Abstract
Total ambient PM2.5 concentrations within six major metropolitan core-based statistical areas (CBSAs) were evaluated using a previously published approach that combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. The results demonstrate differences and similarities across the cities, including seasonal patterns, the magnitude of total PM2.5 concentrations, and local vs. background apportionment. Los Angeles generally had the greatest total and local PM2.5 concentrations, and monitoring locations in Seattle generally had the lowest total and local PM2.5 concentrations. In terms of seasonal change, mean total PM2.5 concentrations varied the most in Dallas and the least in Tampa, while mean total PM2.5 concentrations varied the most spatially in Los Angeles and the least spatially in Dallas. Additionally, mean local PM2.5 concentrations varied the most seasonally in Los Angeles and the least seasonally in Tampa, while mean local PM2.5 concentrations varied the most spatially in Seattle and the least spatially in Charlotte and Dallas. Local attribution in Charlotte, Dallas, Pittsburgh, and Tampa were most similar. Local attribution varied more widely in Los Angeles and Seattle. The analyses found distance to major U.S. roadways as an important indicator for total and local ambient PM2.5 concentrations. Accurate distinctions of local vs. background contributions and point vs. non-point source contributions allow for regulatory, mitigatory, and community efforts to be optimized at the appropriate spatial levels (i.e., local, regional, state, or national) and target toward the most impactful source sectors when seeking to take steps that reduce total community PM2.5 exposures (e.g., reducing vehicular emissions).
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call