American Association for Aerosol Research - Abstract Submission

AAAR 38th Annual Conference
October 5 - October 9, 2020

Virtual Conference

Abstract View


The Relationship between MAIAC Smoke Plume Heights and Surface Particulate Matter

MICHAEL CHEESEMAN, Bonne Ford, John Volckens, Alexei Lyapustin, Jeffrey R. Pierce, Colorado State University

     Abstract Number: 170
     Working Group: Satellite-Data and Environmental Health Applications

Abstract
Biomass burning is a source of fine particulate matter (PM2.5) air pollution, which adversely impacts human health. However, quantifying the health effects from biomass burning PM2.5 is difficult. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of smoke from biomass burning events. Satellite aerosol optical depth (AOD) can be used to fill the spatial gaps in monitoring networks; however, AOD is a column-integrated value that does not distinguish surface-level aerosols. Plume injection heights (PIH), however, may provide constraints on the vertical distribution of smoke. We hypothesized that the ratio of surface PM2.5 and AOD would be correlated with the ratio of PIH and the planetary boundary layer height (PBLH), because when the smoke is aloft (i.e. PIH:PBLH >1) the surface may experience smaller PM2.5 enhancements than if the smoke remained in the boundary layer (PIH:PBLH<1). We investigated the relationship between PIH, AOD, and surface PM2.5, using PIH and AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset and PBLH from the National Centers for Environmental Prediction (NCEP). We assessed regional characteristics of PIH and evaluated its correlation with co-located PM2.5 and AOD measurements downstream of biomass burning events. PIH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PIH:PBLH, which indicates a lower surface PM2.5 enhancement, on average, when plumes are injected into the free troposphere. Thus, we show that PIH has the potential to refine surface PM2.5 estimates during smoke events; however, the relationship is only strong when averaging over large regions or longer time periods.