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Spatial patterns of mobile source particulate matter emissions-to-exposure relationships across the United States
Institution:1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China;2. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China;3. Pacific Northwest National Laboratory, Richland, WA 99352, USA;4. Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA;5. CICERO Center for International Climate Research, N-0318 Oslo, Norway;6. China National Environmental Monitoring Centre, Beijing 100012, China
Abstract:Assessing the public health benefits from air pollution control measures is assisted by understanding the relationship between mobile source emissions and subsequent fine particulate matter (PM2.5) exposure. Since this relationship varies by location, we characterized its magnitude and geographic distribution using the intake fraction (iF) concept. We considered emissions of primary PM2.5 as well as particle precursors SO2 and NOx from each of 3080 counties in the US. We modeled the relationship between these emissions and total US population exposure to PM2.5, making use of a source–receptor matrix developed for health risk assessment. For primary PM2.5, we found a median iF of 1.2 per million, with a range of 0.12–25. Half of the total exposure was reached by a median distance of 150 km from the county where mobile source emissions originated, though this spatial extent varied across counties from within the county borders to 1800 km away. For secondary ammonium sulfate from SO2 emissions, the median iF was 0.41 per million (range: 0.050–10), versus 0.068 per million for secondary ammonium nitrate from NOx emissions (range: 0.00092–1.3). The median distance to half of the total exposure was greater for secondary PM2.5 (450 km for sulfate, 390 km for nitrate). Regression analyses using exhaustive population predictors explained much of the variation in primary PM2.5 iF (R2=0.83) as well as secondary sulfate and nitrate iF (R2=0.74 and 0.60), with greater near-source contribution for primary than for secondary PM2.5. We conclude that long-range dispersion models with coarse geographic resolution are appropriate for risk assessments of secondary PM2.5 or primary PM2.5 emitted from mobile sources in rural areas, but that more resolved dispersion models are warranted for primary PM2.5 in urban areas due to the substantial contribution of near-source populations.
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