Abstract
Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health.
Highlights
Over the past several decades, industrialization and urbanization have caused seriousPM2.5 pollution in China
Simulation times, and input data have a great influence on the analytical results, the research results of previous studies and this paper show that the PM2.5 concentration contribution in the BTH region mainly originates from local pollution sources, and this information can be targeted to control and prevent the occurrence of pollution events
Lagrangian particle dispersion (LPD) can improve PM2.5 concentration forecast accuracy by optimizing the PM2.5 emission inventory and by obtaining real-time analyses of the temporal and spatial distributions and pollution source contributions that lead to changes in the PM2.5 concentration at the receptors
Summary
Over the past several decades, industrialization and urbanization have caused seriousPM2.5 pollution in China. Ambient particles can affect air quality and climate by absorbing and scattering solar irradiation [3,4,5,6], and they have adverse effects on human health. Studies have shown that high concentrations of PM2.5 increase the morbidity and mortality of the public and affect the cardiovascular system [7,8,9,10]. PM2.5 is more harmful to human health than PM10 because PM2.5 has a smaller particle size and a larger specific surface area, which makes it easier to absorb toxic chemicals [11,12]. Increasing attention has been given to PM2.5 in China, from the scientific community and from the public. To reduce PM2.5 concentrations, it is essential to understand the relative contributions to PM2.5 from various sources, and effective management and control strategies can only be developed for major emission sources when this information is obtained
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