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

• Published in: Atmospheric environment (1994) 2021-04, Vol.250, p.118250, Article 118250
• Main Authors: Meskhidze, Nicholas, Sutherland, Bethany, Ling, Xinyi, Dawson, Kyle, Johnson, Matthew S., Henderson, Barron, Hostetler, Chris A., Ferrare, Richard A.
• Format: Article
• Language: English
• Subjects: Aerosol chemical composition; AQS; CATCH; CMAQ; HSRL; PM2.5
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2021.118250

Improved characterization of ambient PM2.5 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 PM2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM2.5 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 PM2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM2.5 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 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 PM2.5 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 PM2.5 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 PM2.5 concentration and chemical composition where HSRL data are available. •New methods for PM2.5 chemical composition retrievals using HSRL and CATCH.•Improves air quality model predicted PM2.5 and chemical composition.•Estimates PM2.5 and chemical composition without air quality model simulations.