Understanding the Relationship Between Urban Green Infrastructure and PM2.5 Based on an Explainable Machine Learning Model: Evidence From 288 Cities in China
文献类型：期刊论文
| 作者 | Lyu, Feinan2; Chen, Kai3; Olhnuud, Aruhan4; Sun, Xiaojie5; Gong, Cheng1 |
| 刊名 | EARTHS FUTURE
 |
| 出版日期 | 2025-11-08 |
| 卷号 | 13期号:11页码:e2025EF006861 |
| 关键词 | urban green infrastructure PM2.5 transparent machine learning models morphological spatial patterns core |
| DOI | 10.1029/2025EF006861 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Urban green infrastructure (UGI) is critical for mitigating fine particulate matter (PM2.5) pollution, a major obstacle to sustainable urban development. However, the morphological spatial patterns of UGI and their impact on PM2.5 remain largely unexplored, as most related studies have focused solely on case studies. This study employed morphological spatial pattern analysis to document the national scale spatial distribution of seven UGI morphology space patterns (MSPs) across 288 Chinese cities. It verified the disparities of each MSP under varying geographic conditions and scalar categories. Using advanced interpretable machine learning methods that account for aggregated contribution of location features, the study confirmed the positive role of UGI proportion in mitigating PM2.5 levels. Significantly, the findings revealed that smaller non-core UGI areas, such as perforation and islet, exert a more pronounced positive impact on reducing PM2.5. Furthermore, the study explored the potential PM2.5 risks facing Chinese cities due to temporal changes of UGI. The study results not only fill the gap in UGI research, but also contributes a feasible urban planning method and provide a basis for reducing PM2.5 to promote sustainable urban development. |
| URL标识 | 查看原文 |
| WOS关键词 | PARTICULATE MATTER ; URBANIZATION ; VEGETATION ; DATASET |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001609564200001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217811]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Gong, Cheng |
| 作者单位 | 1.Taiyuan Normal Univ, Dept Design, Jinzhong, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 3.Shanghai Jiao Tong Univ, Sch Design, Shanghai, Peoples R China; 4.Inner Mongolia Univ, Sch Ecol & Environm, Hohhot, Peoples R China; 5.Taiyuan Normal Univ, Inst Geog Sci, Jinzhong, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Lyu, Feinan,Chen, Kai,Olhnuud, Aruhan,et al. Understanding the Relationship Between Urban Green Infrastructure and PM2.5 Based on an Explainable Machine Learning Model: Evidence From 288 Cities in China[J]. EARTHS FUTURE,2025,13(11):e2025EF006861. |
| APA | Lyu, Feinan,Chen, Kai,Olhnuud, Aruhan,Sun, Xiaojie,&Gong, Cheng.(2025).Understanding the Relationship Between Urban Green Infrastructure and PM2.5 Based on an Explainable Machine Learning Model: Evidence From 288 Cities in China.EARTHS FUTURE,13(11),e2025EF006861. |
| MLA | Lyu, Feinan,et al."Understanding the Relationship Between Urban Green Infrastructure and PM2.5 Based on an Explainable Machine Learning Model: Evidence From 288 Cities in China".EARTHS FUTURE 13.11(2025):e2025EF006861. |
入库方式： OAI收割
来源：地理科学与资源研究所
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