Exploring Natural and Anthropogenic Drivers of PM2.5 Concentrations Based on Random Forest Model: Beijing-Tianjin-Hebei Urban Agglomeration, China
文献类型：期刊论文
| 作者 | Guo, Shasha5; Tao, Xiaoli4; Liang, Longwu2,3 |
| 刊名 | ATMOSPHERE
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 14期号:2页码:381 |
| 关键词 | PM2 5 driving factors random forest Beijing-Tianjin-Hebei urban agglomeration |
| DOI | 10.3390/atmos14020381 |
| 文献子类 | Article |
| 英文摘要 | PM2.5 is the key reason for the frequent occurrence of smog; therefore, identifying its key driving factors has far-reaching significance for the prevention and control of air pollution. Based on long-term remote sensing inversion of PM2.5 data, 21 driving factors in the fields of nature and humanities were selected, and the random forest model was applied to study the influencing factors of PM2.5 concentration in the Beijing-Tianjin-Hebei urban agglomeration (BTH) from 2000 to 2016. The results indicate: (1) The main factors affecting PM2.5 concentration not only include natural factors such as sunshine hours (SSH), relative humidity (RHU), elevation (ELE), normalized difference vegetation index (NDVI), wind speed (WIN), average temperature (TEM), daily temperature range (TEMR), and precipitation (PRE), but also human factors such as urbanization rate (URB), total investment in fixed assets (INV), and the number of employees in the secondary industry (INDU); (2) The concentration of PM2.5 changed into an inverted S-shape with the increase in SSH and WIN, and into an S-shape with the increase in RHU, NDVI, TEM, PRS, URB and INV. As for ELE and TEMR, it fluctuated and decreased with the increase in ELE, while it increased and then decreased with the increase in TEMR. However, its change was less pronounced with the increase in PRE and INDU; (3) The influence of natural factors is higher than that of human factors, but the role of human factors has been continuously strengthened in recent years. The adjustment and control of PM2.5 pollution sources from the perspective of human factors will become an effective way to reduce PM2.5 concentrations in the BTH. |
| WOS关键词 | PARTICULATE MATTER PM2.5 ; YANGTZE-RIVER DELTA ; SOURCE APPORTIONMENT ; SPATIOTEMPORAL VARIATION ; SOCIOECONOMIC-FACTORS ; URBANIZATION ; CITIES ; HAZE ; POLLUTION ; IMPACT |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:000938149100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200768]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Beijing Acad Sci & Technol, Beijing 100089, Peoples R China 4.China Acad Bldg Res, Beijing 100013, Peoples R China 5.Natl Engn Res Ctr Bldg Technol, Beijing 100013, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Shasha,Tao, Xiaoli,Liang, Longwu. Exploring Natural and Anthropogenic Drivers of PM2.5 Concentrations Based on Random Forest Model: Beijing-Tianjin-Hebei Urban Agglomeration, China[J]. ATMOSPHERE,2023,14(2):381. |
| APA | Guo, Shasha,Tao, Xiaoli,&Liang, Longwu.(2023).Exploring Natural and Anthropogenic Drivers of PM2.5 Concentrations Based on Random Forest Model: Beijing-Tianjin-Hebei Urban Agglomeration, China.ATMOSPHERE,14(2),381. |
| MLA | Guo, Shasha,et al."Exploring Natural and Anthropogenic Drivers of PM2.5 Concentrations Based on Random Forest Model: Beijing-Tianjin-Hebei Urban Agglomeration, China".ATMOSPHERE 14.2(2023):381. |
入库方式： OAI收割
来源：地理科学与资源研究所
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