Characterizing traffic-related air pollutant dynamics in a near-road environment
Moutinho, Jennifer Lynn
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Detailed measurements and dispersion modeling were conducted to develop more accurate integrated metrics to assess exposure to potentially high pollutant levels of primary traffic emissions. A 13-week intensive sampling campaign was conducted at six monitoring sites surrounding one of the busiest highway segment in the US with the study area focusing on the Georgia Institute of Technology campus to capture the heterogeneity in pollutant concentrations related to primary traffic emissions. Differences in temporal pollutant concentrations at two near-road monitoring sites along the same road segment showed microenvironment characteristics are a driving factor in observations. Multipollutant metrics and dispersion modeling are two ways to quantify exposure to mobile source emissions. A statistical and biological metric provided insight on how they could be applied in future near-road studies. A dispersion model (R-LINE) was used to develop spatial concentration fields at a fine-spatial resolution over the area of primary exposures. To correct for high near-road bias, the R-LINE results were calibrated using measurement observations after the urban background was removed. Performing the calibration hourly also reduced the bias observed in the diurnal profile. Both the measurement observations and dispersion modeling results show that the highway has a substantial impact on primary traffic pollutant concentrations and captures the prominent spatial gradients across the campus domain, though the gradients were highly species dependent. These improved concentration fields were used to enhance the characterization of pollutant spatial distribution around a traffic hotspot and to quantify personal exposure to primary traffic emissions.