A Hybrid Approach to Forecast Air Quality during High-PM Concentration Pollution Period

Abstract

In this study, a hybrid approach of combining numerical prediction with statistical analysis was proposed to forecast high-PM 10 (aerosol particle with aerodynamic diameter less than 10 μm) concentration events in Beijing, China. This approach was used to forecast the daily PM 10 in Beijing from January 1 to December 30, 2013. The WRF-CMAQ modeling system was also applied to simulate Beijing's PM 10 in the same period. The performance of the two methods was then assessed according to the mean bias (MB), normalized mean bias (NMB), normalized mean gross error (NME), mean normalized bias (MNB), mean normalized gross error (MNE), and root mean square error (RMSE). The results demonstrate that both methods perform well during low-PM 10 concentration periods (PM 10 concentration < 250 μg/m 3 ), the MB, NMB, NME, MNB, MNE and RMSE for hybrid approach during low-PM 10 concentration periods were 26.15, 24.88%, 41.94%, 43.23%, 56.35% and 61.67, respectively. The MB, NMB, NME, MNB, MNE and RMSE for CMAQ during low-PM 10 concentration periods were -6.04, 57.47%, 41.49%, 21.52%, 55.64% and 60.11, respectively. While the MB, NMB, NME, MNB, MNE and RMSE for CMAQ during high-PM 10 concentration periods (PM 10 concentration ≥ 250 μg/m 3 ) were -162.87, -50.37%, 50.37%, -49.86%, 49.86% and 175.93, respectively. The MB, NMB, NME, MNB, MNE and RMSE for hybrid approach during high-PM 10 concentration periods were -30.3, -9.37%, 23.21%, -8.21%, 24.25% and 97.37, respectively. The hybrid approach shows significant improvement in accuracy during high-PM 10 concentration periods.

Air Pollution Forecasts: An Overview

“…Moreover, some cities in China have been selected as the 10 most polluted cities in the world [ 16 ]. In recent years in China, high concentrations of particulate matter have received increasing attention [ 17 ].…”