The relationship between PM2.5 and anticyclonic wave activity during summer over the United States

Published in Atmospheric Chemistry and Physics, 2022

Recommended citation: Wang, Ye, Natalie Mahowald, Peter Hess, Wenxiu Sun and Gang Chen, 2022: The relationship between PM2.5 and anticyclonic wave activity during summer over the United States, Atmospheric Chemistry and Physics, 22, 7575--7592, doi:10.5194/acp-22-7575-2022.

ABSTRACT: . To better understand the role of atmospheric dynamics in modulating surface concentrations of fine particulate matter (PM2.5), we relate the anticyclonic wave activity (AWA) metric and PM2.5 data from the Interagency Monitoring of Protected Visual Environment (IMPROVE) data for the period of 1988–2014 over the US. The observational results are compared with hindcast simulations over the past 2 decades using the National Center for Atmospheric Research–Community Earth System Model (NCAR CESM). We find that PM2.5 is positively correlated (up to R=0.65) with AWA changes close to the observing sites using regression analysis. The composite AWA for high-aerosol days (all daily PM2.5 above the 90th percentile) shows a similarly strong correlation between PM2.5 and AWA. The most prominent correlation occurs in the Midwestern US. Furthermore, the higher quantiles of PM2.5 levels are more sensitive to the changes in AWA. For example, we find that the averaged sensitivity of the 90th-percentile PM2.5 to changes in AWA is approximately 3 times as strong as the sensitivity of 10th-percentile PM2.5 at one site (Arendtsville, Pennsylvania; 39.92∘ N, 77.31∘ W). The higher values of the 90th percentile compared to the 50th percentile in quantile regression slopes are most prominent over the northeastern US. In addition, future changes in US PM2.5 based only on changes in climate are estimated to increase PM2.5 concentrations due to increased AWA in summer over areas where PM2.5 variations are dominated by meteorological changes, especially over the western US. Changes between current and future climates in AWA can explain up to 75 {\%} of PM2.5 variability using a linear regression model. Our analysis indicates that higher PM2.5 concentrations occur when a positive AWA anomaly is prominent, which could be critical for understanding how pollutants respond to changing atmospheric circulation as well as for developing robust pollution projections.

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