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

doi:10.3390/atmos9110428

	Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model
	Guo, Lifeng 1,2; Chen, Baozhang 1,2,3; Zhang, Huifang 1,2; Xu, Guang 1,2; Lu, Lijiang 3; Lin, Xiaofeng 1,2; Kong, Yawen 1,2; Wang, Fei 1,2; Li, Yanpeng 1,3
刊名	ATMOSPHERE
	2018-11-01
卷号	9 期号:11 页码:17
关键词	PM2 5 FLEXPART-WRF real-time concentrations inventory Bayesian optimization method
ISSN号	2073-4433
DOI	10.3390/atmos9110428
通讯作者	Chen, Baozhang(baozhang.chen@igsnrr.ac.cn)
英文摘要	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 mu g/m(3), respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by similar to 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.
资助项目	international partnership program of Chinese Academy of Sciences[131A11KYSB20170025] ; State Key Laboratory of Resources and Environment Information System[O88RA901YA] ; National Natural Science Foundation of China[41771114]
WOS关键词	AIR-QUALITY ; HIGH-RESOLUTION ; DISPERSION ; CHINA ; SIMULATIONS ; AEROSOLS ; EPISODES ; IMPACT ; PM10
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	MDPI
WOS记录号	WOS:000451306900015
资助机构	international partnership program of Chinese Academy of Sciences ; State Key Laboratory of Resources and Environment Information System ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/51568]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Baozhang
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Guo, Lifeng,Chen, Baozhang,Zhang, Huifang,et al. Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model[J]. ATMOSPHERE,2018,9(11):17.
APA	Guo, Lifeng.,Chen, Baozhang.,Zhang, Huifang.,Xu, Guang.,Lu, Lijiang.,...&Li, Yanpeng.(2018).Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model.ATMOSPHERE,9(11),17.
MLA	Guo, Lifeng,et al."Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model".ATMOSPHERE 9.11(2018):17.