资源环境科技发展态势分析平台

To better understand the role of atmospheric dynamics in modulating surface concentrations of fine particulate matter (PM_2.5), we relate the anticyclonic wave activity (AWA) metric and PM_2.5 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 PM_2.5 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 PM_2.5 above the 90th percentile) shows a similarly strong correlation between PM_2.5 and AWA. The most prominent correlation occurs in the Midwestern US. Furthermore, the higher quantiles of PM_2.5 levels are more sensitive to the changes in AWA. For example, we find that the averaged sensitivity of the 90th-percentile PM_2.5 to changes in AWA is approximately 3 times as strong as the sensitivity of 10th-percentile PM_2.5 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 PM_2.5 based only on changes in climate are estimated to increase PM_2.5 concentrations due to increased AWA in summer over areas where PM_2.5 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 PM_2.5 variability using a linear regression model. Our analysis indicates that higher PM_2.5 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.