摘要：Objectives. We assessed how traffic and mobile-source air pollution impacts are distributed across racial/ethnic and socioeconomically diverse groups in port-adjacent communities in southern Los Angeles County, which may experience divergent levels of exposure to port-related heavy-duty diesel truck traffic because of existing residential and land use patterns. Methods. We used spatial regression techniques to assess the association of neighborhood racial/ethnic and socioeconomic composition with residential parcel-level traffic and vehicle-related fine particulate matter exposure after accounting for built environment and land use factors. Results. After controlling for factors associated with traffic generation, we found that a higher percentage of nearby Black and Asian/Pacific Islander residents was associated with higher exposure, a higher percentage of Hispanic residents was associated with higher traffic exposure but lower vehicle particulate matter exposure, and areas with lower socioeconomic status experienced lower exposure. Conclusions. Disparities in traffic and vehicle particulate matter exposure are nuanced depending on the exposure metric used, the distribution of the traffic and emissions, and pollutant dispersal patterns. Future comparative research is needed to assess potential disparities in other transportation and goods movement corridors. Residential proximity to heavy traffic has been associated with adverse health effects, including asthma, reduced lung function, cardiac and pulmonary mortality, and adverse birth outcomes. 1–3 Previous research suggests that non-White and lower income individuals may be exposed to higher levels of traffic-related air pollution 4–8 and that disparities vary with social gradients associated with higher susceptibility to pollution. 9,10 Environmental justice concerns are heightened in goods movement corridors in which substantial volumes of heavy-duty diesel trucks (HDDTs) transport shipping containers on arterials near residences and sensitive land uses through lower socioeconomic status communities. 11,12 Significant questions remain, however, regarding the existence and magnitude of race- and income-based disparities in traffic and air pollution exposure. 13–16 Some studies have found little association between air pollution exposure and socioeconomic status after controlling for confounding factors 17 ; others found greater air pollution and traffic exposure for higher socioeconomic groups. 18,19 Such discrepancies could arise because of methodological differences and challenges in assessing inequities at various scales. 11,20–22 Scale could be an important consideration in assessing traffic impacts because vehicle-related pollutants are highly localized, with pollutant concentrations decaying to background levels within 200 to 300 meters during the day, 23–26 and because ambient air quality monitoring data are likely insufficient to characterize near-roadway pollutant gradients. Our study provides an important environmental justice case study by assessing how traffic and mobile-source air pollution impacts are distributed across groups in port-adjacent communities in southern Los Angeles County, which contain substantial racial/ethnic and socioeconomic diversity and may experience divergent levels of exposure to port-related HDDT traffic because of existing residential and land use patterns. 12,27,28 We have contributed to the environmental justice literature by examining exposures at the parcel property assessment level to determine impacts at a finer spatial resolution, 29 by using spatial regression techniques to account for spatial dependence of data when assessing disparities, 30–33 and by using 3 parcel-level metrics of exposure that could have different spatial distributions and population impacts: total nearby vehicle miles traveled (VMT), nearby truck VMT, and the modeled concentrations of emissions from vehicles on neighborhood roadways. We hypothesized that the first 2 traffic exposure measures would provide a distance-based assessment of near-roadway exposure to traffic-related noise and air pollution and that the third would account for the air pollution “plume” after accounting for the geographic and temporal variation in traffic, wind, and other meteorological patterns.
