State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Chenguang Tian
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Yang Cao
Jiangsu Nanjing Environmental Monitoring Center, Nanjing 210041, China
Rongbin Xu
Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
Yuming Guo
Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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4,212
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5,720
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EndNote: 217
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Total article views: 4,003 (including HTML, PDF, and XML)
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3,033
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4,003
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Supplement: 290
BibTeX: 79
EndNote: 123
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1,179
469
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1,717
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HTML: 1,179
PDF: 469
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Total: 1,717
Supplement: 196
BibTeX: 49
EndNote: 94
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Total article views: 5,720 (including HTML, PDF, and XML)
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Total article views: 4,003 (including HTML, PDF, and XML)
Thereof 3,946 with geography defined
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Total article views: 1,717 (including HTML, PDF, and XML)
Thereof 1,684 with geography defined
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We develop a global dataset of daily fire-sourced PM2.5 concentrations at a spatial resolution of 0.25° for 2000–2023, using a chemical transport model driven with two fire emission inventories and a machine learning approach trained with ground measurements from over 9000 sites. The dataset shows significant spatiotemporal variations of fire PM2.5 in the past decades, serving a useful tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health.
We develop a global dataset of daily fire-sourced PM2.5 concentrations at a spatial resolution...