Publications

Zang, ZL; Wang, WQ; You, W; Li, Y; Ye, F; Wang, CM (2017). Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. SCIENCE OF THE TOTAL ENVIRONMENT, 575, 1219-1227.

Abstract
The aerosol optical depth (AOD) iswidely used to predict surface PM2.5 (particles with an aerodynamic diameter smaller than 2.5 mu m) concentrations using regression methods, as a way to supplement observations from sparse ground PM2.5 monitoring networks. Several meteorological parameters, such as surface humidity, temperature and height of planetary boundary layer (HPBL), are usually combined with AOD to improve the accuracy of the regression model in predicting PM2.5 concentrations. In this paper, we investigate the role of the temperature inversion layer in the prediction of PM2.5 concentrations by the optimal subset regression method. The result indicates that the optimal subset regression model with the parameters of the depth and temperature difference of the inversion can significantly improve the accuracy of the predictions of surface PM2.5 concentrations, compared with the original regressionmodelwith one factor of AOD. The determination coefficient (R-2) increases from0.51 to 0.63, and the Root Mean Square Error (RMSE) decreases from 40.38 to 35.45 mu g/m(3). The optimal subset regressions were also built for each season. The temperature difference of the inversion is introduced into the autumn andwinter optimal subset regression, and the depth of the inversion is introduced into the spring optimal subset regression. The contribution of the inversion parameters to the regression model is affected by the different type of the temperature inversion layer present in each season. (C) 2016 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.scitotenv.2016.09.186

ISSN:
0048-9697