Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset

Abstract

In this study, the influence of aerosol type classification on estimating surface PM2.5 concentrations was assessed by using ground-based aerosol optical depth (AOD) at 22 ground-based observation points in the United States. To clarify the influence, a two-stage model consisting of a multiple regression model (MRM) and an aerosol classification model (ACM) was proposed to estimate surface PM2.5 concentrations, and results from a traditional MRM and the new ACM-MRM were compared. Results show that the average determination coefficient (R2) of the ACM-MRM (0.52) was greater than that of the MRM (0.44), while the root mean square error (RMSE) and mean absolute percent error (MAPE) of the ACM-MRM (3.74 μg/m3 and 34.91%, respectively) were lower than the values obtained with the MRM (4.09 μg/m3 and 37.64%, respectively). The use of ACM improved the estimation of daily PM2.5 concentrations in different regions and different seasons by reducing the deviations caused by aerosol type changes. Further analysis demonstrated that aerosol type changes had adverse influences on the estimation of short-term PM2.5 concentrations and the introduction of ACM can effectively restrain the adverse influences when aerosols change frequently.