Design and application of a hybrid assessment of air quality models for the source apportionment of PM2.5

Abstract Increasing levels of air pollution greatly affect the environment and human health; air quality control is therefore of particular importance. To improve the efficiency of air pollution control, reliable air quality models for source apportionment are critically in need. The multitude of air quality models to find the sources of air pollution, however, have their own limitations. Hence, this study seeks to integrate different air quality models, including a diffusion model (AERMOD) and a grid model (CMAQ), employing initial meteorological fields provided by the Weather Research and Forecasting (WRF) model. Using the advantages of these models, this study builds a hybrid air quality model, which provides a more effective analysis of the distribution of primary pollutants, secondary pollutants, and other environmental information. Two significant fine particulate matter (PM 2.5 ) events were selected in this study to discuss the influence of the Taichung coal-fired power plant and the Taichung traffic source on the PM 2.5 in the Taichung area, as well as to evaluate the performance of the hybrid model. Simulation results for the two cases show that if the coal-fired power loads are reduced by 20% (around 1100 MW), the concentration of PM 2.5 in the Taichung area will decrease by 0.5%; such decrease will reach 1.25% when the power load reduction is 40%. If the traffic source is reduced by 20%, the concentration of PM 2.5 in the Taichung area will decrease by 4.3%, and by 6.6% with 30% traffic source reduction. The hybrid model shows that the contribution of different pollution sources can be illuminated to support air quality control strategies.