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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]

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: 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: 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]

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]

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]