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Switchgrass is a promising bioenergy feedstock, but industrial-scale production may lead to negative environmental effects. This study considers one such potential consequence: the life cycle monetized damages to human health from air pollution. We estimate increases in mortality from long-term exposure to fine particulate matter (PM 2.5 ), which is emitted directly (“primary PM 2.5 ”) and forms in the atmosphere (“secondary PM 2.5 ”) from precursors of nitrogen oxides (NO x ), sulfur oxides (SO x ), ammonia (NH 3 ), and volatile organic compounds (VOCs). Changes in atmospheric concentrations of PM 2.5 (primary + secondary) from on-site production and supporting supply chain activities are considered at 2694 locations (counties in the Central and Eastern US), for two biomass yields (9 and 20 Mg ha −1 ), three nitrogen fertilizer rates (50, 100, and 150 kg ha −1 ), and two nitrogen fertilizer types (urea and urea ammonium nitrate). Results indicate that on-site processes dominate life-cycle emissions of NH 3 , NO x , primary PM 2.5 , and VOCs, whereas SO x is primarily emitted in upstream supply chain processes. Total air quality impacts of switchgrass production, which are dominated by NH 3 emissions from fertilizer application, range widely depending on location, from 2 to 553 $ Mg −1 (mean: 45) of dry switchgrass at a biomass yield of 20 Mg ha −1 and fertilizer application of 100 kg ha −1 N applied as urea. Switching to urea ammonium nitrate solution lowers damages to 2 to 329 $ Mg −1 (mean: 28). This work points to human health damage from air pollution as a potentially large social cost from switchgrass production and suggests means of mitigating that impact via strategic geographical deployment and management. Furthermore, by distinguishing the origin of atmospheric emissions, this paper advances the current emerging literature on ecosystem services and disservices from agricultural and bioenergy systems. [ABSTRACT FROM AUTHOR]

In February 2000, a detailed measurement campaign was conducted over a 9-day period within a mercury (Hg) cell chlor-alkali plant in the southeastern United States (US). The principal focus of this study was to determine the fugitive (non-ducted) airborne Hg emissions from the main production equipment located in the “cell building” during extended period operations, including maintenance events. Other Hg monitoring conducted in and around the plant also included surveys of process areas surrounding the cell building, measurement of Hg fluxes from soil and other exposed surfaces, and speciated Hg measurements of ambient air both “upwind” and “downwind” of the production area as well as the determination of Hg contained in output products and wastes. The study was a collaborative effort among scientists from the US Environmental Protection Agency, the US Department of Energy''s Oak Ridge National Laboratory, and the University of Michigan. This paper provides a description of the facility, the rationale and design of the study, and an introduction to companion papers containing the observational data. [Copyright &y& Elsevier]

Abstract: Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or “land use” regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality by land use regression and the spatial variation in ambient air quality not explained by this regression should be allowed to vary across the continent due to the large scale heterogeneity in topography, climate, and sources of air pollution. This paper introduces a regionalized national universal kriging model for annual average fine particulate matter (PM2.5) monitoring data across the U.S. To take full advantage of an extensive database of land use covariates we chose to use the method of Partial Least Squares, rather than variable selection, for the regression component of the model (the “universal” in “universal kriging”) with regression coefficients and residual variogram models allowed to vary across three regions defined as West Coast, Mountain West, and East. We demonstrate a very high level of cross-validated accuracy of prediction with an overall R 2 of 0.88 and well-calibrated predictive intervals. In accord with the spatially varying characteristics of PM2.5 on a national scale and differing kriging smoothness parameters, the accuracy of the prediction varies by region with predictive intervals being notably wider in the West Coast and Mountain West in contrast to the East. [Copyright &y& Elsevier]

Abstract: Indoor air pollutants at high concentrations in poultry houses can potentially affect workers'' health, and animal welfare and productivity. This paper presents research results of a 2-year continuous monitoring of ammonia (NH3), carbon dioxide (CO2), hydrogen sulfide (H2S), and particulate matter (PM) concentrations from to date the most comprehensive study on a single farm in two 180,000-bird high-rise (HR) and two 200,000-bird manure-belt (MB) layer hen houses located in Indiana, USA. Air was sampled at ventilation fans of the mechanically-ventilated houses. Concentrations of NH3 and CO2 were measured with photoacoustic multi-gas monitors. Concentrations of H2S and PM10 were monitored with pulsed fluorescence analyzers and Tapered Element Oscillating Microbalances (TEOM), respectively. The 2-year mean ± standard deviation concentrations at ventilation fans of the four layer hen houses were 48.9 ± 39 and 51.9 ± 40.7 ppm in HR, and 13.3 ± 9.1 and 12.9 ± 10.5 ppm in MB for NH3; 26.4 ± 17.6 and 24.9 ± 19 ppb in HR, 40.0 ± 21.1 and 41.2 ± 31.5 ppb in MB for H2S; 1755 ± 848 and 1804 ± 887 ppm in HR, and 2295 ± 871 and 2285 ± 946 ppm in MB for CO2; and 540 ± 303 and 552 ± 338 μg m−3 in HR, and 415 ± 428 and 761 ± 661 μg m−3 in MB for PM10. Compared with the MB houses, concentrations of the HR houses were higher for NH3, and lower for CO2, H2S, and PM10 (P < 0.05). High concentrations of NH3 detected in winter represent potential challenges to workers'' health and animal welfare. Variations in pollutant concentrations at the exhaust fans were affected by outdoor temperature, ventilation, bird condition, and farm operation. A new weekly variation, characterized by significantly lower PM10 concentrations on Sundays, was identified and was related to the weekly schedule of house operational activities. [Copyright &y& Elsevier]

Abstract: Increased use of ethanol in the United States fuel supply will impact emissions and ambient concentrations of greenhouse gases, “criteria” pollutants for which the U. S. EPA sets ambient air quality standards, and a variety of air toxic compounds. This paper focuses on impacts of increased ethanol use on ozone and air toxics under a potential implementation scenario resulting from mandates in the U. S. Energy Independence and Security Act (EISA) of 2007. The assessment of impacts was done for calendar year 2022, when 36 billion gallons of renewable fuels must be used. Impacts were assessed relative to a baseline which assumed ethanol volumes mandated by the first renewable fuels standard promulgated by U. S. EPA in early 2007. This assessment addresses both impacts of increased ethanol use on vehicle and other engine emissions, referred to as “downstream” emissions, and “upstream” impacts, i.e., those connected with fuel production and distribution. Air quality modeling was performed for the continental United States using the Community Multi-scale Air Quality Model (CMAQ), version 4.7. Pollutants included in the assessment were ozone, acetaldehyde, ethanol, formaldehyde, acrolein, benzene, and 1,3-butadiene. Results suggest that increased ethanol use due to EISA in 2022 will adversely increase ozone concentrations over much of the U.S., by as much as 1ppb. However, EISA is projected to improve ozone air quality in a few highly-populated areas that currently have poor air quality. Most of the ozone improvements are due to our assumption of increases in nitrogen oxides (NO x ) in volatile organic compound (VOC)-limited areas. While there are some localized impacts, the EISA renewable fuel standards have relatively little impact on national average ambient concentrations of most air toxics, although ethanol concentrations increase substantially. Significant uncertainties are associated with all results, due to limitations in available data. These uncertainties are discussed in detail. [Copyright &y& Elsevier]

Abstract: An emission pre-processing tool is generally needed to transform a bulk annual emission inventory into gridded, temporal (monthly, weekly, and hourly), and speciated emissions in order to use complex air quality models such as the Community Multi-scale Air Quality (CMAQ) model to assess control strategies, forecast air quality, and investigate pollution formation and transport processes. To support the regional air quality modeling in the Pearl River Delta (PRD), we developed an emission pre-processing system, Sparse Matrix Operator Kernel Emissions-PRD (SMOKE-PRD), based on the U.S. Environmental Protection Agency (USEPA)/SMOKE model. This paper introduces the methods and procedures for adapting the SMOKE model to the PRD regional bulk emissions. These include the compilation of the PRD local source-class clarification codes, the incorporation of the PRD local emission inventory, and the updating of spatial, temporal, and chemical speciation information. The SMOKE-PRD system was evaluated, and a case study on ozone simulation was conducted to demonstrate the applicability of the SMOKE-PRD system. Results show that the model can properly simulate temporal variations, spatial patterns, and peak values of O3 concentrations in the PRD region, suggesting the SMOKE-PRD system was successfully localized and can be used to provide model-ready emissions for regional air quality modeling in the PRD region. This work can be extended for adapting the SMOKE model to process emissions for modeling use in other regions of China. [Copyright &y& Elsevier]

Abstract: The U.S. EPA periodically evaluates ambient concentrations, human exposures, and health risks for 180 hazardous air pollutants plus diesel particulate matter using modeled estimates from the National-Scale Air Toxics Assessment (NATA). NATA publishes estimates at the spatial resolution of U.S. Census tracts, which are subdivisions of a county. These local scale, model-predicted estimates from NATA are used extensively in community-based assessments; however, evaluation of NATA’s ambient concentrations and human exposure estimates against measurement data has been limited to date. This paper compares modeled annual average benzene results from the 2002 NATA with measured results from the 2004 to 2007 Detroit Exposure and Aerosol Research Study (DEARS) as a case study of the quality of NATA results. NATA model estimates support community-scale characterization and assessment. Benzene is particularly important as it was estimated by the 2002 NATA as the largest single air toxic pollutant in terms of cancer risk in the U.S. We found that the average ambient concentrations of benzene predicted by NATA were within 5 percent, on average, of the 24-h integrated average ambient concentrations measured in DEARS. The NATA human exposure estimates, which include only outdoor sources for benzene, were, on average, approximately half the measured breathing zone concentrations from DEARS. Our analyses support that the factors driving higher DEARS personal benzene concentrations relative to the NATA predicted exposure values are likely due, at least in part, to indoor sources. This work points to further community-scale modeling research to improve characterizations and assessments of human exposures. [Copyright &y& Elsevier]

Abstract: This paper describes the details of the Atmospheric Model Evaluation Tool (AMET) v1.1 created by scientists in the Atmospheric Modeling and Analysis Division (AMAD) of the U.S. Environmental Protection Agency (EPA). AMET was first developed to evaluate the performance of the 5th Generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological model output and was later extended to include capabilities for evaluating output data from the Community Multiscale Air Quality (CMAQ) model as well. AMET is designed to leverage several open-source software packages that are used in combination to 1) pair the modeled and observed values in time and space, 2) store these paired datasets in an easily accessible and searchable database and 3) access and analyze these data using a statistical package. Through this process, AMET is able to provide a convenient method for evaluating meteorological and air quality model predictions. The use of a searchable, relational database allows the entire dataset to be quickly subset into only those data that are of the most interest for the current analysis, a process that is often tedious and time consuming without the use of a database. In addition to common summary statistics (e.g. RMSE, bias, and correlation), several of the many analysis features available in AMET include scatter plots, time series plots, box plot and spatial plots as part of operational model evaluation. Additionally, several unique analysis functions are also available in AMET, and the system provides a framework within which users may extend the current functionality for their own custom analyses. While AMET was designed to work specifically with MM5, WRF and CMAQ model output, it could easily be modified to work with output data from other meteorological and air quality models. [Copyright &y& Elsevier]

Abstract: This paper compares spatial and seasonal variations and temporal trends in modeled and measured concentrations of sulfur and nitrogen compounds in wet and dry deposition over an 18-year period (1988–2005) over a portion of the northeastern United States. Substantial emissions reduction programs occurred over this time period, including Title IV of the Clean Air Act Amendments of 1990 which primarily resulted in large decreases in sulfur dioxide (SO2) emissions by 1995, and nitrogen oxide (NOx) trading programs which resulted in large decreases in warm season NOx emissions by 2004. Additionally, NOx emissions from mobile sources declined more gradually over this period. The results presented here illustrate the use of both operational and dynamic model evaluation and suggest that the modeling system largely captures the seasonal and long-term changes in sulfur compounds. The modeling system generally captures the long-term trends in nitrogen compounds, but does not reproduce the average seasonal variation or spatial patterns in nitrate. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines the impact of tripled anthropogenic emissions from China and India over the base level (gaseous species and carbonaceous aerosols for 2000) on air quality over the U.S. using the WRF/Chem (Weather Research and Forecasting – Chemistry) model at 1° resolution. WRF/Chem is a state-of-the-science, fully coupled chemistry and meteorology system suitable for simulating the transport and dispersion of pollutants and their impacts. The analyses in this work were focused on MAM (March, April and May). The simulations indicate an extensive area of elevated pollutant concentrations spanning from the Arabian Sea to the Northern Pacific and to the Northern Atlantic. MAM mean contributions from the tripled Asian emissions over the U.S. are found to be: 6–12 ppbv for CO, 1.0–2.5 ppbv for O3, and 0.6–1.6 μg m−3 for PM2.5 on a daily basis. [Copyright &y& Elsevier]

Abstract: 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 (PM2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O3 and PM2.5 forecasts at discrete monitoring locations. The operational surface-level O3 and PM2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O3 and PM2.5 observations obtained from EPA’s AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O3 and PM2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain. The results show that the real-time KF bias-adjusted forecasts for both O3 and PM2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O3 and PM2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O3 and daily mean PM2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008. [Copyright &y& Elsevier]

Abstract: This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O3 concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA''s AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O3 concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2ppb was 4.2ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50ppb. In fact the model tends to under-predict when concentrations exceed 70ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0ppb (Normalized Mean Error (NME)=20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions. [Copyright &y& Elsevier]

Windblown dust is known to impede visibility, deteriorate air quality and modify the radiation budget. Arid and semiarid areas with unpaved and unvegetated land cover are particularly prone to windblown dust, which is often attributed to high particulate matter (PM) pollution in such areas. Yet, windblown dust is poorly represented in existing regulatory air quality models. In a study by the authors on modeling episodic high PM events along the US/Mexico border using the state-of-the-art CMAQ/MM5/SMOKE air quality modeling system [Choi, Y.-J., Hyde, P., Fernando, H.J.S., 2006. Modeling of episodic particulate matter events using a 3D air quality model with fine grid: applications to a pair of cities in the US/Mexico border. Atmospheric Environment 40, 5181–5201], some of the observed PM10 NAAQS exceedances were inferred as due to windblown dust, but the modeling system was incapable of dealing with time-dependent episodic dust entrainment during high wind periods. In this paper, a time-dependent entrainment parameterization for windblown dust is implemented in the CMAQ/MM5/SMOKE modeling system with the hope of improving PM predictions. An approach for realizing windblown dust emission flux for each grid cell over the study domain on an hourly basis, which accounts for the influence of factors such as soil moisture content, atmospheric stability and wind speed, is presented in detail. Comparison of model predictions with observational data taken at a pair of US/Mexico border towns shows a clear improvement of model performance upon implementation of the dust emission flux parameterization. [Copyright &y& Elsevier]

We estimate the contributions from biomass burning (summer wildfires, other fires, residential biofuel, and industrial biofuel) to seasonal and annual aerosol concentrations in the United States. Our approach is to use total carbonaceous (TC) and non-soil potassium (ns-K) aerosol mass concentrations for 2001–2004 from the nationwide IMPROVE network of surface sites, together with satellite fire data. We find that summer wildfires largely drive the observed interannual variability of TC aerosol concentrations in the United States. TC/ns-K mass enhancement ratios from fires range from 10 for grassland and shrub fires in the south to 130 for forest fires in the north. The resulting summer wildfire contributions to annual TC aerosol concentrations for 2001–2004 are 0.26μgCm−3 in the west and 0.14μgCm−3 in the east; Canadian fires are a major contributor in the east. Non-summer wildfires and prescribed burns contribute on an annual mean basis 0.27 and 0.31μgCm−3 in the west and the east, highest in the southeast because of prescribed burning. Residential biofuel is a large contributor in the northeast with annual mean concentration of up to 2.2μgCm−3 in Maine. Industrial biofuel (mainly paper and pulp mills) contributes up to 0.3μgCm−3 in the southeast. Total annual mean fine aerosol concentrations from biomass burning average 1.2 and 1.6μgm−3 in the west and east, respectively, contributing about 50% of observed annual mean TC concentrations in both regions and accounting for 30% (west) and 20% (east) of total observed fine aerosol concentrations. Our analysis supports bottom-up source estimates for the contiguous United States of 0.7–0.9TgCyr−1 from open fires (climatological) and 0.4TgCyr−1 from biofuel use. Biomass burning is thus an important contributor to US air quality degradation, which is likely to grow in the future. [Copyright &y& Elsevier]

The United States Environmental Protection Agency issues periodic reports that describe air quality trends in the US. For some pollutants, such as ozone, both observed and meteorologically adjusted trends are displayed. This paper describes an improved statistical methodology for meteorologically adjusting ozone trends as well as characterizes the relationships between individual meteorological parameters and ozone. A generalized linear model that accommodates the nonlinear effects of the meteorological variables was fit to data collected for 39 major eastern US urban areas. Overall, the model performs very well, yielding R 2 statistics as high as 0.80. The analysis confirms that ozone is generally increasing with increasing temperature and decreasing with increasing relative humidity. Examination of the spatial gradients of these responses show that the effect of temperature on ozone is most pronounced in the north while the opposite is true of relative humidity. By including HYSPLIT-derived transport wind direction and distance in the model, it is shown that the largest incremental impact of wind direction on ozone occurs along the periphery of the study domain, which encompasses major NO x emission sources. [Copyright &y& Elsevier]

Abstract: This paper examines the use of Moderate Resolution Imaging Spectroradiometer (MODIS) observed active fire data (pixel counts) to refine the National Emissions Inventory (NEI) fire emission estimates for major wildfire events. This study was motivated by the extremely limited information available for many years of the United States Environmental Protection Agency (US EPA) NEI about the specific location and timing of major fire events. The MODIS fire data provide twice-daily snapshots of the locations and breadth of fires, which can be helpful for identifying major wildfires that typically persist for a minimum of several days. A major wildfire in Mallory Swamp, FL, is used here as a case study to test a reallocation approach for temporally and spatially distributing the state-level fire emissions based on the MODIS fire data. Community Multiscale Air Quality (CMAQ) model simulations using these reallocated emissions are then compared with another simulation based on the original NEI fire emissions. We compare total carbon (TC) predictions from these CMAQ simulations against observations from the Inter-agency Monitoring of Protected Visual Environments (IMPROVE) surface network. Comparisons at three IMPROVE sites demonstrate substantial improvements in the temporal variability and overall correlation for TC predictions when the MODIS fire data is used to refine the fire emission estimates. These results suggest that if limited information is available about the spatial and temporal extent of a major wildfire fire, remotely sensed fire data can be a useful surrogate for developing the fire emissions estimates for air quality modeling purposes. [Copyright &y& Elsevier]

Abstract: In this paper, we describe two advanced statistical techniques suited to address the following questions: which source categories of emissions affect given areas and where do these source categories come from? A source category is defined as a combination of volatile organic compounds (VOCs) associated with a specific industrial process. A discussion of the positive matrix factorization (PMF) multivariate receptor model is presented, and this PMF technique applied to hourly average concentrations of VOCs measured at five Photochemical Assessment Monitoring Stations (PAMS) located near the emission-rich Houston Ship Channel region in Texas. The observations were made between June and October 2003, and the PMF analysis was limited to nighttime measurements (21:00–06:00 CDT) to remove the complexity of photochemical processing and associated changes in the concentrations of primary and secondary VOCs. Six to eight VOCs source categories were identified for the five Ship Channel sites. Specific geographic areas associated with each source category were identified through the use of conditional probability functions that identify source regions when superimposed on maps of VOC emissions. [Copyright &y& Elsevier]

Abstract: Exposure to carbonyls (aldehydes and ketones) can produce adverse health effects. It is known that various sources of carbonyls are often present inside residences but little is known about their indoor source strengths. In the present paper, we used a database established in the relationships of indoor, outdoor, and personal air (RIOPA) study to estimate indoor source strengths of 10 carbonyls and outdoor contributions to measured indoor concentrations of these carbonyls. We applied a mass balance model to analyze paired indoor and outdoor carbonyl concentrations simultaneously measured in 234 RIOPA homes. Among all the measured carbonyls, formaldehyde, and acetaldehyde had the strongest indoor source strengths with the estimated median values of 3.9 and 2.6mgh−1, respectively. Hexaldehyde also had large indoor source strengths with a median of 0.56mgh−1. Acetone had the largest variations in indoor source strengths ranging from undetected to 14mgh−1. The medians of the estimated indoor source strengths were 0.15mgh−1 for propionaldehyde, 0.18mgh−1 for glyoxal, 0.17mgh−1 for methylglyoxal, and 0.23mgh−1 for benzaldehyde. Acrolein and crotonaldehyde had the weakest indoor source strengths with no indoor sources detected in the majority of the RIOPA homes that were selected to have only nonsmoker residents. Consistent with the indoor source strength results, our estimated outdoor contributions to indoor concentrations were low for formaldehyde. In contrast, more than 90% of the indoor concentrations for acrolein and crotonaldehyde were from outdoor sources. The outdoor contributions to indoor concentrations for acetone, propionaldehyde, benzaldehyde, glyoxal, and methylglyoxal ranged from 10 to 90% across the RIOPA homes, suggesting that both indoor and outdoor sources had contributions to indoor concentrations of these compounds. [Copyright &y& Elsevier]

Abstract: Cities in the Southwestern United States (Southwest) are often close to violating tropospheric ozone (ozone) and particulate matter (PM) federal air quality standards, and local climate and weather conditions play a large part in determining whether or not pollutant levels exceed the federally mandated limits and by what magnitude. The Kolmogorov–Zurbenko (KZ) filter method has been used in a number of studies in the Eastern United States to determine meteorological controls on ozone concentrations and to separate out those effects in order to examine underlying trends. The Southwest, however, experiences a different climate regime than other parts of the country, and atmospheric controls on air quality in the region have not been examined in this manner. This paper determines which meteorological variables most influence ozone and PM in the Southwest and examines patterns of underlying pollutant trends due to emissions. Ozone and PM data were analyzed over the time period 1990–2003 for the Southwest''s five major metropolitan areas: Albuquerque, NM; El Paso, TX; Las Vegas, NV; Phoenix, AZ; and Tucson, AZ. Results indicate that temperature and mixing height most strongly influence ozone conditions, while moisture levels (particularly relative humidity) are the strongest predictors of PM concentrations in all five cities examined. Meteorological variability typically accounts for 40–70% of ozone variability and 20–50% of PM variability. Long-term ozone trends are highly variable, but records from most stations indicate increasing concentrations over the last decade. Long-term trends in PM concentrations were relatively flat in all five cities analyzed but contained coincident extremes unrelated to meteorology. [Copyright &y& Elsevier]

To study the potential impacts of climate change on air quality and public health over the eastern United States, a coupled global/regional-scale modeling system consisting of the NASA-Goddard Institute for Space Studies Atmosphere–Ocean model, the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) model for air quality has been developed. Evaluation results of the modeling system used to simulate climate and ozone air quality over the eastern United States during the five summers of 1993–1997 are presented in this paper. The results indicate that MM5 and CMAQ capture interannual and synoptic-scale variability present in surface temperature and ozone observations in the current climate, while the magnitude of fluctuations on shorter time scales is underestimated. A comparison of observed and predicted spatial patterns of daily maximum ozone concentrations shows best performance in predicting patterns for average and above-average ozone concentrations. The frequency distributions of the duration of extreme heat and ozone events show similar features for both model predictions and observations. Finally, application of a synoptic map-typing procedure reveals that the MM5/CMAQ system succeeded in simulating the average ozone concentrations associated with several frequent pressure patterns, indicating that the effects of synoptic-scale meteorology on ozone concentrations are captured by the modeling system. It is concluded that the GCM/MM5/CMAQ system is a suitable tool for the simulation of summertime surface temperature and ozone air quality conditions over the eastern United States in the present climate. [Copyright &y& Elsevier]

Current modeling methods are inadequate for producing gridded mobile source inventories suitable for simulating fine-scale changes in emissions for transportation planning or regulatory control purposes. In this paper, we present a new model that more precisely simulates small-scale emission changes that can arise from, for example, implementation of traffic control measures in transportation networks. The new model, UCDrive, directly interfaces with most travel demand models and with California''s regulatory emission factor model EMFAC2001/2002. The model improves the sensitivity of emission estimates to variations in vehicle speeds and uses a theoretically consistent approach to spatially disaggregating mobile emissions. To demonstrate application of the model we use the Sacramento area network to perform a comparison study. We show that regional emissions with the new model replicate regulatory inventories, which is critical for using the model for regulatory purposes. We also show that the new model''s results better replicate regulatory totals than those derived with a frequently used gridded emissions inventory model. The new model should improve the ability to better simulate changes in mobile emissions from transportation projects and help to bridge the gap between gridded mobile emission inventories and regional mobile emission inventories. [Copyright &y& Elsevier]

Public education campaigns, to be successful, must repeat simple, consistent messages over time, using various sources and media. During the 2000, 2001, and 2002 ozone seasons, the North Central Texas Council of Governments (NCTCOG) employed a four-pronged, multimedia approach to educate the public about air quality in the Dallas/Fort Worth (DFW) region. The four-pronged approach included several varied information sources:

In 2020, most countries around the world have observed varying degrees of public lockdown measures to mitigate the transmission of SARS-CoV-2. As an unintended consequence of reduced transportation and industrial activities, air quality has dramatically improved in many major cities around the world. In this paper, we analyze the environmental impact of the lockdown measures on P M 2.5 concentration levels in 48 core-based statistical areas (CBSA) of the United States, during the pre and post-lockdown period of January to June 2020. We model the effect of lockdown on the P M 2.5 concentration in different CBSAs while adjusting for various meteorological factors like temperature, wind-speed, precipitation and snow. Linear mixed effects models and functional regression methods with random intercepts are employed to capture the heterogeneity of the effect across different regions. Our analysis shows there is a statistically significant reduction in levels of P M 2.5 across most of the regions during the lock-down period, although interestingly, this effect is not uniform across all the CBSAs under consideration. • We examine the impact of the COVID-19 lockdown measures on the PM2.5 concentration level in USA. • We also investigate if there is any spatio-temporal heterogeneity in the effect of lockdown on air quality. • We use Linear mixed effects models and functional regression methods as primary tools of our analysis. • We adjust for local meteorological variables to remove their confounding effect on PM2.5 concentration. [ABSTRACT FROM AUTHOR]

To effectively control and prevent air pollution, it is necessary to study the influential factors of air quality. A number of previous studies have explored the relationships between air pollution and related factors. However, the methods currently used either cannot well address the multicollinearity problem or fail to explain the importance of the influential factors. Moreover, most of the existing literature limited their studied area in a city or a small region and studied factors in one aspect. There is a lack of studies that analyze the influential factors from the perspective of a country or take into consideration multiple variables. To fill the research gap, this paper proposes a multivariate analysis in the national scale to investigate the most important factors of air quality. In order to study as much influential factors as possible, 171 features ranging from environmental, demographical, economic, meteorological, and energy, were collected and analyzed. To tackle such a "big data" problem, a non-linear machine learning algorithm namely Extreme Gradient Boosting (XGBoost) is utilized to model the relationship and measure the variable importance. Geographical Information System (GIS) is employed to preprocess the diversified variables and visualize the results. Performance of XGBoost is compared with other models and its parameters are tuned using Bayesian Optimization. Experimental results of a case study in the U.S. show that our methodology framework can effectively uncover the important factors of air quality. Six kinds of factors are found to have the largest impact on air quality. Practical suggestions are also proposed from the six aspects to control and prevent air pollution. • This study analyzed the impact factors on AQI of different counties in the U.S. • 171 variables were considered to study the most influential ones. • XGBoost outperforms other commonly seen algorithms in modeling. • Six kinds of factors are found to be most influential on AQI on a national scale. [ABSTRACT FROM AUTHOR]