Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-943-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-943-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Feb 2022
Data description paper |
| 25 Feb 2022
| 25 Feb 2022
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005–2017 based on a multi-variable random forest model
Runmei Ma
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Jie Ban
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Qing Wang
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Yayi Zhang
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
Yang Yang
Institute of Urban Meteorology, China Meteorological Administration,
Beijing, 100089, China
Shenshen Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Wenjiao Shi
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy
of Sciences, Beijing 100049, China
Zhen Zhou
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
School of Marine Technology and Geomatics, Jiangsu Ocean University,
Lianyungang 222000, China
Jiawei Zang
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
School of Marine Technology and Geomatics, Jiangsu Ocean University,
Lianyungang 222000, China
China CDC Key Laboratory of Environment and Population Health,
National Institute of Environmental Health, Chinese Center for Disease
Control and Prevention, Beijing, 100021, China
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Cited
19 citations as recorded by crossref.
- Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework Q. Xiao et al. 10.5194/acp-22-13229-2022
- Predicting the Potential Habitat Distribution of Relict Plant Davidia involucrata in China Based on the MaxEnt Model T. Wang et al. 10.3390/f15020272
- Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning Q. He et al. 10.1021/acs.est.4c02926
- Ambient ozone pollution and prevalence of chronic kidney disease: A nationwide study based on the China National survey of chronic kidney disease C. Yang et al. 10.1016/j.chemosphere.2022.135603
- A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020 J. Wang 10.1016/j.eiar.2024.107630
- Impact of residential solid fuel usage and fuel conversion on children’s lung function Y. Wang et al. 10.1038/s41467-024-53386-z
- Associations between heat waves and chronic kidney disease in China: The modifying role of land cover W. Wang et al. 10.1016/j.envint.2024.108657
- Ambient PM2.5, ozone and mortality in Chinese older adults: A nationwide cohort analysis (2005–2018) Y. Zhang et al. 10.1016/j.jhazmat.2023.131539
- Related health burden with the improvement of air quality across China H. Xu et al. 10.1097/CM9.0000000000002974
- Exploring Global Land Coarse-Mode Aerosol Changes from 2001–2021 Using a New Spatiotemporal Coaction Deep-Learning Model Z. Zang et al. 10.1021/acs.est.3c07982
- Exposure to Concurrent Heatwaves and Ozone Pollution and Associations with Mortality Risk: A Nationwide Study in China H. Du et al. 10.1289/EHP13790
- Ambient PM2.5 components and prevalence of chronic kidney disease: a nationwide cross-sectional survey in China C. Yang et al. 10.1007/s10653-024-01867-x
- Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China Y. Du et al. 10.1016/j.ecoenv.2024.117285
- State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods L. Gianquintieri et al. 10.1007/s10668-024-04781-5
- Lower regional urbanicity and socioeconomic status attenuate associations of green spaces with hypertension and diabetes mellitus: a national representative cross-sectional study in China W. Wang et al. 10.1265/ehpm.24-00121
- Associations of long-term fine particulate matter exposure with all-cause and cause-specific mortality: results from the ChinaHEART project W. Li et al. 10.1016/j.lanwpc.2023.100908
- Assessing the effects of air pollution and residential greenness on frailty in older adults: a prospective cohort study from China X. Guo et al. 10.1007/s11356-023-31741-9
- Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China X. Chen et al. 10.5194/essd-14-5233-2022
- Full-coverage 1 km daily ambient PM<sub>2.5</sub> and O<sub>3</sub> concentrations of China in 2005–2017 based on a multi-variable random forest model R. Ma et al. 10.5194/essd-14-943-2022
18 citations as recorded by crossref.
- Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework Q. Xiao et al. 10.5194/acp-22-13229-2022
- Predicting the Potential Habitat Distribution of Relict Plant Davidia involucrata in China Based on the MaxEnt Model T. Wang et al. 10.3390/f15020272
- Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning Q. He et al. 10.1021/acs.est.4c02926
- Ambient ozone pollution and prevalence of chronic kidney disease: A nationwide study based on the China National survey of chronic kidney disease C. Yang et al. 10.1016/j.chemosphere.2022.135603
- A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020 J. Wang 10.1016/j.eiar.2024.107630
- Impact of residential solid fuel usage and fuel conversion on children’s lung function Y. Wang et al. 10.1038/s41467-024-53386-z
- Associations between heat waves and chronic kidney disease in China: The modifying role of land cover W. Wang et al. 10.1016/j.envint.2024.108657
- Ambient PM2.5, ozone and mortality in Chinese older adults: A nationwide cohort analysis (2005–2018) Y. Zhang et al. 10.1016/j.jhazmat.2023.131539
- Related health burden with the improvement of air quality across China H. Xu et al. 10.1097/CM9.0000000000002974
- Exploring Global Land Coarse-Mode Aerosol Changes from 2001–2021 Using a New Spatiotemporal Coaction Deep-Learning Model Z. Zang et al. 10.1021/acs.est.3c07982
- Exposure to Concurrent Heatwaves and Ozone Pollution and Associations with Mortality Risk: A Nationwide Study in China H. Du et al. 10.1289/EHP13790
- Ambient PM2.5 components and prevalence of chronic kidney disease: a nationwide cross-sectional survey in China C. Yang et al. 10.1007/s10653-024-01867-x
- Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China Y. Du et al. 10.1016/j.ecoenv.2024.117285
- State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods L. Gianquintieri et al. 10.1007/s10668-024-04781-5
- Lower regional urbanicity and socioeconomic status attenuate associations of green spaces with hypertension and diabetes mellitus: a national representative cross-sectional study in China W. Wang et al. 10.1265/ehpm.24-00121
- Associations of long-term fine particulate matter exposure with all-cause and cause-specific mortality: results from the ChinaHEART project W. Li et al. 10.1016/j.lanwpc.2023.100908
- Assessing the effects of air pollution and residential greenness on frailty in older adults: a prospective cohort study from China X. Guo et al. 10.1007/s11356-023-31741-9
- Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China X. Chen et al. 10.5194/essd-14-5233-2022
Latest update: 13 Dec 2024
Short summary
We constructed multi-variable random forest models based on 10-fold cross-validation and estimated daily PM2.5 and O3 concentration of China in 2005–2017 at a resolution of 1 km. The daily R2 values of PM2.5 and O3 were 0.85 and 0.77. The meteorological variables can significantly affect both PM2.5 and O3 modeling. During 2005–2017, PM2.5 exhibited an overall downward trend, while O3 experienced the opposite. The temporal trend of PM2.5 and O3 had spatial characteristics during the study period.
We constructed multi-variable random forest models based on 10-fold cross-validation and...
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