A new approach combining a simplified FLEXPART model and a Bayesian-RAT method for forecasting PM10 and PM2.5.

In this study, we evaluated atmospheric particulate matter (PM) concentration predictions at a regional scale using a simplified Lagrangian particle dispersion modeling system and the Bayesian and multiplicative ratio correction optimization (Bayesian-RAT) method to improve the mixing ratio forecast of PM10 and PM2.5. We first examined the forecast performance of the LPD (i.e., the simplified FLEXPART model combined with the Bayesian-RAT method) by comparing the model predictions with the PM concentration observations from 95 observation stations in Xingtai city and its surrounding areas. The first 2 months (i.e., Oct. and Nov. 2017) of the study period represented the typical spin-up time period, and the analysis period was December 2017. The LPD forecast system was much better (correlation coefficient: R=0.64 vs. 0.48 and 0.67 vs. 0.50 for PM10 and PM2.5, respectively; root mean square error: RMSE = 74.98 vs. 105.96 μg/m3 for PM10 and 54.89 vs. 72.81 μg/m3 for PM2.5) than the pre-calibration results. We also compared the LPD forecasting model with other models (WRF-Chem and Camx) using data from monitoring stations in Xingtai, China, and the LPD forecasting model had higher accuracy than the other models. In particular, the RMSE scores for hourly PM10 concentrations were reduced by 36.51% and 42.21% compared to WRF-Chem and to Camx, respectively. The PM2.5 forecast results, as in the case of PM10, showed a better performance when applying the LPD model to the data from the monitoring stations. The RMSE was reduced by 26.44% and 18.47% relative to the WRF-Chem and Camx, respectively. The results confirm that there is much advantage of the LPD forecast system for predicting PM and may be for other pollutants.