Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-3741-2025
© Author(s) 2025. 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-17-3741-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
04 Aug 2025
Data description paper |
| 04 Aug 2025
| 04 Aug 2025
Global high-resolution fire-sourced PM2.5 concentrations for 2000–2023
Yonghang Hu
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
Yadong Lei
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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Weijie Fu, Xu Yue, Chenguang Tian, Rongbin Xu, and Yuming Guo
Atmos. Chem. Phys., 25, 13103–13121, https://doi.org/10.5194/acp-25-13103-2025, https://doi.org/10.5194/acp-25-13103-2025, 2025
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Stratospheric aerosol geoengineering could cool Earth by blocking sunlight, but its impacts on extreme droughts are unclear. Our analysis of global climate simulations shows this approach might reduce extreme droughts overall. However, benefits are uneven: poorer nations face far higher drought risks compared to wealthier regions. These findings suggest that stratospheric aerosol geoengineering strategies may induce the risk of unintentionally worsening regional hydroclimatic disparities.
Jun Zhu, Yingying Wang, Xu Yue, Huizheng Che, Xiangao Xia, Xiaofei Lu, Chenguang Tian, and Hong Liao
EGUsphere, https://doi.org/10.5194/egusphere-2025-4464, https://doi.org/10.5194/egusphere-2025-4464, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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The radiative forcing (RF) of PM2.5 heavy pollution, its influencing factors and importance to precipitation in the Bohai Rim regions (China) during 2014–2023 is analyzed. The results show that the variations in PM2.5 and RF values under different temperature profiles are not consistent. Pollution RFs were as important as vertical winds to the total precipitation. The results may improve understanding of the radiative effect of pollution and provide some assistance in precipitation forecasting.
Yuxin Zheng, Xu Yue, Xiaofei Lu, and Jun Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2025-1515, https://doi.org/10.5194/egusphere-2025-1515, 2025
Preprint archived
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The sensitivity of gross primary productivity (GPP) to climate varies across regions and ecosystem types. Our study shows that warming enhances GPP in boreal regions but suppresses it in the tropics. Increased precipitation generally promotes GPP but has limited effects on tree species. However, current models tend to overestimate GPP responses to precipitation, resulting in greater GPP variability during drought events.
Yufen Wang, Ke Li, Xi Chen, Zhenjiang Yang, Minglong Tang, Pascoal M. D. Campos, Yang Yang, Xu Yue, and Hong Liao
Atmos. Chem. Phys., 25, 4455–4475, https://doi.org/10.5194/acp-25-4455-2025, https://doi.org/10.5194/acp-25-4455-2025, 2025
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The impacts of biomass burning and anthropogenic emissions on high tropospheric ozone levels are not well studied in southern Africa. We combined model simulations with recent observations at the surface and from space to quantify tropospheric ozone and its drivers in southern Africa. Our work focuses on the impact of emissions from different sources at different spatial scales, contributing to a comprehensive understanding of air pollution drivers and their uncertainties in southern Africa.
Ling Kang, Hong Liao, Ke Li, Xu Yue, Yang Yang, and Ye Wang
Atmos. Chem. Phys., 25, 3603–3621, https://doi.org/10.5194/acp-25-3603-2025, https://doi.org/10.5194/acp-25-3603-2025, 2025
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Using the model of Global Change and Air Pollution version 2.0, climate change over 2010–2045 under a carbon neutrality scenario is simulated to increase summertime maximum daily 8 h average ozone (O3) levels in eastern China, the North China Plain, and the Yangtze River Delta by 2.3, 4.7, and 3.0 ppbv, respectively. Temperature, radiation, and relative humidity are the key meteorological parameters and net chemical production is the key process in climate-driven O3 increases in eastern China.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Xinyi Zhou, Xu Yue, Chenguang Tian, and Xiaofei Lu
Atmos. Chem. Phys., 24, 9923–9937, https://doi.org/10.5194/acp-24-9923-2024, https://doi.org/10.5194/acp-24-9923-2024, 2024
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With a climate–vegetation–chemistry coupled model, we explore global climatic responses to the ozone–vegetation interactions of the present day. We find strong warming and drying effects due to the ozone-induced inhibition on plant stomatal conductance, especially over polluted regions such as the eastern US and China. These climatic perturbations further enhance surface ozone by decreasing dry deposition but reduce aerosol optical depth by increasing cloudiness and the drought tendency.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Jiachen Cao, Xu Yue, and Mingrui Ma
Atmos. Chem. Phys., 24, 3973–3987, https://doi.org/10.5194/acp-24-3973-2024, https://doi.org/10.5194/acp-24-3973-2024, 2024
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We implemented two widely used ozone damage schemes into a same regional model. Although the two schemes yielded distinct ozone vegetation damages, they predicted similar feedbacks to surface air temperature and ozone air quality in China. Our results highlighted the significance of ozone pollution control given its detrimental impacts on ecosystem functions, contributions to global warming, and amplifications of ozone pollution through ozone–vegetation coupling.
Yang Yang, Yang Zhou, Hailong Wang, Mengyun Li, Huimin Li, Pinya Wang, Xu Yue, Ke Li, Jia Zhu, and Hong Liao
Atmos. Chem. Phys., 24, 1177–1191, https://doi.org/10.5194/acp-24-1177-2024, https://doi.org/10.5194/acp-24-1177-2024, 2024
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This study reveals that extreme ozone pollution over the North China Plain and Yangtze River Delta is due to the chemical production related to hot and dry conditions, and the regional transport explains the ozone pollution over the Sichuan Basin and Pearl River Delta. The frequency of meteorological conditions of the extreme ozone pollution increases from the past to the future. The sustainable scenario is the optimal path to retaining clean air in China in the future.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6833–6856, https://doi.org/10.5194/gmd-16-6833-2023, https://doi.org/10.5194/gmd-16-6833-2023, 2023
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In addition to the dominant role of the PBL scheme on the results of the meteorological field, many factors in the model are influenced by large uncertainties. This study focuses on the uncertainties that influence numerical simulation results (including horizontal resolution, vertical resolution, near-surface scheme, initial and boundary conditions, underlying surface update, and update of model version), hoping to provide a reference for scholars conducting research on the model.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, https://doi.org/10.5194/gmd-16-6635-2023, 2023
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Most current studies on planetary boundary layer (PBL) parameterization schemes are relatively fragmented and lack systematic in-depth analysis and discussion. In this study, we comprehensively evaluate the performance capability of the PBL scheme in five typical regions of China in different seasons from the mechanism of the scheme and the effects of PBL schemes on the near-surface meteorological parameters, vertical structures of the PBL, PBL height, and turbulent diffusion.
Yuan Zhao, Xu Yue, Yang Cao, Jun Zhu, Chenguang Tian, Hao Zhou, Yuwen Chen, Yihan Hu, Weijie Fu, and Xu Zhao
Atmos. Chem. Phys., 23, 7823–7838, https://doi.org/10.5194/acp-23-7823-2023, https://doi.org/10.5194/acp-23-7823-2023, 2023
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We project the future changes of dust emissions and loading using an ensemble of model outputs from the Coupled Model Intercomparison Project version 6 under four scenarios. We find increased dust emissions and loading in North Africa, due to increased drought and strengthened surface wind, and decreased dust loading over Asia, following enhanced precipitation. Such a spatial pattern remains similar, though the regional intensity varies among different scenarios.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Yingfang Li, Zhili Wang, Yadong Lei, Huizheng Che, and Xiaoye Zhang
Atmos. Chem. Phys., 23, 2499–2523, https://doi.org/10.5194/acp-23-2499-2023, https://doi.org/10.5194/acp-23-2499-2023, 2023
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Since few studies have assessed the impacts of future combined reductions in aerosols, ozone, and their precursors on future climate change, we use models with an interactive representation of tropospheric aerosols and atmospheric chemistry schemes to quantify the impact of their reductions on the Asian climate. Our results suggest that their reductions will exacerbate the warming effect caused by greenhouse gases, increasing future climate extremes and associated population exposure risk.
Huibin Dai, Hong Liao, Ke Li, Xu Yue, Yang Yang, Jia Zhu, Jianbing Jin, Baojie Li, and Xingwen Jiang
Atmos. Chem. Phys., 23, 23–39, https://doi.org/10.5194/acp-23-23-2023, https://doi.org/10.5194/acp-23-23-2023, 2023
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We apply the 3-D global chemical transport model (GEOS-Chem) to simulate co-polluted days by O3 and PM2.5 (O3–PM2.5PDs) in Beijing–Tianjin–Hebei in 2013–2020 and investigate the chemical and physical characteristics of O3–PM2.5PDs by composited analyses of such days that are captured by both the observations and the model. We report for the first time the unique features in vertical distributions of aerosols during O3–PM2.5PDs and the physical and chemical characteristics of O3–PM2.5PDs.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Chenguang Tian, Xu Yue, Jun Zhu, Hong Liao, Yang Yang, Yadong Lei, Xinyi Zhou, Hao Zhou, Yimian Ma, and Yang Cao
Atmos. Chem. Phys., 22, 12353–12366, https://doi.org/10.5194/acp-22-12353-2022, https://doi.org/10.5194/acp-22-12353-2022, 2022
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We quantify the impacts of fire aerosols on climate through direct, indirect, and albedo effects. In atmosphere-only simulations, we find global fire aerosols cause surface cooling and rainfall inhibition over many land regions. These fast atmospheric perturbations further lead to a reduction in regional leaf area index and lightning activities. By considering the feedback of fire aerosols on humidity, lightning, and leaf area index, we predict a slight reduction in fire emissions.
Lei Li, Yevgeny Derimian, Cheng Chen, Xindan Zhang, Huizheng Che, Gregory L. Schuster, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Christian Matar, Fabrice Ducos, Yana Karol, Benjamin Torres, Ke Gui, Yu Zheng, Yuanxin Liang, Yadong Lei, Jibiao Zhu, Lei Zhang, Junting Zhong, Xiaoye Zhang, and Oleg Dubovik
Earth Syst. Sci. Data, 14, 3439–3469, https://doi.org/10.5194/essd-14-3439-2022, https://doi.org/10.5194/essd-14-3439-2022, 2022
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A climatology of aerosol composition concentration derived from POLDER-3 observations using GRASP/Component is presented. The conceptual specifics of the GRASP/Component approach are in the direct retrieval of aerosol speciation without intermediate retrievals of aerosol optical characteristics. The dataset of satellite-derived components represents scarce but imperative information for validation and potential adjustment of chemical transport models.
Jiyuan Gao, Yang Yang, Hailong Wang, Pinya Wang, Huimin Li, Mengyun Li, Lili Ren, Xu Yue, and Hong Liao
Atmos. Chem. Phys., 22, 7131–7142, https://doi.org/10.5194/acp-22-7131-2022, https://doi.org/10.5194/acp-22-7131-2022, 2022
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China has been implementing a sequence of policies for clean air since the year 2013. The aerosol decline produced a 0.09 ± 0.10°C warming during 2013–2017 estimated in this study, and the increase in ozone in the lower troposphere during this time period accelerated the warming, leading to a total 0.16 ± 0.15°C temperature increase in eastern China. Residential emission reductions led to a cooling effect because of a substantial decrease in light-absorbing aerosols.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Hao Zhou, Xu Yue, Yadong Lei, Chenguang Tian, Jun Zhu, Yimian Ma, Yang Cao, Xixi Yin, and Zhiding Zhang
Atmos. Chem. Phys., 22, 693–709, https://doi.org/10.5194/acp-22-693-2022, https://doi.org/10.5194/acp-22-693-2022, 2022
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Aerosols enhance plant photosynthesis by increasing diffuse radiation. In this study, we found that the aerosol impacts are quite different for varied species. Scattering aerosols such as sulfate and organic carbon promote photosynthesis while absorbing aerosols such as black carbon have negative impacts. Earth system models should consider the impacts of cloud and aerosol species on terrestrial ecosystems so as to better predict carbon cycles under different emission scenarios.
Yadong Lei, Xu Yue, Hong Liao, Lin Zhang, Yang Yang, Hao Zhou, Chenguang Tian, Cheng Gong, Yimian Ma, Lan Gao, and Yang Cao
Atmos. Chem. Phys., 21, 11531–11543, https://doi.org/10.5194/acp-21-11531-2021, https://doi.org/10.5194/acp-21-11531-2021, 2021
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We present the first estimate of ozone enhancement by fire emissions through ozone–vegetation interactions using a fully coupled chemistry–vegetation model (GC-YIBs). In fire-prone areas, fire-induced ozone causes a positive feedback to surface ozone mainly because of the inhibition effects on stomatal conductance.
Jasdeep Singh Anand, Alessandro Anav, Marcello Vitale, Daniele Peano, Nadine Unger, Xu Yue, Robert J. Parker, and Hartmut Boesch
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-125, https://doi.org/10.5194/bg-2021-125, 2021
Publication in BG not foreseen
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Ozone damages plants, which prevents them from absorbing CO2 from the atmosphere. This poses a potential threat to preventing dangerous climate change. In this work, satellite observations of forest cover, ozone, climate, and growing season are combined with an empirical model to estimate the carbon lost due to ozone exposure over Europe. The estimated carbon losses agree well with prior modelled estimates, showing for the first time that satellites can be used to better understand this effect.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Cited articles
Atuyambe, L. M., Etajak, S., Walyawula, F., Kasasa, S., Nyabigambo, A., Bazeyo, W., Wipfli, H., Samet, J. M., and Berhane, K. T.: Air quality and attributable mortality among city dwellers in Kampala, Uganda: results from 4 years of continuous PM2.5 concentration monitoring using BAM 1022 reference instrument, J. Expo. Sci. Env. Epid., 35, 288–293, https://doi.org/10.1038/s41370-024-00684-9, 2024.
Benchrif, A., Wheida, A., Tahri, M., Shubbar, R. M., and Biswas, B.: Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants, Sustain. Cities Soc., 74, 103170, https://doi.org/10.1016/j.scs.2021.103170, 2021.
Bistinas, I., Harrison, S. P., Prentice, I. C., and Pereira, J. M. C.: Causal relationships versus emergent patterns in the global controls of fire frequency, Biogeosciences, 11, 5087–5101, https://doi.org/10.5194/bg-11-5087-2014, 2014.
Breider, T. J., Mickley, L. J., Jacob, D. J., Wang, Q., Fisher, J. A., Chang, R. Y. W., and Alexander, B.: Annual distributions and sources of Arctic aerosol components, aerosol optical depth, and aerosol absorption, J. Geophys. Res.-Atmos., 119, 4107–4124, https://doi.org/10.1002/2013JD020996, 2014.
Burke, M., Childs, M. L., de la Cuesta, B., Qiu, M., Li, J., Gould, C. F., Heft-Neal, S., and Wara, M.: The contribution of wildfire to PM2.5 trends in the USA, Nature, 622, 761–766, https://doi.org/10.1038/s41586-023-06522-6, 2023.
Chen, G., Guo, Y., Yue, X., Tong, S., Gasparrini, A., Bell, M. L., Armstrong, B., Schwartz, J., Jaakkola, J. J. K., Zanobetti, A., Lavigne, E., Nascimento Saldiva, P. H., Kan, H., Royé, D., Milojevic, A., Overcenco, A., Urban, A., Schneider, A., Entezari, A., Vicedo-Cabrera, A. M., Zeka, A., Tobias, A., Nunes, B., Alahmad, B., Forsberg, B., Pan, S.-C., Íñiguez, C., Ameling, C., De la Cruz Valencia, C., Åström, C., Houthuijs, D., Van Dung, D., Samoli, E., Mayvaneh, F., Sera, F., Carrasco-Escobar, G., Lei, Y., Orru, H., Kim, H., Holobaca, I.-H., Kyselý, J., Teixeira, J. P., Madureira, J., Katsouyanni, K., Hurtado-Díaz, M., Maasikmets, M., Ragettli, M. S., Hashizume, M., Stafoggia, M., Pascal, M., Scortichini, M., de Sousa Zanotti Stagliorio Coêlho, M., Valdés Ortega, N., Ryti, N. R. I., Scovronick, N., Matus, P., Goodman, P., Garland, R. M., Abrutzky, R., Garcia, S. O., Rao, S., Fratianni, S., Dang, T. N., Colistro, V., Huber, V., Lee, W., Seposo, X., Honda, Y., Guo, Y. L., Ye, T., Yu, W., Abramson, M. J., Samet, J. M., and Li, S.: Mortality risk attributable to wildfire-related PM2.5 pollution: a global time series study in 749 locations, The Lancet Planetary Health, 5, e579–e587, https://doi.org/10.1016/S2542-5196(21)00200-X, 2021.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016, San Francisco, California, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Childs, M. L., Li, J., Wen, J., Heft-Neal, S., Driscoll, A., Wang, S., Gould, C. F., Qiu, M., Burney, J., and Burke, M.: Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US, Environ. Sci. Technol., 56, 13607–13621, https://doi.org/10.1021/acs.est.2c02934, 2022.
Connolly, R., Marlier, M. E., Garcia-Gonzales, D. A., Wilkins, J., Su, J., Bekker, C., Jung, J., Bonilla, E., Burnett, R. T., Zhu, Y., and Jerrett, M.: Mortality attributable to PM2.5 from wildland fires in California from 2008 to 2018, Science Advances, 10, eadl1252, https://doi.org/10.1126/sciadv.adl1252, 2024.
David, L. M., Ravishankara, A. R., Brewer, J. F., Sauvage, B., Thouret, V., Venkataramani, S., and Sinha, V.: Tropospheric ozone over the Indian subcontinent from 2000 to 2015: Data set and simulation using GEOS-Chem chemical transport model, Atmos. Environ., 219, 117039, https://doi.org/10.1016/j.atmosenv.2019.117039, 2019.
Delp, W. W. and Singer, B. C.: Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors, Sensors-Basel, 20, 3683, https://doi.org/10.3390/s20133683, 2020.
Desservettaz, M. J., Fisher, J. A., Luhar, A. K., Woodhouse, M. T., Bukosa, B., Buchholz, R. R., Wiedinmyer, C., Griffith, D. W. T., Krummel, P. B., Jones, N. B., Deutscher, N. M., and Greenslade, J. W.: Australian Fire Emissions of Carbon Monoxide Estimated by Global Biomass Burning Inventories: Variability and Observational Constraints, J. Geophys. Res.-Atmos., 127, e2021JD035925, https://doi.org/10.1029/2021JD035925, 2022.
Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., Xue, T., Tong, D., Zheng, B., Peng, Y., Huang, X., He, K., and Zhang, Q.: Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion, Environ. Sci. Technol., 55, 12106–12115, https://doi.org/10.1021/acs.est.1c01863, 2021.
Gu, B., Zhang, L., Van Dingenen, R., Vieno, M., Van Grinsven, H. J. M., Zhang, X., Zhang, S., Chen, Y., Wang, S., Ren, C., Rao, S., Holland, M., Winiwarter, W., Chen, D., Xu, J., and Sutton, M. A.: Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution, Science, 374, 758–762, https://doi.org/10.1126/science.abf8623, 2021.
Gui, K., Che, H., Zeng, Z., Wang, Y., Zhai, S., Wang, Z., Luo, M., Zhang, L., Liao, T., Zhao, H., Li, L., Zheng, Y., and Zhang, X.: Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model, Environ. Int., 141, 105801, https://doi.org/10.1016/j.envint.2020.105801, 2020.
Heilman, W. E., Liu, Y., Urbanski, S., Kovalev, V., and Mickler, R.: Wildland fire emissions, carbon, and climate: Plume rise, atmospheric transport, and chemistry processes, Forest Ecol. Manag., 317, 70–79, https://doi.org/10.1016/j.foreco.2013.02.001, 2014.
Hu, Y., Yue, X., and Tian, C.: Climatic drivers of the Canadian wildfire episode in 2023, Atmospheric and Oceanic Science Letters, 17, 100483, https://doi.org/10.1016/j.aosl.2024.100483, 2024.
Hu, Y., Tian, C., and Yue, X.: GFED: Fire-sourced PM2.5 concentrations dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.15493914, 2025a.
Hu, Y., Tian, C., and Yue, X.: QFED: Fire-sourced PM2.5 concentrations dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.15496596, 2025b.
Jin, L., Permar, W., Selimovic, V., Ketcherside, D., Yokelson, R. J., Hornbrook, R. S., Apel, E. C., Ku, I.-T., Collett Jr., J. L., Sullivan, A. P., Jaffe, D. A., Pierce, J. R., Fried, A., Coggon, M. M., Gkatzelis, G. I., Warneke, C., Fischer, E. V., and Hu, L.: Constraining emissions of volatile organic compounds from western US wildfires with WE-CAN and FIREX-AQ airborne observations, Atmos. Chem. Phys., 23, 5969–5991, https://doi.org/10.5194/acp-23-5969-2023, 2023.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.
Keller, C. A., Long, M. S., Yantosca, R. M., Da Silva, A. M., Pawson, S., and Jacob, D. J.: HEMCO v1.0: a versatile, ESMF-compliant component for calculating emissions in atmospheric models, Geosci. Model Dev., 7, 1409–1417, https://doi.org/10.5194/gmd-7-1409-2014, 2014.
Kim, P. S., Jacob, D. J., Fisher, J. A., Travis, K., Yu, K., Zhu, L., Yantosca, R. M., Sulprizio, M. P., Jimenez, J. L., Campuzano-Jost, P., Froyd, K. D., Liao, J., Hair, J. W., Fenn, M. A., Butler, C. F., Wagner, N. L., Gordon, T. D., Welti, A., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Teng, A. P., Millet, D. B., Schwarz, J. P., Markovic, M. Z., and Perring, A. E.: Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model, Atmos. Chem. Phys., 15, 10411–10433, https://doi.org/10.5194/acp-15-10411-2015, 2015.
Knorr, W., Kaminski, T., Arneth, A., and Weber, U.: Impact of human population density on fire frequency at the global scale, Biogeosciences, 11, 1085–1102, https://doi.org/10.5194/bg-11-1085-2014, 2014.
Konovalov, I. B., Golovushkin, N. A., and Beekmann, M.: Wildfire-smoke-precipitation interactions in Siberia: Insights from a regional model study, Sci. Total Environ., 951, 175518, https://doi.org/10.1016/j.scitotenv.2024.175518, 2024.
Larkin, N. K., Raffuse, S. M., and Strand, T. M.: Wildland fire emissions, carbon, and climate: U. S. emissions inventories, Forest Ecol. Manag., 317, 61–69, https://doi.org/10.1016/j.foreco.2013.09.012, 2014.
Lei, Y., Yue, X., Liao, H., Zhang, L., Yang, Y., Zhou, H., Tian, C., Gong, C., Ma, Y., Gao, L., and Cao, Y.: Indirect contributions of global fires to surface ozone through ozone–vegetation feedback, Atmos. Chem. Phys., 21, 11531–11543, https://doi.org/10.5194/acp-21-11531-2021, 2021.
Li, F., Zhang, X., Kondragunta, S., Lu, X., Csiszar, I., and Schmidt, C. C.: Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications, Remote Sens. Environ., 281, 113237, https://doi.org/10.1016/j.rse.2022.113237, 2022.
Li, S., Zhang, L., Cai, K., Ge, W., and Zhang, X.: Comparisons of the vertical distributions of aerosols in the CALIPSO and GEOS-Chem datasets in China, Atmospheric Environment: X, 3, 100036, https://doi.org/10.1016/j.aeaoa.2019.100036, 2019.
Li, Y., Li, M., Li, C., and Liu, Z.: Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms, Sci. Rep.-UK, 10, 9952, https://doi.org/10.1038/s41598-020-67024-3, 2020.
Lu, X., Zhang, L., Chen, Y., Zhou, M., Zheng, B., Li, K., Liu, Y., Lin, J., Fu, T.-M., and Zhang, Q.: Exploring 2016–2017 surface ozone pollution over China: source contributions and meteorological influences, Atmos. Chem. Phys., 19, 8339–8361, https://doi.org/10.5194/acp-19-8339-2019, 2019.
Ma, J., Cheng, J. C. P., Xu, Z., Chen, K., Lin, C., and Jiang, F.: Identification of the most influential areas for air pollution control using XGBoost and Grid Importance Rank, J. Clean. Prod., 274, 122835, https://doi.org/10.1016/j.jclepro.2020.122835, 2020.
Mao, J., Paulot, F., Jacob, D. J., Cohen, R. C., Crounse, J. D., Wennberg, P. O., Keller, C. A., Hudman, R. C., Barkley, M. P., and Horowitz, L. W.: Ozone and organic nitrates over the eastern United States: Sensitivity to isoprene chemistry, J. Geophys. Res.-Atmos., 118, 11256–11268, https://doi.org/10.1002/jgrd.50817, 2013.
Markakis, K., Valari, M., Perrussel, O., Sanchez, O., and Honore, C.: Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology, Atmos. Chem. Phys., 15, 7703–7723, https://doi.org/10.5194/acp-15-7703-2015, 2015.
Marques, J. F., Alves, M. B., Silveira, C. F., Amaral e Silva, A., Silva, T. A., dos Santos, V. J., and Calijuri, M. L.: Fires dynamics in the Pantanal: Impacts of anthropogenic activities and climate change, J. Environ. Manage., 299, 113586, https://doi.org/10.1016/j.jenvman.2021.113586, 2021.
Melia, N., Dean, S., Pearce, H. G., Harrington, L., Frame, D. J., and Strand, T.: Aotearoa New Zealand's 21st-Century Wildfire Climate, Earths Future, 10, e2022EF002853, https://doi.org/10.1029/2022EF002853, 2022.
O'Dell, K., Ford, B., Fischer, E. V., and Pierce, J. R.: Contribution of Wildland-Fire Smoke to US PM2.5 and Its Influence on Recent Trends, Environ. Sci. Technol., 53, 1797–1804, https://doi.org/10.1021/acs.est.8b05430, 2019.
Pan, X., Ichoku, C., Chin, M., Bian, H., Darmenov, A., Colarco, P., Ellison, L., Kucsera, T., da Silva, A., Wang, J., Oda, T., and Cui, G.: Six global biomass burning emission datasets: intercomparison and application in one global aerosol model, Atmos. Chem. Phys., 20, 969–994, https://doi.org/10.5194/acp-20-969-2020, 2020.
Pechony, O. and Shindell, D. T.: Fire parameterization on a global scale, J. Geophys. Res.-Atmos., 114, D16115, https://doi.org/10.1029/2009JD011927, 2009.
Perry, G. L. W. and Dickson, M. E.: Using Machine Learning to Predict Geomorphic Disturbance: The Effects of Sample Size, Sample Prevalence, and Sampling Strategy, J. Geophys. Res.-Earth, 123, 2954–2970, https://doi.org/10.1029/2018JF004640, 2018.
Petrenko, M., Kahn, R., Chin, M., Soja, A., Kucsera, T., and Harshvardhan: The use of satellite-measured aerosol optical depth to constrain biomass burning emissions source strength in the global model GOCART, J. Geophys. Res.-Atmos., 117, D18212, https://doi.org/10.1029/2012JD017870, 2012.
Protonotariou, A., Tombrou, M., Giannakopoulos, C., Kostopoulou, E., and Le Sager, P.: Study of CO surface pollution in Europe based on observations and nested-grid applications of GEOS-CHEM global chemical transport model, Tellus B, 62, 209–227, https://doi.org/10.1111/j.1600-0889.2010.00462.x, 2010.
Qiu, M., Kelp, M., Heft-Neal, S., Jin, X., Gould, C. F., Tong, D. Q., and Burke, M.: Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM2.5: Implications for Assessment of Health Impacts, Environ. Sci. Technol., 58, 22880–22893, https://doi.org/10.1021/acs.est.4c05922, 2024.
Roberts, G. and Wooster, M. J.: Global impact of landscape fire emissions on surface level PM2.5 concentrations, air quality exposure and population mortality, Atmos. Environ., 252, 118210, https://doi.org/10.1016/j.atmosenv.2021.118210, 2021.
Salana, S., Yu, H., Dai, Z., Subramanian, P. S. G., Puthussery, J. V., Wang, Y., Singh, A., Pope, F. D., Leiva G, M. A., Rastogi, N., Tripathi, S. N., Weber, R. J., and Verma, V.: Inter-continental variability in the relationship of oxidative potential and cytotoxicity with PM2.5 mass, Nat. Commun., 15, 5263, https://doi.org/10.1038/s41467-024-49649-4, 2024.
Song, Z., Chen, B., Zhang, P., Guan, X., Wang, X., Ge, J., Hu, X., Zhang, X., and Wang, Y.: High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China, Atmos. Res., 274, 106199, https://doi.org/10.1016/j.atmosres.2022.106199, 2022.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
Wang, L., Liu, B., Li, R., Chen, X., Liu, L., Tang, X., Liu, J., Liao, Z., Xin, J., Wang, Y., and Hu, B.: Prediction of daily PM2.5 and ozone based on high-density weather stations in China: Nonlinear effects of meteorology, human and ecosystem health risks, Atmos. Res., 293, 106889, https://doi.org/10.1016/j.atmosres.2023.106889, 2023.
Ward, D. S., Kloster, S., Mahowald, N. M., Rogers, B. M., Randerson, J. T., and Hess, P. G.: The changing radiative forcing of fires: global model estimates for past, present and future, Atmos. Chem. Phys., 12, 10857–10886, https://doi.org/10.5194/acp-12-10857-2012, 2012.
Ward, D. S., Shevliakova, E., Malyshev, S., and Rabin, S.: Trends and Variability of Global Fire Emissions Due To Historical Anthropogenic Activities, Global Biogeochem. Cy., 32, 122–142, https://doi.org/10.1002/2017gb005787, 2018.
Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M.: Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach, Remote Sens. Environ., 231, 111221, https://doi.org/10.1016/j.rse.2019.111221, 2019.
Wei, J., Li, Z., Lyapustin, A. I., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021.
Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T.: First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact, Nat. Commun., 14, 8349, https://doi.org/10.1038/s41467-023-43862-3, 2023.
Wolke, R., Schröder, W., Schrödner, R., and Renner, E.: Influence of grid resolution and meteorological forcing on simulated European air quality: A sensitivity study with the modeling system COSMO–MUSCAT, Atmos. Environ., 53, 110–130, https://doi.org/10.1016/j.atmosenv.2012.02.085, 2012.
Wong, P.-Y., Lee, H.-Y., Chen, Y.-C., Zeng, Y.-T., Chern, Y.-R., Chen, N.-T., Candice Lung, S.-C., Su, H.-J., and Wu, C.-D.: Using a land use regression model with machine learning to estimate ground level PM2.5, Environ. Pollut., 277, 116846, https://doi.org/10.1016/j.envpol.2021.116846, 2021.
Xiao, Q., Geng, G., Cheng, J., Liang, F., Li, R., Meng, X., Xue, T., Huang, X., Kan, H., Zhang, Q., and He, K.: Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models, Atmos. Environ., 244, 117921, https://doi.org/10.1016/j.atmosenv.2020.117921, 2021.
Xiao, Y., Wang, Y., Yuan, Q., He, J., and Zhang, L.: Generating a long-term (2003–2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS), Sci. Total Environ., 848, 157747, https://doi.org/10.1016/j.scitotenv.2022.157747, 2022.
Xie, Y., Zhou, M., Hunt, K. M. R., and Mauzerall, D. L.: Recent PM2.5 air quality improvements in India benefited from meteorological variation, Nature Sustainability, 7, 983–993, https://doi.org/10.1038/s41893-024-01366-y, 2024.
Xu, R., Yu, P., Abramson, M. J., Johnston, F. H., Samet, J. M., Bell, M. L., Haines, A., Ebi, K. L., Li, S., and Guo, Y.: Wildfires, Global Climate Change, and Human Health, New Engl. J. Med., 383, 2173–2181, https://doi.org/10.1056/NEJMsr2028985, 2020.
Xu, R., Ye, T., Yue, X., Yang, Z., Yu, W., Zhang, Y., Bell, M. L., Morawska, L., Yu, P., Zhang, Y., Wu, Y., Liu, Y., Johnston, F., Lei, Y., Abramson, M. J., Guo, Y., and Li, S.: Global population exposure to landscape fire air pollution from 2000 to 2019, Nature, 621, 521–529, https://doi.org/10.1038/s41586-023-06398-6, 2023.
Xu, X., Wang, J., Henze, D. K., Qu, W., and Kopacz, M.: Constraints on aerosol sources using GEOS-Chem adjoint and MODIS radiances, and evaluation with multisensor (OMI, MISR) data, J. Geophys. Res.-Atmos., 118, 6396–6413, https://doi.org/10.1002/jgrd.50515, 2013.
Yan, Y., Lin, J., and He, C.: Ozone trends over the United States at different times of day, Atmos. Chem. Phys., 18, 1185–1202, https://doi.org/10.5194/acp-18-1185-2018, 2018.
Yue, X., Hu, Y., Tian, C., Xu, R., Yu, W., and Guo, Y.: Increasing impacts of fire air pollution on public and ecosystem health, The Innovation, 5, 100609, https://doi.org/10.1016/j.xinn.2024.100609, 2024.
Zhang, D., Wang, W., Xi, Y., Bi, J., Hang, Y., Zhu, Q., Pu, Q., Chang, H., and Liu, Y.: Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States, Environ. Sci. Technol., 57, 19990–19998, https://doi.org/10.1021/acs.est.3c05143, 2023.
Zhou, S., Collier, S., Jaffe, D. A., and Zhang, Q.: Free tropospheric aerosols at the Mt. Bachelor Observatory: more oxidized and higher sulfate content compared to boundary layer aerosols, Atmos. Chem. Phys., 19, 1571–1585, https://doi.org/10.5194/acp-19-1571-2019, 2019.
Zhu, A., Xu, H., Deng, J., Ma, J., and Li, S.: El Niño–Southern Oscillation (ENSO) effect on interannual variability in spring aerosols over East Asia, Atmos. Chem. Phys., 21, 5919–5933, https://doi.org/10.5194/acp-21-5919-2021, 2021.
Short summary
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...
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