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Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach
Open accessAbstract
Abstract Fine particulate matter (PM2.5) is closely related to the atmospheric environment and human life. Satellite-based aerosol optical depth (AOD) products have been widely applied in estimating daily PM2.5 concentrations over large areas using statistical regression models. However, they are often given at coarse spatial resolutions which limit their applications in small or medium scales. This study aims to produce PM2.5 concentrations at a high spatial resolution (1 km) across China based on the newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product using a newly developed space-time random forest (STRF) model. Daily PM2.5 concentrations for 2016 were estimated from in-situ surface PM2.5 measurements, and meteorological and ancillary variables. The 10-fold cross-validation (CV) approach and three popular models, including the multiple linear regression model, the geographically weighted regression model, and the two-stage model, are employed for validation and cross-comparison. A sample-based CV of the STRF model shows a high and stable accuracy with a coefficient of determination equal to 0.85, a root-mean-square error of 15.57 μg m−3, and a mean prediction error of 9.77 μg m−3. This finding suggests that the STRF model can predict PM2.5 daily, monthly, and annual concentrations at an unprecedented spatial resolution and accuracy across China. It also appears to have outperformed the above popular models and the previous related studies. In general, the STRF model is robust and can accurately estimate PM2.5 concentrations by taking advantage of the ensemble regression approach, the synergy of space-time information, and the high-resolution, high-quality, and wide-spatial-coverage of the MAIAC AOD product. It may thus also be useful for applications in related air pollution studies, especially those focused on urban areas.
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Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890 · cited 1×
“The samplebased CV is a widely used validation approach (Rodriguez et al., 2010) that involves randomly selecting 90% of the samples for modeling and using the remaining 10% for validation.”
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Cited by
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., & Cribb, M. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112136 · Cited by 1,346
Self-citationCOVID-19 lockdowns cause global air pollution declines
Venter, Z. S., Aunan, K., Chowdhury, S., & Lelieveld, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006853117 · Cited by 1,081
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., et al. (2020) · Atmospheric Chemistry and Physics · doi:10.5194/acp-20-3273-2020 · Cited by 706
Self-citationStronger policy required to substantially reduce deaths from PM2.5 pollution in China
Yue, H., He, C., Huang, Q., Yin, D., & Bryan, B. A. (2020) · Nature Communications · doi:10.1038/s41467-020-15319-4 · Cited by 419
Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R. T., Dickerson, R. R., Lin, J., Boersma, K. F., et al. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.2c03834 · Cited by 361
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Metadata
- Authors
- Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., & Cribb, M.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 231
- Pages
- 111221
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2953978338
- Cited by
- 534
- References
- 3
- FWCI
- 30.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., & Cribb, M. (2019). Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111221
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