Assessment of Traffic-Related Air Pollution: Case Study of Pregnant Women in South Texas

Population groups vulnerable to adverse effects of traffic-related air pollution correspond to children, pregnant women and elderly. Despite these effects, literature is limited in terms of studies focusing on these groups and a reason often cited is the limited information on their mobility important for exposure assessment. The current study presents a method for assessing individual-level exposure to traffic-related air pollution by integrating mobility patterns tracked by global positioning system (GPS) devices with dynamics of air pollutant concentrations. The study is based on a pool of 17 pregnant women residing in Hidalgo County, Texas. The traffic-related particulate matter with diameter of less than 2.5 micrometer (PM2.5) emissions and air pollutant concentrations are predicted using MOVES and AERMOD models, respectively. The daily average traffic-related PM2.5 concentration was found to be 0.32 µg/m3, with the highest concentration observed in transit (0.56 µg/m3), followed by indoors (0.29 µg/m3), and outdoor (0.26 µg/m3) microenvironment. The obtained exposure levels exhibited considerable variation between time periods, with higher levels during peak commuting periods, close to the US–Mexico border region and lower levels observed during midday periods. The study also assessed if there is any difference between traffic-related dynamic exposure, based on time-varying mobility patterns, and static exposure, based solely on residential locations, and found a difference of 9%, which could be attributed to the participants’ activity patterns being focused mostly indoors

Near-Road Traffic-Related Air Pollution: Resuspended PM2.5 from Highways and Arterials

Recent studies suggest that the transportation sector is a major contributor to fine particulate matter (PM2.5) in urban areas. A growing body of literature indicates PM2.5 exposure can lead to adverse health effects, and that PM2.5 concentrations are often elevated close to roadways. The transportation sector produces PM2.5 emissions from combustion, brake wear, tire wear, and resuspended dust. Traffic-related resuspended dust is particulate matter, previously deposited on the surface of roadways that becomes resuspended into the air by the movement of traffic. The objective of this study was to use regulatory guidelines to model the contribution of resuspended dust to near-road traffic-related PM2.5 concentrations. The U.S. Environmental Protection Agency (EPA) guidelines for quantitative hotspot analysis were used to predict traffic-related PM2.5 concentrations for a small network in Dallas, Texas. Results show that the inclusion of resuspended dust in the emission and dispersion modeling chain increases prediction of near-road PM2.5 concentrations by up to 74%. The results also suggest elevated PM2.5 concentrations near arterial roads. Our results are discussed in the context of human exposure to traffic-related air pollution

Near-Road Traffic-Related Air Pollution: Monitoring, Modeling, and Data Analysis

Long-term exposure to air pollution has been shown to be associated to many different adverse health outcomes. Vehicles considerably contribute to pollutant emissions in urban areas. Air quality modeling is being widely used for research and regulatory studies to predict future traffic-related air pollution or in cases where air quality monitoring data are not available. In this study, near-road traffic-related particulate matter (PM) data were investigated. The application of dispersion modeling for research and regulatory analysis was explored. In addition, the effect of influential variables on traffic-related dispersion modeling was investigated, followed by a comprehensive evaluation of AERMOD, developed by the United States Environmental Protection Agency for regulatory air dispersion modeling. Traffic contribution to a 24-hour PM2.5 increment in the near-road environment was estimated to be about 27% of background concentration. A multiple linear regression model can explain 85% of the variability of 24-hour PM2.5 concentrations in the near-road environment and shows improvement in near-road concentration predictions when accounting for wind speed and wind direction. In this study, dispersion modeling was used to perform a worst-case particulate matter hot-spot scenario analysis specific to El Paso, Texas. In addition, a novel application of dispersion modeling was developed to assess traffic-related air pollution exposure by integrating mobility patterns tracked by Global Positioning System (GPS) devices. The results exhibit a significant variation of traffic-related air pollution exposure across different time periods and spatial locations that cannot be captured by simpler metrics such as traffic density and near-road distance or even modeling air pollution without accounting for mobility. The sensitivity of traffic-related air pollution dispersion modeling to a variety of input sets was investigated. Results show a significant effect of meteorological variables on near-road traffic-related air pollution. As such, annual average pollutant concentrations dispersed during nighttime conditions were shown to be higher by 100% to 120% compared to daytime periods for identical emission rates. This relative difference increased to 150% to 200% for rural land-use conditions. Emission and dispersion modeling based on regulatory guidelines was conducted to evaluate the effect of emission rate variation (due to the inclusion of resuspended dust in traffic-related PM2.5 emissions) on near-road traffic-related air pollution. Results show a nonlinearity between emission rates and concentrations due to the effect of meteorological variables and the geometry of the network, which emphasizes the importance of dispersion modeling for traffic-related air quality analysis. Results show the increase in PM2.5 emission rates due to resuspended dust inclusion on arterials range between 39% and 108% and between 16% and 19% on highways. This increase in emission rates is associated with an overall increase in near-road traffic-related PM2.5 concentrations by between 49% and 74%, an important percentage range from an exposure and health point of view. Sensitivity analysis of dispersion modeling to source and dispersion parameters shows the importance of parametrization when assessing near-road traffic-related exposure. A comprehensive evaluation of AERMOD shows the necessity of using volume representation of vehicular emission sources. Results show general weaknesses of area representation of emission sources in predicting concentrations at upwind locations, at higher elevations, and in cases of low wind speed