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.
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.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Aug 2024
Data description paper |
| 27 Aug 2024
| 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
CORRESPONDING AUTHOR
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Hubei 430000, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China
Yu Ding
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Hubei 430000, China
Jia Xing
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, the University of Tennessee, Knoxville, TN 37996, USA
Joshua S. Fu
Department of Civil and Environmental Engineering, the University of Tennessee, Knoxville, TN 37996, USA
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Ming-Tung Chuang, Maggie Chel Gee Ooi, Neng-Huei Lin, Joshua S. Fu, Chung-Te Lee, Sheng-Hsiang Wang, Ming-Cheng Yen, Steven Soon-Kai Kong, and Wei-Syun Huang
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Hajime Akimoto, Tatsuya Nagashima, Natsumi Kawano, Li Jie, Joshua S. Fu, and Zifa Wang
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In order to perform proper model simulation of ozone near the ground in the coastal area of northeastern Asia, it has been found that it is very important to select appropriate dry deposition velocities of ozone on the oceanic water of specific area of the northwestern Pacific. Empirical measurement of the mixing ratios and dry deposition flux of ozone over the ocean in this area is highly recommended.
Jia Xing, Siwei Li, Yueqi Jiang, Shuxiao Wang, Dian Ding, Zhaoxin Dong, Yun Zhu, and Jiming Hao
Atmos. Chem. Phys., 20, 14347–14359, https://doi.org/10.5194/acp-20-14347-2020, https://doi.org/10.5194/acp-20-14347-2020, 2020
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Quantifying emission changes is a prerequisite for assessment of control effectiveness in improving air quality. However, traditional bottom-up methods usually take months to perform and limit timely assessments. A novel method was developed by using a response model that provides real-time estimation of emission changes based on air quality observations. It was successfully applied to quantify emission changes on the North China Plain due to the COVID-19 pandemic shutdown.
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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.
Surface PM2.5 data have gained widespread application in health assessments and related fields,...
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