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Spatiotemporal patterns and drivers of public concern about air pollution in China: Leveraging online big data and interpretable machine learning

文献类型：期刊论文

作者Xu, Gang6,7; Liu, Haimeng5; Jia, Chunwang4; Zhou, Tianyu3; Shang, Jing2; Zhang, Xuejie1; Wang, Yunge6,7; Wu, Mengke7
刊名ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
出版日期2025-07-01
卷号114页码:107897
关键词Public environmental concern Air quality Environmental perception Machine learning XGBoost-SHAP Environmental assessment Environmental governance
ISSN号0195-9255
DOI10.1016/j.eiar.2025.107897
产权排序3
文献子类Article
英文摘要Public concern about air pollution directly shape residents' risk adaptation behaviors, government policies, and environmental sustainability. However, long-term nationwide studies in China are limited, with even fewer examining the nonlinear mechanisms driving these dynamics. Using Baidu search data from 290 cities across China (2011-2022), we analyzed the spatiotemporal patterns of public concern about air pollution and its mismatch with actual observed pollution levels. We further employed an XGBoost-SHAP model to reveal nonlinear effects of various factors on public concern. The results show a rise-and-fall trend in public concern from 2011 to 2022, with a clear correlation between declining concern and reduced PM2.5 levels after 2016. Concern was highest in coastal areas, the North China Plain, and northern coal-producing regions. In 2011, 980 million people had high pollution-low concern, dropping to around 200 million since 2016. Meanwhile, the population with low pollution-high concern steadily grew, highlighting China's progress in air pollution control and public environmental awareness. Actual air pollution levels are not the primary driver of public concern; education and income have the strongest influence. Public concern shows a roughly linear relationship with education, urban development, and media access. However, income and PM2.5 levels display nonlinear effects: concern plateaus above a per capita income of 28,000 yuan and declines after 60,000 yuan. Similarly, concern stabilizes once PM2.5 levels exceed 80 mu g/m3. This study reveals the nonlinear effects and threshold dynamics driving public environmental concern, offering valuable insights to inform strategies for advancing public environmental awareness and strengthening environmental governance in China.
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WOS关键词QUALITY PERCEPTION ; RISK PERCEPTION ; ENVIRONMENTAL CONCERN ; CLIMATE-CHANGE ; SOCIAL MEDIA ; HEALTH ; WELL ; EXPERIENCE ; ANNOYANCE ; EXPOSURE
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001440132800001
出版者ELSEVIER SCIENCE INC
源URL[http://ir.igsnrr.ac.cn/handle/311030/213200]  
专题区域可持续发展分析与模拟院重点实验室_外文论文
通讯作者Liu, Haimeng
作者单位1.Peking Univ, Shenzhen Grad Sch, Sch Urban Planning & Design, Shenzhen 518055, Peoples R China
2.China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China;
3.Nanhu Lab, Res Centra Big Data Technol, Jiaxing 314002, Peoples R China;
4.Qufu Normal Univ, Sch Geog & Tourism, Rizhao 276826, Peoples R China;
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
6.Wenzhou Future City Res Inst, Wenzhou 325000, Peoples R China;
7.Zhejiang Coll Secur Technol, Wenzhou 325000, Peoples R China;
推荐引用方式
GB/T 7714		 Xu, Gang,Liu, Haimeng,Jia, Chunwang,et al. Spatiotemporal patterns and drivers of public concern about air pollution in China: Leveraging online big data and interpretable machine learning[J]. ENVIRONMENTAL IMPACT ASSESSMENT REVIEW,2025,114:107897.
APA Xu, Gang.,Liu, Haimeng.,Jia, Chunwang.,Zhou, Tianyu.,Shang, Jing.,...&Wu, Mengke.(2025).Spatiotemporal patterns and drivers of public concern about air pollution in China: Leveraging online big data and interpretable machine learning.ENVIRONMENTAL IMPACT ASSESSMENT REVIEW,114,107897.
MLA Xu, Gang,et al."Spatiotemporal patterns and drivers of public concern about air pollution in China: Leveraging online big data and interpretable machine learning".ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 114(2025):107897.

入库方式： OAI收割

来源：地理科学与资源研究所

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