Finely Resolved On‐Road PM_2.5 and Estimated Premature Mortality in Central North Carolina

Abstract

Abstract To quantify the on‐road PM 2.5 ‐related premature mortality at a national scale, previous approaches to estimate concentrations at a 12‐km × 12‐km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on‐road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on‐road PM 2.5 ‐related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36‐km × 36‐km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM 2.5 concentrations at a Census‐block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on‐road PM 2.5 ‐related premature mortality than CMAQ. The major difference is from the primary on‐road PM 2.5 where the hybrid approach estimated 2.5 times more primary on‐road PM 2.5 ‐related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on‐road PM 2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near‐road primary PM 2.5 and suggesting that previous studies may have underestimated premature mortality due to PM 2.5 from traffic‐related emissions.