Full-coverage 1 km daily ambient PM2.5 and O-3 concentrations of China in 2005-2017 based on a multi-variable random forest model
文献类型：期刊论文
| 作者 | Ma, Runmei6; Ban, Jie6; Wang, Qing6; Zhang, Yayi6; Yang, Yang4; Li, Shenshen3; Shi, Wenjiao1,2; Zhou, Zhen5,6; Zang, Jiawei5,6; Li, Tiantian6 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2022-02-25 |
| 卷号 | 14期号:2页码:943-954 |
| ISSN号 | 1866-3508 |
| DOI | 10.5194/essd-14-943-2022 |
| 英文摘要 | The health risks of fine particulate matter (PM2.5) and ambient ozone (O-3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O-3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance and estimate daily average PM2.5 concentration and O-3 daily maximum of 8 h average concentration (O-3-8 hmax) of China in 2005-2017 at a spatial resolution of 1 km x 1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on 10-fold cross-validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly estimations of PM2.5 from test datasets gave average model-fitting R-2 values of 0.85, 0.88 and 0.90, respectively; these R-2 values were 0.77, 0.77 and 0.69 for O-3-8 hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O-3-8 hmax estimations. During 2005-2017, PM2.5 concentration exhibited an overall downward trend, while ambient O-3 concentration experienced an upward trend. Whilst the spatial patterns of PM2.5 and O-3-8 hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristics. |
| 资助项目 | National Natural Science Foundation of China[92043301] ; National Natural Science Foundation of China[42071433] |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| WOS记录号 | WOS:000763325800001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/171699]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 4.China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China 5.Jiangsu Ocean Univ, Sch Marine Technol & Geomat, Lianyungang 222000, Peoples R China 6.Chinese Ctr Dis Control & Prevent, Natl Inst Environm Hlth, China CDC Key Lab Environm & Populat Hlth, Beijing 100021, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Runmei,Ban, Jie,Wang, Qing,et al. Full-coverage 1 km daily ambient PM2.5 and O-3 concentrations of China in 2005-2017 based on a multi-variable random forest model[J]. EARTH SYSTEM SCIENCE DATA,2022,14(2):943-954. |
| APA | Ma, Runmei.,Ban, Jie.,Wang, Qing.,Zhang, Yayi.,Yang, Yang.,...&Li, Tiantian.(2022).Full-coverage 1 km daily ambient PM2.5 and O-3 concentrations of China in 2005-2017 based on a multi-variable random forest model.EARTH SYSTEM SCIENCE DATA,14(2),943-954. |
| MLA | Ma, Runmei,et al."Full-coverage 1 km daily ambient PM2.5 and O-3 concentrations of China in 2005-2017 based on a multi-variable random forest model".EARTH SYSTEM SCIENCE DATA 14.2(2022):943-954. |
入库方式： OAI收割
来源：地理科学与资源研究所
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