IGSNRR OpenIR
Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model
Guo, Lifeng1,2; Chen, Baozhang1,2,3; Zhang, Huifang1,2; Xu, Guang1,2; Lu, Lijiang3; Lin, Xiaofeng1,2; Kong, Yawen1,2; Wang, Fei1,2; Li, Yanpeng1,3
2018-11-01
Source PublicationATMOSPHERE
ISSN2073-4433
Volume9Issue:11Pages:17
Corresponding AuthorChen, Baozhang(baozhang.chen@igsnrr.ac.cn)
AbstractIn 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.
KeywordPM2 5 FLEXPART-WRF real-time concentrations inventory Bayesian optimization method
DOI10.3390/atmos9110428
WOS KeywordAIR-QUALITY ; HIGH-RESOLUTION ; DISPERSION ; CHINA ; SIMULATIONS ; AEROSOLS ; EPISODES ; IMPACT ; PM10
Indexed BySCI
Language英语
Funding Projectinternational 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]
Funding Organizationinternational partnership program of Chinese Academy of Sciences ; State Key Laboratory of Resources and Environment Information System ; National Natural Science Foundation of China
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000451306900015
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51569
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Baozhang
Affiliation1.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
Recommended Citation
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.
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