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Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT plus CRF Model

文献类型：期刊论文

作者Wang, Xiaoshuang1,5,6; Zhu, Yunqiang5; Zeng, Hongyun2; Cheng, Quanying5,6; Zhao, Xiaohong3; Xu, Haihong3; Zhou, Tianmo4,5,6
刊名ATMOSPHERE
出版日期2022-07-01
卷号13期号:7页码:13
关键词air pollution address matching BERT plus CRF model air pollution complaint air pollution control spatial representation
DOI10.3390/atmos13071023
通讯作者Zhu, Yunqiang(zhuyq@igsnrr.ac.cn) ; Xu, Haihong(xuhh@acee.org.cn)
英文摘要(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for air pollution control by combining regional air monitoring data. (2) Methods: By selecting the air pollution complaint information in Beijing from 2019 to 2020, in this study, we extract the names and addresses of complaint points, as well as the complaint times and types by adopting the BERT (bidirectional encoder representations from transformers) + CRF (conditional random field) model deep learning method. Moreover, through further filtering and processing of the complaint points' address information, we achieve address matching and spatial positioning of the complaint points, and realize the regional spatial representation of air pollution complaints in Beijing in the form of a heat map. (3) Results: The experimental results are compared and analyzed with the ranking data of total suspended particulate (TSP) concentration of townships (streets) in Beijing during the same period, indicating that the key areas of air pollution complaints have a high correlation with the key polluted township (street) areas. The distribution of complaints and the types of complaints in each township (street) differ according to the population density in each township (street), the level of education, and economic activity. (4) Conclusions: The results of this study show that the public, as the intuitive perceiver of air pollution, is sensitive to the air pollution situation at a smaller spatial scale; furthermore, complaints can provide guidance and reference for the direction of air pollution control and law enforcement investigations when coupled with geographical features and economic status.
WOS关键词QUALITY
资助项目National Natural Science Foundation of China[42050101] ; Strategic Pilot Science and Technology Project of Chinese Academy of Sciences[XDA23100100] ; Pre-research project of the Ministry of Ecology and Environmental of China[DQGG202034]
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000833240500001
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Pilot Science and Technology Project of Chinese Academy of Sciences ; Pre-research project of the Ministry of Ecology and Environmental of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/181438]  
专题中国科学院地理科学与资源研究所
通讯作者Zhu, Yunqiang; Xu, Haihong
作者单位1.Beijing Municipal Ecol Environm Bur, Comprehens Serv Ctr, Beijing 100048, Peoples R China
2.Yunnan Univ, Sch Earth Sci, Kunming 650500, Yunnan, Peoples R China
3.Minist Ecol & Environm, Appraisal Ctr Environm & Engn, Beijing 100012, Peoples R China
4.Informat Res Inst, Emergency Management Dept, Beijing 100029, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xiaoshuang,Zhu, Yunqiang,Zeng, Hongyun,et al. Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT plus CRF Model[J]. ATMOSPHERE,2022,13(7):13.
APA Wang, Xiaoshuang.,Zhu, Yunqiang.,Zeng, Hongyun.,Cheng, Quanying.,Zhao, Xiaohong.,...&Zhou, Tianmo.(2022).Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT plus CRF Model.ATMOSPHERE,13(7),13.
MLA Wang, Xiaoshuang,et al."Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT plus CRF Model".ATMOSPHERE 13.7(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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