A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information
Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level. Objectives: To estimate dai...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/53028/ |
| _version_ | 1848798862297268224 |
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| author | Chen, Gongbo Li, Shanshan Knibbs, Luke D. Hamm, Nicholas A.S. Cao, Wei Li, Tiantian Guo, Jianping Ren, Hongyan Abramson, Michael J. Guo, Yuming |
| author_facet | Chen, Gongbo Li, Shanshan Knibbs, Luke D. Hamm, Nicholas A.S. Cao, Wei Li, Tiantian Guo, Jianping Ren, Hongyan Abramson, Michael J. Guo, Yuming |
| author_sort | Chen, Gongbo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level.
Objectives: To estimate daily concentrations of PM2.5 across China during 2005–2016.
Methods: Daily ground-level PM 2.5 data were obtained from 1479 stations across China during 2014–2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM 2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM 2.5 across China with a resolution of 0.1° (≈10 km) during 2005–2016.
Results: The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM 2.5 [10-fold cross-validation (CV) R2=83%, root mean squared prediction error (RMSE) = 28.1μg/m3]. At the monthly and annual time-scale, the explained variability of average PM 2.5 increased up to 86% (RMSE = 10.7μg/m3 and 6.9μg/m3, respectively).
Conclusions: Taking advantage of a novel application of modeling framework and the most recent ground-level PM 2.5observations, the machine learning method showed higher predictive ability than previous studies. |
| first_indexed | 2025-11-14T20:26:31Z |
| format | Article |
| id | nottingham-53028 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:26:31Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-530282020-05-04T19:49:08Z https://eprints.nottingham.ac.uk/53028/ A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information Chen, Gongbo Li, Shanshan Knibbs, Luke D. Hamm, Nicholas A.S. Cao, Wei Li, Tiantian Guo, Jianping Ren, Hongyan Abramson, Michael J. Guo, Yuming Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level. Objectives: To estimate daily concentrations of PM2.5 across China during 2005–2016. Methods: Daily ground-level PM 2.5 data were obtained from 1479 stations across China during 2014–2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM 2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM 2.5 across China with a resolution of 0.1° (≈10 km) during 2005–2016. Results: The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM 2.5 [10-fold cross-validation (CV) R2=83%, root mean squared prediction error (RMSE) = 28.1μg/m3]. At the monthly and annual time-scale, the explained variability of average PM 2.5 increased up to 86% (RMSE = 10.7μg/m3 and 6.9μg/m3, respectively). Conclusions: Taking advantage of a novel application of modeling framework and the most recent ground-level PM 2.5observations, the machine learning method showed higher predictive ability than previous studies. Elsevier 2018-09-15 Article PeerReviewed Chen, Gongbo, Li, Shanshan, Knibbs, Luke D., Hamm, Nicholas A.S., Cao, Wei, Li, Tiantian, Guo, Jianping, Ren, Hongyan, Abramson, Michael J. and Guo, Yuming (2018) A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information. Science of The Total Environment, 636 . pp. 52-60. ISSN 1879-1026 PM2.5 Aerosol optical depth Random forests Machine learning China https://www.sciencedirect.com/science/article/pii/S0048969718314281?via%3Dihub doi:10.1016/j.scitotenv.2018.04.251 doi:10.1016/j.scitotenv.2018.04.251 |
| spellingShingle | PM2.5 Aerosol optical depth Random forests Machine learning China Chen, Gongbo Li, Shanshan Knibbs, Luke D. Hamm, Nicholas A.S. Cao, Wei Li, Tiantian Guo, Jianping Ren, Hongyan Abramson, Michael J. Guo, Yuming A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information |
| title | A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information |
| title_full | A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information |
| title_fullStr | A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information |
| title_full_unstemmed | A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information |
| title_short | A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information |
| title_sort | machine learning method to estimate pm 2.5 concentrations across china with remote sensing, meteorological and land use information |
| topic | PM2.5 Aerosol optical depth Random forests Machine learning China |
| url | https://eprints.nottingham.ac.uk/53028/ https://eprints.nottingham.ac.uk/53028/ https://eprints.nottingham.ac.uk/53028/ |