Abstract View
Improving Estimates of PM2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar
BETHANY SUTHERLAND, Nicholas Meskhidze, NC State University
Abstract Number: 393
Working Group: Satellite-Data and Environmental Health Applications
Abstract
Remote sensing is an effective means of monitoring aerosol mass loadings. Retrievals of aerosol optical depth (AOD) have been used to improve model simulations of PM2.5 concentration and to infer ground-level PM2.5. However, recent advances in active remote-sensing techniques (e.g., HSRL -High Spectral Resolution Lidar) and algorithm development (e.g., CATCH- Creating Aerosol Types from Chemistry) allow for retrievals of both aerosol PM2.5 mass concentrations and chemical composition.
In this presentation, we offer two new methodologies that combine the products of the CATCH algorithm with HSRL-retrieved AOD and aerosol types to derive the mass concentration and chemical composition of PM2.5. The methods are validated against the data from the NASA DISCOVER-AQ BWC Campaign (2011) and ground measurements from EPA’s Air Quality System network. In Method 1, the CATCH-HSRL combination is used to improve the regional air-quality model-predicted PM2.5 concentrations and chemical composition. In Method 2, PM2.5 concentrations and chemical composition are derived using the CATCH-HSRL combination alone.
Results show good agreement for both methods with the ground measurements. Method 1 and 2 yield r2 values of 0.61 and 0.69 and RMSE of 6.1 μg/m3 and 6.8 μg/m3, respectively. By comparison, the unconstrained CMAQ simulations produced r2=0.30 and RMSE=4.0 μg/m3. The estimates of PM2.5 chemical composition by both methods were similar (or better) compared to CMAQ simulations.