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Atmosphere 2018, 9(11), 428;

Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model

State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China
College of Resources and Environmental, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Author to whom correspondence should be addressed.
Received: 27 August 2018 / Revised: 25 October 2018 / Accepted: 29 October 2018 / Published: 5 November 2018
(This article belongs to the Section Air Quality)
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In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM2.5. We first examined the inversion modeling performance by comparing the model predictions with PM2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m3, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM2.5 concentrations and the corresponding monthly emission fluxes. View Full-Text
Keywords: PM2.5; FLEXPART-WRF; real-time concentrations; inventory; Bayesian optimization method PM2.5; FLEXPART-WRF; real-time concentrations; inventory; Bayesian optimization method

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Guo, L.; Chen, B.; Zhang, H.; Xu, G.; Lu, L.; Lin, X.; Kong, Y.; Wang, F.; Li, Y. Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model. Atmosphere 2018, 9, 428.

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