding ground-level ozone and PM10 concentrations in summer and especially during heat wave periods. We use the Weather Research and Forecasting Model with coupled atmospheric chemistry (WRF-CHEM) to quantify these feedbacks in the Berlin-Brandenburg region, Germany during the two summers of 2006 (heat wave) and 2014 (reference period). VOC emissions from vegetation are calculated by MEGAN 2.0 coupled online with WRF-CHEM. Our preliminary results indicate that the contribution of VOCs from vegetation to ozone formation may increase by more than twofold during heat wave periods. We highlight the importance of the vegetation for urban areas in the context of a changing climate and discuss potential tradeoffs of urban greening programs.

Investigation of chemical properties and transport phenomena associated with pollutants in the atmospheric boundary layer

NASA Astrophysics Data System (ADS)

Holmes, Heather A.

Under the Clean Air Act, the U.S. Environmental Protection Agency is required to determine which air pollutants are harmful to human health, then regulate, monitor and establish criteria levels for these pollutants. To accomplish this and for scientific advancement, integration of knowledge from several disciplines is required including: engineering, atmospheric science, chemistry and public health. Recently, a shift has been made to establish interdisciplinary research groups to better understand the atmospheric processes that govern the transport of pollutants and chemical reactions of species in the atmospheric boundary layer (ABL). The primary reason for interdisciplinary collaboration is the need for atmospheric processes to be treated as a coupled system, and to design experiments that measure meteorological, chemical and physical variables simultaneously so forecasting models can be improved (i.e., meteorological and chemical process models). This dissertation focuses on integrating research disciplines to provide a more complete framework to study pollutants in the ABL. For example, chemical characterization of particulate matter (PM) and the physical processes governing PM distribution and mixing are combined to provide more comprehensive data for source apportionment. Data from three field experiments were utilized to study turbulence, meteorological and chemical parameters in the ABL. Two air quality field studies were conducted on the U.S./Mexico border. The first was located in Yuma, AZ to investigate the spatial and temporal variability of PM in an urban environment and relate chemical properties of ambient aerosols to physical findings. The second border air quality study was conducted in Nogales, Sonora, Mexico to investigate the relationship between indoor and outdoor air quality in order to better correlate cooking fuel types and home activities to elevated indoor PM concentrations. The final study was executed in southern Idaho and focused on comparing two gaseous dry deposition models to determine the fluxes of gaseous elemental mercury and reactive gaseous mercury using the measured concentrations and calculated deposition velocities for each species. Results indicate a large dependence on coupled physical, chemical and biological interactions for atmospheric processes, signifying the need for interdisciplinary collaboration.

IMPROVING NATIONAL AIR QUALITY FORECASTS WITH SATELLITE AEROSOL OBSERVATIONS

EPA Science Inventory

Air quality forecasts for major US metropolitan areas have been provided to the public through a partnership between the US Environmental Protection Agency and state and local air agencies since 1997. Recent years have witnessed improvement in forecast skill and expansion of fore...

Air pollution from gas flaring: new emission factor estimates and detection in a West African aerosol remote-sensing climatology

NASA Astrophysics Data System (ADS)

MacKenzie, Rob; Fawole, Olusegun Gabriel; Levine, James; Cai, Xiaoming

2016-04-01

Gas flaring, the disposal of gas through stacks in an open-air flame, is a common feature in the processing of crude oil, especially in oil-rich regions of the world. Gas flaring is a prominent source of volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAH), CO, CO2, nitrogen oxides (NOx), SO2 (in "sour" gas only), and soot (black carbon), as well as the release of locally significant amounts of heat. The rates of emission of these pollutants from gas flaring depend on a number of factors including, but not limited to, fuel composition and quantity, stack geometry, flame/combustion characteristics, and prevailing meteorological conditions. Here, we derive new estimated emission factors (EFs) for carbon-containing pollutants (excluding PAH). The air pollution dispersion model, ADMS5, is used to simulate the dispersion of the pollutants from flaring stacks in the Niger delta. A seasonal variation of the dispersion pattern of the pollutant within a year is studied in relation to the movements of the West Africa Monsoon (WAM) and other prevailing meteorological factors. Further, we have clustered AERONET aerosol signals using trajectory analysis to identify dominant aerosol sources at the Ilorin site in West Africa (4.34 oE, 8.32 oN). A 10-year trajectory-based analysis was undertaken (2005-2015, excluding 2010). Of particular interest are air masses that have passed through the gas flaring region in the Niger Delta area en-route the AERONET site. 7-day back trajectories were calculated using the UK Universities Global Atmospheric Modelling Programme (UGAMP) trajectory model which is driven by analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). From the back-trajectory calculations, dominant sources are identified, using literature classifications: desert dust (DD); Biomass burning (BB); and Urban-Industrial (UI). We use a combination of synoptic trajectories and aerosol optical properties to distinguish a fourth source: that due to gas flaring. We discuss the relative impact of these different aerosol sources on the overall radiative forcing at Ilorin AERONET site.

Evaluation of the high resolution DEHM/UBM model system over Denmark

NASA Astrophysics Data System (ADS)

Im, Ulas; Christensen, Jesper H.; Ellermann, Thomas; Ketzel, Matthias; Geels, Camilla; Hansen, Kaj M.; Plejdrup, Marlene S.; Brandt, Jørgen

2015-04-01

The air pollutant levels over Denmark are simulated using the high resolution DEHM/UBM model system for the years 2006 to 2014. The system employs a hemispheric chemistry-transport model, the Danish Eulerian Hemispheric Model (DEHM; Brandt et al., 2012) that runs on a 150 km x 150 km resolution over the Northern Hemisphere, with nesting capability for higher resolutions over Europe, Northern Europe and Denmark on 50 km x 50 km, 16.7 km x 16.7 km and 5.6 km x 5.6 km resolutions, respectively, coupled to the Urban Background Model (UBM; Berkowicz, 2000; Brandt et al., 2001) that covers the whole of Denmark with a 1 km x 1 km spatial resolution. Over Denmark, the system uses the SPREAD emission model (Plejdrup and Gyldenkærne, 2011) that distributes the Danish emissions for all pollutants and all sectors in the national emission database on a 1 km x 1 km resolution grid covering Denmark and its national sea territory. The study will describe the model system and we will evaluate the performance of the model system in simulating hourly and daily ozone (O3), carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) concentrations against surface measurements from eight monitoring stations. Finally we investigate the spatial variation of air pollutants over Denmark on different time scales. References Berkowicz, R., 2000. A Simple Model for Urban Background Pollution. Environmental Monitoring and Assessment, 65, 1/2, 259-267. Brandt, J., J. H. Christensen, L. M. Frohn, F. Palmgren, R. Berkowicz and Z. Zlatev, 2001: "Operational air pollution forecasts from European to local scale". Atmospheric Environment, Vol. 35, Sup. No. 1, pp. S91-S98, 2001 Brandt et al., 2012. An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport. Atmospheric Environment, 53, 156-176. Plejdrup, M.S., Gyldenkærne, S., 2011. Spatial distribution of pollutants to air - the SPREAD model. NERI Technical Report No. 823.

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.

Feedbacks between Air-Quality, Meteorology, and the Forest Environment

NASA Astrophysics Data System (ADS)

Makar, Paul; Akingunola, Ayodeji; Stroud, Craig; Zhang, Junhua; Gong, Wanmin; Moran, Michael; Zheng, Qiong; Brook, Jeffrey; Sills, David

2017-04-01

The outcome of air quality forecasts depend in part on how the local environment surrounding the emissions regions influences chemical reaction rates and transport from those regions to the larger spatial scales. Forested areas alter atmospheric chemistry through reducing photolysis rates and vertical diffusivities within the forest canopy. The emitted pollutants, and their reaction products, are in turn capable of altering meteorology, through the well-known direct and indirect effects of particulate matter on radiative transfer. The combination of these factors was examined using version 2 of the Global Environmental Multiscale - Modelling Air-quality and CHemistry (GEM-MACH) on-line air pollution model. The model configuration used for this study included 12 aerosol size bins, eight aerosol species, homogeneous core Mie scattering, the Milbrandt-Yao two-moment cloud microphysics scheme with cloud condensation nuclei generated from model aerosols using the scheme of Abdul-Razzak and Ghan, and a new parameterization for forest canopy shading and turbulence. The model was nested to 2.5km resolution for a domain encompassing the lower Great Lakes, for simulations of a period in August of 2015 during the Pan American Games, held in Toronto, Canada. Four scenarios were carried out: (1) a "Base Case" scenario (the original model, in which coupling between chemistry and weather is not permitted; instead, the meteorological model's internal climatologies for aerosol optical and cloud condensation properties are used for direct and indirect effect calculations); (2) a "Feedback" scenario (the aerosol properties were derived from the internally simulated chemistry, and coupled to the meteorological model's radiative transfer and cloud formation modules); (3) a "Forest" scenario (canopy shading and turbulence were added to the Base Case); (4) a "Combined" scenario (including both direct and indirect effect coupling between meteorology and chemistry, as well as the forest canopy parameterization). The simulations suggest that the feedbacks between simulated aerosols and meteorology may strengthen the existing lake breeze circulation, modifying the resulting meteorological and air-quality forecasts, while the forest canopy's influence may extend throughout the planetary boundary layer, and may also influence the weather. The simulations will be compared to available observations, in order to determine their relative impact on model performance.

Automated system for smoke dispersion prediction due to wild fires in Alaska

NASA Astrophysics Data System (ADS)

Kulchitsky, A.; Stuefer, M.; Higbie, L.; Newby, G.

2007-12-01

Community climate models have enabled development of specific environmental forecast systems. The University of Alaska (UAF) smoke group was created to adapt a smoke forecast system to the Alaska region. The US Forest Service (USFS) Missoula Fire Science Lab had developed a smoke forecast system based on the Weather Research and Forecasting (WRF) Model including chemistry (WRF/Chem). Following the successful experience of USFS, which runs their model operationally for the contiguous U.S., we develop a similar system for Alaska in collaboration with scientists from the USFS Missoula Fire Science Lab. Wildfires are a significant source of air pollution in Alaska because the climate and vegetation favor annual summer fires that burn huge areas. Extreme cases occurred in 2004, when an area larger than Maryland (more than 25000~km2) burned. Small smoke particles with a diameter less than 10~μm can penetrate deep into lungs causing health problems. Smoke also creates a severe restriction to air transport and has tremendous economical effect. The smoke dispersion and forecast system for Alaska was developed at the Geophysical Institute (GI) and the Arctic Region Supercomputing Center (ARSC), both at University of Alaska Fairbanks (UAF). They will help the public and plan activities a few days in advance to avoid dangerous smoke exposure. The availability of modern high performance supercomputers at ARSC allows us to create and run high-resolution, WRF-based smoke dispersion forecast for the entire State of Alaska. The core of the system is a Python program that manages the independent pieces. Our adapted Alaska system performs the following steps \\begin{itemize} Calculate the medium-resolution weather forecast using WRF/Met. Adapt the near real-time satellite-derived wildfire location and extent data that are received via direct broadcast from UAF's "Geographic Information Network of Alaska" (GINA) Calculate fuel moisture using WRF forecasts and National Fire Danger Rating System (NFDRS) fuel maps Calculate smoke emission components using a first order fire emission model Model the smoke plume rise yielding a vertically distribution that accounts for one-dimensional (vertical) concentrations of smoke constituents in the atmosphere above the fire Run WRF/Chem at high resolution for the forecast Use standard graphical tools to provide accessible smoke dispersion The system run twice each day at ARSC. The results will be freely available from a dedicated wildfire smoke web portal at ARSC.

The Co-Benefits of Global and Regional Greenhouse Gas Mitigation on US Air Quality at Fine Resolution

NASA Astrophysics Data System (ADS)

Zhang, Y.; Bowden, J. H.; Adelman, Z.; Naik, V.; Horowitz, L. W.; Smith, S.; West, J. J.

2014-12-01

Reducing greenhouse gases (GHGs) not only slows climate change, but can also have co-benefits for improved air quality. In this study, we examine the co-benefits of global and regional GHG mitigation on US air quality at fine resolution through dynamical downscaling, using the latest Community Multi-scale Air Quality (CMAQ) model. We will investigate the co-benefits on US air quality due to domestic GHG mitigation alone, and due to mitigation outside of the US. We also quantity the co-benefits resulting from reductions in co-emitted air pollutants versus slowing climate change and its effects on air quality. Projected climate in the 2050s from the IPCC RCP4.5 and RCP8.5 scenarios is dynamically downscaled with the Weather Research and Forecasting model (WRF). Anthropogenic emissions projections from the RCP4.5 scenario and its reference (REF), are directly processed in SMOKE to provide temporally- and spatially-resolved CMAQ emission input files. Chemical boundary conditions (BCs) are obtained from West et al. (2013), who studied the co-benefits of global GHG reductions on global air quality and human health. Our preliminary results show that the global GHG reduction (RCP4.5 relative to REF) reduces the 1hr daily maximum ozone by 3.3 ppbv annually over entire US, as high as 6 ppbv in September. The west coast of California and the Northeast US are the regions that benefit most. By comparing different scenarios, we find that foreign countries' GHGs mitigation has a larger influence on the US ozone decreases (accounting for 77% of the total decrease), compared with 23% from domestic GHG mitigation only, highlighting the importance of methane reductions and the intercontinental transport of air pollutants. The reduction of global co-emitted air pollutants has a more pronounced effect on ozone decreasing, relative to the effect from slowing climate and its effects on air quality. We also plan to report co-benefits for PM2.5 in the US.

Demonstrating the Operational Value of Atmospheric Infrared Sounder (AIRS) Retrieved Profiles in the Pre-Convective Environment

NASA Technical Reports Server (NTRS)

Kozlowski, Danielle M.; Zavodsky, T.; Jedloved, Gary J.

2011-01-01

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One operational forecast challenge is forecasting convective weather in data-void regions such as large bodies of water (e.g. Gulf of Mexico). To address this forecast challenge, SPoRT produces a twice-daily three-dimensional analysis that blends a model first-guess from the Advanced Research Weather Research and Forecasting (WRF-ARW) model with retrieved profiles from the Atmospheric Infrared Sounder (AIRS) -- a hyperspectral sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. AIRS profiles are unique in that they give a three dimensional view of the atmosphere that is not available through the current rawinsonde network. AIRS has two overpass swaths across North America each day, one valid in the 0700-0900 UTC timeframe and the other in the 1900-2100 UTC timeframe. This is helpful because the rawinsonde network only has data from 0000 UTC and 1200 UTC at specific land-based locations. Comparing the AIRS analysis product with control analyses that include no AIRS data demonstrates the value of the retrieved profiles to situational awareness for the pre-convective (and convective) environment. In an attempt to verify that the AIRS analysis was a good representation of the vertical structure of the atmosphere, both the AIRS and control analyses are compared to a Rapid Update Cycle (RUC) analysis used by operational forecasters. Using guidance from operational forecasters, convective available potential energy (CAPE) was determined to be a vital variable in making convective forecasts and is used herein to demonstrate the utility of the AIRS profiles in changing the vertical thermodynamic structure of the atmosphere in the pre-convective and convective environment. CAPE is an important metric because of it is a quantitative measure of atmospheric stability, which is necessary information when forecasting for convective weather. Case studies from the summer of 2010 were examined, and most impact from the AIRS retrieved profiles occurred over the data-void Gulf of Mexico with fields of convective potential closer to the RUC than the CNTL. Mixed results were found when AIRS retrieved profiles were used over land, so more cases need to be examined to determine whether AIRS would be an effective tool over land. Additional analyses of problematic convective forecasts over the Gulf Coast will be needed to determine the operational impact of AIRS. SPoRT eventually plans to transition the AIRS product to select Weather Forecast Office (WFO) partners, pending the outcome of these additional analyses.

Toward a US National Air Quality Forecast Capability: Current and Planned Capabilities

EPA Science Inventory

As mandated by Congress, NOAA is establishing a US national air quality forecast capability. This capability is being built with EPA, to provide air quality forecast information with enough accuracy and lead-time so that people can take actions to limit harmful effects of poor a...

A Unified Data Assimilation Strategy for Regional Coupled Atmosphere-Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xie, Lian; Liu, Bin; Zhang, Fuqing; Weng, Yonghui

2014-05-01

Improving tropical cyclone (TC) forecasts is a top priority in weather forecasting. Assimilating various observational data to produce better initial conditions for numerical models using advanced data assimilation techniques has been shown to benefit TC intensity forecasts, whereas assimilating large-scale environmental circulation into regional models by spectral nudging or Scale-Selective Data Assimilation (SSDA) has been demonstrated to improve TC track forecasts. Meanwhile, taking into account various air-sea interaction processes by high-resolution coupled air-sea modelling systems has also been shown to improve TC intensity forecasts. Despite the advances in data assimilation and air-sea coupled models, large errors in TC intensity and track forecasting remain. For example, Hurricane Nate (2011) has brought considerable challenge for the TC operational forecasting community, with very large intensity forecast errors (27, 25, and 40 kts for 48, 72, and 96 h, respectively) for the official forecasts. Considering the slow-moving nature of Hurricane Nate, it is reasonable to hypothesize that air-sea interaction processes played a critical role in the intensity change of the storm, and accurate representation of the upper ocean dynamics and thermodynamics is necessary to quantitatively describe the air-sea interaction processes. Currently, data assimilation techniques are generally only applied to hurricane forecasting in stand-alone atmospheric or oceanic model. In fact, most of the regional hurricane forecasting models only included data assimilation techniques for improving the initial condition of the atmospheric model. In such a situation, the benefit of adjustments in one model (atmospheric or oceanic) by assimilating observational data can be compromised by errors from the other model. Thus, unified data assimilation techniques for coupled air-sea modelling systems, which not only simultaneously assimilate atmospheric and oceanic observations into the coupled air-sea modelling system, but also nudging the large-scale environmental flow in the regional model towards global model forecasts are of increasing necessity. In this presentation, we will outline a strategy for an integrated approach in air-sea coupled data assimilation and discuss its benefits and feasibility from incremental results for select historical hurricane cases.

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley T.; Jedlovee, Gary J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF-IFS Analysis Data

NASA Astrophysics Data System (ADS)

Borsdorff, T.; Aan de Brugh, J.; Hu, H.; Aben, I.; Hasekamp, O.; Landgraf, J.

2018-03-01

The Tropospheric Monitoring Instrument (TROPOMI) was launched onboard of the European Space Agency's (ESA) Sentinel-5P satellite. One of the mission's key products is the total column density of carbon monoxide, inferred from TROPOMI's 2.3 μm measurements. Using the operational processing algorithm, we analyze six subsequent days of measurements during the commissioning phase. The TROPOMI product is compared with CO fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation system. Globally, a small mean difference between the data sets of 3.2 ± 5.5% with a correlation coefficient of 0.97 is found. The daily global coverage of TROPOMI enables it to capture day-to-day evolution of the atmospheric composition. As an example, we discuss the air pollution event of India in November 2017 with high carbon monoxide (CO) concentrations, which partly dispersed when the CO polluted air was transported north alongside the Himalaya to China. The striking agreement and also regional differences with ECMWF indicate new exciting applications for the TROPOMI CO data product.

THE NEW ENGLAND AIR QUALITY FORECASTING PILOT PROGRAM: DEVELOPMENT OF AN EVALUATION PROTOCOL AND PERFORMANCE BENCHMARK

EPA Science Inventory

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implemen...

A statistical framework for the validation of a population exposure model based on personal exposure data

NASA Astrophysics Data System (ADS)

Rodriguez, Delphy; Valari, Myrto; Markakis, Konstantinos; Payan, Sébastien

2016-04-01

Currently, ambient pollutant concentrations at monitoring sites are routinely measured by local networks, such as AIRPARIF in Paris, France. Pollutant concentration fields are also simulated with regional-scale chemistry transport models such as CHIMERE (http://www.lmd.polytechnique.fr/chimere) under air-quality forecasting platforms (e.g. Prev'Air http://www.prevair.org) or research projects. These data may be combined with more or less sophisticated techniques to provide a fairly good representation of pollutant concentration spatial gradients over urban areas. Here we focus on human exposure to atmospheric contaminants. Based on census data on population dynamics and demographics, modeled outdoor concentrations and infiltration of outdoor air-pollution indoors we have developed a population exposure model for ozone and PM2.5. A critical challenge in the field of population exposure modeling is model validation since personal exposure data are expensive and therefore, rare. However, recent research has made low cost mobile sensors fairly common and therefore personal exposure data should become more and more accessible. In view of planned cohort field-campaigns where such data will be available over the Paris region, we propose in the present study a statistical framework that makes the comparison between modeled and measured exposures meaningful. Our ultimate goal is to evaluate the exposure model by comparing modeled exposures to monitor data. The scientific question we address here is how to downscale modeled data that are estimated on the county population scale at the individual scale which is appropriate to the available measurements. To assess this question we developed a Bayesian hierarchical framework that assimilates actual individual data into population statistics and updates the probability estimate.

Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health

PubMed Central

2012-01-01

Background Characterizing factors which determine susceptibility to air pollution is an important step in understanding the distribution of risk in a population and is critical for setting appropriate policies. We evaluate general and specific measures of community health as modifiers of risk for asthma and congestive heart failure following an episode of acute exposure to wildfire smoke. Methods A population-based study of emergency department visits and daily concentrations of fine particulate matter during a wildfire in North Carolina was performed. Determinants of community health defined by County Health Rankings were evaluated as modifiers of the relative risk. A total of 40 mostly rural counties were included in the study. These rankings measure factors influencing health: health behaviors, access and quality of clinical care, social and economic factors, and physical environment, as well as, the outcomes of health: premature mortality and morbidity. Pollutant concentrations were obtained from a mathematically modeled smoke forecasting system. Estimates of relative risk for emergency department visits were based on Poisson mixed effects regression models applied to daily visit counts. Results For asthma, the strongest association was observed at lag day 0 with excess relative risk of 66%(28,117). For congestive heart failure the excess relative risk was 42%(5,93). The largest difference in risk was observed after stratifying on the basis of Socio-Economic Factors. Difference in risk between bottom and top ranked counties by Socio-Economic Factors was 85% and 124% for asthma and congestive heart failure respectively. Conclusions The results indicate that Socio-Economic Factors should be considered as modifying risk factors in air pollution studies and be evaluated in the assessment of air pollution impacts. PMID:23006928

Cardio-respiratory outcomes associated with exposure to wildfire smoke are modified by measures of community health.

PubMed

Rappold, Ana G; Cascio, Wayne E; Kilaru, Vasu J; Stone, Susan L; Neas, Lucas M; Devlin, Robert B; Diaz-Sanchez, David

2012-09-24

Characterizing factors which determine susceptibility to air pollution is an important step in understanding the distribution of risk in a population and is critical for setting appropriate policies. We evaluate general and specific measures of community health as modifiers of risk for asthma and congestive heart failure following an episode of acute exposure to wildfire smoke. A population-based study of emergency department visits and daily concentrations of fine particulate matter during a wildfire in North Carolina was performed. Determinants of community health defined by County Health Rankings were evaluated as modifiers of the relative risk. A total of 40 mostly rural counties were included in the study. These rankings measure factors influencing health: health behaviors, access and quality of clinical care, social and economic factors, and physical environment, as well as, the outcomes of health: premature mortality and morbidity. Pollutant concentrations were obtained from a mathematically modeled smoke forecasting system. Estimates of relative risk for emergency department visits were based on Poisson mixed effects regression models applied to daily visit counts. For asthma, the strongest association was observed at lag day 0 with excess relative risk of 66% (28,117). For congestive heart failure the excess relative risk was 42% (5,93). The largest difference in risk was observed after stratifying on the basis of Socio-Economic Factors. Difference in risk between bottom and top ranked counties by Socio-Economic Factors was 85% and 124% for asthma and congestive heart failure respectively. The results indicate that Socio-Economic Factors should be considered as modifying risk factors in air pollution studies and be evaluated in the assessment of air pollution impacts.

Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.

PubMed

Paschalidou, Anastasia K; Karakitsios, Spyridon; Kleanthous, Savvas; Kassomenos, Pavlos A

2011-02-01

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.

P.88 Regional Precipitation Forecast with Atmospheric Infrared Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary

2010-01-01

Prudent assimulation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. In general, AIRS-enhanced analysis more closely resembles radiosondes than the CNTL; forecasts with AIRS profiles are generally closer to NAM analyses than CNTL for sensible weather parameters (not shown here). Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecases. Including AIRS profiles in assimilation process enhances the low-level instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

Studying and analyzing the formation and dynamics of the black cloud over Cairo, Egypt, using a multi sensor approach

NASA Astrophysics Data System (ADS)

El-Askary, H. M.; Prasad, A. K.; Marey, H. M.; El-Raey, M. E.; Asrar, G. R.; Kafatos, M.

2012-04-01

In the past decade, episodes of severe air pollution from biomass burning and/or industrial activities, known as the "black cloud" have occurred over Cairo, and the Nile Delta region situated on the eastern side of the Sahara desert in Egypt, during the autumn season. Previous studies have attributed the increased pollution levels during the black cloud season only to the biomass or open burning of agricultural waste, vehicular, industrial emissions, and secondary aerosols. However, new multi-sensor observations (column and vertical profiles) from satellites, dust transport models and associated meteorology present a different picture of the autumn pollution. It was found that the same region receives as well numerous dust storms along with the anthropogenic aerosols during same season. Such complex combination of these aerosols results in poor air quality and poses significant health hazards for the population in this region. In this study, data from the Moderate Resolution Imaging Spectrometer (MODIS) along with the Multiangle Imaging Spectroradiometer (MISR) are used with meteorological data and trajectory analyses to determine the cause of these events. MODIS fire counts highlighted the anthropogenic component of the dense cloud resulting from the burning of agricultural waste after harvest season. Synchronous MISR data show that these fires create low altitude (<500 m) plumes of smoke and aerosols which flow over Cairo in a few hours, as confirmed by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) forward trajectory analysis. Much of the burning occurs at night, when a thermal inversion constrains the plumes to remain in the boundary layer (BL). Convection during the day raises the BL, dispersing these smoke particles until the next night. However, we have found a dust transport pathway along the Mauritania/Mali/Algeria/Libya/Egypt axis that significantly affects NE Africa, especially the Nile Delta region, during the biomass burning season. The increase in aerosol loading (A0D>0.9) along with corresponding decrease in the Angstrom Exponent, a typical feature of desert dust, point towards the presence of desert dust over the Delta region. Hence, the high aerosol concentration episodes cannot be solely attributed to biomass burning or local pollution. Our results show that high altitude long range transported dust is a major contributing factor to the black cloud pollution during the biomass burning season. Our new findings may create a new outlook to investigate the chemical and physical nature of air pollution by scientists and informed decisions by policymakers. The complexity of aerosol transport and different sources of origin is a most challenging issue, not just for pollution control in densely populated areas, but also for effects on the overall climate system. We have found that current models such as DREAM that forecasts dust aerosols require revision in estimates during the autumn season since they could show some events observed by satellites. The satellite data such as MODIS provide useful complementary information to validate and constrain forecast from dust models. Our results indicate that hastily assigning origin to aerosols (such as black cloud which implies anthropogenic pollution), may mask the more complex origin of aerosol loadings.

Reducing residential solid fuel combustion through electrified space heating leads to substantial air quality, health and climate benefits in China's Beijing-Tianjin-Hebei region

NASA Astrophysics Data System (ADS)

Yang, J.; Mauzerall, D. L.

2017-12-01

During periods of high pollution in winter, household space heating can contribute more than half of PM2.5 concentrations in China's Beijing-Tianjin-Hebei (BTH) region. The majority of rural households and some urban households in the region still heat with small stoves and solid fuels such as raw coal, coal briquettes and biomass. Thus, reducing emissions from residential space heating has become a top priority of the Chinese government's air pollution mitigation plan. Electrified space heating is a promising alternative to solid fuel. However, there is little analysis of the air quality and climate implications of choosing various electrified heating devices and utilizing different electricity sources. Here we conduct an integrated assessment of the air quality, human health and climate implications of various electrified heating scenarios in the BTH region using the Weather Research and Forecasting model with Chemistry. We use the Multi-resolution Emission Inventory for China for the year 2012 as our base case and design two electrification scenarios in which either direct resistance heaters or air source heat pumps are installed to replace all household heating stoves. We initially assume all electrified heating devices use electricity from supercritical coal-fired power plants. We find that installing air source heat pumps reduces CO2 emissions and premature deaths due to PM2.5 pollution more than resistance heaters, relative to the base case. The increased health and climate benefits of heat pumps occur because they have a higher heat conversion efficiency and thus require less electricity for space heating than resistance heaters. We also find that with the same heat pump installation, a hybrid electricity source (40% of the electricity generated from renewable sources and the rest from coal) further reduces both CO2 emissions and premature deaths than using electricity only from coal. Our study demonstrates the air pollution and CO2 mitigation potential and public health benefits of using electrified space heating. In particular, we find air source heat pumps could bring more climate and health benefits than direct resistance heaters. Our results also support policies to integrate renewable energy sources with the reduction of solid fuel combustion for residential space heating.

Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China

NASA Astrophysics Data System (ADS)

Pang, Jiongming; Liu, Zhiquan; Wang, Xuemei; Bresch, Jamie; Ban, Junmei; Chen, Dan; Kim, Jhoon

2018-04-01

In this study, Geostationary Ocean Color Imager (GOCI) AOD and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data were assimilated to forecast surface PM2.5 concentrations over Eastern China, by using the three-dimensional variational (3DAVR) data assimilation (DA) system, to compare DA impacts by assimilating AOD retrievals from these two types of satellites. Three experiments were conducted, including a CONTROL without the AOD assimilation, and GOCIDA and VIIRSDA with the assimilation of AOD retrievals from GOCI and VIIRS, respectively. By utilizing the Weather Research and Forecasting with Chemistry (WRF/Chem) model, 48-h forecasts were initialized at each 06 UTC from 19 November to 06 December 2013. These forecasts were evaluated with 248 ground-based measurements from the air quality monitoring network across 67 China cities. The results show that overall the CONTROL underestimated surface PM2.5 concentrations, especially over Jing-Jin-Ji (JJJ) region and Yangtze River Delta (YRD) region. Both the GOCIDA and VIIRSDA produced higher surface PM2.5 concentrations mainly over Eastern China, which fits well with the PM2.5 measurements at these eastern sites, with more than 8% error reductions (ER). Moreover, compared to CONTROL, GOCIDA reduced 14.0% and 6.4% error on JJJ region and YRD region, respectively, while VIIRSDA reduced respectively 2.0% and 13.4% error over the corresponding areas. During the heavy polluted period, VIIRSDA improved all sites within YRD region, and GOCIDA enhanced 84% sites. Meanwhile, GOCIDA improved 84% sites on JJJ region, while VIIRSDA did not affect that region. These geographic distinctions might result from spatial dissimilarity between GOCI AOD and VIIRS AOD at time intervals. Moreover, the larger increment produced by AOD DA under stable meteorological conditions could lead to a longer duration (e.g., 1-2 days, > 2 days) of AOD DA impacts. Even though with AOD DA, surface PM2.5 concentrations were still underestimated clearly over heavy polluted periods. And 3% sites performed worse, where low PM2.5 values were observed and CONTROL performed well. With this study, the results indicate that AOD DA can partially improve the accuracy of PM2.5 forecasts. And the obvious geographic differences on forecasts emphasize the potential and importance of combining AOD retrievals from GOCI and VIIRS into data assimilation.

A regional high-resolution emission inventory of primary air pollutants in 2012 for Beijing and the surrounding five provinces of North China

NASA Astrophysics Data System (ADS)

Liu, Huanjia; Wu, Bobo; Liu, Shuhan; Shao, Panyang; Liu, Xiangyang; Zhu, Chuanyong; Wang, Yong; Wu, Yiming; Xue, Yifeng; Gao, Jiajia; Hao, Yan; Tian, Hezhong

2018-05-01

A high resolution regional emission inventory of typical primary air pollutants (PAPs) for the year 2012 in Beijing and the surrounding five provinces (BSFP) of North China is developed. It is compiled with the combination of bottom-up and top-down methods, based on city-level collected activity data and the latest updated specific emission factors for different sources. The considered sources are classified into 12 major categories and totally 36 subcategories with respect to their multi-dimensional characteristics, such as economic sector, combustion facility or industrial process, installed air pollution control devices, etc. Power plant sector is the dominant contributor of NOX emissions with an average contribution of 34.1%, while VOCs emissions are largely emitted from industrial process sources (33.9%). Whereas, other stationary combustion sources represent major sources of primary PM2.5, PM10 and BC emissions, accounting for 22.7%, 30.0% and 33.9% of the total emissions, respectively. Hebei province contributes over 34% of the regional total CO emissions because of huge volume of iron and steel production. By comparison, Shandong province ranks as the biggest contributor for NOX, PM10, PM2.5, SO2, VOCs and OC. Further, the BSFP regional total emissions are spatially distributed into grid cells with a high resolution of 9 km × 9 km using GIS tools and surrogate indexes, such regional population, gross domestic product (GDP) and the types of arable soils. The highest emission intensities are mainly located in Beijing-Tianjin-Tangshan area, Jinan-Laiwu-Zibo area and several other cities such as Shijiazhuang, Handan, and Zhengzhou. Furthermore, in order to establish a simple method to estimate and forecast PAPs emissions with macroscopic provincial-level statistical parameters in China, multi-parameter regression equations are firstly developed to estimate emissions outside the BSFP region with routine statistics (e.g. population, total final coal consumption, area of cultivated land and possession of civil vehicles) using the software 1stOpt. We find the estimated PAPs emissions of 31 provinces show close correlation with the well-recognized MEIC inventory. This high resolution multi-pollutants inventory provides necessary input data for regional air quality models that could help to identify and appoint the major influence sources, better understand the complex regional air pollution formation mechanism, and benefit for developing the corresponding joint prevention and control policies of regional complex air pollution in North China.

Potentially efficient forest and range applications of remote sensing using earth orbital space craft, circa 1980

NASA Technical Reports Server (NTRS)

Wilson, R. C.

1970-01-01

Sixteen remote sensing applications or groups of related applications judged to be most important of any in the forestry and range disciplines were evaluated. In one application, major land classification, large amounts of useful data are anticipated to be contributed by space sensors in 1980. In four applications moderate amounts are anticipated to be so contributed. These are timber inventory, range inventory, fire weather forecasting, and monitoring snowfields. In the following seven applications small but significant amounts of data are anticipated to be contributed by space sensors: (1) detailed land classification; (2) inventory of wildlife habitat; (3) recreation resource inventory; (4) detecting stresses on the vegetation (5) monitoring air pollution caused by wildfires and prescribed burning; (6) monitoring water cycle, (7) pollution and erosion; and (8) evaluating damage to forests and ranges.

Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in situ aircraft, ground-level, and satellite measurements from the DISCOVER-AQ Colorado campaign

NASA Astrophysics Data System (ADS)

Battye, William H.; Bray, Casey D.; Aneja, Viney P.; Tong, Daniel; Lee, Pius; Tang, Youhua

2016-09-01

The U.S. National Oceanic and Atmospheric Administration (NOAA) is responsible for forecasting elevated levels of air pollution within the National Air Quality Forecast Capability (NAQFC). The current research uses measurements gathered in the DISCOVER-AQ Colorado field campaign and the concurrent Front Range Air Pollution and Photochemistry Experiment (FRAPPE) to test performance of the NAQFC CMAQ modeling framework for predicting NH3. The DISCOVER-AQ and FRAPPE field campaigns were carried out in July and August 2014 in Northeast Colorado. Model predictions are compared with measurements of NH3 gas concentrations and the NH4+ component of fine particulate matter concentrations measured directly by the aircraft in flight. We also compare CMAQ predictions with NH3 measurements from ground-based monitors within the DISCOVER-AQ Colorado geographic domain, and from the Tropospheric Emission Spectrometer (TES) on the Aura satellite. In situ aircraft measurements carried out in July and August of 2014 suggest that the NAQFC CMAQ model underestimated the NH3 concentration in Northeastern Colorado by a factor of ∼2.7 (NMB = -63%). Ground-level monitors also produced a similar result. Average satellite-retrieved NH3 levels also exceeded model predictions by a factor of 1.5-4.2 (NMB = -33 to -76%). The underestimation of NH3 was not accompanied by an underestimation of particulate NH4+, which is further controlled by factors including acid availability, removal rate, and gas-particle partition. The average measured concentration of NH4+ was close to the average predication (NMB = +18%). Seasonal patterns measured at an AMoN site in the region suggest that the underestimation of NH3 is not due to the seasonal allocation of emissions, but to the overall annual emissions estimate. The underestimation of NH3 varied across the study domain, with the largest differences occurring in a region of intensive agriculture near Greeley, Colorado, and in the vicinity of Denver. The NAQFC modeling framework did not include a recently developed bidirectional flux algorithm for NH3, which has shown to considerably improve NH3 modeling in agricultural regions. The bidirectional flux algorithm, however, is not expected to obtain the magnitude of this increase sufficient to overcome the underestimation of NH3 found in this study. Our results suggest that further improvement of the emission inventories and modeling approaches are required to reduce the bias in NAQFC NH3 modeling predictions.

Multitemporal Monitoring of the Air Quality in Bulgaria by Satellite Based Instruments

NASA Astrophysics Data System (ADS)

Nikolov, Hristo; Borisova, Denitsa

2015-04-01

Nowadays the effect on climate changes on the population and environment caused by air pollutants at local and regional scale by pollution concentrations higher than allowed is undisputable. Main sources of gas releases are due to anthropogenic emissions caused by the economic and domestic activities of the inhabitants, and to less extent having natural origin. Complementary to pollutants emissions the local weather parameters such as temperature, precipitation, wind speed, clouds, atmospheric water vapor, and wind direction control the chemical reactions in the atmosphere. It should be noted that intrinsic property of the air pollution is its "transboundary-ness" and this is why the air quality (AQ) is not affecting the population of one single country only. This why the exchange of information concerning AQ at EU level is subject to well established legislation and one of EU flagship initiatives for standardization in data exchange, namely INSPIRE, has to cope with. It should be noted that although good reporting mechanism with regard to AQ is already established between EU member states national networks suffer from a serious disadvantage - they don't form a regular grid which is a prerequisite for verification of pollutants transport modeling. Alternative sources of information for AQ are the satellite observations (i.e. OMI, TOMS instruments) providing daily data for ones of the major contributors to air pollution such as O3, NOX and SO2. Those data form regular grids and are processed the same day of the acquisition so they could be used in verification of the outputs generated by numerical modeling of the AQ and pollution transfer. In this research we present results on multitemporal monitoring of several regional "hot spots" responsible for greenhouse gases emissions in Bulgaria with emphasis on satellite-based instruments. Other output from this study is a method for validation of the AQ forecasts and also providing feedback to the service that prepares them. The following sources of in-situ data for the different types of gases and dust particles have been used - the National Institute of Meteorology and Hydrology of Bulgaria (NIMH) and National System for Environmental Monitoring managed by Bulgarian Executive Environmental Agency (ExEA). Both authorities provide data for concentration of several gases just to mention CO, CO2, NO2, SO2, and fine suspended dust (PM10, PM2.5) on monthly (for some data on daily) basis. Considered satellite-based instruments for data provision are OMI instrument aboard EOS-Aura satellite and from TROPOMI instrument which is basic payload for the future Sentinel-5P mission.

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.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

NASA Astrophysics Data System (ADS)

Hao, Yufang; Xie, Shaodong

2018-03-01

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.

Evaluation of Air Force and Navy Demand Forecasting Systems

DTIC Science & Technology

1994-01-01

forecasting approach, the Air Force Material Command is questioning the adoption of the Navy’s Statistical Demand Forecasting System ( Gitman , 1994). The...Recoverable Item Process in the Requirements Data Bank System is to manage reparable spare parts ( Gitman , 1994). Although RDB will have the capability of...D062) ( Gitman , 1994). Since a comparison is made to address Air Force concerns, this research only limits its analysis to the range of Air Force

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.

The Aura Mission and Its Application to Climate and Air Quality

NASA Technical Reports Server (NTRS)

Hilsenrath, Ernest; Schoeberl, Mark; Douglass, Anne

2003-01-01

NASA's Aura satellite is scheduled to launch in the second quarter of 2004 into a polar orbit. The Aura mission is designed to collect data to address three high priority environmental science questions: (1) Is the ozone layer recovering as expected? (2) What are the sources and processes that control tropospheric pollutants? And (3) what is the quantitative impact of constituents on climate change? Aura will answer these questions by globally measuring a comprehensive set of trace gases and aerosols in the troposphere and stratosphere. Aura data will also have applications for monitoring and predicting climate and air quality parameters. Aura s observations will continue the TOMS ozone trend record and provide an assessment as to whether the Montreal Protocol is achieving its objective. Aura will measure gases and aerosols in the upper troposphere and lower stratosphere that contribute to climate forcing. These data will be of sufficient coverage, vertical resolution, and accuracy to help constrain climate models. In addition, Aura observations of tropospheric ozone and its precursors will have regional as well as intercontinental coverage, which could improve emission inventories. Near real time data will tested for local air quality forecasts in collaboration with the US's Environmental Protection UV-B forecasts from Aura ozone and cloud cover data. An overview of Aura s instruments, data products, validation, and examples of data applications will be presented.

Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006

NASA Astrophysics Data System (ADS)

Ngan, Fong; Byun, Daewon; Kim, Hyuncheol; Lee, Daegyun; Rappenglück, Bernhard; Pour-Biazar, Arastoo

2012-07-01

To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

Intra and inter-continental aerosol transport and local and regional impacts

NASA Astrophysics Data System (ADS)

Charles, Leona Ann Marie

Under the Clean Air Act, the Environmental Protection Agency (EPA) is required to establish a nationally uniform air quality index for the reporting of air quality. In 1976, the EPA established this index, then called the Pollutant Standards Index, for use by state and local communities across the country. The Index provides information on pollutant concentrations for ground-level ozone, particulate matter, carbon monoxide, sulfur dioxide, and nitrogen dioxide. On July 18, 1997, the EPA revised the ozone and particulate matter standards, in light of a comprehensive review of new scientific evidence including refined fine particulate matter standards.* Any program which is designed to improve air quality must devise tools in which emissions, meteorology, air chemistry and transport are understood. Clearly, the complexity of this task requires measurements at both regional and mesoscale ranges, as well as on a continental scale to investigate long range transport. Unfortunately, determination of fine particulate matter (PM) concentrations is particularly difficult since an accurate measurement of PM2.5 relies on costly equipment which cannot provide the complete transport story and the mixing and dispersion of particulate matter is much more complex than that for trace gases. Besides the need for accurate measurements as a way of documenting air quality standards, the EPA is required in the near future to implement a 24 hour Air Quality Forecast. Current forecast tools are usually based on emission inventories and meteorological forecasts, but significant work is being done in trying to assimilate both ground measurements as well as satellite measurements into these schemes. Clearly, the 'Holy Grail' would be the capability of assimilating full 3D (+ time) measurements. However, since satellite measurements are primarily passive, only total air column properties such as aerosol optical depth can be retrieved. In particular, it is not possible to determine the vertical layering of aerosols in the troposphere from passive remote sensing measurements. Therefore, the connection with air pollution is very poor. Furthermore, the vertical structure of the aerosol is very important in assessing transport events and how they mix with the Planetary Boundary Layer (PBL). The need to fill this data gap and supply vertical information on plume detection has led to the launch of the Cloud Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) space borne lidar system, which can in principle provide vertical profiles of aerosol backscatter that can be used in the assimilation schemes. One particular problem which needs to be addressed, is the fact that the relationship between the optical scattering coefficients (or AOD) and the PM2.5 mass is not simple. Finally, regarding non-attainment of National Ambient Air Quality Standards (NAAQS), it has also been shown that a significant portion of the PM2.5 aerosol mass can be due to non-local sources. This fact is critical in assessing the appropriate strategy in emission controls, as part of the state implementation plan (SIP) to come into compliance. However, these studies are usually based on statistical analysis tools such as Positive Factor Analysis (PFA), and are not applicable to any single measurement. In addition, little is known about the impact of episodic long range transport as a possible mechanism for affecting local pollution. Such a mechanism cannot be investigated by statistical means or by any existing air transport models which do not consider high altitude plumes (aerosol layers), and must be studied solely with an appropriate suite of measurements including the simultaneous use of sky radiometers, lidars and satellites. Furthermore, since fine particulate matter is so crucial to identify, multi-wavelength determination of aerosol properties such as angstrom coefficient are necessary. It is our purpose to investigate the possibility that such long range transport events can indeed affect local air-quality. This may first seem improbable due to the high plume altitudes, but we will show by case studies that significant mixing into the PBL can occur and affect local air quality. In particular, in chapters 5 and 6 we investigate dust and smoke transport events respectively, showing the usefulness of multi-wavelength lidar measurements to study the interaction of aerosols in the PBL with long range advected aerosol plumes. Our measurements are used to determine the plume angstrom exponent, which allows us to differentiate smoke events from dust events, as well as partitioning the total aerosol optical depth obtained from a CIMEL sky radiometer between the PBL and the high altitude plumes.* (Abstract shortened by UMI.) *Please refer to dissertation for diagrams.

High-Resolution Modelling of Health Impacts from Air Pollution for Denmark using the Integrated Model System EVA

NASA Astrophysics Data System (ADS)

Brandt, Jørgen; Andersen, Mikael S.; Bønløkke, Jakob; Christensen, Jesper H.; Hansen, Kaj M.; Hertel, Ole; Im, Ulas; Jensen, Steen S.; Ketzel, Matthias; Nielsen, Ole-Kenneth; Plejdrup, Marlene S.; Sigsgaard, Torben; Geels, Camilla

2015-04-01

We have developed an integrated health impact assessment system EVA (Economic Valuation of Air pollution; Brandt et al., 2013a; 2013b), based on the impact-pathway chain, to assess the health impacts and health-related economic externalities of air pollution resulting from specific emission sources or sectors. The system is used to support policymaking with respect to emission control. The EVA system has previously been used to assess the health impacts based on results from a regional model DEHM (the Danish Eulerian Hemispheric Model; Brandt et al., 2012). In this study we have used a coupling of two chemistry transport models to calculate the air pollution concentration at different scales; the DEHM model to calculate the air pollution levels with a resolution down to 5.6 km x 5.6 km and the UBM model (Urban Background Model ; Berkowicz, 2000; Brandt et al., 2001) to further calculate the air pollution at 1 km x 1 km resolution for Denmark using results from DEHM as boundary conditions. Both the emission data based on the SPREAD model (Plejdrup and Gyldenkærne, 2011) as well as the population density has been represented in the model system with the same high resolution. The new developments of the integrated model system will be presented as well as results for health impacts and related external costs over the years 2006-2014 for Denmark. Furthermore, a sensitivity study of the health impact using coarse and fine resolutions in the model system has been carried out to evaluate the effect of improved description of the geographical population distribution with respect to location of local emissions. References Berkowicz, R., 2000. A Simple Model for Urban Background Pollution. Environmental Monitoring and Assessment, 65, 1/2, 259-267. Brandt, J., J. H. Christensen, L. M. Frohn, F. Palmgren, R. Berkowicz and Z. Zlatev, 2001: "Operational air pollution forecasts from European to local scale". Atmospheric Environment, Vol. 35, Sup. No. 1, pp. S91-S98, 2001 Brandt, J., J. D. Silver, L. M. Frohn, C. Geels, A. Gross, A. B. Hansen, K. M. Hansen, G. B. Hedegaard, C. A. Skjøth, H. Villadsen, A. Zare, and J. H. Christensen, 2012: An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport. Atmospheric Environment, Volume 53, June 2012, pp. 156-176, doi:10.1016/j.atmosenv.2012.01.011 Brandt, J., J. D. Silver, J. H. Christensen, M. S. Andersen, J. Bønløkke, T. Sigsgaard, C. Geels, A. Gross, A. B. Hansen, K. M. Hansen, G. B. Hedegaard, E. Kaas and L. M. Frohn, 2013a: "Contribution from the ten major emission sectors in Europe to the Health-Cost Externalities of Air Pollution using the EVA Model System - an integrated modelling approach". Atmospheric Chemistry and Physics, Vol. 13, pp. 7725-7746, 2013. www.atmos-chem-phys.net/13/7725/2013/, doi:10.5194/acp-13-7725-2013. Brandt, J., J. D. Silver, J. H. Christensen, M. S. Andersen, J. Bønløkke, T. Sigsgaard, C. Geels, A. Gross, A. B. Hansen, K. M. Hansen, G. B. Hedegaard, E. Kaas and L. M. Frohn, 2013b: "Assessment of Past, Present and Future Health-Cost Externalities of Air Pollution in Europe and the contribution from international ship traffic using the EVA Model System". Atmospheric Chemistry and Physics. Vol. 13, pp. 7747-7764, 2013. www.atmos-chem-phys.net/13/7747/2013/. doi:10.5194/acp-13-7747-2013. Plejdrup, M.S., Gyldenkærne, S., 2011. Spatial distribution of pollutants to air - the SPREAD model. NERI Technical Report No. 823.

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.

Developing air quality forecasts

NASA Astrophysics Data System (ADS)

Lee, Pius; Saylor, Rick; Meagher, James

2012-05-01

Third International Workshop on Air Quality Forecasting Research; Potomac, Maryland, 29 November to 1 December 2011 Elevated concentrations of both near-surface ozone (O3) and fine particulate matter smaller than 2.5 micrometers in diameter have been implicated in increased mortality and other human health impacts. In light of these known influences on human health, many governments around the world have instituted air quality forecasting systems to provide their citizens with advance warning of impending poor air quality so that they can take actions to limit exposure. In an effort to improve the performance of air quality forecasting systems and provide a forum for the exchange of the latest research in air quality modeling, the International Workshop on Air Quality Forecasting Research (IWAQFR) was established in 2009 and is cosponsored by the U.S. National Oceanic and Atmospheric Administration (NOAA), Environment Canada (EC), and the World Meteorological Organization (WMO). The steering committee for IWAQFR's establishment was composed of Véronique Bouchet, Mike Howe, and Craig Stoud (EC); Greg Carmichael (University of Iowa); Paula Davidson and Jim Meagher (NOAA); and Liisa Jalkanen (WMO). The most recent workshop took place in Maryland.

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants.

PubMed

Dai, C; Cai, X H; Cai, Y P; Guo, H C; Sun, W; Tan, Q; Huang, G H

2014-06-01

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk. A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.

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 ...

Application of Weather Research and Forecasting Model with Chemistry (WRF/Chem) over northern China: Sensitivity study, comparative evaluation, and policy implications

NASA Astrophysics Data System (ADS)

Wang, Litao; Zhang, Yang; Wang, Kai; Zheng, Bo; Zhang, Qiang; Wei, Wei

2016-01-01

An extremely severe and persistent haze event occurred over the middle and eastern China in January 2013, with the record-breaking high concentrations of fine particulate matter (PM2.5). In this study, an online-coupled meteorology-air quality model, the Weather Research and Forecasting Model with Chemistry (WRF/Chem), is applied to simulate this pollution episode over East Asia and northern China at 36- and 12-km grid resolutions. A number of simulations are conducted to examine the sensitivities of the model predictions to various physical schemes. The results show that all simulations give similar predictions for temperature, wind speed, wind direction, and humidity, but large variations exist in the prediction for precipitation. The concentrations of PM2.5, particulate matter with aerodynamic diameter of 10 μm or less (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are overpredicted partially due to the lack of wet scavenging by the chemistry-aerosol option with the 1999 version of the Statewide Air Pollution Research Center (SAPRC-99) mechanism with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Volatility Basis Set (VBS) for secondary organic aerosol formation. The optimal set of configurations with the best performance is the simulation with the Gorddard shortwave and RRTM longwave radiation schemes, the Purdue Lin microphysics scheme, the Kain-Fritsch cumulus scheme, and a nudging coefficient of 1 × 10-5 for water vapor mixing ratio. The emission sensitivity simulations show that the PM2.5 concentrations are most sensitive to nitrogen oxide (NOx) and SO2 emissions in northern China, but to NOx and ammonia (NH3) emissions in southern China. 30% NOx emission reductions may result in an increase in PM2.5 concentrations in northern China because of the NH3-rich and volatile organic compound (VOC) limited conditions over this area. VOC emission reductions will lead to a decrease in PM2.5 concentrations in eastern China. However, 30% reductions in the emissions of SO2, NOx, NH3, and VOC, individually or collectively, are insufficient to effectively mitigate the severe pollution over northern China. More aggressive emission controls, which needs to be identified in further studies, are needed in this area to reach the objective of 25% PM2.5 concentration reduction in 2017 proposed in the Action Plan for Air Pollution Prevention and Control by the State Council in 2013.

Transportation Sector Model of the National Energy Modeling System. Volume 1

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

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. Themore » current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.« less

Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

NASA Astrophysics Data System (ADS)

Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang

2017-11-01

Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and MNE of 0.16-0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

Evaluation of the Impact of AIRS Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of a long-term series of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

Evaluation of the Impact of Atmospheric Infrared Sounder (AIRS) Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of 6 weeks of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

Winds over Japan.

NASA Astrophysics Data System (ADS)

Plumley, William J.

1994-01-01

Before World War II, weather forecasters had little knowledge of upper-air wind patterns above 20000 feet. Data were seldom avai able at these heights, and the need was not great because commercial aircraft seldom flew at these altitudes. The war in the Pacific changed all that. Wind forecasts for 30000 feet plus became urgent to support the XXI Bomber Command in its bombing mission over Japan.The U.S. Army Air Force Pacific Ocean Area (AAFPOA) placed a Weather Central in the Marianas Islands in 1944 (Saipan in 1944 and Guam in 1945) to provide forecasting support for this mission. A forecasting procedure was put into operation that combined the elements known as "single-station forecasting" and an advanced procedure that used "altirmeter corrections" to analyze upper-airdata and make prognoses. Upper-air charts were drawn for constant pressure surfaces rather than constant height surfaces. The constant pressure surfaces were tied together by means of the atmospheric temperature field represented by specific temperature anomalies between pressure surfaces. Wind forecasts over the Marianas-Japan route made use of space cross sections that provided the data to forecast winds at each 5000-ft level to 35000 ft along the mission flight path. The new procedures allowed the forecaster to construct internally consistent meteorological charts in three dimensions in regions of sparse data.Army air force pilots and their crews from the Marianas were among the first to experience the extreme wind conditions now known as the "jet stream". Air force forecasters demonstrated that, with experience, such winds could reasonably be forecast under difficult operational conditions.

Climate Change and Health Risks from Extreme Heat and Air Pollution in the Eastern United States

NASA Astrophysics Data System (ADS)

Limaye, V.; Vargo, J.; Harkey, M.; Holloway, T.; Meier, P.; Patz, J.

2013-12-01

Climate change is expected to exacerbate health risks from exposure to extreme heat and air pollution through both direct and indirect mechanisms. Directly, warmer ambient temperatures promote biogenic emissions of ozone precursors and favor the formation of ground-level ozone, while an anticipated increase in the frequency of stagnant air masses will allow fine particulates to accumulate. Indirectly, warmer summertime temperatures stimulate energy demand and exacerbate polluting emissions from the electricity sector. Thus, while technological adaptations such as air conditioning can reduce risks from exposures to extreme heat, they can trigger downstream damage to air quality and public health. Through an interdisciplinary modeling effort, we quantify the impacts of climate change on ambient temperatures, summer energy demand, air quality, and public health. The first phase of this work explores how climate change will directly impact the burden of heat-related mortality. Climatic patterns, demographic trends, and epidemiologic risk models suggest that populations in the eastern United States are likely to experience an increasing heat stress mortality burden in response to rising summertime air temperatures. We use North American Regional Climate Change Assessment Program modeling data to estimate mid-century 2-meter air temperatures and humidity across the eastern US from June-August, and quantify how long-term changes in actual and apparent temperatures from present-day will affect the annual burden of heat-related mortality across this region. With the US Environmental Protection Agency's Environmental Benefits Mapping and Analysis Program, we estimate health risks using concentration-response functions, which relate temperature increases to changes in annual mortality rates. We compare mid-century summertime temperature data, downscaled using the Weather Research and Forecasting model, to 2007 baseline temperatures at a 12 km resolution in order to estimate the number of annual excess deaths attributable to increased summer temperatures. Warmer average temperatures are expected to cause 173 additional deaths due to cardiovascular stress, while higher minimum temperatures will cause 67 additional deaths. This work particularly improves on the spatial resolution of published analyses of heat-related mortality in the US.

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper.

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...

The Impacts of Urbanization on Meteorology and Air Quality in the Los Angeles Basin

NASA Astrophysics Data System (ADS)

Li, Y.; Zhang, J.; Sailor, D.; Ban-Weiss, G. A.

2017-12-01

Urbanization has a profound influence on regional meteorology in mega cities like Los Angeles. This influence is driven by changes in land surface physical properties and urban processes, and their corresponding influence on surface-atmosphere coupling. Changes in meteorology from urbanization in turn influences air quality through weather-dependent chemical reaction, pollutant dispersion, etc. Hence, a real-world representation of the urban land surface properties and urban processes should be accurately resolved in regional climate-chemistry models for better understanding the role of urbanization on changing urban meteorology and associated pollutant dynamics. By incorporating high-resolution land surface data, previous research has improved model-observation comparisons of meteorology in urban areas including the Los Angeles basin, and indicated that historical urbanization has increased urban temperatures and altered wind flows significantly. However, the impact of urban expansion on air quality has been less studied. Thus, in this study, we aim to evaluate the effectiveness of resolving high-resolution heterogeneity in urban land surface properties and processes for regional weather and pollutant concentration predictions. We coupled the Weather Research and Forecasting model with Chemistry to the single-layer Urban Canopy Model to simulate a typical summer period in year 2012 for Southern California. Land cover type and urban fraction were determined from National Land Cover Data. MODIS observations were used to determine satellite-derived albedo, green vegetation fraction, and leaf area index. Urban morphology was determined from GIS datasets of 3D building geometries. An urban irrigation scheme was also implemented in the model. Our results show that the improved model captures the diurnal cycle of 2m air temperature (T2) and Ozone (O3) concentrations. However, it tends to overestimate wind speed and underestimate T2, which leads to an underestimation of O3 and fine particulate matter concentrations. By comparing simulations assuming current land cover of the Los Angeles basin versus pre-urbanization land cover, we find that land cover change through urbanization has led to important shifts in regional air pollution via the aforementioned physical and chemical mechanisms.

Numerical study of the effects of local atmospheric circulations on a pollution event over Beijing-Tianjin-Hebei, China.

PubMed

Miao, Yucong; Liu, Shuhua; Zheng, Yijia; Wang, Shu; Chen, Bicheng; Zheng, Hui; Zhao, Jingchuan

2015-04-01

Currently, the Chinese central government is considering plans to build a trilateral economic sphere in the Bohai Bay area, including Beijing, Tianjin and Hebei (BTH), where haze pollution frequently occurs. To achieve sustainable development, it is necessary to understand the physical mechanism of the haze pollution there. Therefore, the pollutant transport mechanisms of a haze event over the BTH region from 23 to 24 September 2011 were studied using the Weather Research and Forecasting model and the FLEXible-PARTicle dispersion model to understand the effects of the local atmospheric circulations and atmospheric boundary layer structure. Results suggested that the penetration by sea-breeze could strengthen the vertical dispersion by lifting up the planetary boundary layer height (PBLH) and carry the local pollutants to the downstream areas; in the early night, two elevated pollution layers (EPLs) may be generated over the mountain areas: the pollutants in the upper EPL at the altitude of 2-2.5 km were favored to disperse by long-range transport, while the lower EPL at the altitude of 1 km may serve as a reservoir, and the pollutants there could be transported downward and contribute to the surface air pollution. The intensity of the sea-land and mountain-valley breeze circulations played an important role in the vertical transport and distribution of pollutants. It was also found that the diurnal evolution of the PBLH is important for the vertical dispersion of the pollutants, which is strongly affected by the local atmospheric circulations and the distribution of urban areas. Copyright © 2015. Published by Elsevier B.V.

Data Assimilation of AIRS Water Vapor Profiles: Impact on Precipitation Forecasts for Atmospheric River Cases Affecting the Western of the United States

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Bradley; Jedlovec, Gary; Wick, Gary; Neiman, Paul

2013-01-01

Atmospheric rivers are transient, narrow regions in the atmosphere responsible for the transport of large amounts of water vapor. These phenomena can have a large impact on precipitation. In particular, they can be responsible for intense rain events on the western coast of North America during the winter season. This paper focuses on attempts to improve forecasts of heavy precipitation events in the Western US due to atmospheric rivers. Profiles of water vapor derived from from Atmospheric Infrared Sounder (AIRS) observations are combined with GFS forecasts by a three-dimensional variational data assimilation in the Gridpoint Statistical Interpolation (GSI). Weather Research and Forecasting (WRF) forecasts initialized from the combined field are compared to forecasts initialized from the GFS forecast only for 3 test cases in the winter of 2011. Results will be presented showing the impact of the AIRS profile data on water vapor and temperature fields, and on the resultant precipitation forecasts.

THE EMERGENCE OF NUMERICAL AIR QUALITY FORCASTING MODELS AND THEIR APPLICATIONS

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

High-resolution visibility and air quality forecasting using multi-layer urban canopy model for highly urbanized Hong Kong and the Pearl River Delta

NASA Astrophysics Data System (ADS)

Piu NG, Chak; HAO, Song; Fat LAM, Yun

2015-04-01

Visibility is a universally critical element which affects the public in many aspects, including economic activities, health of local citizens and safety of marine transportation and aviation. The Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility equation, an empirical equation developed by USEPA, has been modified by various studies to fit into the application upon the Asian continent including Hong Kong and China. Often these studies focused on the improvement of the existing IMPROVE equation by modifying its particulate speciation using local observation data. In this study, we developed an Integrated Forecast System (IFS) to predict the next-day air quality and visibility using Weather Research and Forecasting model with Building Energy Parameterization and Building Energy Model (WRF-BEP+BEM) and Community Multi-scale Air Quality Model (CMAQ). Unlike the other studies, the core of this study is to include detailed urbanization impacts with calibrated "IMPROVE equation for PRD" into the modeling system for Hong Kong's environs. The ultra-high resolution land cover information (~1km x 1km) from Google images, was digitized into the Geographic Information System (GIS) for preparing the model-ready input for IFS. The NCEP FNL (Final) Operation Global Analysis (FNL) and the Global Forecasting System (GFS) datasets were tested for both hind-cast and forecast cases, in order to calibrate the input of urban parameters in the WRF-BEP+BEM model. The evaluation of model performance with sensitivity cases was performed on sea surface temperature (SST), surface temperature (T), wind speed/direction with the major pollutants (i.e., PM10, PM2.5, NOx, SO2 and O3) using local observation and will be presented/discussed in this paper. References: 1. Y. L. Lee, R. Sequeira, Visibility degradation across Hong Kong its components and their relative contribution. Atmospheric Environment 2001, 35, 5861-5872. doi:10.1016/S1352-2310(01)00395-8 2. R. Zhang, Q. Bian, J. C. H. Fung, A. K. H. Lau, Mathematical modeling of seasonal variations in visibility in Hong Kong and the Pearl River Delta region. Atmospheric Environment 2013, 77, 803-816. http://dx.doi.org/10.1016/j.atmosenv.2013.05.048

Modeling the convective transport of pollutants from eastern Colorado, USA into Rocky Mountain National Park

NASA Astrophysics Data System (ADS)

Pina, A.; Schumacher, R. S.; Denning, S.

2015-12-01

Rocky Mountain National Park (RMNP) is a Class I Airshed designated under the Clean Air Act. Atmospheric nitrogen (N) deposition in the Park has been a known problem since weekly measurements of wet deposition of inorganic N began in the 1980s by the National Atmospheric Deposition Program (NADP). The addition of N from urban and agriculture emissions along the Colorado Front Range to montane ecosystems degrades air quality/visibility, water quality, and soil pH levels. Based on NADP data during summers 1994-2014, wet N deposition at Beaver Meadows in RMNP exhibited a bimodal gamma distribution. In this study, we identified meteorological transport mechanisms for 3 high wet-N deposition events (all events were within the secondary peak of the gamma distribution) using the North American Regional Reanalysis (NARR) and the Weather Research and Forecasting (WRF) model. The NARR was used to identify synoptic-scale influences on the transport; the WRF model was used to analyze the convective transport of pollutants from a concentrated animal feeding operation near Greeley, Colorado, USA. The WRF simulation included a passive tracer from the feeding operation and a convection-permitting horizontal spacing of 4/3 km. The three cases suggest (a) synoptic-scale moisture and flow patterns are important for priming summer transport events and (b) convection plays a vital role in the transport of Front Range pollutants into RMNP.

Air pollution in Polish cities during January 2017 - an episode study

NASA Astrophysics Data System (ADS)

Durka, Pawel; Kaminski, Jacek W.; Struzewska, Joanna

2017-04-01

Poor air quality is a health issue in Poland, especially during winter. Six cities in Poland are in the top 10 and 33 cities are in the top 50 most polluted cities in Europe. In the first days of January 2017, there was a drastic change in the weather patterns over Central Europe. Temperatures dropped below -20oC in only a couple of days with calm wind conditions. Meteorological soundings showed inversions up to hundreds of meters above the surface. In such conditions PM10 and PM2.5 concentrations were unusually high. Thresholds were exceeded in most of the cities in Poland. In some of the cities PM concentrations were very high: reaching 1600 µg/m3 in Rybnik, 1300 µg/m3 in Zabrze, 1000 µg/m3 in Gliwice, 700 µg/m3 in Katowice. Most likely the main source of the pollution event were domestic emissions as well as traffic emissions. High concentration values were due to the stratification of the atmosphere. Nearly all cities issued smog alerts. The majority of the cities introduced free public transportation and schools were closed. We will show the evolution of this episode in the selected cities for the period when the highest concentrations measured and forecasted - from 7 to 11 January 2017. The synoptic situation for the episode will be discussed.

Heat Waves, Urban Vegetation, and Air Pollution

NASA Astrophysics Data System (ADS)

Churkina, G.; Grote, R.; Butler, T. M.

2014-12-01

Fast-track programs to plant millions of trees in cities around the world aim at the reduction of summer temperatures, increase carbon storage, storm water control, provision of space for recreation, as well as poverty alleviation. Although these multiple benefits speak positively for urban greening programs, the programs do not take into account how close human and natural systems are coupled in urban areas. Elevated temperatures together with anthropogenic emissions of air and water pollutants distinguish the urban system. Urban and sub-urban vegetation responds to ambient changes and reacts with pollutants. Neglecting the existence of this coupling may lead to unforeseen drawbacks of urban greening programs. The potential for emissions from urban vegetation combined with anthropogenic emissions to produce ozone has long been recognized. This potential increases under rising temperatures. Here we investigate how global change induced heat waves affect emissions of volatile organic compounds (VOC) from urban vegetation and corresponding ground-level ozone levels. We also quantify other ecosystem services provided by urban vegetation (e.g., cooling and carbon storage) and their sensitivity to climate change. In this study we use Weather Research and Forecasting Model with coupled atmospheric chemistry (WRF-CHEM) to quantify these feedbacks in Berlin, Germany during the heat waves in 2003 and 2006. We highlight the importance of the vegetation for urban areas under changing climate and discuss associated tradeoffs.

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...

A PERFORMANCE EVALUATION OF THE ETA- CMAQ AIR QUALITY FORECAST SYSTEM FOR THE SUMMER OF 2005

EPA Science Inventory

This poster presents an evaluation of the Eta-CMAQ Air Quality Forecast System's experimental domain using O₃ observations obtained from EPA's AIRNOW program and a suite of statistical metrics examining both discrete and categorical forecasts.

Potential impacts of urban land expansion on Asian airborne pollutant outflows

NASA Astrophysics Data System (ADS)

Tao, Wei; Liu, Junfeng; Ban-Weiss, George A.; Zhang, Lin; Zhang, Jiachen; Yi, Kan; Tao, Shu

2017-07-01

Eastern part of China (EPC) has experienced rapid urbanization during the past few decades. Here we investigate the impacts of urban land expansion over EPC on the export of Asian pollutants to the western Pacific during January, April, July, and October of 2009 using the Weather Research and Forecasting model coupled to Chemistry (WRF/Chem) and a single-layer urban canopy scheme. Over urbanizing areas, increases in the urban land fraction result in a linearly enhanced uplift of surface primary pollutants to higher altitudes. We further examine how this local effect would change outflows of Asian pollutants to the western Pacific using the tagged black carbon (BC) and carbon monoxide (CO) tracers emitted from EPC (denoted by BCt and COt, respectively). Overall, a 0.1 increase in the fraction of land area that is urban over EPC would linearly (R2 = 0.70-0.96) increase the mean tropospheric eastward export of BCt and COt across meridional planes (i.e., 135°E and 150°E) by 4-40% and 1-6% in different months, respectively. The relative perturbation in exporting efficiency generally maximizes during July while minimizes during April. The urbanization-export relationship is largely driven by the elevation effect and is also impacted by urbanization-forced changes in zonal winds. The spatial pattern of the response of BCt over the downwind Pacific differs from that of COt mainly due to aerosol-cloud interactions. Our findings demonstrate that extensive urban land expansion could substantially impact climate and air quality from a local scale to a regional scale, especially for shorter-lived air pollutants such as BC and other aerosols.

Environmentalists take the offensive

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

Eason, H.

1983-04-01

The unfortunate polarization between businessmen and environmentalists will intensify this year as Congress, manned with newly-elected allies of the Environmental Protection Agency (EPA), reviews the nation's fundamental pollution-control and conservation laws, the Clean Air Act, and the Clean Water Act. Emotions and controversy over EPA's management of its toxic-waste Superfund cleanup program may prevent careful, reasonable review of the environmental issues at stake, and EPA forecasts the issues will be discussed politically, rather than substantively. Business lobbyists argue that their people support clean air and water and safe disposal of wastes too, but are also concerned with the entanglements ofmore » expensive red tape, unenforceable timetables, and counterproductive procedures. Especially sensitive areas of debate are those dealing with acid rain legislation, wilderness area designations, and budget cuts in natural resources and ecology protection.« less

Quantifying sources of elemental carbon over the Guanzhong Basin of China: A consistent network of measurements and WRF-Chem modeling.

PubMed

Li, Nan; He, Qingyang; Tie, Xuexi; Cao, Junji; Liu, Suixin; Wang, Qiyuan; Li, Guohui; Huang, Rujin; Zhang, Qiang

2016-07-01

We conducted a year-long WRF-Chem (Weather Research and Forecasting Chemical) model simulation of elemental carbon (EC) aerosol and compared the modeling results to the surface EC measurements in the Guanzhong (GZ) Basin of China. The main goals of this study were to quantify the individual contributions of different EC sources to EC pollution, and to find the major cause of the EC pollution in this region. The EC measurements were simultaneously conducted at 10 urban, rural, and background sites over the GZ Basin from May 2013 to April 2014, and provided a good base against which to evaluate model simulation. The model evaluation showed that the calculated annual mean EC concentration was 5.1 μgC m(-3), which was consistent with the observed value of 5.3 μgC m(-3). Moreover, the model result also reproduced the magnitude of measured EC in all seasons (regression slope = 0.98-1.03), as well as the spatial and temporal variations (r = 0.55-0.78). We conducted several sensitivity studies to quantify the individual contributions of EC sources to EC pollution. The sensitivity simulations showed that the local and outside sources contributed about 60% and 40% to the annual mean EC concentration, respectively, implying that local sources were the major EC pollution contributors in the GZ Basin. Among the local sources, residential sources contributed the most, followed by industry and transportation sources. A further analysis suggested that a 50% reduction of industry or transportation emissions only caused a 6% decrease in the annual mean EC concentration, while a 50% reduction of residential emissions reduced the winter surface EC concentration by up to 25%. In respect to the serious air pollution problems (including EC pollution) in the GZ Basin, our findings can provide an insightful view on local air pollution control strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impacts of compact growth and electric vehicles on future air quality and urban exposures may be mixed.

PubMed

Yu, Haofei; Stuart, Amy L

2017-01-15

'Smart' growth and electric vehicles are potential solutions to the negative impacts of worldwide urbanization on air pollution and health. However, the effects of planning strategies on distinct types of pollutants, and on human exposures, remain understudied. The goal of this work was to investigate the potential impacts of alternative urban designs for the area around Tampa, Florida USA, on emissions, ambient concentrations, and exposures to oxides of nitrogen (NO x ), 1,3-butadiene, and benzene. We studied three potential future scenarios: sprawling growth, compact growth, and 100% vehicle fleet electrification with compact growth. We projected emissions in the seven-county region to 2050 based on One Bay regional visioning plan data. We estimated pollutant concentrations in the county that contains Tampa using the CALPUFF dispersion model. We applied residential population projections to forecast acute (highest hour) and chronic (annual average) exposure. The compact scenario was projected to result in lower regional emissions of all pollutants than sprawl, with differences of -18%, -3%, and -14% for NO x , butadiene, and benzene, respectively. Within Hillsborough County, the compact form also had lower emissions, concentrations, and exposures than sprawl for NO x (-16%/-5% for acute/chronic exposures, respectively), but higher exposures for butadiene (+41%/+30%) and benzene (+21%/+9%). The addition of complete vehicle fleet electrification to the compact scenario mitigated these in-county increases for the latter pollutants, lowering predicted exposures to butadiene (-25%/-39%) and benzene (-5%/-19%), but also resulted in higher exposures to NO x (+81%/+30%) due to increased demand on power plants. These results suggest that compact forms may have mixed impacts on exposures and health. 'Smart' urban designs should consider multiple pollutants and the diverse mix of pollutant sources. Cleaner power generation will also likely be needed to support aggressive adoption of electric vehicles. Copyright © 2016 Elsevier B.V. All rights reserved.

Impact of AIRS Thermodynamic Profiles on Precipitation Forecasts for Atmospheric River Cases Affecting the Western United States

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley T.; Jedlovec, Gary J.; Blakenship, Clay B.; Wick, Gary A.; Neiman, Paul J.

2013-01-01

This project is a collaborative activity between the NASA Short-term Prediction Research and Transition (SPoRT) Center and the NOAA Hydrometeorology Testbed (HMT) to evaluate a SPoRT Advanced Infrared Sounding Radiometer (AIRS: Aumann et al. 2003) enhanced moisture analysis product. We test the impact of assimilating AIRS temperature and humidity profiles above clouds and in partly cloudy regions, using the three-dimensional variational Gridpoint Statistical Interpolation (GSI) data assimilation (DA) system (Developmental Testbed Center 2012) to produce a new analysis. Forecasts of the Weather Research and Forecasting (WRF) model initialized from the new analysis are compared to control forecasts without the additional AIRS data. We focus on some cases where atmospheric rivers caused heavy precipitation on the US West Coast. We verify the forecasts by comparison with dropsondes and the Cooperative Institute for Research in the Atmosphere (CIRA) Blended Total Precipitable Water product.

Impacts of air pollution wave on years of life lost: A crucial way to communicate the health risks of air pollution to the public.

PubMed

Huang, Jing; Pan, Xiaochuan; Guo, Xinbiao; Li, Guoxing

2018-04-01

Limited studies have explored the impacts of exposure to sustained high levels of air pollution (air pollution wave) on mortality. Given that the frequency, intensity and duration of air pollution wave has been increasing in highly polluted regions recently, understanding the impacts of air pollution wave is crucial. In this study, air pollution wave was defined as 2 or more consecutive days with air pollution index (API) > 100. The impacts of air pollution wave on years of life lost (YLL) due to non-accidental, cardiovascular and respiratory deaths were evaluated by considering both consecutive days with high levels of air pollution and daily air pollution levels in Tianjin, China, from 2006 to 2011. The results showed the durational effect of consecutive days with high levels of air pollution was substantial in addition to the effect of daily air pollution. For instance, the durational effect was related to an increase in YLL of 116.6 (95% CI: 4.8, 228.5) years from non-accidental deaths when the air pollution wave was sustained for 4 days, while the corresponding daily air pollution's effect was 121.2 (95% CI: 55.2, 187.1) years. A better interpretation of the health risks of air pollution wave is crucial for air pollution control policy making and public health interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Influence of Asian dust storms on air quality in Taiwan.

PubMed

Liu, Chung-Ming; Young, Chea-Yuan; Lee, Yen-Chih

2006-09-15

In each year, dust storms triggered by cold air masses passing through northern China and Mongolia enhance the PM10 concentration over Taiwan region during winter and spring. On average, there are four to five dust events and 6.1 dust days in a year in Taiwan. Each event lasts for 1 day or even longer. A procedure to identify a dust event is rationalized and exercised on data collected during 1994-2005. Also, a ranking method named as the dust intensity rank (DIR) is developed to distinguish the intensity of each event affecting the local air quality. About 86% of dust days belong to ranks 1 and 2. In general, poorer air quality is associated with higher ranks. Ranks 4 and 5 correspond to a PSI (Pollution Standard Index) larger than 100. Linking DIR with the popular PSI is useful for both the public and the official forecasting system. It is also useful for inter-comparison between dust influences on air quality at different downstream regions in Taiwan. Composite analyses of the temporal and spatial variation of the hourly PM10 level indicate that dust particles usually arrive 12 h before the time of the peak PM10 concentration and last for 36 h at northern Taiwan, while the time of the peak concentration at eastern or western Taiwan, due to the evolution of the synoptic weather system, is about 3-12 h later. It is noted that the increase of PM10 level at the western side of Taiwan results from a mixture of upstream Asian dust inputs and local pollutants.

Modeling Urban Air Quality in the Berlin-Brandenburg Region: Evaluation of a WRF-Chem Setup

NASA Astrophysics Data System (ADS)

Kuik, F.; Churkina, G.; Butler, T. M.; Lauer, A.; Mar, K. A.

2015-12-01

Air pollution is the number one environmental cause of premature deaths in Europe. Despite extensive regulations, air pollution remains a challenging issue, especially in urban areas. For studying air quality in the Berlin-Brandenburg region of Germany the Weather Research and Forecasting Model with Chemistry (WRF-Chem) is set up and evaluated against meteorological and air quality observations from monitoring stations as well as from a field campaign conducted in 2014 (incl. black carbon, VOCs as well as mobile measurements of particle size distribution and particle mass). The model setup includes 3 nested domains with horizontal resolutions of 15km, 3km, and 1km, online biogenic emissions using MEGAN 2.0, and anthropogenic emissions from the TNO-MACC-II inventory. This work serves as a basis for future studies on different aspects of air pollution in the Berlin-Brandenburg region, including how heat waves affect emissions of biogenic volatile organic compounds (BVOC) from urban vegetation (summer 2006) and the impact of selected traffic measures on air quality in the Berlin-Brandenburg area (summer 2014). The model represents the meteorology as observed in the region well for both periods. An exception is the heat wave period in 2006, where the temperature simulated with 3km and 1km resolutions is biased low by around 2°C for urban built-up stations. First results of simulations with chemistry show that, on average, WRF-Chem simulates concentrations of O3 well. However, the 8 hr maxima are underestimated, and the minima are overestimated. While NOx daily means are modeled reasonably well for urban stations, they are overestimated for suburban stations. PM10 concentrations are underestimated by the model. The biases and correlation coefficients of simulated O3, NOx, and PM10 in comparison to surface observations do not show improvements for the 1km domain in comparison to the 3km domain. To improve the model performance of the 1km domain we will include an updated emission inventory (TNO-MACC-III) as well as the interpolation of the emission data from 7km to a 1km resolution.

Developing a system of multi-evaluation of the impact of global climate change on human health in Russia

NASA Astrophysics Data System (ADS)

Granberg, I.; Golitsyn, G.; Istoshin, N.; Efimenko, N.; Alekhin, A.; Rogoza, A.; Povolotskaya, N.; Artamonova, M.; Maximenkov, L.; Pogarski, F.

2009-04-01

High people sensitivity to weather and space factors, particularly encumbered by various illnesses, was from time immemorial. Now, in terms of global climate change, accompanied by frequent and severe restructuring of atmospheric processes, thermal anomalies, droughts, environmental change through meteorological and heliogeophysical factors affect the human body particularly intense, causing adverse effects to health. There are currently beginning to develop methods for evaluating multifactor of the external environment and prevention of their negative influence on people. For those sensitive to such influences, with adverse weather in response to sudden changes in weather factors pathological of meteopathic reactions may arise. In doing so, even among healthy individuals it is up to 35-45% of meteosensitive. Meteopathic reactions lead to the appearance and progression of pathological disorders, and the associated increase in chronic diseases. In this connection the tasks solution related to assessing the impact of meteorological and climatic variations of different space-time scale on the health of the population of Russia becomes extremely important, especially for the people with cardiovascular disease. This is confirmed by as clinical observations, and the state of vital systems of meteosensitive people. Based on the results of comprehensive research of Pyatigorsk State Research Institute of Curortology (PSRIC), the A. M. Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences (IAP), and Hydrometeocenter of Russia in the region of Caucasian Mineral Waters (CMW) by scientists of IAP and PSRIC there was established a system of Operational Medical Weather Forecast (OMWF), which aims to have possibility on time to host events for the prevention of meteopathic reactions of people with high meteodependence. Also, we have introduced improved definition of Weather Pathogenicity Index (WPI) for medical weather forecast. As a basis of medical weather forecasts we use developed by the joint efforts of our two institutions (PSRIC and IAP) typification of biotropic weather conditions on the basis of climatograms analysis (synoptic-meteorological conditions, helio-geomagnetic activity) and monitoring the health of people with various illnesses in the process of health resort treatment for CMW low-altitude resorts. This classification in modified form is now being adapted to the megapolis conditions by the example of Moscow. In the originating methodology of multiple-factor estimation of the impact of global climate change on human health in Russia a complex study of the atmosphere condition (especially in the case of inversions, leading to a sharp escalation of air pollution) is included, with simultaneous control of weather biotropy degree at meteosensitivity patients during different types of weather: anticyclonic, cyclonic, and frontal, causing changes in blood pressure and other adverse reactions of the organism. In the course of the works there are studied meteopathic reactions in patients with ischemic heart disease (IHD), including those with concomitant hypertension (CH) in connection with the combined influence of dynamic, meteorological, geophysical and environmental factors in the Moscow megalopolis and at the mountain cardiology resort of Kislovodsk. There are compared the results of resort treatment from the group of patients who had in obtaining information about the occurrence of pathogenic meteotropic weathers appointed preventive measures, designed for individual nosological forms, with a control group of patients, for which, regardless the type of weather, the standard complex of resort treatment had been set, without special measures for meteotropic reactions prevention. As a measure to improve meteo-prophylaxis of patients with cardiovascular diseases and improve the system of medical weather forecast for low-mountain resort a methodology of planned meteo-prophylaxis by directed using unique natural healing factors (natural aero-ionization, volatile phytoorganic substances, etc.) in the complex with a standard resort treatment has been developed and proposed to introduce. It was found that on days with high temperature, there is a higher level of air pollution in the territory of Moscow, that may be related to temperature inversions in the surface layer, the lack of movement of air masses (calm), which impede the scattering of chemicals air pollutants. The criteria for calculating the pathogenicity index of different types of weather depending on ozone and submicron aerosol concentration in the surface air in Moscow and Kislovodsk are specified. The results indicate a crucial theoretical and practical significance and prospects of organization of occurring everywhere monitoring of air, features of cross-border transfer of aerosol pollution of the atmosphere (for the concentration and physical and chemical characteristics of the aerosol and its distribution on the territory). The results of the work serve as the basis for a system of multi-evaluation of the impact of global climate change on human health in Russia, with the effective use of medical weather forecast that, in general, aimed at raising the health of Russian citizens within the National Project "Health". The investigation was fulfilled in the frames of Program of Presidium of the Russian Academy of Sciences "Basic Sciences - for Medicine" and RFBR grant No. 07-05-12069-ofi_a.

Proceedings of the Conference on Coal Use for California

NASA Technical Reports Server (NTRS)

1978-01-01

The papers, statements, and panel session transcriptions that resulted from the conference are presented. The conference brought together approximately 400 specialists, students, interest groups and general public for the examination of technological, institutional, and social issues surrounding coal use for California and the identification of attendant constraints, impediments, advantages, and target opportunities. The expertise of the participants cover a wide range of subject matter that includes systems examination of coal opportunities, energy demand forecasting, environmental aspects of coal use, coal supply and transport, viewpoint of neighboring states, air pollution control, direct firing, coal gasification and liquefaction technologies, economics of coal use, and the regulatory system.

The Relationship Between Turbulence and Air Quality in California's Central Valley

NASA Astrophysics Data System (ADS)

Caputi, D.; Faloona, I. C.; Trousdell, J.; Conley, S. A.

2017-12-01

The San Joaquin valley is known for excessive air pollution, owing to local production combined with flow patterns that channel in air from the bay area, with surrounding mountains trapping the air inside. Understanding the role of boundary layer in the context of these dynamics is a particular challenge that will aid in effective air quality attainment planning. During the summers of 2015 and 2016, a Mooney aircraft operated by Scientific Aviation Inc. collected 170 hours of airborne data between Fresno and Bakersfield, CA. Combining this data with WRF forecast output, it is possible to use a simple budget technique to estimate the kinematic surface heat fluxes and thus the convective velocity scale. The 1 Hz wind measurements on the aircraft are provided by a newly developed low-cost system that utilizes the placement of dual GPS antennae on fixed positions of the airframe. Power spectra from the data indicates that the inertial subrange of turbulence is detectable from wavelengths of 150-500 m. Using Kolmogorov scaling laws, it is possible to estimate that about 20% of the total variance is not being captured by the system (at spatial scales under 150 m). Similarity relationships can then be employed to estimate the convective velocity scale as a function of sampling length, which levels off at about 22 km to a value within 5% of the estimate obtained by the budgeting method. A larger goal of this work is to connect these turbulence parameters with observations of air quality, noting that a major finding of the field campaign is that the entrainment between the polluted boundary layer and cleaner free troposphere plays a significant role in the local daytime pollutant concentration. Nighttime dynamics are being explored as well. Using a combination of 915 MHz sounder data from Visalia, ground ozone monitors, and flight data, a relationship can be seen between the nocturnal low level jet speed and ozone concentrations the following day. This suggests a significant role of sheer-induced mixing in the overall pollutant budget equation. Further work will explore the relationship of measured horizontal wind variability at night and the observed low level jet speed to determine if turbulent mixing to the surface can increase depletion of the residual layer ozone.

Health Impact Assessment of Asian Dust/Cross-border Air Pollutant and Necessary Preventive Measure.

PubMed

Onishi, Kazunari

2017-01-01

The health effects of Asian dust (mineral dust) originating from dry lands such as the Gobi Desert and Taklamakan Desert have recently been a concern. In addition to Asian dust, transboundary airborne microparticles that reach Japan include various types of aerosol, such as artificial air pollutants and smoke from combustion. They originate from densely populated areas and are transported along the same route as Asian dust. We analyzed environmental factors and subjective symptoms involving the respiratory organ, nose, eyes, and skin using a conventional equation for estimation, and found that symptoms with a significant risk of worsening varied according to the type of pollutants reaching Japan. We also analyzed the synergistic effects of Asian dust and pollens on nasal symptoms using a two-pollutant model. The odds ratio for symptoms at the time of arrival of a high concentration of Asian dust was 1.37 (95% confidence interval: 1.19-1.58), but the odds ratio adjusted for pollens was 1.18 (95% confidence interval: 1.04-1.34). Although the influence on nasal symptoms overlapped somewhat between Asian dust and pollens, that of Asian dust remained significant. Regarding preventive measures against symptoms, we examined the rate of particle leakage into masks. We found that it is important to wear a mask that fits an individual's facial features and has no gap between the face and the mask. In addition, we report our attempt to construct models for predicting aerosol arrival and forecasting health to establish preventive measures against aerosols.

Operational atmospheric modeling system CARIS for effective emergency response associated with hazardous chemical releases in Korea.

PubMed

Kim, Cheol-Hee; Park, Jin-Ho; Park, Cheol-Jin; Na, Jin-Gyun

2004-03-01

The Chemical Accidents Response Information System (CARIS) was developed at the Center for Chemical Safety Management in South Korea in order to track and predict the dispersion of hazardous chemicals in the case of an accident or terrorist attack involving chemical companies. The main objective of CARIS is to facilitate an efficient emergency response to hazardous chemical accidents by rapidly providing key information in the decision-making process. In particular, the atmospheric modeling system implemented in CARIS, which is composed of a real-time numerical weather forecasting model and an air pollution dispersion model, can be used as a tool to forecast concentrations and to provide a wide range of assessments associated with various hazardous chemicals in real time. This article introduces the components of CARIS and describes its operational modeling system. Some examples of the operational modeling system and its use for emergency preparedness are presented and discussed. Finally, this article evaluates the current numerical weather prediction model for Korea.

Forecasting long-range atmospheric transport episodes of polychlorinated biphenyls using FLEXPART

NASA Astrophysics Data System (ADS)

Halse, Anne Karine; Eckhardt, Sabine; Schlabach, Martin; Stohl, Andreas; Breivik, Knut

2013-06-01

The analysis of concentrations of persistent organic pollutants (POPs) in ambient air is costly and can only be done for a limited number of samples. It is thus beneficial to maximize the information content of the samples analyzed via a targeted observation strategy. Using polychlorinated biphenyls (PCBs) as an example, a forecasting system to predict and evaluate long-range atmospheric transport (LRAT) episodes of POPs at a remote site in southern Norway has been developed. The system uses the Lagrangian particle transport model FLEXPART, and can be used for triggering extra ("targeted") sampling when LRAT episodes are predicted to occur. The system was evaluated by comparing targeted samples collected over 12-25 h during individual LRAT episodes with monitoring samples regularly collected over one day per week throughout a year. Measured concentrations in all targeted samples were above the 75th percentile of the concentrations obtained from the regular monitoring program and included the highest measured values of all samples. This clearly demonstrates the success of the targeted sampling strategy.

The Impact of Atmospheric InfraRed Sounder (AIRS) Profiles on Short-term Weather Forecasts

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Brad; Jedlovec, Gary J.; Lapenta, William

2007-01-01

The Atmospheric Infrared Sounder (AIRS), together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced spacebased atmospheric sounding systems. The combined AlRS/AMSU system provides radiance measurements used to retrieve temperature profiles with an accuracy of 1 K over 1 km layers under both clear and partly cloudy conditions, while the accuracy of the derived humidity profiles is 15% in 2 km layers. Critical to the successful use of AIRS profiles for weather and climate studies is the use of profile quality indicators and error estimates provided with each profile Aside form monitoring changes in Earth's climate, one of the objectives of AIRS is to provide sounding information of sufficient accuracy such that the assimilation of the new observations, especially in data sparse region, will lead to an improvement in weather forecasts. The purpose of this paper is to describe a procedure to optimally assimilate highresolution AIRS profile data in a regional analysis/forecast model. The paper will focus on the impact of AIRS profiles on a rapidly developing east coast storm and will also discuss preliminary results for a 30-day forecast period, simulating a quasi-operation environment. Temperature and moisture profiles were obtained from the prototype version 5.0 EOS science team retrieval algorithm which includes explicit error information for each profile. The error profile information was used to select the highest quality temperature and moisture data for every profile location and pressure level for assimilation into the ARPS Data Analysis System (ADAS). The AIRS-enhanced analyses were used as initial fields for the Weather Research and Forecast (WRF) system used by the SPORT project for regional weather forecast studies. The ADASWRF system will be run on CONUS domain with an emphasis on the east coast. The preliminary assessment of the impact of the AIRS profiles will focus on quality control issues associated with AIRS, intelligent use of the quality indicators, and forecast verification.

Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments.

PubMed

Dunea, Daniel; Pohoata, Alin; Iordache, Stefania

2015-07-01

The paper presents the screening of various feedforward neural networks (FANN) and wavelet-feedforward neural networks (WFANN) applied to time series of ground-level ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10 and PM2.5 fractions) recorded at four monitoring stations located in various urban areas of Romania, to identify common configurations with optimal generalization performance. Two distinct model runs were performed as follows: data processing using hourly-recorded time series of airborne pollutants during cold months (O3, NO2, and PM10), when residential heating increases the local emissions, and data processing using 24-h daily averaged concentrations (PM2.5) recorded between 2009 and 2012. Dataset variability was assessed using statistical analysis. Time series were passed through various FANNs. Each time series was decomposed in four time-scale components using three-level wavelets, which have been passed also through FANN, and recomposed into a single time series. The agreement between observed and modelled output was evaluated based on the statistical significance (r coefficient and correlation between errors and data). Daubechies db3 wavelet-Rprop FANN (6-4-1) utilization gave positive results for O3 time series optimizing the exclusive use of the FANN for hourly-recorded time series. NO2 was difficult to model due to time series specificity, but wavelet integration improved FANN performances. Daubechies db3 wavelet did not improve the FANN outputs for PM10 time series. Both models (FANN/WFANN) overestimated PM2.5 forecasted values in the last quarter of time series. A potential improvement of the forecasted values could be the integration of a smoothing algorithm to adjust the PM2.5 model outputs.

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

Carson, K.S.

The presence of overpopulation or unsustainable population growth may place pressure on the food and water supplies of countries in sensitive areas of the world. Severe air or water pollution may place additional pressure on these resources. These pressures may generate both internal and international conflict in these areas as nations struggle to provide for their citizens. Such conflicts may result in United States intervention, either unilaterally, or through the United Nations. Therefore, it is in the interests of the United States to identify potential areas of conflict in order to properly train and allocate forces. The purpose of thismore » research is to forecast the probability of conflict in a nation as a function of it s environmental conditions. Probit, logit and ordered probit models are employed to forecast the probability of a given level of conflict. Data from 95 countries are used to estimate the models. Probability forecasts are generated for these 95 nations. Out-of sample forecasts are generated for an additional 22 nations. These probabilities are then used to rank nations from highest probability of conflict to lowest. The results indicate that the dependence of a nation`s economy on agriculture, the rate of deforestation, and the population density are important variables in forecasting the probability and level of conflict. These results indicate that environmental variables do play a role in generating or exacerbating conflict. It is unclear that the United States military has any direct role in mitigating the environmental conditions that may generate conflict. A more important role for the military is to aid in data gathering to generate better forecasts so that the troops are adequntely prepared when conflicts arises.« less

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

2011-01-01

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

NASA Technical Reports Server (NTRS)

Chou, S.-H.; Zavodsky, B. T.; Jedloved, G. J.

2010-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

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 ...

Assessment and forecasting of lightning potential and its effect on launch operations at Cape Canaveral Air Force Station and John F. Kennedy Space Center

NASA Technical Reports Server (NTRS)

Weems, J.; Wyse, N.; Madura, J.; Secrist, M.; Pinder, C.

1991-01-01

Lightning plays a pivotal role in the operation decision process for space and ballistic launches at Cape Canaveral Air Force Station and Kennedy Space Center. Lightning forecasts are the responsibility of Detachment 11, 4th Weather Wing's Cape Canaveral Forecast Facility. These forecasts are important to daily ground processing as well as launch countdown decisions. The methodology and equipment used to forecast lightning are discussed. Impact on a recent mission is summarized.

76 FR 39357 - Revisions to the California State Implementation Plan, Imperial County Air Pollution Control...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-06

... the California State Implementation Plan, Imperial County Air Pollution Control District, Kern County Air Pollution Control District, and Ventura County Air Pollution Control District AGENCY... the Imperial County Air Pollution Control District (ICAPCD), Kern County Air Pollution Control...

Techniques for Forecasting Air Passenger Traffic

NASA Technical Reports Server (NTRS)

Taneja, N.

1972-01-01

The basic techniques of forecasting the air passenger traffic are outlined. These techniques can be broadly classified into four categories: judgmental, time-series analysis, market analysis and analytical. The differences between these methods exist, in part, due to the degree of formalization of the forecasting procedure. Emphasis is placed on describing the analytical method.

Studies of air traffic forecasts, airspace load and the effect of ADS-B via satellites on flight times

NASA Astrophysics Data System (ADS)

Zhong, Z. W.; Ridhwan Salleh, Saiful; Chow, W. X.; Ong, Z. M.

2016-10-01

Air traffic forecasting is important as it helps stakeholders to plan their budgets and facilities. Thus, three most commonly used forecasting models were compared to see which model suited the air passenger traffic the best. General forecasting equations were also created to forecast the passenger traffic. The equations could forecast around 6.0% growth from 2015 onwards. Another study sought to provide an initial work for determining a theoretical airspace load with relevant calculations. The air traffic was simulated to investigate the current airspace load. Logical and reasonable results were obtained from the modelling and simulations. The current utilization percentages for airspace load per hour and the static airspace load in the interested airspace were found to be 6.64% and 11.21% respectively. Our research also studied how ADS-B would affect the time taken for aircraft to travel. 6000 flights departing from and landing at the airport were studied. New flight plans were simulated with improved flight paths due to the implementation of ADS-B, and flight times of all studied flights could be improved.

Potential Impact of Land Use Change on Future Regional Climate in the Southeastern U.S.: Reforestation and Crop Land Conversion

NASA Technical Reports Server (NTRS)

Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, Konstantinos; Hu, Y.; Nenes, A.; Stone, B.; Russell, A. G.

2013-01-01

The impact of future land use and land cover changes (LULCC) on regional and global climate is one of the most challenging aspects of understanding anthropogenic climate change. We study the impacts of LULCC on regional climate in the southeastern U.S. by downscaling the NASA Goddard Institute for Space Studies global climate model E to the regional scale using a spectral nudging technique with the Weather Research and Forecasting Model. Climate-relevant meteorological fields are compared for two southeastern U.S. LULCC scenarios to the current land use/cover for four seasons of the year 2050. In this work it is shown that reforestation of cropland in the southeastern U.S. tends to warm surface air by up to 0.5 K, while replacing forested land with cropland tends to cool the surface air by 0.5 K. Processes leading to this response are investigated and sensitivity analyses conducted. The sensitivity analysis shows that results are most sensitive to changes in albedo and the stomatal resistance. Evaporative cooling of croplands also plays an important role in regional climate. Implications of LULCC on air quality are discussed. Summertime warming associated with reforestation of croplands could increase the production of some secondary pollutants, while a higher boundary layer will decrease pollutant concentrations; wintertime warming may decrease emissions from biomass burning from wood stoves

Effects of Grid Resolution on Modeled Air Pollutant Concentrations Due to Emissions from Large Point Sources: Case Study during KORUS-AQ 2016 Campaign

NASA Astrophysics Data System (ADS)

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

2017-12-01

Large point sources in the Chungnam area received a nation-wide attention in South Korea because the area is located southwest of the Seoul Metropolitan Area whose population is over 22 million and the summertime prevalent winds in the area is northeastward. Therefore, emissions from the large point sources in the Chungnam area were one of the major observation targets during the KORUS-AQ 2016 including aircraft measurements. In general, horizontal grid resolutions of eulerian photochemical models have profound effects on estimated air pollutant concentrations. It is due to the formulation of grid models; that is, emissions in a grid cell will be assumed to be mixed well under planetary boundary layers regardless of grid cell sizes. In this study, we performed series of simulations with the Comprehensive Air Quality Model with eXetension (CAMx). For 9-km and 3-km simulations, we used meteorological fields obtained from the Weather Research and Forecast model while utilizing the "Flexi-nesting" option in the CAMx for the 1-km simulation. In "Flexi-nesting" mode, CAMx interpolates or assigns model inputs from the immediate parent grid. We compared modeled concentrations with ground observation data as well as aircraft measurements to quantify variations of model bias and error depending on horizontal grid resolutions.

Managing Air Quality - Air Pollutant Types

EPA Pesticide Factsheets

Describes the types of air pollutants, including common or criteria pollutants, and hazardous air pollutants and links to additional information. Also links to resources on other air pollution issues.

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...

The Co-benefits of Domestic and Foreign GHG Mitigation on US Air Quality

NASA Astrophysics Data System (ADS)

Zhang, Y.; Bowden, J.; Adelman, Z.; Naik, V.; Horowitz, L. W.; West, J. J.

2013-12-01

Authors: Yuqiang Zhang1, Jared Bowden2 , Zachariah Adelman1,2, Vaishali Naik3, Larry W. Horowitz4 , J. Jason West1 1 University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 2 Institute for the Environment, Chapel Hill, NC 27599 3 UCAR/NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540 4 NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540 Abstract: Actions to mitigate greenhouse gas (GHG) emissions will reduce co-emitted air pollutants, which can immediately affect air quality; slowing climate change through GHG mitigation also influences air quality in the long term. We previously used a global model (MOZART-4) to show that global GHG mitigation will have significant co-benefits for air quality and human health. In doing so, we contrasted the Representative Concentration Pathway Scenario 4.5 (RCP4.5), treated as a GHG mitigation scenario, with its associated reference case scenario (REF). Using these same scenarios, we investigate here the air quality co-benefits due to domestic GHGs mitigation in the US alone at fine resolution, and compare these co-benefits with those resulting from foreign GHG mitigation. This work focuses on downscaling the meteorology and air pollutant chemistry to the US scale. We use the latest Weather Research and Forecasting (WRF) model as a Regional Climate Model (RCM) to dynamically downscale the GFDL AM3 Global Climate Model (GCM) over the US at 36 km resolution, in 2000 and 2050. The 2000 simulation will be compared with the multi-year surface observation data, satellite data, and all simulations with the GCM simulation. These simulations will be used as inputs for the newest Community Multiscale Air Quality (CMAQ) modeling system. Initial conditions (IC) and dynamic boundary conditions (BC) for CMAQ will be derived from the global MOZART-4 simulations. Anthropogenic emissions for the REF and RCP4.5 scenarios will be processed through SMOKE to prepare temporally- and spatially-resolved emission files. We will evaluate the co-benefits of GHG mitigation by changing the meteorological and air pollutant emissions inputs for RCP4.5 and REF, as well as the fixed methane level, and will separate the co-benefits of domestic vs. foreign GHG mitigation by using RCP4.5 emissions in the US only, but REF boundary conditions and REF emissions elsewhere.

Modelling two-way interactions between atmospheric pollution and weather using high-resolution GEM-MACH

NASA Astrophysics Data System (ADS)

Makar, Paul; Gong, Wanmin; Pabla, Balbir; Cheung, Philip; Milbrandt, Jason; Gravel, Sylvie; Moran, Michael; Gilbert, Samuel; Zhang, Junhua; Zheng, Qiong

2013-04-01

The Global Environmental Multiscale (GEM) model is the source of the Canadian government's operational numerical weather forecast guidance, and GEM-MACH is the Canadian operational air-quality forecast model. GEM-MACH comprises GEM and the 'Modelling Air-quality and Chemistry' module, a gas-phase, aqueous-phase and aerosol chemistry and microphysics subroutine package called from within GEM's physics module. The present operational GEM-MACH model is "on-line" (both chemistry and meteorology are part of the same modelling structure) but is not fully coupled (weather variables are provided as inputs to the chemistry, but the chemical variables are not used to modify the weather). In this work, we describe modifications made to GEM-MACH as part of the 2nd phase of the Air Quality Model Evaluation International Initiative, in order to bring the model to a fully coupled status and present the results of initial tests comparing uncoupled and coupled versions of the model to observations for a high-resolution forecasting system. Changes to GEM's cloud microphysics and radiative transfer packages were carried out to allow two-way coupling. The cloud microphysics package used here is the Milbrandt-Yau 2-moment (MY2) bulk microphysics scheme, which solves prognostic equations for the total droplet number concentration and the mass mixing ratios of six hydrometeor categories. Here, we have replaced the original cloud condensation nucleation parameterization of MY2 (empirically relating supersaturation and CCN number) with the aerosol activation scheme of Abdul-Razzak and Ghan (2002). The latter scheme makes use of the particle size and speciation distribution of GEM-MACH's chemistry code as well as meteorological inputs to predict the number of aerosol particles activated to form cloud droplets, which is then used in the MY2 microphysics. The radiative transfer routines of GEM assume a default constant concentration aerosol profile between the surface and 1500m, and a single set of optical properties for extinction, single scattering albedo, and asymmetry factor. Ozone in GEM is taken from a default 2D (latitude-height) monthly climatology. We have replaced the ozone below the model top with the ozone calculated from GEM-MACH's chemistry, and the default optical parameters associated with particulate matter have been replaced by those calculated with a Mie scattering algorithm. These changes were found to have a significant local impact on both weather and air-quality predictions for short-term test runs of 24 hours duration. In that particular case, the maximum number concentration of cloud droplets decreased by an order of magnitude, while the number of raindrops increased by an order of magnitude and changed in spatial distribution, but surface rainfall was found to decrease. The differences in meteorology had a profound effect on local pollutant plume concentrations at specific locations and times. We compare results over a longer time period, using two parallel forecast systems, one with feedbacks between meteorology and chemistry, one without. Both nest GEM-MACH from a North American domain (10 km horizontal grid spacing) to a 1535 x 1360 km, 2.5 km domain. These systems will be evaluated against monitoring networks within the high resolution domain.

Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model

NASA Astrophysics Data System (ADS)

Lim, Kyo-Sun Sunny; Lim, Jong-Myoung; Shin, Hyeyum Hailey; Hong, Jinkyu; Ji, Young-Yong; Lee, Wanno

2018-06-01

A substantial over-prediction bias at low-to-moderate wind speeds in the Weather Research and Forecasting (WRF) model has been reported in the previous studies. Low-level wind fields play an important role in dispersion of air pollutants, including radionuclides, in a high-resolution WRF framework. By implementing two subgrid-scale orography parameterizations (Jimenez and Dudhia in J Appl Meteorol Climatol 51:300-316, 2012; Mass and Ovens in WRF model physics: problems, solutions and a new paradigm for progress. Preprints, 2010 WRF Users' Workshop, NCAR, Boulder, Colo. http://www.mmm.ucar.edu/wrf/users/workshops/WS2010/presentations/session%204/4-1_WRFworkshop2010Final.pdf, 2010), we tried to compare the performance of parameterizations and to enhance the forecast skill of low-level wind fields over the central western part of South Korea. Even though both subgrid-scale orography parameterizations significantly alleviated the positive bias at 10-m wind speed, the parameterization by Jimenez and Dudhia revealed a better forecast skill in wind speed under our modeling configuration. Implementation of the subgrid-scale orography parameterizations in the model did not affect the forecast skills in other meteorological fields including 10-m wind direction. Our study also brought up the problem of discrepancy in the definition of "10-m" wind between model physics parameterizations and observations, which can cause overestimated winds in model simulations. The overestimation was larger in stable conditions than in unstable conditions, indicating that the weak diurnal cycle in the model could be attributed to the representation error.

Routine High-Resolution Forecasts/Analyses for the Pacific Disaster Center: User Manual

NASA Technical Reports Server (NTRS)

Roads, John; Han, J.; Chen, S.; Burgan, R.; Fujioka, F.; Stevens, D.; Funayama, D.; Chambers, C.; Bingaman, B.; McCord, C.;

Spacebuoy: A University Nanosat Space Weather Mission (III)

DTIC Science & Technology

2013-10-11

ionospheric forecasting models; specifically the operational Global Assimilation of Ionospheric Measurements (GAIM) model currently used by the Air Force... ionospheric forecasting models; specifically the operational Global Assimilation of Ionospheric Measurements (GAIM) model currently used by the Air...Mission Objectives • Provide critical space weather data for use in ionospheric forecasting efforts, particularly assimilated data used in the GAIM

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...

A comparison of self reported air pollution problems and GIS-modeled levels of air pollution in people with and without chronic diseases

PubMed Central

Piro, Fredrik Niclas; Madsen, Christian; Næss, Øyvind; Nafstad, Per; Claussen, Bjørgulf

2008-01-01

Objective To explore various contributors to people's reporting of self reported air pollution problems in area of living, including GIS-modeled air pollution, and to investigate whether those with respiratory or other chronic diseases tend to over-report air pollution problems, compared to healthy people. Methods Cross-sectional data from the Oslo Health Study (2000–2001) were linked with GIS-modeled air pollution data from the Norwegian Institute of Air Research. Multivariate regression analyses were performed. 14 294 persons aged 30, 40, 45, 60 or 75 years old with complete information on modeled and self reported air pollution were included. Results People who reported air pollution problems were exposed to significantly higher GIS-modeled air pollution levels than those who did not report such problems. People with chronic disease, reported significantly more air pollution problems after adjustment for modeled levels of nitrogen dioxides, socio-demographic variables, smoking, depression, dwelling conditions and an area deprivation index, even if they had a non-respiratory disease. No diseases, however, were significantly associated with levels of nitrogen dioxides. Conclusion Self reported air pollution problems in area of living are strongly associated with increased levels of GIS-modeled air pollution. Over and above this, those who report to have a chronic disease tend to report more air pollution problems in area of living, despite no significant difference in air pollution exposure compared to healthy people, and no associations between these diseases and NO2. Studies on the association between self reported air pollution problems and health should be aware of the possibility that disease itself may influence the reporting of air pollution. PMID:18307757

Experiments with data assimilation in comprehensive air quality models: Impacts on model predictions and observation requirements (Invited)

NASA Astrophysics Data System (ADS)

Mathur, R.

2009-12-01

Emerging regional scale atmospheric simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions. Sophisticated air quality modeling systems are needed to develop effective abatement strategies that focus on simultaneously controlling multiple criteria pollutants as well as use in providing short term air quality forecasts. In recent years the applications of such models is 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 physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. Preliminary applications of assimilation of remote sensing and aloft observations within a comprehensive regional scale atmospheric chemistry-transport modeling system will be presented: (1) A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts long-range transport associated with the summer 2004 Alaskan fires on surface-level regional fine particulate matter (PM2.5) concentrations across the Eastern U.S. The episodic impact of this pollution transport event on PM2.5 concentrations over the eastern U.S. during mid-July 2004, is quantified through the complementary use of the model with remotely-sensed, aloft, and surface measurements; (2) Simple nudging experiments with limited aloft measurements are performed to identify uncertainties in model representations of physical processes and assess the potential use of such measurements in improving the predictive capability of atmospheric chemistry-transport models. The results from these early applications will be discussed in context of uncertainties in the model and in the remote sensing data and needs for defining a future optimum observing strategy.

Assessment of inter-city transport of particulate matter in the Beijing-Tianjin-Hebei region

NASA Astrophysics Data System (ADS)

Chang, Xing; Wang, Shuxiao; Zhao, Bin; Cai, Siyi; Hao, Jiming

2018-04-01

The regional transport of particulate matter with diameter less than 2.5 µm (PM2.5) plays an important role in the air pollution of the Beijing-Tianjin-Hebei (BTH) region in China. However, previous studies on regional transport of PM2.5 mainly aim at province level, which is insufficient for the development of an optimal joint PM2.5 control strategy. In this study, we calculate PM2.5 inflows and outflows through the administrative boundaries of three major cities in the BTH region, i.e., Beijing, Tianjin and Shijiazhuang, using the WRF (Weather Research and Forecasting model)-CMAQ (Community Multiscale Air Quality) modeling system. The monthly average inflow fluxes indicate the major directions of PM2.5 transport. For Beijing, the PM2.5 inflow fluxes from Zhangjiakou (in the northwest) and Baoding (in the southwest) constitute 57 % of the total in winter, and Langfang (in the southeast) and Baoding constitute 73 % in summer. Based on the net PM2.5 fluxes and their vertical distributions, we find there are three major transport pathways in the BTH region: the northwest-southeast pathway in winter (at all levels below 1000 m), the northwest-southeast pathway in summer (at all levels below 1000 m), and the southwest-northeast pathway in both winter and in summer (mainly at 300-1000 m). In winter, even if surface wind speeds are low, the transport at above 300 m can still be strong. Among the three pathways, the southwest-northeast happens along with PM2.5 concentrations 30 and 55 % higher than the monthly average in winter and summer, respectively. Analysis of two heavy pollution episodes in January and July in Beijing show a much (8-16 times) stronger transport than the monthly average, emphasizing the joint air pollution control of the cities located on the transport pathways, especially during heavy pollution episodes.

Feedbacks between air pollution and weather, Part 1: Effects on weather

NASA Astrophysics Data System (ADS)

Makar, P. A.; Gong, W.; Milbrandt, J.; Hogrefe, C.; Zhang, Y.; Curci, G.; Žabkar, R.; Im, U.; Balzarini, A.; Baró, R.; Bianconi, R.; Cheung, P.; Forkel, R.; Gravel, S.; Hirtl, M.; Honzak, L.; Hou, A.; Jiménez-Guerrero, P.; Langer, M.; Moran, M. D.; Pabla, B.; Pérez, J. L.; Pirovano, G.; San José, R.; Tuccella, P.; Werhahn, J.; Zhang, J.; Galmarini, S.

2015-08-01

The meteorological predictions of fully coupled air-quality models running in ;feedback; versus ;no-feedback; simulations were compared against each other and observations as part of Phase 2 of the Air Quality Model Evaluation International Initiative. In the ;no-feedback; mode, the aerosol direct and indirect effects were disabled, with the models reverting to either climatologies of aerosol properties, or a no-aerosol weather simulation. In the ;feedback; mode, the model-generated aerosols were allowed to modify the radiative transfer and/or cloud formation parameterizations of the respective models. Annual simulations with and without feedbacks were conducted on domains over North America for the years 2006 and 2010, and over Europe for the year 2010. The incorporation of feedbacks was found to result in systematic changes to forecast predictions of meteorological variables, both in time and space, with the largest impacts occurring in the summer and near large sources of pollution. Models incorporating only the aerosol direct effect predicted feedback-induced reductions in temperature, surface downward and upward shortwave radiation, precipitation and PBL height, and increased upward shortwave radiation, in both Europe and North America. The feedback response of models incorporating both the aerosol direct and indirect effects varied across models, suggesting the details of implementation of the indirect effect have a large impact on model results, and hence should be a focus for future research. The feedback response of models incorporating both direct and indirect effects was also consistently larger in magnitude to that of models incorporating the direct effect alone, implying that the indirect effect may be the dominant process. Comparisons across modelling platforms suggested that direct and indirect effect feedbacks may often act in competition: the sign of residual changes associated with feedbacks often changed between those models incorporating the direct effect alone versus those incorporating both feedback processes. Model comparisons to observations for no-feedback and feedback implementations of the same model showed that differences in performance between models were larger than the performance changes associated with implementing feedbacks within a given model. However, feedback implementation was shown to result in improved forecasts of meteorological parameters such as the 2 m surface temperature and precipitation. These findings suggest that meteorological forecasts may be improved through the use of fully coupled feedback models, or through incorporation of improved climatologies of aerosol properties, the latter designed to include spatial, temporal and aerosol size and/or speciation variations.

The impact of satellite temperature soundings on the forecasts of a small national meteorological service

NASA Technical Reports Server (NTRS)

Wolfson, N.; Thomasell, A.; Alperson, Z.; Brodrick, H.; Chang, J. T.; Gruber, A.; Ohring, G.

1984-01-01

The impact of introducing satellite temperature sounding data on a numerical weather prediction model of a national weather service is evaluated. A dry five level, primitive equation model which covers most of the Northern Hemisphere, is used for these experiments. Series of parallel forecast runs out to 48 hours are made with three different sets of initial conditions: (1) NOSAT runs, only conventional surface and upper air observations are used; (2) SAT runs, satellite soundings are added to the conventional data over oceanic regions and North Africa; and (3) ALLSAT runs, the conventional upper air observations are replaced by satellite soundings over the entire model domain. The impact on the forecasts is evaluated by three verification methods: the RMS errors in sea level pressure forecasts, systematic errors in sea level pressure forecasts, and errors in subjective forecasts of significant weather elements for a selected portion of the model domain. For the relatively short range of the present forecasts, the major beneficial impacts on the sea level pressure forecasts are found precisely in those areas where the satellite sounding are inserted and where conventional upper air observations are sparse. The RMS and systematic errors are reduced in these regions. The subjective forecasts of significant weather elements are improved with the use of the satellite data. It is found that the ALLSAT forecasts are of a quality comparable to the SAR forecasts.

Stroke: Temporal Trends and Association with Atmospheric Variables and Air Pollutants in Northern Spain.

PubMed

Santurtún, Ana; Ruiz, Patricia Bolivar; López-Delgado, Laura; Sanchez-Lorenzo, Arturo; Riancho, Javier; Zarrabeitia, María T

2017-07-01

Stroke, the second cause of death and the most frequent cause of severe disability among adults in developed countries, is related to a large variety of risk factors. This paper assesses the temporal patterns in stroke episodes in a city in Northern Spain during a 12-year period and analyzes the possible effects that atmospheric pollutants and meteorological variables may have on stroke on a daily scale. Our results show that there is an increase in stroke admissions (r = 0.818, p = 0.001) especially in patients over 85 years old. On a weekly scale, the number of hospital admissions due to stroke remains stable from Monday to Friday, whereas it abruptly decreases during the weekends, reaching its minimum values on Sunday (p < 0.005); however, mortality in patients admitted to the hospital is higher on Sundays than on other days of the week. Finally, a statistically significant positive correlation between the number of stroke hospital admissions and NO 2 levels (p = 0.012) and an inverse correlation with relative humidity (p = 0.032) were found. The analysis of the relationship between ischemic strokes and atmospheric circulation shows a higher frequency of the former in Santander with enhanced negative air pressure anomalies over western Spain; the fact that under these conditions the region studied registers very low values of relative humidity is in line with the aforementioned inverse correlation, which has not been described elsewhere in the literature. This study could be a first step for implementing stroke alert protocols depending on air pollution levels and circulation patterns forecasts.

Year-long simulation of gaseous and particulate air pollutants in India

NASA Astrophysics Data System (ADS)

Kota, Sri Harsha; Guo, Hao; Myllyvirta, Lauri; Hu, Jianlin; Sahu, Shovan Kumar; Garaga, Rajyalakshmi; Ying, Qi; Gao, Aifang; Dahiya, Sunil; Wang, Yuan; Zhang, Hongliang

2018-05-01

Severe pollution events occur frequently in India but few studies have investigated the characteristics, sources, and control strategies for the whole country. A year-long simulation was carried out in India to provide detailed information of spatial and temporal distribution of gas species and particulate matter (PM). The concentrations of O3, NO2, SO2, CO, as well as PM2.5 and its components in 2015 were predicted using Weather Research Forecasting (WRF) and the Community Multiscale Air Quality (CMAQ) models. Model performance was validated against available observations from ground based national ambient air quality monitoring stations in major cities. Model performance of O3 does not always meet the criteria suggested by the US Environmental Protection Agency (EPA) but that of PM2.5 meets suggested criteria by previous studies. The performance of model was better on days with high O3 and PM2.5 levels. Concentrations of PM2.5, NO2, CO and SO2 were highest in the Indo-Gangetic region, including northern and eastern India. PM2.5 concentrations were higher during winter and lower during monsoon season. Winter nitrate concentrations were 160-230% higher than yearly average. In contrast, the fraction of sulfate in total PM2.5 was maximum in monsoon and least in winter, due to decrease in temperature and solar radiation intensity in winter. Except in southern India, where sulfate was the major component of PM2.5, primary organic aerosol (POA) fraction in PM2.5 was highest in all regions of the country. Fractions of secondary components were higher on bad days than on good days in these cities, indicating the importance of control of precursors for secondary pollutants in India.

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.

An Elevated Reservoir of Air Pollutants over the Mid-Atlantic States During the 2011 DISCOVER-AQ Campaign: Airborne Measurements and Numerical Simulations

NASA Technical Reports Server (NTRS)

He, Hao; Loughner, Christopher P.; Stehr, Jeffrey W.; Arkinson, Heather L.; Brent, Lacey C.; Follette-Cook, Melanie B.; Tzortziou, Maria A.; Pickering, Kenneth E.; Thompson, Anne M.; Martins, Douglas K.;

Forecasting daily attendances at an emergency department to aid resource planning

PubMed Central

Sun, Yan; Heng, Bee Hoon; Seow, Yian Tay; Seow, Eillyne

2009-01-01

Background Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning. Methods Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15. Results By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50. After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data. Conclusion Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning. PMID:19178716

Spatiotemporal Variations and Driving Factors of Air Pollution in China.

PubMed

Zhan, Dongsheng; Kwan, Mei-Po; Zhang, Wenzhong; Wang, Shaojian; Yu, Jianhui

2017-12-08

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio.

Spatiotemporal Variations and Driving Factors of Air Pollution in China

PubMed Central

Zhan, Dongsheng; Zhang, Wenzhong; Wang, Shaojian; Yu, Jianhui

2017-01-01

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio. PMID:29292783

Future directions of meteorology related to air-quality research.

PubMed

Seaman, Nelson L

2003-06-01

Meteorology is one of the major factors contributing to air-pollution episodes. More accurate representation of meteorological fields has been possible in recent years through the use of remote sensing systems, high-speed computers and fine-mesh meteorological models. Over the next 5-20 years, better meteorological inputs for air quality studies will depend on making better use of a wealth of new remotely sensed observations in more advanced data assimilation systems. However, for fine mesh models to be successful, parameterizations used to represent physical processes must be redesigned to be more precise and better adapted for the scales at which they will be applied. Candidates for significant overhaul include schemes to represent turbulence, deep convection, shallow clouds, and land-surface processes. Improvements in the meteorological observing systems, data assimilation and modeling, coupled with advancements in air-chemistry modeling, will soon lead to operational forecasting of air quality in the US. Predictive capabilities can be expected to grow rapidly over the next decade. This will open the way for a number of valuable new services and strategies, including better warnings of unhealthy atmospheric conditions, event-dependent emissions restrictions, and now casting support for homeland security in the event of toxic releases into the atmosphere.

Estimating Mixing Heights Using Microwave Temperature Profiler

NASA Technical Reports Server (NTRS)

Nielson-Gammon, John; Powell, Christina; Mahoney, Michael; Angevine, Wayne

2008-01-01

A paper describes the Microwave Temperature Profiler (MTP) for making measurements of the planetary boundary layer thermal structure data necessary for air quality forecasting as the Mixing Layer (ML) height determines the volume in which daytime pollution is primarily concentrated. This is the first time that an airborne temperature profiler has been used to measure the mixing layer height. Normally, this is done using a radar wind profiler, which is both noisy and large. The MTP was deployed during the Texas 2000 Air Quality Study (TexAQS-2000). An objective technique was developed and tested for estimating the ML height from the MTP vertical temperature profiles. In order to calibrate the technique and evaluate the usefulness of this approach, estimates from a variety of measurements during the TexAQS-2000 were compared. Estimates of ML height were used from radiosondes, radar wind profilers, an aerosol backscatter lidar, and in-situ aircraft measurements in addition to those from the MTP.

Application of GIS to modified models of vehicle emission dispersion

NASA Astrophysics Data System (ADS)

Jin, Taosheng; Fu, Lixin

This paper reports on a preliminary study of the forecast and evaluation of transport-related air pollution dispersion in urban areas. Some modifications of the traditional Gauss dispersion models are provided, and especially a crossroad model is built, which considers the great variation of vehicle emission attributed to different driving patterns at the crossroad. The above models are combined with a self-developed geographic information system (GIS) platform, and a simulative system with graphical interfaces is built. The system aims at visually describing the influences on the urban environment by urban traffic characteristics and therefore gives a reference to the improvement of urban air quality. Due to the introduction of a self-developed GIS platform and a creative crossroad model, the system is more effective, flexible and accurate. Finally, a comparison of the simulated (predicted) and observed hourly concentration is given, which indicates a good simulation.

Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: A real-data study

NASA Astrophysics Data System (ADS)

Hu, Xiao-Ming; Zhang, Fuqing; Nielsen-Gammon, John W.

2010-04-01

This study explores the treatment of model error and uncertainties through simultaneous state and parameter estimation (SSPE) with an ensemble Kalman filter (EnKF) in the simulation of a 2006 air pollution event over the greater Houston area during the Second Texas Air Quality Study (TexAQS-II). Two parameters in the atmospheric boundary layer parameterization associated with large model sensitivities are combined with standard prognostic variables in an augmented state vector to be continuously updated through assimilation of wind profiler observations. It is found that forecasts of the atmosphere with EnKF/SSPE are markedly improved over experiments with no state and/or parameter estimation. More specifically, the EnKF/SSPE is shown to help alleviate a near-surface cold bias and to alter the momentum mixing in the boundary layer to produce more realistic wind profiles.

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.

Operational use of the AIRS Total Column Ozone Retrievals along with the RGB Airmass Product as Part of the GOES-R Proving Ground

NASA Technical Reports Server (NTRS)

Folmer, M.; Zavodsky, Bradley; Molthan, Andrew

2012-01-01

The Red, Green, Blue (RGB) Air Mass product has been demonstrated in the GOES ]R Proving Ground as a possible decision aid. Forecasters have been trained on the usefulness of identifying stratospheric intrusions and potential vorticity (PV) anomalies that can lead to explosive cyclogenesis, genesis of mesoscale convective systems (MCSs), or the transition of tropical cyclones to extratropical cyclones. It has also been demonstrated to distinguish different air mass types from warm, low ozone air masses to cool, high ozone air masses and the various interactions with the PV anomalies. To assist the forecasters in understanding the stratospheric contribution to high impact weather systems, the Atmospheric Infrared Sounder (AIRS) Total Column Ozone Retrievals have been made available as an operational tool. These AIRS retrievals provide additional information on the amount of ozone that is associated with the red coloring seen in the RGB Air Mass product. This paper discusses how the AIRS retrievals can be used to quantify the red coloring in RGB Air Mass product. These retrievals can be used to diagnose the depth of the stratospheric intrusions associated with different types of weather systems and provide the forecasters decision aid tools that can improve the quality of forecast products.

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...

78 FR 37757 - Revisions to the California State Implementation Plan, South Coast Air Quality Management District

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-24

... the California State Implementation Plan, South Coast Air Quality Management District AGENCY... the South Coast Air Quality Management District (SCAQMD) portion of the California State... Quality Index rather than on 1-hour ozone forecasted values; (b) forecast criteria for allowing a...

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) ...

FORECASTING AIR QUALITY OVER THE UNITED STATES

EPA Science Inventory

Increased awareness of national air quality issues on the part of the media and the general public have recently led to more demand for short-term (1-2 day) air quality forecasts for use in assessing potential health impacts (e.g., on children, the elderly, and asthmatics) and po...

Effects of traffic-related outdoor air pollution on respiratory illness and mortality in children, taking into account indoor air pollution, in Indonesia.

PubMed

Kashima, Saori; Yorifuji, Takashi; Tsuda, Toshihide; Ibrahim, Juliani; Doi, Hiroyuki

2010-03-01

To evaluate the effects of outdoor air pollution, taking into account indoor air pollution, in Indonesia. The subjects were 15,242 children from 2002 to 2003 Indonesia Demographic and Health Survey. The odds ratios and their confidence intervals for adverse health effects were estimated. Proximity increased the prevalence of acute respiratory infection both in urban and rural areas after adjusting for indoor air pollution. In urban areas, the prevalence of acute upper respiratory infection increased by 1.012 (95% confidence intervals: 1.005 to 1.019) per 2 km proximity to a major road. Adjusted odds ratios tended to be higher in the high indoor air pollution group. Exposure to traffic-related outdoor air pollution would increase adverse health effects after adjusting for indoor air pollution. Furthermore, indoor air pollution could exacerbate the effects of outdoor air pollution.

Demonstrating the Operational Value of Atmospheric Infrared Sounder (AIRS) Profiles in the Pre-Convective Environment

NASA Technical Reports Server (NTRS)

Kozlowski, Danielle; Zavodsky, Bradley; Stano, Geoffrey; Jedlovec, Gary

2011-01-01

The Short-term Prediction Research and Transition (SPoRT) is a project to transition those NASA observations and research capabilities to the weather forecasting community to improve the short-term regional forecasts. This poster reviews the work to demonstrate the value to these forecasts of profiles from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite with particular assistance in predicting thunderstorm forecasts by the profiles of the pre-convective environment.

Improving 7-Day Forecast Skill by Assimilation of Retrieved AIRS Temperature Profiles

NASA Technical Reports Server (NTRS)

Susskind, Joel; Rosenberg, Bob

2016-01-01

We conducted a new set of Data Assimilation Experiments covering the period January 1 to February 29, 2016 using the GEOS-5 DAS. Our experiments assimilate all data used operationally by GMAO (Control) with some modifications. Significant improvement in Global and Southern Hemisphere Extra-tropical 7-day forecast skill was obtained when: We assimilated AIRS Quality Controlled temperature profiles in place of observed AIRS radiances, and also did not assimilate CrISATMS radiances, nor did we assimilate radiosonde temperature profiles or aircraft temperatures. This new methodology did not improve or degrade 7-day Northern Hemispheric Extra-tropical forecast skill. We are conducting experiments aimed at further improving of Northern Hemisphere Extra-tropical forecast skill.

76 FR 39303 - Revisions to the California State Implementation Plan, Imperial County Air Pollution Control...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-06

... the California State Implementation Plan, Imperial County Air Pollution Control District, Kern County Air Pollution Control District, and Ventura County Air Pollution Control District AGENCY... approve revisions to the Imperial County Air Pollution Control District (ICAPCD), Kern County Air...

Assimilation of Quality Controlled AIRS Temperature Profiles using the NCEP GFS

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste; Iredell, Lena; Rosenberg, Robert

2013-01-01

We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.

Air pollution and chronic airway diseases: what should people know and do?

PubMed

Jiang, Xu-Qin; Mei, Xiao-Dong; Feng, Di

2016-01-01

The health effects of air pollution remain a public health concern worldwide. Exposure to air pollution has many substantial adverse effects on human health. Globally, seven million deaths were attributable to the joint effects of household and ambient air pollution. Subjects with chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma are especially vulnerable to the detrimental effects of air pollutants. Air pollution can induce the acute exacerbation of COPD and onset of asthma, increase the respiratory morbidity and mortality. The health effects of air pollution depend on the components and sources of pollutants, which varied with countries, seasons, and times. Combustion of solid fuels is a major source of air pollutants in developing countries. To reduce the detrimental effects of air pollution, people especially those with COPD or asthma should be aware of the air quality and take extra measures such as reducing the time outdoor and wearing masks when necessary. For reducing the air pollutants indoor, people should use clean fuels and improve the stoves so as to burn fuel more efficiently and vent emissions to the outside. Air cleaners that can improve the air quality efficiently are recommended.

Air pollution and chronic airway diseases: what should people know and do?

PubMed Central

Jiang, Xu-Qin; Feng, Di

2016-01-01

The health effects of air pollution remain a public health concern worldwide. Exposure to air pollution has many substantial adverse effects on human health. Globally, seven million deaths were attributable to the joint effects of household and ambient air pollution. Subjects with chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma are especially vulnerable to the detrimental effects of air pollutants. Air pollution can induce the acute exacerbation of COPD and onset of asthma, increase the respiratory morbidity and mortality. The health effects of air pollution depend on the components and sources of pollutants, which varied with countries, seasons, and times. Combustion of solid fuels is a major source of air pollutants in developing countries. To reduce the detrimental effects of air pollution, people especially those with COPD or asthma should be aware of the air quality and take extra measures such as reducing the time outdoor and wearing masks when necessary. For reducing the air pollutants indoor, people should use clean fuels and improve the stoves so as to burn fuel more efficiently and vent emissions to the outside. Air cleaners that can improve the air quality efficiently are recommended. PMID:26904251

On The Usage Of Fire Smoke Emissions In An Air Quality Forecasting System To Reduce Particular Matter Forecasting Error

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; DiMego, G.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2016-12-01

Wildfires contribute to air quality problems not only towards primary emissions of particular matters (PM) but also emitted ozone precursor gases that can lead to elevated ozone concentration. Wildfires are unpredictable and can be ignited by natural causes such as lightning or accidently by human negligent behavior such as live cigarette. Although wildfire impacts on the air quality can be studied by collecting fire information after events, it is extremely difficult to predict future occurrence and behavior of wildfires for real-time air quality forecasts. Because of the time constraints of operational air quality forecasting, assumption of future day's fire behavior often have to be made based on observed fire information in the past. The United States (U.S.) NOAA/NWS built the National Air Quality Forecast Capability (NAQFC) based on the U.S. EPA CMAQ to provide air quality forecast guidance (prediction) publicly. State and local forecasters use the forecast guidance to issue air quality alerts in their area. The NAQFC fine particulates (PM2.5) prediction includes emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and fires. The fire emission input to the NAQFC is derived from the NOAA NESDIS HMS fire and smoke detection product and the emission module of the US Forest Service BlueSky Smoke Modeling Framework. This study focuses on the error estimation of NAQFC PM2.5 predictions resulting from fire emissions. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that present operational NAQFC fire emissions assumption can lead to a huge error in PM2.5 prediction as fire emissions are sometimes placed at wrong location and time. This PM2.5 prediction error can be propagated from the fire source in the Northwest U.S. to downstream areas as far as the Southeast U.S. From this study, a new procedure has been identified to minimize the aforementioned error. An additional 24 hours reanalysis-run of NAQFC using same-day observed fire emission are being tested. Preliminary results have shown that this procedure greatly improves the PM2.5 predictions at both nearby and downstream areas from fire sources. The 24 hours reanalysis-run is critical and necessary especially during extreme fire events to provide better PM2.5 predictions.

AirData

EPA Pesticide Factsheets

The AirData site provides access to yearly summaries of United States air pollution data, taken from EPA's air pollution databases. AirData has information about where air pollution comes from (emissions) and how much pollution is in the air outside our homes and work places (monitoring).

Present and future emissions of HAPs from crematories in China

NASA Astrophysics Data System (ADS)

Xue, Yifeng; Tian, Hezhong; Yan, Jing; Xiong, Chengcheng; Pan, Tao; Nie, Lei; Wu, Xiaoqing; Li, Jing; Wang, Wei; Gao, Jiajia; Zhu, Chuanyong; Wang, Kun

2016-01-01

China is the most populous country in the world. The amount of death population has reached 9.65 million and 49.5% of human corpses are cremated by about 1700 crematories spread throughout the country in 2012, leading to considerable discharge of various hazardous air pollutants (HAPs) into the atmosphere and great concerns on regional air quality and health risks for surrounding residents. By using the practicable or best available emission factors, for the first time, a multiple-year emission inventory of typical hazardous air pollutants discharged from crematories in the Chinese mainland, has been established for the historical period of 1990-2012, and the future trends of HAPs emissions until 2030 are forecasted based on three scenarios analysis. Our results show that the total emissions have gradually increased to 906 t of NOX, 443 t of SO2, 2713 t of CO, 477.7 t of PM, 377 t of HCl, 36 t of H2S, 25 t of NH3, 62 t of NMVOCs, 592 kg of Hg, 48 kg of Pb, 14 kg of Cd, 53 kg of As, 40 kg of Cr, 37 kg of Cu, 51 kg of Ni, and 96 g of PCDD/Fs as TEQ (toxic equivalent quantity) by the year 2012. Under the business-as-usual (BAU) scenario, various HAPs emitted from cremators would continuously increase with an average growth rate of 3% till to 2030; whereas the emissions will peak at around 2015 and then decline gradually with varied speed under the two improved control scenarios. To mitigate the associated air pollution and health risks caused by crematories, it is of great necessary for implementing more strict emission standards, applying combustion optimization and requiring installation of best available flue gas purification system, as well as powerful supervision for sound operation of crematories.

Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

2013-12-01

It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more serious before occurring.

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

Uncertainty characterization and quantification in air pollution models. Application to the ADMS-Urban model.

NASA Astrophysics Data System (ADS)

Debry, E.; Malherbe, L.; Schillinger, C.; Bessagnet, B.; Rouil, L.

2009-04-01

Evaluation of human exposure to atmospheric pollution usually requires the knowledge of pollutants concentrations in ambient air. In the framework of PAISA project, which studies the influence of socio-economical status on relationships between air pollution and short term health effects, the concentrations of gas and particle pollutants are computed over Strasbourg with the ADMS-Urban model. As for any modeling result, simulated concentrations come with uncertainties which have to be characterized and quantified. There are several sources of uncertainties related to input data and parameters, i.e. fields used to execute the model like meteorological fields, boundary conditions and emissions, related to the model formulation because of incomplete or inaccurate treatment of dynamical and chemical processes, and inherent to the stochastic behavior of atmosphere and human activities [1]. Our aim is here to assess the uncertainties of the simulated concentrations with respect to input data and model parameters. In this scope the first step consisted in bringing out the input data and model parameters that contribute most effectively to space and time variability of predicted concentrations. Concentrations of several pollutants were simulated for two months in winter 2004 and two months in summer 2004 over five areas of Strasbourg. The sensitivity analysis shows the dominating influence of boundary conditions and emissions. Among model parameters, the roughness and Monin-Obukhov lengths appear to have non neglectable local effects. Dry deposition is also an important dynamic process. The second step of the characterization and quantification of uncertainties consists in attributing a probability distribution to each input data and model parameter and in propagating the joint distribution of all data and parameters into the model so as to associate a probability distribution to the modeled concentrations. Several analytical and numerical methods exist to perform an uncertainty analysis. We chose the Monte Carlo method which has already been applied to atmospheric dispersion models [2, 3, 4]. The main advantage of this method is to be insensitive to the number of perturbed parameters but its drawbacks are its computation cost and its slow convergence. In order to speed up this one we used the method of antithetic variable which takes adavantage of the symmetry of probability laws. The air quality model simulations were carried out by the Association for study and watching of Atmospheric Pollution in Alsace (ASPA). The output concentrations distributions can then be updated with a Bayesian method. This work is part of an INERIS Research project also aiming at assessing the uncertainty of the CHIMERE dispersion model used in the Prev'Air forecasting platform (www.prevair.org) in order to deliver more accurate predictions. (1) Rao, K.S. Uncertainty Analysis in Atmospheric Dispersion Modeling, Pure and Applied Geophysics, 2005, 162, 1893-1917. (2) Beekmann, M. and Derognat, C. Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the PAris Area (ESQUIF) campaign, Journal of Geophysical Research, 2003, 108, 8559-8576. (3) Hanna, S.R. and Lu, Z. and Frey, H.C. and Wheeler, N. and Vukovich, J. and Arunachalam, S. and Fernau, M. and Hansen, D.A. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain, Atmospheric Environment, 2001, 35, 891-903. (4) Romanowicz, R. and Higson, H. and Teasdale, I. Bayesian uncertainty estimation methodology applied to air pollution modelling, Environmetrics, 2000, 11, 351-371.

Long-term Changes in Extreme Air Pollution Meteorology and the Implications for Air Quality.

PubMed

Hou, Pei; Wu, Shiliang

2016-03-31

Extreme air pollution meteorological events, such as heat waves, temperature inversions and atmospheric stagnation episodes, can significantly affect air quality. Based on observational data, we have analyzed the long-term evolution of extreme air pollution meteorology on the global scale and their potential impacts on air quality, especially the high pollution episodes. We have identified significant increasing trends for the occurrences of extreme air pollution meteorological events in the past six decades, especially over the continental regions. Statistical analysis combining air quality data and meteorological data further indicates strong sensitivities of air quality (including both average air pollutant concentrations and high pollution episodes) to extreme meteorological events. For example, we find that in the United States the probability of severe ozone pollution when there are heat waves could be up to seven times of the average probability during summertime, while temperature inversions in wintertime could enhance the probability of severe particulate matter pollution by more than a factor of two. We have also identified significant seasonal and spatial variations in the sensitivity of air quality to extreme air pollution meteorology.

Time Series Forecasting of the Number of Malaysia Airlines and AirAsia Passengers

NASA Astrophysics Data System (ADS)

Asrah, N. M.; Nor, M. E.; Rahim, S. N. A.; Leng, W. K.

2018-04-01

The standard practice in forecasting process involved by fitting a model and further analysis on the residuals. If we know the distributional behaviour of the time series data, it can help us to directly analyse the model identification, parameter estimation, and model checking. In this paper, we want to compare the distributional behaviour data from the number of Malaysia Airlines (MAS) and AirAsia passenger’s. From the previous research, the AirAsia passengers are govern by geometric Brownian motion (GBM). The data were normally distributed, stationary and independent. Then, GBM was used to forecast the number of AirAsia passenger’s. The same methods were applied to MAS data and the results then were compared. Unfortunately, the MAS data were not govern by GBM. Then, the standard approach in time series forecasting will be applied to MAS data. From this comparison, we can conclude that the number of AirAsia passengers are always in peak season rather than MAS passengers.

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...

Air Pollution

MedlinePlus

Air pollution is a mixture of solid particles and gases in the air. Car emissions, chemicals from factories, ... Ozone, a gas, is a major part of air pollution in cities. When ozone forms air pollution, it's ...

BP-Broker use-cases in the UncertWeb framework

NASA Astrophysics Data System (ADS)

Roncella, Roberto; Bigagli, Lorenzo; Schulz, Michael; Stasch, Christoph; Proß, Benjamin; Jones, Richard; Santoro, Mattia

2013-04-01

The UncertWeb framework is a distributed, Web-based Information and Communication Technology (ICT) system to support scientific data modeling in presence of uncertainty. We designed and prototyped a core component of the UncertWeb framework: the Business Process Broker. The BP-Broker implements several functionalities, such as: discovery of available processes/BPs, preprocessing of a BP into its executable form (EBP), publication of EBPs and their execution through a workflow-engine. According to the Composition-as-a-Service (CaaS) approach, the BP-Broker supports discovery and chaining of modeling resources (and processing resources in general), providing the necessary interoperability services for creating, validating, editing, storing, publishing, and executing scientific workflows. The UncertWeb project targeted several scenarios, which were used to evaluate and test the BP-Broker. The scenarios cover the following environmental application domains: biodiversity and habitat change, land use and policy modeling, local air quality forecasting, and individual activity in the environment. This work reports on the study of a number of use-cases, by means of the BP-Broker, namely: - eHabitat use-case: implements a Monte Carlo simulation performed on a deterministic ecological model; an extended use-case supports inter-comparison of model outputs; - FERA use-case: is composed of a set of models for predicting land-use and crop yield response to climatic and economic change; - NILU use-case: is composed of a Probabilistic Air Quality Forecasting model for predicting concentrations of air pollutants; - Albatross use-case: includes two model services for simulating activity-travel patterns of individuals in time and space; - Overlay use-case: integrates the NILU scenario with the Albatross scenario to calculate the exposure to air pollutants of individuals. Our aim was to prove the feasibility of describing composite modeling processes with a high-level, abstract notation (i.e. BPMN 2.0), and delegating the resolution of technical issues (e.g. I/O matching) as much as possible to an external service. The results of the experimented solution indicate that this approach facilitates the integration of environmental model workflows into the standard geospatial Web Services framework (e.g. the GEOSS Common Infrastructure), mitigating its inherent complexity. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement n° 248488.

Specific weather biotrop factors in the mountain resorts of North Caucasus

NASA Astrophysics Data System (ADS)

Efimenko, Natalia; Chalaya, Elena; Povolotckaia, Nina; Senik, Irina; Slepykh, Victor

2015-04-01

Key aspects of weather therapeutic action in the mountain resorts of the Northern Caucasus (RNC) are formed under the combined influence of global, regional and local atmospheric processes, picturesque landscape, vegetation which create specificity and attraction of the weather regime for the interests of resort rehabilitation, recreation and tourism practically during the whole year. They include high purity of surface atmosphere (background level of aerosols for the countryside, the transparency of the atmosphere 0.780 -0.890); natural hypo barium and hypoxia (5-10%); increased natural aeroionization (N+=400-1000 ion/cm3; N- = 600-1200 ion/cm3; KUI = 0.8 -1.0); the softness of temperature rate (± 600 W/m ); regime of solar radiation favourable for heliotherapy. Pathogenic effects in the mountains can occur both in front atmospheric processes and in conditions of relatively favorable weather. For example, in a stable anti-cyclonic air mass with the sunny weather, foehn effects can happen that are accompanied by excessively low air humidity (lower than 20-30%), the air temperature rises in the afternoon (in winter until 15- 20°C, in summer - up to 25-35°C). The situation can be worsened by ozone intrusion (O3) with the increase of its concentration by 20 ppb or more, temperature stratification change, formation of pollution accumulation conditions in the gorges and valleys where the resort towns are located. We can observe: the increase in the concentration of aerosol pollution from 1.78 to 4 and even up to 8-10 particles/cm (particle diameter is 500-1000 nm); the rise in mass concentration of submicron aerosol up to 75 mkg/m3 and the gas pollution (CO, COx, O3) of the surface atmosphere. Against this backdrop the effects of rapid changes in the chemical composition of natural ions due to the formation of positive nitrogen ions (often with a prevalence of positive over negative air ions) can be sometimes developed. In such situations people suffering from disadaptation are under the risk of expanding meteopathic reactions which require medical intervention. Long-term performance of medical weather forecast system (MWFS) has proved its high social role - the effectiveness of spa rehabilitation of people with disadaptation in RNC through planned meteoprophylaxis increases by 20-30% [1]. Unfortunately, there are still many methodological aspects of forecasting biotropic situations for balneology which are insufficiently studied in the aspect of MWFS. It is necessary to develop new directions in the field of Biometeorology. Reference 1. The health of the population of Russia: the influence of the environment in a changing climate/monograph. Under the editorship of Academician A. Grigoriev; The Russian Academy of Sciences. -Moscow: Nauka, 2014. - P. 355-370.

Forest fires and PM10 pollution: the March 2012 case in Northern Spain

NASA Astrophysics Data System (ADS)

Rasilla Álvarez, Domingo; García Codron, Juan Carlos; Carracedo Martín, Virginia

2016-04-01

Forest fires are one of the largest sources of particulate matter, carbon monoxide, volatile organic compounds and other pollutants at regional scale. They significantly impact on local air quality and human health, even far from their original sources. March 2012 was one of the largest fire activity late winter and early spring seasons across northern Spain and Portugal. Official statistics from the Spanish and Portuguese authorities show that, during that month, approximately 35.000 ha were burned, representing the top March season in Cantabria (N. Spain) and the northern distritos of Portugal since 1981, most of them occurring in the mountainous areas, as depicted from the FIRMS database (https://firms.modaps.eosdis.nasa.gov/). At the same time, an analysis of the pollution data (Airbase dataset; http://www.eea.europa.eu/) show an increase in PM10 average values and exceedences of the limit values across the same area simultaneously or immediately after the main fire activity episodes. A comprehensive analysis of this fire and pollution event was undertaken to analyze the possible contribution of forest fires and other sources of PM10 to the high levels of this pollutant at ground level. Besides statistics of fire activity, satellite "hot spots" and ground level pollution data, we have included in our analysis meteorological records (synoptic stations, upper air soundings), backtrajectories (http://ready.arl.noaa.gov/HYSPLIT.php) and dust forecast models (https://www.bsc.es/earth-sciences/mineral-dust/catalogo-datos-dust). The results show a good agreement between the spatial and temporal variability of the levels of PM10 and the direction of the pollution plumes downwind the forest fires. The activity was mostly concentrated during 3 events, the first one between February 25th to March 3rd; the second spanning from 10th to 17th, and the last one, the most severe of the three, at the end of March. The climatological background was favourable, because most of the Iberian Peninsula recorded severe moisture deficits at the end of the winter, as shown by the drought indices. At synoptic time scale, the episodes of generalized burning coincided with warmer and drier than usual conditions, although wind speed was low, in agreement with the prevailing stable atmosphere. Saharian dust advections seem to have an indirect contribution to the high levels of PM10, probably by resuspension of old air masses. Moreover, the possible advection of old polluted layers from Eastern Europe, through a European blocking circulation (cut off high), is also considered.

Investigating the impact of a northerly low-level jet on GHG flux estimates for Indianapolis using WRF-LETKF

NASA Astrophysics Data System (ADS)

Dickerson, R. R.; Shou, Y.; Ide, K.; Zhang, D. L.; Ren, X.; Shepson, P. B.

2016-12-01

Northerly, low-level jets (LLJ's) often developed over the Great Lakes region during the cool season and are linked to wintertime blizzards, development and spread of large wildfires, and the transport and dispersion of air pollutants. However, our knowledge regarding northerly LLJ's is still limited. In the present study, characteristics and mechanisms of the formation and evolution of the northerly LLJ are investigated based on a case study by using the local ensemble transform Kalman Filter data (LETKF) assimilation system and Weather Research Forecasting model (WRF). Results are carefully evaluated using data sets collected by the HALO Photonics Lidar Profiler and aircraft flight over Indianapolis during the INFLUX experiment on October 1, 2014. It is found that the northerly LLJ exhibited typical meso-scale, but non-typical diurnal variation characteristics. During its life cycle the northerly LLJ is shown to be supergeostrophic which suggests that except for the synoptic system forcing and upper and low-level jets interactions the inertial oscillation may also contribute to the formation of the northerly LLJ and it may be triggered by the weakening of the turbulent mixing related to a strong temperature inversion developed by frontogenesis. The frontal inversion associated with the LLJ is found to be critical to the dispersion and transportation of the trace gases over Indiana. A backward trajectory analysis based on the HYSPLIT dispersion model suggests that the inversion forces the pollution to be advected by the LLJ aloft. The flux of pollutants including GHG's is heavily impacted by upwind sources and downward mixing into the PBL over Indianapolis. Consideration of these circulations improves estimates of urban GHG emissions. Keywords WRF-LETKF, Northerly low-level jet, air pollution, frontal inversion

The Impact of a Potential Shale Gas Development in Germany and the United Kingdom on Pollutant and Greenhouse Gas Emissions

NASA Astrophysics Data System (ADS)

Weger, L.; Cremonese, L.; Bartels, M. P.; Butler, T. M.

2016-12-01

Several European countries with domestic shale gas reserves are considering extracting this natural gas resource to complement their energy transition agenda. Natural gas, which produces lower CO2 emissions upon combustion compared to coal or oil, has the potential to serve as a bridge in the transition from fossil fuels to renewables. However, the generation of shale gas leads to emissions of CH4 and pollutants such as PM, NOx and VOCs, which in turn impact climate as well as local and regional air quality. In this study, we explore the impact of a potential shale gas development in Europe, specifically in Germany and the United Kingdom, on emissions of greenhouse gases and pollutants. In order to investigate the effect on emissions, we first estimate a range of wells drilled per year and production volume for the two countries under examination based on available geological information and on regional infrastructural and economic limitations. Subsequently we assign activity data and emissions factors to the well development, gas production and processing stages of shale gas generation to enable emissions quantification. We then define emissions scenarios to explore different storylines of potential shale gas development, including low emissions (high level of regulation), high emissions (low level of regulation) and middle emissions scenarios, which influence fleet make-up, emission factor and activity data choices for emissions quantification. The aim of this work is to highlight important variables and their ranges, to promote discussion and communication of potential impacts, and to construct possible visions for a future shale gas development in the two study countries. In a follow-up study, the impact of pollutant emissions from these scenarios on air quality will be explored using the Weather Research and Forecasting model with chemistry (WRF-Chem) model.

The Emerging Role of Outdoor and Indoor Air Pollution in Cardiovascular Disease

PubMed Central

Uzoigwe, Jacinta C.; Prum, Thavaleak; Bresnahan, Eric; Garelnabi, Mahdi

2013-01-01

Outdoor and indoor air pollution poses a significant cardiovascular risk, and has been associated with atherosclerosis, the main underlying pathology in many cardiovascular diseases. Although, it is well known that exposure to air pollution causes pulmonary disease, recent studies have shown that cardiovascular health consequences of air pollution generally equal or exceed those due to pulmonary diseases. The objective of this article is to evaluate the current evidence on the emerging role of environmental air pollutions in cardiovascular disease, with specific focus on the types of air pollutants and mechanisms of air pollution-induced cardiotoxicity. Published literature on pollution was systematically reviewed and cited in this article. It is hoped that this review will provide a better understanding of the harmful cardiovascular effects induced by air pollution exposure. This will help to bring a better understanding on the possible preventive health measures and will also serve regulatory agencies and researchers. In addition, elucidating the biological mechanisms underlying the link between air pollution and cardiovascular disease is an essential target in developing novel pharmacological strategies aimed at decreasing adverse effects of air pollution on cardiovascular system. PMID:24083218

Response mechanisms of conifers to air pollutants

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

Matyssek, R.; Reich, P.; Oren, R.

1995-07-01

Conifers are known to respond to SO{sub 2}, O{sub 3}, NO{sub x} and acid deposition. Of these pollutants, O{sub 3} is likely the most widespread and phytotoxic compound, and therefore of great interest to individuals concerned with forest resources Direct biological responses have a toxicological effects on metabolism which can then scale to effects on tree growth and forest ecology, including processes of competition and succession. Air pollution can cause reductions in photosynthesis and stomatal conductance, which are the physiological parameters most rigorously studied for conifers. Some effects air pollutants can have on plants are influenced by the presence ofmore » co-occurring environmental stresses. For example, drought usually reduces vulnerability of plants to air pollution. In addition, air pollution sensitivity may differ among species and with plant/leaf age. Plants may make short-term physiological adjustments to compensate for air pollution or may evolve resistance to air pollution through the processes of selection. Models are necessary to understand how physiological processes, growth processes, and ecological processes are affected by air pollutants. The process of defining the ecological risk that air pollutants pose for coniferous forests requires approaches that exploit existing databases, environmental monitoring of air pollutants and forest resources, experiments with well-defined air pollution treatments and environmental control/monitoring, modeling, predicting air pollution-caused changes in productivity and ecological processes over time and space, and integration of social values.« less

What can individuals do to reduce personal health risks from air pollution?

PubMed Central

Laumbach, Robert; Meng, Qingyu

2015-01-01

In many areas of the world, concentrations of ambient air pollutants exceed levels associated with increased risk of acute and chronic health problems. While effective policies to reduce emissions at their sources are clearly preferable, some evidence supports the effectiveness of individual actions to reduce exposure and health risks. Personal exposure to ambient air pollution can be reduced on high air pollution days by staying indoors, reducing outdoor air infiltration to indoors, cleaning indoor air with air filters, and limiting physical exertion, especially outdoors and near air pollution sources. Limited evidence suggests that the use of respirators may be effective in some circumstances. Awareness of air pollution levels is facilitated by a growing number of public air quality alert systems. Avoiding exposure to air pollutants is especially important for susceptible individuals with chronic cardiovascular or pulmonary disease, children, and the elderly. Research on mechanisms underlying the adverse health effects of air pollution have suggested potential pharmaceutical or chemopreventive interventions, such as antioxidant or antithrombotic agents, but in the absence of data on health outcomes, no sound recommendations can be made for primary prevention. Health care providers and their patients should carefully consider individual circumstances related to outdoor and indoor air pollutant exposure levels and susceptibility to those air pollutants when deciding on a course of action to reduce personal exposure and health risks from ambient air pollutants. Careful consideration is especially warranted when interventions may have unintended negative consequences, such as when efforts to avoid exposure to air pollutants lead to reduced physical activity or when there is evidence that dietary supplements, such as antioxidants, have potential adverse health effects. These potential complications of partially effective personal interventions to reduce exposure or risk highlight the primary importance of reducing emissions of air pollutants at their sources. PMID:25694820

What can individuals do to reduce personal health risks from air pollution?

PubMed

Laumbach, Robert; Meng, Qingyu; Kipen, Howard

2015-01-01

In many areas of the world, concentrations of ambient air pollutants exceed levels associated with increased risk of acute and chronic health problems. While effective policies to reduce emissions at their sources are clearly preferable, some evidence supports the effectiveness of individual actions to reduce exposure and health risks. Personal exposure to ambient air pollution can be reduced on high air pollution days by staying indoors, reducing outdoor air infiltration to indoors, cleaning indoor air with air filters, and limiting physical exertion, especially outdoors and near air pollution sources. Limited evidence suggests that the use of respirators may be effective in some circumstances. Awareness of air pollution levels is facilitated by a growing number of public air quality alert systems. Avoiding exposure to air pollutants is especially important for susceptible individuals with chronic cardiovascular or pulmonary disease, children, and the elderly. Research on mechanisms underlying the adverse health effects of air pollution have suggested potential pharmaceutical or chemopreventive interventions, such as antioxidant or antithrombotic agents, but in the absence of data on health outcomes, no sound recommendations can be made for primary prevention. Health care providers and their patients should carefully consider individual circumstances related to outdoor and indoor air pollutant exposure levels and susceptibility to those air pollutants when deciding on a course of action to reduce personal exposure and health risks from ambient air pollutants. Careful consideration is especially warranted when interventions may have unintended negative consequences, such as when efforts to avoid exposure to air pollutants lead to reduced physical activity or when there is evidence that dietary supplements, such as antioxidants, have potential adverse health effects. These potential complications of partially effective personal interventions to reduce exposure or risk highlight the primary importance of reducing emissions of air pollutants at their sources.

Improved Satellite Techniques for Monitoring and Forecasting the Transition of Hurricanes to Extratropical Storms

NASA Technical Reports Server (NTRS)

Folmer, Michael; Halverson, Jeffrey; Berndt, Emily; Dunion, Jason; Goodman, Steve; Goldberg, Mitch

2014-01-01

The Geostationary Operational Environmental Satellites R-Series (GOES-R) and Joint Polar Satellite System (JPSS) Satellite Proving Grounds have introduced multiple proxy and operational products into operations over the last few years. Some of these products have proven to be useful in current operations at various National Weather Service (NWS) offices and national centers as a first look at future satellite capabilities. Forecasters at the National Hurricane Center (NHC), Ocean Prediction Center (OPC), NESDIS Satellite Analysis Branch (SAB) and the NASA Hurricane and Severe Storms Sentinel (HS3) field campaign have had access to a few of these products to assist in monitoring extratropical transitions of hurricanes. The red, green, blue (RGB) Air Mass product provides forecasters with an enhanced view of various air masses in one complete image to help differentiate between possible stratospheric/tropospheric interactions, moist tropical air masses, and cool, continental/maritime air masses. As a compliment to this product, a new Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) Ozone product was introduced in the past year to assist in diagnosing the dry air intrusions seen in the RGB Air Mass product. Finally, a lightning density product was introduced to forecasters as a precursor to the new Geostationary Lightning Mapper (GLM) that will be housed on GOES-R, to monitor the most active regions of convection, which might indicate a disruption in the tropical environment and even signal the onset of extratropical transition. This presentation will focus on a few case studies that exhibit extratropical transition and point out the usefulness of these new satellite techniques in aiding forecasters forecast these challenging events.

Enhancing indoor air quality –The air filter advantage

PubMed Central

Vijayan, Vannan Kandi; Paramesh, Haralappa; Salvi, Sundeep Santosh; Dalal, Alpa Anil Kumar

2015-01-01

Air pollution has become the world's single biggest environmental health risk, linked to around 7 million deaths in 2012 according to a recent World Health Organisation (WHO) report. The new data further reveals a stronger link between, indoor and outdoor air pollution exposure and cardiovascular diseases, such as strokes and ischemic heart disease, as well as between air pollution and cancer. The role of air pollution in the development of respiratory diseases, including acute respiratory infections and chronic obstructive pulmonary diseases, is well known. While both indoor and outdoor pollution affect health, recent statistics on the impact of household indoor pollutants (HAP) is alarming. The WHO factsheet on HAP and health states that 3.8 million premature deaths annually - including stroke, ischemic heart disease, chronic obstructive pulmonary disease (COPD) and lung cancer are attributed to exposure to household air pollution. Use of air cleaners and filters are one of the suggested strategies to improve indoor air quality. This review discusses the impact of air pollutants with special focus on indoor air pollutants and the benefits of air filters in improving indoor air quality. PMID:26628762

Enhancing indoor air quality -The air filter advantage.

PubMed

Vijayan, Vannan Kandi; Paramesh, Haralappa; Salvi, Sundeep Santosh; Dalal, Alpa Anil Kumar

2015-01-01

Air pollution has become the world's single biggest environmental health risk, linked to around 7 million deaths in 2012 according to a recent World Health Organisation (WHO) report. The new data further reveals a stronger link between, indoor and outdoor air pollution exposure and cardiovascular diseases, such as strokes and ischemic heart disease, as well as between air pollution and cancer. The role of air pollution in the development of respiratory diseases, including acute respiratory infections and chronic obstructive pulmonary diseases, is well known. While both indoor and outdoor pollution affect health, recent statistics on the impact of household indoor pollutants (HAP) is alarming. The WHO factsheet on HAP and health states that 3.8 million premature deaths annually - including stroke, ischemic heart disease, chronic obstructive pulmonary disease (COPD) and lung cancer are attributed to exposure to household air pollution. Use of air cleaners and filters are one of the suggested strategies to improve indoor air quality. This review discusses the impact of air pollutants with special focus on indoor air pollutants and the benefits of air filters in improving indoor air quality.

Air pollution, deprivation and health: understanding relationships to add value to local air quality management policy and practice in Wales, UK.

PubMed

Brunt, H; Barnes, J; Jones, S J; Longhurst, J W S; Scally, G; Hayes, E

2017-09-01

Air pollution exposure reduces life expectancy. Air pollution, deprivation and poor-health status combinations can create increased and disproportionate disease burdens. Problems and solutions are rarely considered in a broad public health context, but doing so can add value to air quality management efforts by reducing air pollution risks, impacts and inequalities. An ecological study assessed small-area associations between air pollution (nitrogen dioxide and particulate matter), deprivation status and health outcomes in Wales, UK. Air pollution concentrations were highest in 'most' deprived areas. When considered separately, deprivation-health associations were stronger than air pollution-health associations. Considered simultaneously, air pollution added to deprivation-health associations; interactions between air pollution and deprivation modified and strengthened associations with all-cause and respiratory disease mortality, especially in 'most' deprived areas where most-vulnerable people lived and where health needs were greatest. There is a need to reduce air pollution-related risks for all. However, it is also the case that greater health gains can result from considering local air pollution problems and solutions in the context of wider health-determinants and acting on a better understanding of relationships. Informed and co-ordinated air pollution mitigation and public health action in high deprivation and pollution areas can reduce risks and inequalities. To achieve this, greater public health integration and collaboration in local air quality management policy and practice is needed. © The Author 2016. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Relationship between Air Pollution and Weather Conditions under Complicated Geographical conditions

NASA Astrophysics Data System (ADS)

Cheng, Q.; Jiang, P.; Li, M.

2017-12-01

Air pollution is one of the most serious issues all over the world, especially in megacities with constrained geographical conditions for air pollution diffusion. However, the dynamic mechanism of air pollution diffusion under complicated geographical conditions is still be confused. Researches to explore relationship between air pollution and weather conditions from the perspective of local atmospheric circulations can contribute more to solve such problem. We selected three megacities (Beijing, Shanghai and Guangzhou) under different geographical condition (mountain-plain transition region, coastal alluvial plain and coastal hilly terrain) to explore the relationship between air pollution and weather conditions. RDA (Redundancy analysis) model was used to analyze how the local atmospheric circulation acts on the air pollutant diffusion. The results show that there was a positive correlation between the concentration of air pollutants and air pressure, while temperature, precipitation and wind speed have negative correlations with the concentration of air pollutants. Furthermore, geographical conditions, such as topographic relief, have significant effects on the direction, path and intensity of local atmospheric circulation. As a consequence, air pollutants diffusion modes in different cities under various geographical conditions are diverse from each other.

A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies

PubMed Central

Coker, Eric; Kizito, Samuel

2018-01-01

An important aspect of the new sustainable development goals (SDGs) is a greater emphasis on reducing the health impacts from ambient air pollution in developing countries. Meanwhile, the burden of human disease attributable to ambient air pollution in sub-Saharan Africa is growing, yet estimates of its impact on the region are possibly underestimated due to a lack of air quality monitoring, a paucity of air pollution epidemiological studies, and important population vulnerabilities in the region. The lack of ambient air pollution epidemiologic data in sub-Saharan Africa is also an important global health disparity. Thousands of air pollution health effects studies have been conducted in Europe and North America, rather than in urban areas that have some of the highest measured air pollution levels in world, including urban areas in sub-Saharan Africa. In this paper, we provide a systematic and narrative review of the literature on ambient air pollution epidemiological studies that have been conducted in the region to date. Our review of the literature focuses on epidemiologic studies that measure air pollutants and relate air pollution measurements with various health outcomes. We highlight the gaps in ambient air pollution epidemiological studies conducted in different sub-regions of sub-Saharan Africa and provide methodological recommendations for future environmental epidemiology studies addressing ambient air pollution in the region. PMID:29494501

A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies.

PubMed

Coker, Eric; Kizito, Samuel

2018-03-01

An important aspect of the new sustainable development goals (SDGs) is a greater emphasis on reducing the health impacts from ambient air pollution in developing countries. Meanwhile, the burden of human disease attributable to ambient air pollution in sub-Saharan Africa is growing, yet estimates of its impact on the region are possibly underestimated due to a lack of air quality monitoring, a paucity of air pollution epidemiological studies, and important population vulnerabilities in the region. The lack of ambient air pollution epidemiologic data in sub-Saharan Africa is also an important global health disparity. Thousands of air pollution health effects studies have been conducted in Europe and North America, rather than in urban areas that have some of the highest measured air pollution levels in world, including urban areas in sub-Saharan Africa. In this paper, we provide a systematic and narrative review of the literature on ambient air pollution epidemiological studies that have been conducted in the region to date. Our review of the literature focuses on epidemiologic studies that measure air pollutants and relate air pollution measurements with various health outcomes. We highlight the gaps in ambient air pollution epidemiological studies conducted in different sub-regions of sub-Saharan Africa and provide methodological recommendations for future environmental epidemiology studies addressing ambient air pollution in the region.

40 CFR 52.11 - Prevention of air pollution emergency episodes.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 3 2010-07-01 2010-07-01 false Prevention of air pollution emergency... Prevention of air pollution emergency episodes. (a) Each subpart identifies portions of the air pollution.... (c) Where a State plan does not provide for public announcement regarding air pollution emergency...

40 CFR 52.11 - Prevention of air pollution emergency episodes.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 3 2011-07-01 2011-07-01 false Prevention of air pollution emergency... Prevention of air pollution emergency episodes. (a) Each subpart identifies portions of the air pollution.... (c) Where a State plan does not provide for public announcement regarding air pollution emergency...

40 CFR 60.2141 - By what date must I conduct the initial air pollution control device inspection?

Code of Federal Regulations, 2011 CFR

2011-07-01

... air pollution control device inspection? 60.2141 Section 60.2141 Protection of Environment... initial air pollution control device inspection? (a) The initial air pollution control device inspection... startup. (b) Within 10 operating days following an air pollution control device inspection, all necessary...

40 CFR 52.11 - Prevention of air pollution emergency episodes.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 3 2013-07-01 2013-07-01 false Prevention of air pollution emergency... Prevention of air pollution emergency episodes. (a) Each subpart identifies portions of the air pollution.... (c) Where a State plan does not provide for public announcement regarding air pollution emergency...

40 CFR 60.2141 - By what date must I conduct the initial air pollution control device inspection?

Code of Federal Regulations, 2012 CFR

2012-07-01

... air pollution control device inspection? 60.2141 Section 60.2141 Protection of Environment... initial air pollution control device inspection? (a) The initial air pollution control device inspection... startup. (b) Within 10 operating days following an air pollution control device inspection, all necessary...

40 CFR 52.11 - Prevention of air pollution emergency episodes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 3 2012-07-01 2012-07-01 false Prevention of air pollution emergency... Prevention of air pollution emergency episodes. (a) Each subpart identifies portions of the air pollution.... (c) Where a State plan does not provide for public announcement regarding air pollution emergency...

40 CFR 52.11 - Prevention of air pollution emergency episodes.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 3 2014-07-01 2014-07-01 false Prevention of air pollution emergency... Prevention of air pollution emergency episodes. (a) Each subpart identifies portions of the air pollution.... (c) Where a State plan does not provide for public announcement regarding air pollution emergency...

78 FR 52857 - Approval and Promulgation of Implementation Plans; State of Iowa

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-27

... Health Rules and Regulations, Chapter V, Air Pollution. The revisions reflect updates to the Iowa..., Chapter V, ``Air Pollution,'' as a revision to the SIP. In order for the local program's ``Air Pollution..., ``Air Pollution.'' The local agency routinely revises its ``Air Pollution'' regulations to be consistent...

Challenges and future direction of molecular research in air pollution-related lung cancers.

PubMed

Shahadin, Maizatul Syafinaz; Ab Mutalib, Nurul Syakima; Latif, Mohd Talib; Greene, Catherine M; Hassan, Tidi

2018-04-01

Hazardous air pollutants or chemical release into the environment by a variety of natural and/or anthropogenic activities may give adverse effects to human health. Air pollutants such as sulphur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), heavy metals and particulate matter (PM) affect number of different human organs, especially the respiratory system. The International Agency for Research on Cancer (IARC) reported that ambient air pollution is a cause of lung cancer. Recently, the agency has classified outdoor air pollution as well as PM air pollution as Group 1 carcinogens. In addition, several epidemiological studies have shown a positive association between air pollutants to lung cancer risks and mortality. However, there are only a few studies examining the molecular effects of air pollution exposure specifically in lung cancer due to multiple challenges to mimic air pollution exposure in basic experimentation. Another major issue is the lack of adequate adjustments for exposure misclassification as air pollution may differ temporo-spatially and socioeconomically. Thus, the purpose of this paper is to review the current molecular understanding of air pollution-related lung cancer and potential future direction in this challenging yet important research field. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of Health Impact of Air Pollution Between China and Other Countries.

PubMed

Tian, Linwei; Sun, Shengzhi

2017-01-01

Air pollution is the world's largest single environmental risk according to the World Health Organization (WHO), which caused around seven million deaths in 2012. Extensive epidemiological studies have been carried out worldwide to examine the health impacts of ambient air pollution, consistently demonstrating significant health impacts of ambient air pollution. Air pollution problem in China is especially serious; it has become the fourth biggest threat to the health of the Chinese people. In this review, we summarized existing literature, compared health impact of air pollution between China and other countries, and found substantial heterogeneity in the risk estimates of air pollution. The effect heterogeneities may be due to the differences in the characteristics of populations (e.g., the proportion of the elder population and people with preexisting diseases), exposure profile (e.g., air pollution concentrations and composition), and regional climate. Although the magnitude of relative risk estimates of air pollution is generally similar with that in other parts of the world, air pollution is one of China's most serious environmental health problems given the huge number of people exposed to high concentration levels of air pollution in China.

Improving Forecast Skill by Assimilation of Quality-controlled AIRS Temperature Retrievals under Partially Cloudy Conditions

NASA Technical Reports Server (NTRS)

Reale, O.; Susskind, J.; Rosenberg, R.; Brin, E.; Riishojgaard, L.; Liu, E.; Terry, J.; Jusem, J. C.

2007-01-01

The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite has been long recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid-lower tropospheric sounding AIRS channels are assimilated primarily under completely clear-sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS-derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud-contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS-induced anomaly propagates through the model's dynamics producing improved 5-day forecasts.

The Transition of Atmospheric Infrared Sounder Total Ozone Products to Operations

NASA Technical Reports Server (NTRS)

Berndt, E. B.; Zavodsky, B. T.; Jedlovec, G. J.

2014-01-01

The National Aeronautics and Space Administration Short-term Prediction Research and Transition Center (NASA SPoRT) has transitioned a total column ozone product from the Atmospheric Infrared Sounder (AIRS) retrievals to the Weather Prediction Center and Ocean Prediction Center. The total column ozone product is used to diagnose regions of warm, dry, ozone-rich, stratospheric air capable of descending to the surface to create high-impact non-convective winds. Over the past year, forecasters have analyzed the Red, Green, Blue (RGB) Air Mass imagery in conjunction with the AIRS total column ozone to aid high wind forecasts. One of the limitations of the total ozone product is that it is difficult for forecasters to determine whether elevated ozone concentrations are related to stratospheric air or climatologically high values of ozone in certain regions. During the summer of 2013, SPoRT created an AIRS ozone anomaly product which calculates the percent of normal ozone based on a global stratospheric ozone mean climatology. With the knowledge that ozone values 125 percent of normal and greater typically represent stratospheric air; the anomaly product can be used with the total column ozone product to confirm regions of stratospheric air. This paper describes the generation of these products along with forecaster feedback concerning the use of the AIRS ozone products in conjunction with the RGB Air Mass product to access the utility and transition of the products.

National Centers for Environmental Prediction

Science.gov Websites

Products Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model PARALLEL/EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration /EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION / DIAGNOSTICS Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

Air pollution and stroke - an overview of the evidence base.

PubMed

Maheswaran, Ravi

2016-08-01

Air pollution is being increasingly recognized as a significant risk factor for stroke. There are numerous sources of air pollution including industry, road transport and domestic use of biomass and solid fuels. Early reports of the association between air pollution and stroke come from studies investigating health effects of severe pollution episodes. Several daily time series and case-crossover studies have reported associations with stroke. There is also evidence linking chronic air pollution exposure with stroke and with reduced survival after stroke. A conceptual framework linking air pollution exposure and stroke is proposed. It links acute and chronic exposure to air pollution with pathways to acute and chronic effects on stroke risk. Current evidence regarding potential mechanisms mainly relate to particulate air pollution. Whilst further evidence would be useful, there is already sufficient evidence to support consideration of reduction in air pollution as a preventative measure to reduce the stroke burden globally. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

NASA Technical Reports Server (NTRS)

Garner, Gregory G.; Thompson, Anne M.

2013-01-01

An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for

Evaluating the Impact of Atmospheric Infrared Sounder (AIRS) Data On Convective Forecasts

NASA Technical Reports Server (NTRS)

Kozlowski, Danielle; Zavodsky, Bradley

2011-01-01

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. The mission of SPoRT is to transition observations and research capabilities into operations to help improve short-term weather forecasts on a regional scale. Two areas of focus are data assimilation and modeling, which can to help accomplish SPoRT's programmatic goals of transitioning NASA data to operational users. Forecasting convective weather is one challenge that faces operational forecasters. Current numerical weather prediction (NWP) models that operational forecasters use struggle to properly forecast location, timing, intensity and/or mode of convection. Given the proper atmospheric conditions, convection can lead to severe weather. SPoRT's partners in the National Oceanic and Atmospheric Administration (NOAA) have a mission to protect the life and property of American citizens. This mission has been tested as recently as this 2011 severe weather season, which has seen more than 300 fatalities and injuries and total damages exceeding $10 billion. In fact, during the three day period from 25-27 April, 1,265 storms reports (362 tornado reports) were collected making this three day period one of most active in American history. To address the forecast challenge of convective weather, SPoRT produces a real-time NWP model called the SPoRT Weather Research and Forecasting (SPoRT-WRF), which incorporates unique NASA data sets. One of the NASA assets used in this unique model configuration is retrieved profiles from the Atmospheric Infrared Sounder (AIRS).The goal of this project is to determine the impact that these AIRS profiles have on the SPoRT-WRF forecasts by comparing to a current operational model and a control SPoRT-WRF model that does not contain AIRS profiles.

Spatial Interpolation of Aerosol Optical Depth Pollution: Comparison of Methods for the Development of Aerosol Distribution

NASA Astrophysics Data System (ADS)

Safarpour, S.; Abdullah, K.; Lim, H. S.; Dadras, M.

2017-09-01

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.

Methods for Environments and Contaminants: Criteria Air Pollutants

EPA Pesticide Factsheets

EPA’s Office of Air Quality Planning and Standards (OAQPS) has set primary (health-based) National Ambient Air Quality Standards (NAAQS) for six common air pollutants, often referred to as criteria air pollutants (or simply criteria pollutants).

Public-health impact of outdoor air pollution for 2(nd) air pollution management policy in Seoul metropolitan area, Korea.

PubMed

Leem, Jong Han; Kim, Soon Tae; Kim, Hwan Cheol

2015-01-01

Air pollution contributes to mortality and morbidity. We estimated the impact of outdoor air pollution on public health in Seoul metropolitan area, Korea. Attributable cases of morbidity and mortality were estimated. Epidemiology-based exposure-response functions for a 10 μg/m3 increase in particulate matter (PM2.5 and PM10) were used to quantify the effects of air pollution. Cases attributable to air pollution were estimated for mortality (adults ≥ 30 years), respiratory and cardiovascular hospital admissions (all ages), chronic bronchitis (all ages), and acute bronchitis episodes (≤18 years). Environmental exposure (PM2.5 and PM10) was modeled for each 3 km × 3 km. In 2010, air pollution caused 15.9% of total mortality or approximately 15,346 attributable cases per year. Particulate air pollution also accounted for: 12,511 hospitalized cases of respiratory disease; 20,490 new cases of chronic bronchitis (adults); 278,346 episodes of acute bronchitis (children). After performing the 2(nd) Seoul metropolitan air pollution management plan, the reducible death number associated with air pollution is 14,915 cases per year in 2024. We can reduce 57.9% of death associated with air pollution. This assessment estimates the public-health impacts of current patterns of air pollution. Although individual health risks of air pollution are relatively small, the public-health consequences are remarkable. Particulate air pollution remains a key target for public-health action in the Seoul metropolitan area. Our results, which have also been used for economic valuation, should guide decisions on the assessment of environmental health-policy options.

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...

Meteodrones - Meteorological Planetary Boundary Layer Measurements by Vertical Drone Soundings

NASA Astrophysics Data System (ADS)

Lauer, Jonas; Fengler, Martin

2017-04-01

As of today, there is a gap in the operational data collection of meteorological observations in the Planetary Boundary Layer (PBL). This lack of spatially and temporally reliable knowledge of PBL conditions and energy fluxes with the surface causes shortcomings in the prediction of micro- and mesoscale phenomena such as convection, temperature inversions, local wind systems or fog. The currently used remote sensing instruments share the drawback of only partially covering necessary variables. To fill this data gap, since 2012, Meteomatics has been developing a drone measurement system, the Meteodrone, to measure the parameters wind speed, wind direction, dewpoint, temperature and air pressure of the PBL up to 1.5 km above ground. Both the data quality and the assimilation into a regional numerical weather model could be determined in several pilot studies. Besides, a project in cooperation with the NSSL (National Severe Storms Laboratory) was launched in October 2016 with the goal of capturing pre-convective conditions for improved severe storm forecasts in Oklahoma. Also, related measurements, such as air pollution measurements in the Misox valley to determine LDSP values, were successfully conducted. The main goal of the project is the operational data collection of PBL measurements and the assimilation of this data into regional numerical weather forecast models. Considering the high data quality indicated in all conducted studies as well as the trouble-free execution, this goal is both worthwhile and realistic.

Postprocessing for Air Quality Predictions

NASA Astrophysics Data System (ADS)

Delle Monache, L.

2017-12-01

In recent year, air quality (AQ) forecasting has made significant progress towards better predictions with the goal of protecting the public from harmful pollutants. This progress is the results of improvements in weather and chemical transport models, their coupling, and more accurate emission inventories (e.g., with the development of new algorithms to account in near real-time for fires). Nevertheless, AQ predictions are still affected at times by significant biases which stem from limitations in both weather and chemistry transport models. Those are the result of numerical approximations and the poor representation (and understanding) of important physical and chemical process. Moreover, although the quality of emission inventories has been significantly improved, they are still one of the main sources of uncertainties in AQ predictions. For operational real-time AQ forecasting, a significant portion of these biases can be reduced with the implementation of postprocessing methods. We will review some of the techniques that have been proposed to reduce both systematic and random errors of AQ predictions, and improve the correlation between predictions and observations of ground-level ozone and surface particulate matter less than 2.5 µm in diameter (PM2.5). These methods, which can be applied to both deterministic and probabilistic predictions, include simple bias-correction techniques, corrections inspired by the Kalman filter, regression methods, and the more recently developed analog-based algorithms. These approaches will be compared and contrasted, and strength and weaknesses of each will be discussed.

Data Assimilation and Regional Forecasts Using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to optimally assimilate AIRS thermodynamic profiles--obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm-into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses will be used to conduct a month-long series of regional forecasts over the continental U.S. The long-tern1 impact of AIRS profiles on forecast will be assessed against verifying radiosonde and stage IV precipitation data.

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Zabodsky, Brad; Chou, Shih-Hung; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which, together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.

40 CFR 60.2151 - By what date must I conduct the annual air pollution control device inspection?

Code of Federal Regulations, 2011 CFR

2011-07-01

... air pollution control device inspection? 60.2151 Section 60.2151 Protection of Environment... annual air pollution control device inspection? On an annual basis (no more than 12 months following the previous annual air pollution control device inspection), you must complete the air pollution control...

40 CFR 60.2151 - By what date must I conduct the annual air pollution control device inspection?

Code of Federal Regulations, 2012 CFR

2012-07-01

... air pollution control device inspection? 60.2151 Section 60.2151 Protection of Environment... annual air pollution control device inspection? On an annual basis (no more than 12 months following the previous annual air pollution control device inspection), you must complete the air pollution control...

“Air pollution in Delhi: Its Magnitude and Effects on Health”

PubMed Central

Rizwan, SA; Nongkynrih, Baridalyne; Gupta, Sanjeev Kumar

2013-01-01

Air pollution is responsible for many health problems in the urban areas. Of late, the air pollution status in Delhi has undergone many changes in terms of the levels of pollutants and the control measures taken to reduce them. This paper provides an evidence-based insight into the status of air pollution in Delhi and its effects on health and control measures instituted. The urban air database released by the World Health Organization in September 2011 reported that Delhi has exceeded the maximum PM10 limit by almost 10-times at 198 μg/m3. Vehicular emissions and industrial activities were found to be associated with indoor as well as outdoor air pollution in Delhi. Studies on air pollution and mortality from Delhi found that all-natural-cause mortality and morbidity increased with increased air pollution. Delhi has taken several steps to reduce the level of air pollution in the city during the last 10 years. However, more still needs to be done to further reduce the levels of air pollution. PMID:23559696

75 FR 18061 - Approval and Promulgation of Air Quality Implementation Plans; Texas; Control of Air Pollution...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-09

... Promulgation of Air Quality Implementation Plans; Texas; Control of Air Pollution From Motor Vehicles AGENCY... Chapter 114, Control of Air Pollution from Motor Vehicles. These revisions consist of the new Rebate Grant... air pollution regulations and control strategies to ensure that air quality meets the National Ambient...

Enviro-HIRLAM online integrated meteorology-chemistry modelling system: strategy, methodology, developments and applications (v7.2)

NASA Astrophysics Data System (ADS)

Baklanov, Alexander; Smith Korsholm, Ulrik; Nuterman, Roman; Mahura, Alexander; Pagh Nielsen, Kristian; Hansen Sass, Bent; Rasmussen, Alix; Zakey, Ashraf; Kaas, Eigil; Kurganskiy, Alexander; Sørensen, Brian; González-Aparicio, Iratxe

2017-08-01

The Environment - High Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully online integrated numerical weather prediction (NWP) and atmospheric chemical transport (ACT) model for research and forecasting of joint meteorological, chemical and biological weather. The integrated modelling system is developed by the Danish Meteorological Institute (DMI) in collaboration with several European universities. It is the baseline system in the HIRLAM Chemical Branch and used in several countries and different applications. The development was initiated at DMI more than 15 years ago. The model is based on the HIRLAM NWP model with online integrated pollutant transport and dispersion, chemistry, aerosol dynamics, deposition and atmospheric composition feedbacks. To make the model suitable for chemical weather forecasting in urban areas, the meteorological part was improved by implementation of urban parameterisations. The dynamical core was improved by implementing a locally mass-conserving semi-Lagrangian numerical advection scheme, which improves forecast accuracy and model performance. The current version (7.2), in comparison with previous versions, has a more advanced and cost-efficient chemistry, aerosol multi-compound approach, aerosol feedbacks (direct and semi-direct) on radiation and (first and second indirect effects) on cloud microphysics. Since 2004, the Enviro-HIRLAM has been used for different studies, including operational pollen forecasting for Denmark since 2009 and operational forecasting atmospheric composition with downscaling for China since 2017. Following the main research and development strategy, further model developments will be extended towards the new NWP platform - HARMONIE. Different aspects of online coupling methodology, research strategy and possible applications of the modelling system, and fit-for-purpose model configurations for the meteorological and air quality communities are discussed.

Air Pollution in the World's Megacities.

ERIC Educational Resources Information Center

Richman, Barbara T., Ed.

1994-01-01

Reports findings of the Global Environment Monitoring System study concerning air pollution in the world's megacities. Discusses sources of air pollution, air pollution impacts, air quality monitoring, air quality trends, and control strategies. Provides profiles of the problem in Beijing, Los Angeles, Mexico City, India, Cairo, Sao Paulo, and…

An Experiment with Air Purifiers in Delhi during Winter 2015-2016

PubMed Central

Vyas, Sangita

2016-01-01

Particulate pollution has important consequences for human health, and is an issue of global concern. Outdoor air pollution has become a cause for alarm in India in particular because recent data suggest that ambient pollution levels in Indian cities are some of the highest in the world. We study the number of particles between 0.5μm and 2.5μm indoors while using affordable air purifiers in the highly polluted city of Delhi. Though substantial reductions in indoor number concentrations are observed during air purifier use, indoor air quality while using an air purifier is frequently worse than in cities with moderate pollution, and often worse than levels observed even in polluted cities. When outdoor pollution levels are higher, on average, indoor pollution levels while using an air purifier are also higher. Moreover, the ratio of indoor air quality during air purifier use to two comparison measures of air quality without an air purifier are also positively correlated with outdoor pollution levels, suggesting that as ambient air quality worsens there are diminishing returns to improvements in indoor air quality during air purifier use. The findings of this study indicate that although the most affordable air purifiers currently available are associated with significant improvements in the indoor environment, they are not a replacement for public action in regions like Delhi. Although private solutions may serve as a stopgap, reducing ambient air pollution must be a public health and policy priority in any region where air pollution is as high as Delhi’s during the winter. PMID:27978542

Assessment of air pollution of settlement areas in Ulaanbaatar city, Mongolia

NASA Astrophysics Data System (ADS)

Ch, Sonomdagva; Ch, Byambatseren; Batdelger, B.

2017-05-01

The purpose of this study is to analyses mass concentration varied by its measurement of air pollution in Ulaanbaatar city, Mongolia. Ulaanbaatar city will have been increasing air pollution due to rapidly expanding vehicular population, growing industrial sector in last 10 years ago. In addition, people use to heat the carbon from 10 month in every year. This becomes a base cause of air pollution in Ulaanbaatar. We studied a change of mass concentration an air pollution elements in Ulaanbaatar, Mongolia. To research work, we used information that based on data of my measurements of air pollution and Metropolitan air quality agency until 2006 to 2016. This research important result is air pollution levels are limited to the areas around Ulaanbaatar areas are the most polluted in the center of city are the least polluted areas whereas Tolgoit, Sapporo, 1st Khoroolol, Amgalan, Shar Khad are moderately polluted and the areas around Baruun 4 zam, Factory, Zaisan, Nisekh are normally polluted. The results of pollution are illustrated four zones. By dividing the polluted areas into such zones, we are trying to make it easier to take preventive measures against the pollution itself and protective measures for safeguarding the health of mass population.

[The short-term effects of air pollution on mortality: the results of the EMECAM project in 2 cities of Asturias. Estudio Multicéntrico Español sobre la Relación entre la Contaminación Atmosférica y la Mortalidad].

PubMed

Cañada Martínez, A; García González, J V; Rodríguez Suárez, V; Fernández Noval, F; Fernández Rodríguez, C; Huerta González, I

1999-01-01

The studies conducted to date regarding the possibility that air pollutants, at levels considered safe to date, are capable of having impact are capable of having impact on human health have not led to homogeneous findings. This study is aimed at estimating the degrees of relationship between the daily levels of the pollutants and the death rate on a short-terms basis in the two most populated cities in Austria (Gijón and Oviedo), as well as contributing to increasing the statistical importance and the representative nature of the EMECAM Project, within which this study is comprised. Ecological time series study, Estimate of degrees of group exposure based on the readings taken at the pollution control stations. Modeling of the death rate series, including control variables, by means of Poisson regression. Estimating risks related to each pollutant for the death rate, controlling the series-based autocorrelation. Throughout the 1993-1996 period, the pollution by means of particles in suspension and CO was greater in Gijón, that involving SO2 and NO2 having been greater in Oviedo. In these two cities, the levels can be considered to be low and to fall within what is considered admissible under the laws currently in impact. Most of the relative risk forecasts neared the zero impact point, although significant positive (especially for NO2) as well as negative relationships have been found to exist. The significant relationships found were not proven to be consistent in these two cities for the periods studied. Based on the findings of this study, the conclusion cannot be drawn that a clear-cut relationship exists between the pollutants studied (particles, SO2, NO2, CO) and the death rate on a short-term basis, at least at the levels detected in Gijón and Oviedo.

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations.

PubMed

Oh, TaeSeok; Kim, MinJeong; Lim, JungJin; Kang, OnYu; Shetty, K Vidya; SankaraRao, B; Yoo, ChangKyoo; Park, Jae Hyung; Kim, Jeong Tai

2012-05-01

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO2, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well. Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO2 pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.

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

EPA Science Inventory

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), are developing an operational, nationwide Air Quality Forecasting (AQF) system. An experimental phase of this program, which couples NOAA's Et...

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

Determinants of perceived air pollution annoyance and association between annoyance scores and air pollution (PM 2.5, NO 2) concentrations in the European EXPOLIS study

NASA Astrophysics Data System (ADS)

Rotko, Tuulia; Oglesby, Lucy; Künzli, Nino; Carrer, Paolo; Nieuwenhuijsen, Mark J.; Jantunen, Matti

Apart from its traditionally considered objective impacts on health, air pollution can also have perceived effects, such as annoyance. The psychological effects of air pollution may often be more important to well-being than the biophysical effects. Health effects of perceived annoyance from air pollution are so far unknown. More knowledge of air pollution annoyance levels, determinants and also associations with different air pollution components is needed. In the European air pollution exposure study, EXPOLIS, the air pollution annoyance as perceived at home, workplace and in traffic were surveyed among other study objectives. Overall 1736 randomly drawn 25-55-yr-old subjects participated in six cities (Athens, Basel, Milan, Oxford, Prague and Helsinki). Levels and predictors of individual perceived annoyances from air pollution were assessed. Instead of the usual air pollution concentrations at fixed monitoring sites, this paper compares the measured microenvironment concentrations and personal exposures of PM 2.5 and NO 2 to the perceived annoyance levels. A considerable proportion of the adults surveyed was annoyed by air pollution. Female gender, self-reported respiratory symptoms, downtown living and self-reported sensitivity to air pollution were directly associated with high air pollution annoyance score while in traffic, but smoking status, age or education level were not significantly associated. Population level annoyance averages correlated with the city average exposure levels of PM 2.5 and NO 2. A high correlation was observed between the personal 48-h PM 2.5 exposure and perceived annoyance at home as well as between the mean annoyance at work and both the average work indoor PM 2.5 and the personal work time PM 2.5 exposure. With the other significant determinants (gender, city code, home location) and home outdoor levels the model explained 14% (PM 2.5) and 19% (NO 2) of the variation in perceived air pollution annoyance in traffic. Compared to Helsinki, in Basel and Prague the adult participants were more annoyed by air pollution while in traffic even after taking the current home outdoor PM 2.5 and NO 2 levels into account.

[Papers Presented at the American Medical Association's Air Pollution Medical Research Conference (New Orleans, Louisiana, October 5-7, 1970).

ERIC Educational Resources Information Center

American Medical Association, Chicago, IL.

This is a collection of twenty speeches presented at the American Medical Association's Air Pollution Medical Conference, October 5-7, 1970. Speeches included: Air Pollution Control: The Physician's Role; Air Pollution Problems in Nuclear Power Development; Airway Resistance and Collateral Ventilation; Asbestos Air Pollution in Urban Areas;…

APEX (Air Pollution Exercise) Volume 21: Legal References: Air Pollution Control Regulations.

ERIC Educational Resources Information Center

Environmental Protection Agency, Research Triangle Park, NC. Office of Manpower Development.

The Legal References: Air Pollution Control Regulations Manual is the last in a set of 21 manuals (AA 001 009-001 029) used in APEX (Air Pollution Exercise), a computerized college and professional level "real world" game simulation of a community with urban and rural problems, industrial activities, and air pollution difficulties. The manual…

Air pollution in India and related adverse respiratory health effects: past, present, and future directions.

PubMed

Khilnani, Gopi C; Tiwari, Pawan

2018-03-01

The review describes current status of air pollution in India, summarizes recent research on adverse health effects of ambient and household air pollution, and outlines the ongoing efforts and future actions required to improve air quality and reduce morbidity and mortality because of air pollution in India. Global burden of disease data analysis reveals more than one million premature deaths attributable to ambient air pollution in 2015 in India. More than one million additional deaths can be attributed to household air pollution. Particulate matter with diameter 2.5 μm or less has been causatively linked with most premature deaths. Acute respiratory tract infections, asthma, chronic obstructive pulmonary disease, exacerbations of preexisting obstructive airway disease and lung cancer are proven adverse respiratory effects of air pollution. Targeting air quality standards laid by WHO can significantly reduce morbidity and mortality because of air pollution in India. India is currently exposed to high levels of ambient and household air pollutants. Respiratory adverse effects of air pollution are significant contributors to morbidity and premature mortality in India. Substantial efforts are being made at legislative, administrative, and community levels to improve air quality. However, much more needs to be done to change the 'status quo' and attain the target air quality standards. VIDEO ABSTRACT: http://links.lww.com/COPM/A24.

Effect of aerosol feedback in the Korea Peninsula using WRF-CMAQ two-way coupled model

NASA Astrophysics Data System (ADS)

Yoo, J.; Jeon, W.; Lee, H.; Lee, S.

2017-12-01

Aerosols influence the climate system by scattering and absorption of the solar radiation by altering the cloud radiative properties. For the reason, consideration of aerosol feedback is important numerical weather prediction and air quality models. The purpose of this study was to investigate the effect of aerosol feedback on PM10 simulation in Korean Peninsula using the Weather Research and Forecasting (WRF) and the community multiscale air quality (CMAQ) two-way coupled model. Simulations were conducted with the aerosol feedback (FB) and without (NFB). The results of the simulated solar radiation in the west part of Korea decreased due to the aerosol feedback effect. The feedback effect was significant in the west part of Korea Peninsula, showing high Particulate Matter (PM) estimates due to dense emissions and its long-range transport from China. The decrease of solar radiation lead to planetary boundary layer (PBL) height reduction, thereby dispersion of air pollutants such as PM is suppressed, and resulted in higher PM concentrations. These results indicate that aerosol feedback effects can play an important role in the simulation of meteorology and air quality over Korea Peninsula.

Objective Lightning Probability Forecasts for East-Central Florida Airports

NASA Technical Reports Server (NTRS)

Crawford, Winfred C.

2013-01-01

The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.

Interactive short-term effects of equivalent temperature and air pollution on human mortality in Berlin and Lisbon.

PubMed

Burkart, Katrin; Canário, Paulo; Breitner, Susanne; Schneider, Alexandra; Scherber, Katharina; Andrade, Henrique; Alcoforado, Maria João; Endlicher, Wilfried

2013-12-01

There is substantial evidence that both temperature and air pollution are predictors of mortality. Thus far, few studies have focused on the potential interactive effects between the thermal environment and different measures of air pollution. Such interactions, however, are biologically plausible, as (extreme) temperature or increased air pollution might make individuals more susceptible to the effects of each respective predictor. This study investigated the interactive effects between equivalent temperature and air pollution (ozone and particulate matter) in Berlin (Germany) and Lisbon (Portugal) using different types of Poisson regression models. The findings suggest that interactive effects exist between air pollutants and equivalent temperature. Bivariate response surface models and generalised additive models (GAMs) including interaction terms showed an increased risk of mortality during periods of elevated equivalent temperatures and air pollution. Cold effects were mostly unaffected by air pollution. The study underscores the importance of air pollution control in mitigating heat effects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gulf of Mexico Air/Sea Interaction: Measurements and Initial Data Characterization

NASA Astrophysics Data System (ADS)

MacDonald, C.; Huang, C. H.; Roberts, P. T.; Bariteau, L.; Fairall, C. W.; Gibson, W.; Ray, A.

2011-12-01

Corporate, government, and university researchers collaborated to develop an atmospheric boundary layer environmental observations program on an offshore platform in the Gulf of Mexico. The primary goals of this project were to provide data to (1) improve our understanding of boundary layer processes and air-sea interaction over the Gulf of Mexico; (2) improve regional-scale meteorological and air quality modeling; and (3) provide a framework for advanced offshore measurements to support future needs such as emergency response, exploration and lease decisions, wind energy research and development, and meteorological and air quality forecasting. In October 2010, meteorological and oceanographic sensors were deployed for an extended period (approximately 12 months) on a Chevron service platform (ST 52B, 90.5W, 29N) to collect boundary layer and sea surface data sufficient to support these objectives. This project has significant importance given the large industrial presence in the Gulf, sizeable regional population nearby, and the recognized need for precise and timely pollutant forecasts. Observations from this project include surface meteorology; sodar marine boundary layer winds; microwave radiometer profiles of temperature, relative humidity, and liquid water; ceilometer cloud base heights; water temperature and current profiles; sea surface temperature; wave height statistics; downwelling solar and infrared radiation; and air-sea turbulent momentum and heat fluxes. This project resulted in the collection of an unprecedented set of boundary layer measurements over the Gulf of Mexico that capture the range of meteorological and oceanographic interactions and processes that occur over an entire year. This presentation will provide insight into the logistical and scientific issues associated with the deployment and operations of unique measurements in offshore areas and provide results from an initial data analysis of boundary layer processes over the Gulf of Mexico, with a special focus on the relationship among measured and modeled energy fluxes and other oceanographic and atmospheric conditions.

Review of air pollution and health impacts in Malaysia.

PubMed

Afroz, Rafia; Hassan, Mohd Nasir; Ibrahim, Noor Akma

2003-06-01

In the early days of abundant resources and minimal development pressures, little attention was paid to growing environmental concerns in Malaysia. The haze episodes in Southeast Asia in 1983, 1984, 1991, 1994, and 1997 imposed threats to the environmental management of Malaysia and increased awareness of the environment. As a consequence, the government established Malaysian Air Quality Guidelines, the Air Pollution Index, and the Haze Action Plan to improve air quality. Air quality monitoring is part of the initial strategy in the pollution prevention program in Malaysia. Review of air pollution in Malaysia is based on the reports of the air quality monitoring in several large cities in Malaysia, which cover air pollutants such as Carbon monoxide (CO), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3), and Suspended Particulate Matter (SPM). The results of the monitoring indicate that Suspended Particulate Matter (SPM) and Nitrogen Dioxide (NO2) are the predominant pollutants. Other pollutants such as CO, O(x), SO2, and Pb are also observed in several big cities in Malaysia. The air pollution comes mainly from land transportation, industrial emissions, and open burning sources. Among them, land transportation contributes the most to air pollution. This paper reviews the results of the ambient air quality monitoring and studies related to air pollution and health impacts.

Environmental Perception and Citizen Response: a Denver, Colorado Air Pollution Case Study.

NASA Astrophysics Data System (ADS)

Naomi, Leaura M.

Denver, a high altitude city, suffers from air pollution. Automobile emissions, as well as wood and coal burning contribute to Denver's air pollution. In order to reduce its air pollution, Denver hosted a no-drive campaign, The Better Air Campaign. This study examined how Denver -area citizens perceived their air pollution, responded to their air pollution, and responded to their no-drive campaign. First, I conducted personal interviews of twenty Denver air pollution decision-makers to ascertain their perceptions and definitions of Denver's air pollution problem. Second, I created a theoretical model of environmental perception and behavioral response to air pollution. Third, I conducted a telephone survey of 500 Denver-area residents to examine the usefulness of the model. By segmenting a sample of 500 Denver-area residents via a modified values and lifestyles (VALS) technique included in a telephone survey, the perceptions and behaviors of residents fell into a clear pattern. This values and lifestyles pattern coincided with a conventional innovation-adoption pattern, including innovators, the bandwagon, and laggards. Thus, the research determined the population's perceptions and behavioral responses to their air pollution. The research also pointed a direction for Denver's air pollution decision-makers to follow in order to reduce use of the gasoline-powered automobile. And, for those interested in encouraging public acceptance of ecological sustainability, it suggested application of the VALS technique for reaching the public.

Indoor Air Pollution in Non Ac Passenger Bus

NASA Astrophysics Data System (ADS)

El Husna, Iksiroh; Unzilatirrizqi, Rizal D. Yan El; Karyanto, Yudi; Sunoko, Henna R.

2018-02-01

Passenger buses have been one of favorite means of transportation in Indonesia due to its affordability and flexibility. Intensity of human activities during the trip in the buses have a potential of causing indoor air pollution (polusi udara dalam ruang; PUDR). The indoor air pollution has an impact of 1000-time bigger than outdoor air pollution (polusi udara luar ruang; PULR) on lung. This study aimed to find out indoor air pollution rate of non air conditioned buses using an approach to biological agent pollutant source. The study applied an analysis restricted to microorganisms persistence as one of the sources of the indoor air pollution. The media were placed in different parts of the non AC buses. This study revealed that fungs were found in the non AC buses. They became contaminants and developed pathogenic bacteria that caused air pollution.

Air Pollution Prevention and Control Policy in China.

PubMed

Huang, Cunrui; Wang, Qiong; Wang, Suhan; Ren, Meng; Ma, Rui; He, Yiling

2017-01-01

With rapid urbanization and development of transport infrastructure, air pollution caused by multiple-pollutant emissions and vehicle exhaust has been aggravated year by year in China. In order to improve air quality, the Chinese authorities have taken a series of actions to control air pollution emission load within a permissible range. However, although China has made positive progress on tackling air pollution, these actions have not kept up with its economy growth and fossil-fuel use. The traditional single-pollutant approach is far from enough in China now, and in the near future, air pollution control strategies should move in the direction of the multiple-pollutant approach. In addition, undesirable air quality is usually linked with the combination of high emissions and adverse weather conditions. However, few studies have been done on the influence of climate change on atmospheric chemistry in the global perspective. Available evidence suggested that climate change is likely to exacerbate certain kinds of air pollutants including ozone and smoke from wildfires. This has become a major public health problem because the interactions of global climate change, urban heat islands, and air pollution have adverse effects on human health. In this chapter, we first review the past and current circumstances of China's responses to air pollution. Then we discuss the control challenges and future options for a better air quality in China. Finally, we begin to unravel links between air pollution and climate change, providing new opportunities for integrated research and actions in China.

Mapping urban environmental noise: a land use regression method.

PubMed

Xie, Dan; Liu, Yi; Chen, Jining

2011-09-01

Forecasting and preventing urban noise pollution are major challenges in urban environmental management. Most existing efforts, including experiment-based models, statistical models, and noise mapping, however, have limited capacity to explain the association between urban growth and corresponding noise change. Therefore, these conventional methods can hardly forecast urban noise at a given outlook of development layout. This paper, for the first time, introduces a land use regression method, which has been applied for simulating urban air quality for a decade, to construct an urban noise model (LUNOS) in Dalian Municipality, Northwest China. The LUNOS model describes noise as a dependent variable of surrounding various land areas via a regressive function. The results suggest that a linear model performs better in fitting monitoring data, and there is no significant difference of the LUNOS's outputs when applied to different spatial scales. As the LUNOS facilitates a better understanding of the association between land use and urban environmental noise in comparison to conventional methods, it can be regarded as a promising tool for noise prediction for planning purposes and aid smart decision-making.

Clinical effects of air pollution on the central nervous system; a review.

PubMed

Babadjouni, Robin M; Hodis, Drew M; Radwanski, Ryan; Durazo, Ramon; Patel, Arati; Liu, Qinghai; Mack, William J

2017-09-01

The purpose of this review is to describe recent clinical and epidemiological studies examining the adverse effects of urban air pollution on the central nervous system (CNS). Air pollution and particulate matter (PM) are associated with neuroinflammation and reactive oxygen species (ROS). These processes affect multiple CNS pathways. The conceptual framework of this review focuses on adverse effects of air pollution with respect to neurocognition, white matter disease, stroke, and carotid artery disease. Both children and older individuals exposed to air pollution exhibit signs of cognitive dysfunction. However, evidence on middle-aged cohorts is lacking. White matter injury secondary to air pollution exposure is a putative mechanism for neurocognitive decline. Air pollution is associated with exacerbations of neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. Increases in stroke incidences and mortalities are seen in the setting of air pollution exposure and CNS pathology is robust. Large populations living in highly polluted environments are at risk. This review aims to outline current knowledge of air pollution exposure effects on neurological health. Copyright © 2017 Elsevier Ltd. All rights reserved.

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Treesearch

S. P. Urbanski; J. M. Salmon; B. L. Nordgren; W. M. Hao

2009-01-01

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for...

Combination of synoptical-analogous and dynamical methods to increase skill score of monthly air temperature forecasts over Northern Eurasia

NASA Astrophysics Data System (ADS)

Khan, Valentina; Tscepelev, Valery; Vilfand, Roman; Kulikova, Irina; Kruglova, Ekaterina; Tischenko, Vladimir

2016-04-01

Long-range forecasts at monthly-seasonal time scale are in great demand of socio-economic sectors for exploiting climate-related risks and opportunities. At the same time, the quality of long-range forecasts is not fully responding to user application necessities. Different approaches, including combination of different prognostic models, are used in forecast centers to increase the prediction skill for specific regions and globally. In the present study, two forecasting methods are considered which are exploited in operational practice of Hydrometeorological Center of Russia. One of them is synoptical-analogous method of forecasting of surface air temperature at monthly scale. Another one is dynamical system based on the global semi-Lagrangian model SL-AV, developed in collaboration of Institute of Numerical Mathematics and Hydrometeorological Centre of Russia. The seasonal version of this model has been used to issue global and regional forecasts at monthly-seasonal time scales. This study presents results of the evaluation of surface air temperature forecasts generated with using above mentioned synoptical-statistical and dynamical models, and their combination to potentially increase skill score over Northern Eurasia. The test sample of operational forecasts is encompassing period from 2010 through 2015. The seasonal and interannual variability of skill scores of these methods has been discussed. It was noticed that the quality of all forecasts is highly dependent on the inertia of macro-circulation processes. The skill scores of forecasts are decreasing during significant alterations of synoptical fields for both dynamical and empirical schemes. Procedure of combination of forecasts from different methods, in some cases, has demonstrated its effectiveness. For this study the support has been provided by Grant of Russian Science Foundation (№14-37-00053).

40 CFR 52.274 - California air pollution emergency plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 3 2013-07-01 2013-07-01 false California air pollution emergency plan... pollution emergency plan. (a) Since the California Air Pollution Emergency Plan does not provide complete... District (SCAQMD). (2) Sacramento County Air Pollution Control District. (3) Monterey Bay Unified APCD...

40 CFR 52.274 - California air pollution emergency plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 3 2012-07-01 2012-07-01 false California air pollution emergency plan... pollution emergency plan. (a) Since the California Air Pollution Emergency Plan does not provide complete... District (SCAQMD). (2) Sacramento County Air Pollution Control District. (3) Monterey Bay Unified APCD...

40 CFR 52.274 - California air pollution emergency plan.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 3 2014-07-01 2014-07-01 false California air pollution emergency plan... pollution emergency plan. (a) Since the California Air Pollution Emergency Plan does not provide complete... District (SCAQMD). (2) Sacramento County Air Pollution Control District. (3) Monterey Bay Unified APCD...

40 CFR 52.274 - California air pollution emergency plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 3 2011-07-01 2011-07-01 false California air pollution emergency plan... pollution emergency plan. (a) Since the California Air Pollution Emergency Plan does not provide complete... District (SCAQMD). (2) Sacramento County Air Pollution Control District. (3) Monterey Bay Unified APCD...

76 FR 30025 - Revisions to the California State Implementation Plan, Placer County Air Pollution Control...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-24

... the California State Implementation Plan, Placer County Air Pollution Control District and Ventura County Air Pollution Control District AGENCY: Environmental Protection Agency (EPA). ACTION: Direct final... Pollution Control District (PCAPCD) and Ventura County Air Pollution Control District (VCAPCD) portion of...

77 FR 214 - Revisions to the California State Implementation Plan, San Joaquin Valley Unified Air Pollution...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-04

... the California State Implementation Plan, San Joaquin Valley Unified Air Pollution Control District... approval of revisions to the San Joaquin Valley Air Pollution Control District (SJVUAPCD) portion of the... used by the California Air Resources Board and air districts for evaluating air pollution control...