Improving Estimates of PM2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm

Nicholas Meskhidze; Bethany Sutherland; Xinyi Ling; Kyle Dawson; Matthew S Johnson; Barron Henderson; Chris A Hostetler; Richard A Ferrare

doi:10.1016/j.atmosenv.2021.118250

Improving Estimates of PM_2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm

Atmos Environ (1994). 2021 Apr 1:250:10.1016/j.atmosenv.2021.118250. doi: 10.1016/j.atmosenv.2021.118250.

Authors

Nicholas Meskhidze¹, Bethany Sutherland¹, Xinyi Ling¹, Kyle Dawson², Matthew S Johnson³, Barron Henderson⁴, Chris A Hostetler⁵, Richard A Ferrare⁵

Affiliations

¹ North Carolina State University, Raleigh, NC.
² Bayer Crop Sciences, Chesterfield, MO.
³ NASA Ames Research Center, Moffett Field, CA.
⁴ US EPA.
⁵ NASA Langley Research Center, Hampton, VA.

Abstract

Improved characterization of ambient PM_2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM_2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM_2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM_2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM_2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign (2011) are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM_2.5 concentrations (R² value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m^-3, and the normalized mean bias (NMB) was lowered from -46.0 to 4.6%). The HSRL-CH method showed statistics (R²=0.75, RMSE=8.6 μgm^-3, and NMB=24.0%), which were better than the CMAQ prediction of PM_2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM_2.5 concentration and chemical composition where HSRL data are available.

Keywords: AQS; CATCH; CMAQ; HSRL; PM2.5; aerosol chemical composition.

Grants and funding

EPA999999/ImEPA/Intramural EPA/United States