Explainable GeoAI reveals spatially varying and nonlinear associations of PM2.5 pollution with driving factors in Chinese cities from 2015 to 2022
文献类型：期刊论文
| 作者 | Dai, Xiaoliang1,6,7; Sun, Kai5; Li, Weirong4; Zhu, Yunqiang1,3,7; Ding, Fangyu1; Pan, Peng2; Yang, Yichen1,6,7; Wang, Shu1,7 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2026-07-01 |
| 卷号 | 19期号:1页码:2620872 |
| 关键词 | PM2.5 concentration natural and socioeconomic factors GeoAI geographical random forest explainable AI |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2026.2620872 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | PM2.5 pollution remains a critical environmental and public health challenge in China despite post-2015 improvements. However, our understanding of the spatially heterogeneous and nonlinear associations of its driving factors remains limited. To fill this gap, we employ an explainable geospatial artificial intelligence (GeoAI) framework that integrates the geographical random forest (GRF) model and the Shapley additive explanations (SHAP) approach to examine the associations between 16 determinants and PM2.5 concentrations. Based on a nationwide and multi-year analysis across 288 cities selected from all 336 Chinese cities between 2015 and 2022, the results show that GRF achieves at least a 0.04 higher R-2 than baseline models. Our analysis reveals three findings. First, population density is the most influential factor in 52.39% of cities; combined with temperature, road density, and gas supply, these four dominate over 95% of cities. Second, drivers exhibit significant spatially varying and nonlinear associations. For instance, population density correlates positively with PM2.5 in the North China Plain but negatively in sparsely populated areas; and the association of temperature follows an inverted U-shaped pattern. Third, these spatial and nonlinear associations undergo temporal changes. These findings offer insights for future environmental management strategies to mitigate the negative impacts of various drivers. |
| URL标识 | 查看原文 |
| WOS关键词 | PATTERNS ; IMPACT |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001671177200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/220942]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Sun, Kai; Zhu, Yunqiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 2.Minist Ecol & Environm, Appraisal Ctr Environm & Engn, Beijing, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China; 4.Guangxi Normal Univ, Coll Environm & Resources, Guilin, Peoples R China; 5.Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China; 6.Univ Chinese Acad Sci, Beijing, Peoples R China; 7.State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Dai, Xiaoliang,Sun, Kai,Li, Weirong,et al. Explainable GeoAI reveals spatially varying and nonlinear associations of PM2.5 pollution with driving factors in Chinese cities from 2015 to 2022[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2026,19(1):2620872. |
| APA | Dai, Xiaoliang.,Sun, Kai.,Li, Weirong.,Zhu, Yunqiang.,Ding, Fangyu.,...&Wang, Shu.(2026).Explainable GeoAI reveals spatially varying and nonlinear associations of PM2.5 pollution with driving factors in Chinese cities from 2015 to 2022.INTERNATIONAL JOURNAL OF DIGITAL EARTH,19(1),2620872. |
| MLA | Dai, Xiaoliang,et al."Explainable GeoAI reveals spatially varying and nonlinear associations of PM2.5 pollution with driving factors in Chinese cities from 2015 to 2022".INTERNATIONAL JOURNAL OF DIGITAL EARTH 19.1(2026):2620872. |
入库方式： OAI收割
来源：地理科学与资源研究所
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