Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3781-2024
https://doi.org/10.5194/essd-16-3781-2024
Data description paper
 | 
27 Aug 2024
Data description paper |  | 27 Aug 2024

Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases

Siwei Li, Yu Ding, Jia Xing, and Joshua S. Fu

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Cited articles

Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, https://doi.org/10.5194/gmd-6-883-2013, 2013. 
Appel, K. W., Napelenok, S., Hogrefe, C., Pouliot, G., Foley, K. M., Roselle, S. J., Pleim, J., Bash, J., Pye, H. O. T., Heath, N., Murphy, B., and Mathur, R.: Overview and evaluation of the community multiscale air quality (CMAQ) modeling system version 5.2, in: Air Pollution Modeling and its Application XXV 35, Springer International Publishing, 69–73, https://doi.org/10.1007/978-3-319-57645-9_11, 2018. 
Bai, K., Li, K., Guo, J., and Chang, N. B.: Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? ISPRS J. Photogramm., 184, 31–44, 2022. 
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, 2016. 
Bellouin, N., Boucher, O., Haywood, J., and Reddy, M. S.: Global estimate of aerosol direct radiative forcing from satellite measurements. Nature, 438, 1138–1141, 2005. 
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Short summary
Surface PM2.5 data have gained widespread application in health assessments and related fields, while the inherent uncertainties in PM2.5 data persist due to the lack of ground-truth data across the space. This study provides a novel testbed, enabling comprehensive evaluation across the entire spatial domain. The optimized deep-learning model with spatiotemporal features successfully retrieved surface PM2.5 concentrations in China (2013–2021), with reduced biases induced by sample imbalance.
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