Articles | Volume 18, issue 6
https://doi.org/10.5194/essd-18-4279-2026
© Author(s) 2026. 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-18-4279-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
24 Jun 2026
Data description article |
| 24 Jun 2026
| 24 Jun 2026
Reconstructing two-decade daily high-resolution seamless global land XCO2 records using a hybrid Transformer–BiLSTM model
Yu Qu
MEEKL-AERM, College of Environmental Sciences and Engineering, Institute of Tibetan Plateau, and Center for Environment and Health, Peking University, Beijing 100871, China
School of Geographical Sciences, South China Normal University, Guangzhou 510631, China
Xian Shi
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Yulong Fan
MEEKL-AERM, College of Environmental Sciences and Engineering, Institute of Tibetan Plateau, and Center for Environment and Health, Peking University, Beijing 100871, China
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Zhihui Wang
University of Science and Technology of China, Hefei 230026, China
Anhui Province Key Laboratory of Optical Quantitative Remote Sensing, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
MEEKL-AERM, College of Environmental Sciences and Engineering, Institute of Tibetan Plateau, and Center for Environment and Health, Peking University, Beijing 100871, China
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We developed a machine learning approach to map daily air pollution across China at high resolution, covering six major pollutants. Our results reveal how different pollutants respond differently to changes in human activity and emissions, uncovering the underlying chemical and atmospheric processes. This study provides detailed evidence of air pollution patterns and interactions, offering insights that can guide more effective strategies to protect air quality and public health.
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This study estimated the daily seamless 10 km ambient gaseous pollutants (NO2, SO2, and CO) across China using machine learning with extensive input variables measured on monitors, satellites, and models. Our dataset yields a high data quality via cross-validation at varying spatiotemporal scales and outperforms most previous related studies, making it most helpful to future (especially short-term) air pollution and environmental health-related studies.
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PM2.5 pollution is a pressing environmental issue threatening human health and food security globally. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas emissions. Persistent secondary inorganic aerosol pollution in China is limited by acid gas emissions, while an additional control on NH3 emissions would become more important as reductions in SO2 and NOx emissions progress.
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Short summary
We developed a new global dataset that provides daily seamless observations of XCO2 over land from 2003 to 2022. Using artificial intelligence to integrate multiple satellite missions, atmospheric reanalysis, and environmental data, we filled data gaps and ensured continuity across satellite records. The dataset captures both long-term increases in XCO2 and short-term enhancements associated with events such as wildfires, supporting carbon-emission monitoring and climate-change studies.
We developed a new global dataset that provides daily seamless observations of XCO2 over land...
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