Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3781-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3781-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
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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Latest update: 13 Dec 2024
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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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