The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China
文献类型：期刊论文
| 作者 | Chi, Yufeng3,4; Ren, Yin4; Xu, Chengdong2; Zhan, Yu1 |
| 刊名 | ENVIRONMENTAL POLLUTION
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 342页码:8 |
| ISSN号 | 0269-7491 |
| 关键词 | LightGBM PM2.5 and NO2 spatial distribution Hot spotanalysis Geodeteotor |
| DOI | 10.1016/j.envpol.2023.123122 |
| 通讯作者 | Ren, Yin(yren@iue.ac.cn) |
| 英文摘要 | The spatial distribution characteristics of multi-air pollutants and their impacts are difficult to quantify effectively. As PM2.5 and NO2 are the main air pollutants, it is of great significance to explore the spatial causes of their pollution and their interaction mechanism. This study used machine learning (LightGBM) and hot spot analysis to map the spatial distribution of PM2.5 and NO2 in Southwest Fujian (SWFJ) in 2018 and their key pollution areas. Then, the factors and interactive detection of geographical detectors were used to conduct a detailed analysis of the quantitative impact of potential factors such as human activities, terrain, air pollutants, and meteorology on PM2.5 and NO2 pollution. From this we can learn that 1. LightGBM has good stability for drawing the spatial distribution of PM2.5 and NO2. 2. The spatial mechanism of PM2.5 and NO2 can be effectively interpreted from a massive data and macro perspective. 3. A large amount of evidence shows that potential factors such as human activities, topography, air pollutants and meteorology have direct or indirect effects on PM2.5 and NO2 pollution in the SWFJ area. This includes the direct impact of local road traffic emissions on the distribution of PM2.5 and NO2 pollution, the digestion of both by vegetation, the mutual transformation of atmospheric pollutants themselves, and the impact of meteorological conditions. This study not only confirms the effectiveness of machine learning combined with geographical detectors to promote the study of regional air pollution mechanisms, but also confirms the feasibility of exploring the spatial distribution mechanisms of various air pollutants. Therefore, this study is of great significance for explaining the spatial distribution of PM2.5 and NO2, and can also provide reference for policy formulation to reduce regional PM2.5 and NO2 concentrations. |
| WOS关键词 | AIR-POLLUTION ; CHEMICAL-COMPOSITIONS ; IDENTIFICATION ; GEODETECTOR ; RESOLUTION ; CHILDREN ; BASIN ; CITY |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001161372000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202846]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ren, Yin |
| 作者单位 | 1.Sichuan Univ, Coll Carbon Neutral Future Technol, Chengdu 610065, Sichuan, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Sanming Univ, Sch Informat Engn, Sanming 365004, Peoples R China 4.Chinese Acad Sci, Key Lab Urban Environm & Hlth, Inst Urban Environm, Xiamen 361021, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chi, Yufeng,Ren, Yin,Xu, Chengdong,et al. The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China[J]. ENVIRONMENTAL POLLUTION,2024,342:8. |
| APA | Chi, Yufeng,Ren, Yin,Xu, Chengdong,&Zhan, Yu.(2024).The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China.ENVIRONMENTAL POLLUTION,342,8. |
| MLA | Chi, Yufeng,et al."The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China".ENVIRONMENTAL POLLUTION 342(2024):8. |
入库方式： OAI收割
来源：地理科学与资源研究所
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