170311 Highly-resolved air pollution modeling and its impact on estimating on-road PM 2.5 related premature mortality—an example in central North Carolina

Title:
Highly-resolved air pollution modeling and its impact on estimating on-road PM 2.5 related premature mortality—an example in central North Carolina

Speaker:
Shih Ying “Changsy” Chang, PhD
(Univ. of North Carolina at Chapel Hill/Sonoma Technology, INC.)

Time:
03/11 (Sat.) 15:00 PST, 16:00 MST, 17:00 CST, 18:00 EST,
03/12 (Sun.) 07:00 Taiwan

Link:
part-1
part-2



Prerequisite knowledge:
none

Abstract:

Emission from onroad vehicles is a major contributor of air pollution-related premature death. Previous studies have estimated that onroad emissions in the U.S. cause 29,000 to 53,000 ozone and PM 2.5 -related premature deaths. In these studies, grid-based air quality chemical transport models (CTM) were used to provide ambient concentration estimates. Because these models were usually run at a relatively coarse spatial resolution (i.e. 36 km × 36 km or 12 km × 12 km), they fail to fully characterize the concentration hotspots at the proximity of emission sources and thus fail to capture high-risk areas. Several studies have shown that people living close to major roads have higher risk to develop respiratory diseases than those living several hundred meters away. To capture this sharp gradient and to improve characterization of the exposure and risk from traffic-related air pollutants, spatially resolved concentration is required. However, estimating concentration at a high spatial resolution is challenging for large-scale application and is 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 – a) using the Community Multiscale Air Quality (CMAQ) model, to model concentration at a coarser resolution of a 36 km × 36 km grid resolution, and b) 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 meters 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.