Abstract

To better understand the role of atmospheric dynamics in modulating surface concentrations of fine particulate matter (PM2.5), we relate the anti-cyclone 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 two 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 the averaged sensitivity of the 90th percentile PM2.5 to changes in AWA is approximately three 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 developing robust pollution projections.

Highlights

  • We find that PM2.5 is positively correlated with anti-cyclone wave activity (AWA) changes close to the observing sites using regression analysis

  • We employed a univariate linear regression model to determine the correlation of PM2.5 levels and AWA on synoptic-scales over the US

  • This analysis demonstrates that PM2.5 is positively linked to the local anticyclonic finite-amplitude wave activity 350 over the past two decades during JJA, and the high PM2.5 concentrations are more sensitive to the AWA than those low ones

Read more

Summary

Introduction

Particulate matter less than 2.5 μ m in diameter (PM2.5) poses a considerable air quality concern due to its impacts on human 20 health (Liu et al, 2020). Continuing exposure to PM2.5 can exacerbate existing cardiovascular and respiratory problems, and cause lung damage (Bernard et al, 2001) It can alter the body’s defense system against foreign materials, and even lead to premature death (Kappos et al, 2004). Once deposited onto 25 snow PM2.5 can cause an increase in melting of snow and ice (Painter et al, 2007) or modify land or ocean biogeochemistry (Mahowald et al, 2011). It influences Earth’s energy balance directly by scattering solar incoming radiation back into space (Charlson et al, 1992) or indirectly by altering cloud albedo and lifetime (Albrecht, 1989; Arimoto, 2001). Understanding and predicting potential changes in PM2.5 is crucial to both human 30 health concerns and current environmental issues

Methods
Results
Conclusion

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

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.