United States Department of Transportation
i
Assessing Children’s Spatiotemporal Exposures to Transportation Pollutants in Near-Road Communities
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2019-10-31
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[PDF-23.13 MB]
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Edition:1/1/2018-7/31/2019
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Abstract:Traffic-related air pollution has a profound impact on human health especially for residents living in near-road communities which are constantly exposed these air pollutants. A near-road community is expected to observe significant spatial and temporal variations in pollutant concentrations, as air pollution resulting from emissions from major highways decreases rapidly from the highway. This research conducted on-site traffic and air quality measurements on four critical transportations related air pollutants, PM2.5, PM10, NO2, O3, as well as emission and air dispersion modeling of transportation emission impacts in a near-road community. Using numerical models provided by the EPA, integrated with field measurements of both traffic and air quality, this research developed spatial and temporal pollutant concentration variation patterns in a near-road community using MOVES and AERMOD, EPA emissions and dispersion models. It was observed that modeled-to-monitored comparisons show that air quality impact in near-road communities resulting from traffic-related emissions are dominated by regional background concentrations. Additionally, the AERMOD predictions rendered highest concentration estimates at locations where the traffic volume is the highest and downwind of the prevailing winds. However, impacts of the traffic emissions on the air quality subside rapidly with increasing distance away from the highway, at around 200 meters. This research also apportioned the differences in exposure concentrations to background concentrations and those contributed from major highways. In the near-road community studied, traffic emissions from the highway were 4.8 times higher than the contributions made by local arterial roads. For better transportation air quality impact assessments, higher quality traffic data such as time-specific traffic volume and fleet information as well as meteorological data such as site-specific surface meteorological could help yield more accurate concentration predictions.
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