Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3495-2024
© 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-16-3495-2024
© 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
| 
02 Aug 2024
Data description paper |
| 02 Aug 2024
| 02 Aug 2024
Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Jie Chen
Innovation Center for FengYun Meteorological Satellite, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Wei Zheng
Innovation Center for FengYun Meteorological Satellite, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Tianchan Shan
CORRESPONDING AUTHOR
Innovation Center for FengYun Meteorological Satellite, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Gang Wang
Guangzhou Meteorological Satellite Ground Station, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Guangzhou 510640, China
Related authors
No articles found.
Jie Chen, Qiancheng Lv, Shuang Wu, Yelu Zeng, Manchun Li, Ziyue Chen, Enze Zhou, Wei Zheng, Cheng Liu, Xiao Chen, Jing Yang, and Bingbo Gao
Earth Syst. Sci. Data, 15, 1911–1931, https://doi.org/10.5194/essd-15-1911-2023, https://doi.org/10.5194/essd-15-1911-2023, 2023
Short summary
Short summary
The Himawari-8 fire product is the mainstream fire product with the highest temporal resolution, yet it presents large uncertainties and is not suitable for reliable real-time fire monitoring in China. To address this issue, we proposed an adaptive hourly NSMC (National Satellite Meteorological Center) Himawari-8 fire product for China; the overall accuracy increased from 54 % (original Himawari product) to 80 %. This product can largely enhance real-time fire monitoring and relevant research.
Jie Chen, Qi Yao, Ziyue Chen, Manchun Li, Zhaozhan Hao, Cheng Liu, Wei Zheng, Miaoqing Xu, Xiao Chen, Jing Yang, Qiancheng Lv, and Bingbo Gao
Earth Syst. Sci. Data, 14, 3489–3508, https://doi.org/10.5194/essd-14-3489-2022, https://doi.org/10.5194/essd-14-3489-2022, 2022
Short summary
Short summary
The potential degradation of mainstream global fire products leads to large uncertainty in the effective monitoring of wildfires and their influence. To fill this gap, we produced a Fengyun-3D (FY-3D) global active fire product with a similar spatial and temporal resolution to MODIS fire products, aiming to serve as continuity and a replacement for MODIS fire products. The FY-3D fire product is an ideal tool for global fire monitoring and can be preferably employed for fire monitoring in China.
Related subject area
Domain: ESSD – Atmosphere | Subject: Energy and Emissions
Development of a high-resolution integrated emission inventory of air pollutants for China
State of Wildfires 2023–24
Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil
Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions
Air pollution emission inventory using national high-resolution spatial parameters for the Nordic countries and analysis of PM2.5 spatial distribution for road transport and machinery and off-road sectors
A quality-assured dataset of nine radiation components observed at the Shangdianzi regional GAW station in China (2013–2022)
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories
A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles
Reconciliation of observation- and inventory- based methane emissions for eight large global emitters
Global Carbon Budget 2023
Spatiotemporally resolved emissions and concentrations of styrene, benzene, toluene, ethylbenzene, and xylenes (SBTEX) in the US Gulf region
High-resolution emission inventory of full-volatility organic compounds from cooking in China during 2015–2021
Global carbon uptake of cement carbonation accounts 1930–2021
A dense station-based, long-term and high-accuracy dataset of daily surface solar radiation in China
The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020
Decadal growth in emission load of major air pollutants in Delhi
Improved catalog of NOx point source emissions (version 2)
The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies
Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence
Emission trends of air pollutants and CO2 in China from 2005 to 2021
Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics
The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019
Spatially resolved hourly traffic emission over megacity Delhi using advanced traffic flow data
Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling
Retrievals of XCO2, XCH4 and XCO from portable, near-infrared Fourier transform spectrometer solar observations in Antarctica
Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting
DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data
Nana Wu, Guannan Geng, Ruochong Xu, Shigan Liu, Xiaodong Liu, Qinren Shi, Ying Zhou, Yu Zhao, Huan Liu, Yu Song, Junyu Zheng, Qiang Zhang, and Kebin He
Earth Syst. Sci. Data, 16, 2893–2915, https://doi.org/10.5194/essd-16-2893-2024, https://doi.org/10.5194/essd-16-2893-2024, 2024
Short summary
Short summary
The commonly used method for developing large-scale air pollutant emission datasets for China faces challenges due to limited availability of detailed parameter information. In this study, we develop an efficient integrated framework to gather such information by harmonizing seven heterogeneous inventories from five research institutions. Emission characterizations are analyzed and validated, demonstrating that the dataset provides more accurate emission magnitudes and spatiotemporal patterns.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-218, https://doi.org/10.5194/essd-2024-218, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This inaugural State of Wildfires report catalogues extreme fires of the 2023–24 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Leonardo Hoinaski, Robson Will, and Camilo Bastos Ribeiro
Earth Syst. Sci. Data, 16, 2385–2405, https://doi.org/10.5194/essd-16-2385-2024, https://doi.org/10.5194/essd-16-2385-2024, 2024
Short summary
Short summary
We introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database for air quality studies in Brazil. The database encompasses hourly datasets of meteorology, emission sources, and ambient concentrations of multiple air pollutants covering the Brazilian territory. It combines local inventories, consolidated datasets, and internationally recommended models to provide essential data for developing air pollution control policies, even in data-scarce areas.
Mark Omara, Anthony Himmelberger, Katlyn MacKay, James P. Williams, Joshua Benmergui, Maryann Sargent, Steven C. Wofsy, and Ritesh Gautam
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-72, https://doi.org/10.5194/essd-2024-72, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We review, analyze, and synthesize previous peer-reviewed measurement-based data on facility-level oil and gas methane emissions and use these data to develop a high-resolution spatially explicit inventory of national methane emissions. This work provides improved assessment of national methane emissions relative to government inventories in support of accurate and comprehensive methane emissions assessment, attribution, and mitigation.
Ville-Veikko Paunu, Niko Karvosenoja, David Segersson, Susana López-Aparicio, Ole-Kenneth Nielsen, Marlene Schmidt Plejdrup, Throstur Thorsteinsson, Dam Thanh Vo, Jeroen Kuenen, Hugo Denier van der Gon, Jukka-Pekka Jalkanen, Jørgen Brandt, and Camilla Geels
Earth Syst. Sci. Data, 16, 1453–1474, https://doi.org/10.5194/essd-16-1453-2024, https://doi.org/10.5194/essd-16-1453-2024, 2024
Short summary
Short summary
Air pollution is an important cause of adverse health effects, even in Nordic countries. To assess their health impacts, emission inventories with high spatial resolution are needed. We studied how national data and methods for the spatial distribution of the emissions compare to a European level inventory. For road transport the methods are well established, but for machinery and off-road emissions the current recommendations for the spatial distribution of these emissions should be improved.
Weijun Quan, Zhenfa Wang, Lin Qiao, Xiangdong Zheng, Junli Jin, Yinruo Li, Xiaomei Yin, Zhiqiang Ma, and Martin Wild
Earth Syst. Sci. Data, 16, 961–983, https://doi.org/10.5194/essd-16-961-2024, https://doi.org/10.5194/essd-16-961-2024, 2024
Short summary
Short summary
Radiation components play important roles in various fields such as the Earth’s surface radiation budget, ecosystem productivity, and human health. In this study, a dataset consisting of quality-assured daily data of nine radiation components is presented based on the in situ measurements at the Shangdianzi regional GAW station in China during 2013–2022. The dataset can be applied in the validation of satellite products and numerical models and investigation of atmospheric radiation.
Ruben Urraca, Greet Janssens-Maenhout, Nicolás Álamos, Lucas Berna-Peña, Monica Crippa, Sabine Darras, Stijn Dellaert, Hugo Denier van der Gon, Mark Dowell, Nadine Gobron, Claire Granier, Giacomo Grassi, Marc Guevara, Diego Guizzardi, Kevin Gurney, Nicolás Huneeus, Sekou Keita, Jeroen Kuenen, Ana Lopez-Noreña, Enrique Puliafito, Geoffrey Roest, Simone Rossi, Antonin Soulie, and Antoon Visschedijk
Earth Syst. Sci. Data, 16, 501–523, https://doi.org/10.5194/essd-16-501-2024, https://doi.org/10.5194/essd-16-501-2024, 2024
Short summary
Short summary
CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories providing gridded (0.1×0.1) monthly emissions of anthropogenic CO2. Regional inventories include country-specific information and finer spatial resolution than global inventories. CoCO2-MOSAIC provides harmonized access to these datasets and can be considered as a regionally accepted reference to assess the quality of global inventories, as done in the current paper.
Marc Guevara, Santiago Enciso, Carles Tena, Oriol Jorba, Stijn Dellaert, Hugo Denier van der Gon, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 16, 337–373, https://doi.org/10.5194/essd-16-337-2024, https://doi.org/10.5194/essd-16-337-2024, 2024
Short summary
Short summary
A global dataset of emissions from thermal power plants was created for the year 2018. The resulting catalogue reports annual emissions of CO2 and co-emitted species (NOx, CO, SO2 and CH4) for more than 16000 individual facilities at their exact geographical locations. Information on the temporal and vertical distributions of the emissions is also provided at the facility level. The dataset is intended to support current and future satellite emission monitoring and inverse modelling efforts.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-516, https://doi.org/10.5194/essd-2023-516, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This study provides an overview of data availability from observation and inventory-based CH4 emissions estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more Parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
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
Short summary
Short summary
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.
Chi-Tsan Wang, Bok H. Baek, William Vizuete, Lawrence S. Engel, Jia Xing, Jaime Green, Marc Serre, Richard Strott, Jared Bowden, and Jung-Hun Woo
Earth Syst. Sci. Data, 15, 5261–5279, https://doi.org/10.5194/essd-15-5261-2023, https://doi.org/10.5194/essd-15-5261-2023, 2023
Short summary
Short summary
Hazardous air pollutant (HAP) human exposure studies usually rely on local measurements or dispersion model methods, but those methods are limited under spatial and temporal conditions. We processed the US EPA emission data to simulate the hourly HAP emission patterns and applied the chemical transport model to simulate the HAP concentrations. The modeled HAP results exhibit good agreement (R is 0.75 and NMB is −5.6 %) with observational data.
Zeqi Li, Shuxiao Wang, Shengyue Li, Xiaochun Wang, Guanghan Huang, Xing Chang, Lyuyin Huang, Chengrui Liang, Yun Zhu, Haotian Zheng, Qian Song, Qingru Wu, Fenfen Zhang, and Bin Zhao
Earth Syst. Sci. Data, 15, 5017–5037, https://doi.org/10.5194/essd-15-5017-2023, https://doi.org/10.5194/essd-15-5017-2023, 2023
Short summary
Short summary
This study developed the first full-volatility organic emission inventory for cooking sources in China, presenting high-resolution cooking emissions during 2015–2021. It identified the key subsectors and hotspots of cooking emissions, analyzed emission trends and drivers, and proposed future control strategies. The dataset is valuable for accurately simulating organic aerosol formation and evolution and for understanding the impact of organic emissions on air pollution and climate change.
Zi Huang, Jiaoyue Wang, Longfei Bing, Yijiao Qiu, Rui Guo, Ying Yu, Mingjing Ma, Le Niu, Dan Tong, Robbie M. Andrew, Pierre Friedlingstein, Josep G. Canadell, Fengming Xi, and Zhu Liu
Earth Syst. Sci. Data, 15, 4947–4958, https://doi.org/10.5194/essd-15-4947-2023, https://doi.org/10.5194/essd-15-4947-2023, 2023
Short summary
Short summary
This is about global and regional cement process carbon emissions and CO2 uptake calculations from 1930 to 2019. The global cement production is rising to 4.4 Gt, causing processing carbon emission of 1.81 Gt (95% CI: 1.75–1.88 Gt CO2) in 2021. Plus, in 2021, cement’s carbon accumulated uptake (22.9 Gt, 95% CI: 19.6–22.6 Gt CO2) has offset 55.2% of cement process CO2 emissions (41.5 Gt, 95% CI: 38.7–47.1 Gt CO2) since 1930.
Wenjun Tang, Junmei He, Jingwen Qi, and Kun Yang
Earth Syst. Sci. Data, 15, 4537–4551, https://doi.org/10.5194/essd-15-4537-2023, https://doi.org/10.5194/essd-15-4537-2023, 2023
Short summary
Short summary
In this study, we have developed a dense station-based, long-term dataset of daily surface solar radiation in China with high accuracy. The dataset consists of estimates of global, direct and diffuse radiation at 2473 meteorological stations from the 1950s to 2021. Validation indicates that our station-based radiation dataset clearly outperforms the satellite-based radiation products. Our dataset will contribute to climate change research and solar energy applications in the future.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
Short summary
Short summary
Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Saroj Kumar Sahu, Poonam Mangaraj, and Gufran Beig
Earth Syst. Sci. Data, 15, 3183–3202, https://doi.org/10.5194/essd-15-3183-2023, https://doi.org/10.5194/essd-15-3183-2023, 2023
Short summary
Short summary
The developed emission inventory identifies all the potential anthropogenic sources active in the Delhi NCR. The decadal change (2010–2020) and the changing policies have also been illustrated to observe the modulation in the sectorial emission trend. Emission hotspots with possible source-specific mitigation strategies have also been highlighted to improve the air quality of the Delhi NCR. The provided dataset is a vital tool for air quality and chemical transport modeling studies.
Steffen Beirle, Christian Borger, Adrian Jost, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3051–3073, https://doi.org/10.5194/essd-15-3051-2023, https://doi.org/10.5194/essd-15-3051-2023, 2023
Short summary
Short summary
We present a catalog of nitrogen oxide emissions from point sources (like power plants or metal smelters) based on satellite observations of NO2 combined with meteorological wind fields.
Monica Crippa, Diego Guizzardi, Tim Butler, Terry Keating, Rosa Wu, Jacek Kaminski, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Harrison Suchyta, Marilena Muntean, Efisio Solazzo, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Fabio Monforti-Ferrario, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Tabish Ansari, and Kristen Foley
Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, https://doi.org/10.5194/essd-15-2667-2023, 2023
Short summary
Short summary
This study responds to the global and regional atmospheric modelling community's need for a mosaic of air pollutant emissions with global coverage, long time series, spatially distributed data at a high time resolution, and a high sectoral resolution in order to enhance the understanding of transboundary air pollution. The mosaic approach to integrating official regional emission inventories with a global inventory based on a consistent methodology ensures policy-relevant results.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
Short summary
Short summary
This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Shengyue Li, Shuxiao Wang, Qingru Wu, Yanning Zhang, Daiwei Ouyang, Haotian Zheng, Licong Han, Xionghui Qiu, Yifan Wen, Min Liu, Yueqi Jiang, Dejia Yin, Kaiyun Liu, Bin Zhao, Shaojun Zhang, Ye Wu, and Jiming Hao
Earth Syst. Sci. Data, 15, 2279–2294, https://doi.org/10.5194/essd-15-2279-2023, https://doi.org/10.5194/essd-15-2279-2023, 2023
Short summary
Short summary
This study compiled China's emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0) based on unified emission-source framework. The emission trends and its drivers are analyzed. Key sectors and regions with higher synergistic reduction potential of air pollutants and CO2 are identified. Future control measures are suggested. The dataset and analyses provide insights into the synergistic reduction of air pollutants and CO2 emissions for China and other developing countries.
Wouter B. Mol, Wouter H. Knap, and Chiel C. van Heerwaarden
Earth Syst. Sci. Data, 15, 2139–2151, https://doi.org/10.5194/essd-15-2139-2023, https://doi.org/10.5194/essd-15-2139-2023, 2023
Short summary
Short summary
We describe a dataset of detailed measurements of sunlight reaching the surface, recorded at a rate of one measurement per second for 10 years. The dataset includes detailed information on direct and scattered sunlight; classifications and statistics of variability; and observations of clouds, atmospheric composition, and wind. The dataset can be used to study how the atmosphere influences sunlight variability and to validate models that aim to predict this variability with greater accuracy.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Akash Biswal, Vikas Singh, Leeza Malik, Geetam Tiwari, Khaiwal Ravindra, and Suman Mor
Earth Syst. Sci. Data, 15, 661–680, https://doi.org/10.5194/essd-15-661-2023, https://doi.org/10.5194/essd-15-661-2023, 2023
Short summary
Short summary
This paper presents detailed emission estimates of on-road traffic exhaust emissions of nine major pollutants for Delhi. We use advanced traffic data and emission factors as a function of speed to estimate emissions for each hour and 100 m × 100 m spatial resolution. We examine the source contribution according to the vehicle, fuel, road and Euro types to identify the most polluting vehicles. These data are useful for high-resolution air quality modelling for developing suitable strategies.
Auke M. van der Woude, Remco de Kok, Naomi Smith, Ingrid T. Luijkx, Santiago Botía, Ute Karstens, Linda M. J. Kooijmans, Gerbrand Koren, Harro A. J. Meijer, Gert-Jan Steeneveld, Ida Storm, Ingrid Super, Hubertus A. Scheeren, Alex Vermeulen, and Wouter Peters
Earth Syst. Sci. Data, 15, 579–605, https://doi.org/10.5194/essd-15-579-2023, https://doi.org/10.5194/essd-15-579-2023, 2023
Short summary
Short summary
To monitor the progress towards the CO2 emission goals set out in the Paris Agreement, the European Union requires an independent validation of emitted CO2. For this validation, atmospheric measurements of CO2 can be used, together with first-guess estimates of CO2 emissions and uptake. To quickly inform end users, it is imperative that this happens in near real-time. To aid these efforts, we create estimates of European CO2 exchange at high resolution in near real time.
David F. Pollard, Frank Hase, Mahesh Kumar Sha, Darko Dubravica, Carlos Alberti, and Dan Smale
Earth Syst. Sci. Data, 14, 5427–5437, https://doi.org/10.5194/essd-14-5427-2022, https://doi.org/10.5194/essd-14-5427-2022, 2022
Short summary
Short summary
We describe measurements made in Antarctica using an EM27/SUN, a near-infrared, portable, low-resolution spectrometer from which we can retrieve the average atmospheric concentration of several greenhouse gases. We show that these measurements are reliable and comparable to other, similar ground-based measurements. Comparisons to the ESA's Sentinel-5 precursor (S5P) satellite demonstrate the usefulness of these data for satellite validation.
Giacomo Grassi, Giulia Conchedda, Sandro Federici, Raul Abad Viñas, Anu Korosuo, Joana Melo, Simone Rossi, Marieke Sandker, Zoltan Somogyi, Matteo Vizzarri, and Francesco N. Tubiello
Earth Syst. Sci. Data, 14, 4643–4666, https://doi.org/10.5194/essd-14-4643-2022, https://doi.org/10.5194/essd-14-4643-2022, 2022
Short summary
Short summary
Despite increasing attention on the role of land use CO2 fluxes in climate change mitigation, there are large differences in available databases. Here we present the most updated and complete compilation of land use CO2 data based on country submissions to United Nations Framework Convention on Climate Change and explain differences with other datasets. Our dataset brings clarity of land use CO2 fluxes and helps track country progress under the Paris Agreement.
Thorsten Hoeser, Stefanie Feuerstein, and Claudia Kuenzer
Earth Syst. Sci. Data, 14, 4251–4270, https://doi.org/10.5194/essd-14-4251-2022, https://doi.org/10.5194/essd-14-4251-2022, 2022
Short summary
Short summary
The DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set provides offshore wind energy infrastructure locations and their temporal deployment dynamics from July 2016 until June 2021 on a global scale. It differentiates between offshore wind turbines, platforms under construction, and offshore wind farm substations. It is derived by applying deep-learning-based object detection to Sentinel-1 imagery.
Cited articles
Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011.
Anderson, G. K., Sandberg, D. V., and Norheim, R. A.: Fire emission production simulator (FEPS) user's guide, USDA Forest Service, https://www.frames.gov/catalog/6921 (last access: 31 July 2024), 2004.
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical biomass map using multiple reference datasets, Global Change Biol., 22, 1406–1420, https://doi.org/10.1111/gcb.13139, 2016.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Change, 2, 182–185, https://doi.org/10.1038/nclimate1354, 2012.
Bowman, D. M. J. S., Williamson, G. J., Abatzoglou, J. T., Kolden, C. A., Cochrane, M. A., and Smith, A. M. S.: Human exposure and sensitivity to globally extreme wildfire events, Nat. Ecol. Evol., 1, 1–6, https://doi.org/10.1038/s41559-016-0058, 2017.
Bray, C. D., Battye, W., Aneja, V. P., Tong, D. Q., Lee, P., and Tang, Y.: Ammonia emissions from biomass burning in the continental United States, Atmos. Environ., 187, 50–61, https://doi.org/10.1016/j.atmosenv.2018.05.052, 2018.
Cao, G., Zhang, X., Wang, Y., and Zheng, F.: Estimation of emissions from field burning of crop straw in China, Chinese Sci. Bull., 53, 784–790, https://doi.org/10.1007/s11434-008-0145-4, 2008.
Chang, D. and Song, Y.: Estimates of biomass burning emissions in tropical Asia based on satellite-derived data, Atmos. Chem. Phys., 10, 2335–2351, https://doi.org/10.5194/acp-10-2335-2010, 2010.
Chen, J., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M. S., Wang, S., Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B., Morawska, L., Thai, P., Lam, Y. F., Pereira, G., Ding, A., Huang, X., and Dumka, U. C.: A review of biomass burning: Emissions and impacts on air quality, health and climate in China, Sci. Total Environ., 579, 1000–1034, https://doi.org/10.1016/j.scitotenv.2016.10.146, 2017.
Chen, J., Yao, Q., Chen, Z., Li, M., Hao, Z., Liu, C., Zheng, W., Xu, M., Chen, X., Yang, J., Lv, Q., and Gao, B.: The Fengyun-3D (FY-3D) global active fire product: principle, methodology and validation, Earth Syst. Sci. Data, 14, 3489–3508, https://doi.org/10.5194/essd-14-3489-2022, 2022.
Chen, Y., Morton, D. C., Jin, Y., Collatz, G. J., Kasibhatla, P. S., van der Werf, G. R., DeFries, R. S., and Randerson, J. T.: Long-term trends and interannual variability of forest, savanna and agricultural fires in South America, Carbon Manag., 4, 617–638, https://doi.org/10.4155/cmt.13.61, 2013.
Chen, Y., Morton, D. C., Andela, N., van der Werf, G. R., Giglio, L., and Randerson, J. T.: A pan-tropical cascade of fire driven by El Niño/Southern Oscillation, Nat. Clim. Change, 7, 906–911, https://doi.org/10.1038/s41558-017-0014-8, 2017.
Cochrane, M. A. and Laurance, W. F.: Fire as a large-scale edge effect in Amazonian forests, J. Trop. Ecol., 18, 311–325, https://doi.org/10.1017/S0266467402002237, 2002.
Collins, L., Bradstock, R. A., Clarke, H., Clarke, M. F., Nolan, R. H., and Penman, T. D.: The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire, Environ. Res. Lett., 16, 044029, https://doi.org/10.1088/1748-9326/abeb9e, 2021.
Collins, L., Clarke, H., Clarke, M. F., McColl Gausden, S. C., Nolan, R. H., Penman, T., and Bradstock, R.: Warmer and drier conditions have increased the potential for large and severe fire seasons across south-eastern Australia, Global Ecol. Biogeogr., 31, 1933–1948, https://doi.org/10.1111/geb.13514, 2022.
Confronting the Wildfire Crisis: https://www.fs.usda.gov/managing-land/wildfire-crisis, last access: 12 October 2023.
Copes-Gerbitz, K., Hagerman, S. M., and Daniels, L. D.: Transforming fire governance in British Columbia, Canada: an emerging vision for coexisting with fire, Reg. Environ. Change, 22, 48, https://doi.org/10.1007/s10113-022-01895-2, 2022.
De Sales, F., Xue, Y., and Okin, G. S.: Impact of burned areas on the northern African seasonal climate from the perspective of regional modeling, Clim. Dynam., 47, 3393–3413, https://doi.org/10.1007/s00382-015-2522-4, 2016.
DiMiceli, C., Sohlberg, R., and Townshend, J.: MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V061, https://doi.org/10.5067/MODIS/MOD44B.061, 2022.
Dury, M., Hambuckers, A., Warnant, P., Henrot, A., Favre, E., Ouberdous, M., and François, L.: Responses of European forest ecosystems to 21(st) century climate: assessing changes in interannual variability and fire intensity, iForest, 82–99, https://doi.org/10.3832/ifor0572-004, 2011.
Eames, T., Russell-smith, J., Yates, C., Vernooij, R., and van der Werf, G.: Seasonal skew of tropical savanna fires, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13544, https://doi.org/10.5194/egusphere-egu23-13544, 2023.
Eufemia, L., Dias Turetta, A. P., Bonatti, M., Da Ponte, E., and Sieber, S.: Fires in the Amazon Region: Quick Policy Review, Dev. Policy Rev., 40, e12620, https://doi.org/10.1111/dpr.12620, 2022.
Fagre, D. B., Peterson, D. L., and Hessl, A. E.: Taking the Pulse of Mountains: Ecosystem Responses to Climatic Variability, Climatic Change, 59, 263–282, https://doi.org/10.1023/A:1024427803359, 2003.
Fagua, J. C. and Ramsey, R. D.: Geospatial modeling of land cover change in the Chocó-Darien global ecoregion of South America; One of most biodiverse and rainy areas in the world, PLOS ONE, 14, e0211324, https://doi.org/10.1371/journal.pone.0211324, 2019.
Fang, Z., Deng, W., Zhang, Y., Ding, X., Tang, M., Liu, T., Hu, Q., Zhu, M., Wang, Z., Yang, W., Huang, Z., Song, W., Bi, X., Chen, J., Sun, Y., George, C., and Wang, X.: Open burning of rice, corn and wheat straws: primary emissions, photochemical aging, and secondary organic aerosol formation, Atmos. Chem. Phys., 17, 14821–14839, https://doi.org/10.5194/acp-17-14821-2017, 2017.
Filizzola, C., Falconieri, A., Lacava, T., Marchese, F., Masiello, G., Mazzeo, G., Pergola, N., Pietrapertosa, C., Serio, C., and Tramutoli, V.: Fire characterization by using an original RST-based approach for fire radiative power (FRP) computation, Fire, 6, 48, https://doi.org/10.3390/fire6020048, 2023.
Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M., and Gowman, L. M.: Implications of changing climate for global wildland fire, Int. J. Wildland Fire, 18, 483–507, https://doi.org/10.1071/WF08187, 2009.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V061, https://doi.org/10.5067/MODIS/MCD12Q1.061, 2022.
Gallagher, R. V., Allen, S., Mackenzie, B. D. E., Yates, C. J., Gosper, C. R., Keith, D. A., Merow, C., White, M. D., Wenk, E., Maitner, B. S., He, K., Adams, V. M., and Auld, T. D.: High fire frequency and the impact of the 2019–2020 megafires on Australian plant diversity, Divers. Distrib., 27, 1166–1179, https://doi.org/10.1111/ddi.13265, 2021.
Gautam, R., Hsu, N. C., Eck, T. F., Holben, B. N., Janjai, S., Jantarach, T., Tsay, S.-C., and Lau, W. K.: Characterization of aerosols over the Indochina peninsula from satellite-surface observations during biomass burning pre-monsoon season, Atmos. Environ., 78, 51–59, https://doi.org/10.1016/j.atmosenv.2012.05.038, 2013.
Giglio, L., Csiszar, I., and Justice, C. O.: Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, J. Geophys. Res.-Biogeo., 111, G02016, https://doi.org/10.1029/2005JG000142, 2006.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Griffin, D., Chen, J., Anderson, K., Makar, P., McLinden, C. A., Dammers, E., and Fogal, A.: Towards an improved understanding of wildfire CO emissions: a satellite remote-sensing perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-649, 2023.
Hoffman, K. M., Christianson, A. C., Gray, R. W., and Daniels, L.: Western Canada's new wildfire reality needs a new approach to fire management, Environ. Res. Lett., 17, 061001, https://doi.org/10.1088/1748-9326/ac7345, 2022.
Hoffmann, W. A. and Jackson, R. B.: Vegetation–Climate Feedbacks in the Conversion of Tropical Savanna to Grassland, J. Climate, 13, 1593–1602, https://doi.org/10.1175/1520-0442(2000)013<1593:VCFITC>2.0.CO;2, 2000.
Huang, J., Loría-Salazar, S. M., Deng, M., Lee, J., and Holmes, H. A.: Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US, Atmos. Chem. Phys., 24, 3673–3698, https://doi.org/10.5194/acp-24-3673-2024, 2024.
Ichoku, C. and Ellison, L.: Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements, Atmos. Chem. Phys., 14, 6643–6667, https://doi.org/10.5194/acp-14-6643-2014, 2014.
Ichoku, C., Ellison, L. T., Willmot, K. E., Matsui, T., Dezfuli, A. K., Gatebe, C. K., Wang, J., Wilcox, E. M., Lee, J., Adegoke, J., Okonkwo, C., Bolten, J., Policelli, F. S., and Habib, S.: Biomass burning, land-cover change, and the hydrological cycle in Northern sub-Saharan Africa, Environ. Res. Lett., 11, 095005, https://doi.org/10.1088/1748-9326/11/9/095005, 2016.
Ito, A. and Penner, J. E.: Global estimates of biomass burning emissions based on satellite imagery for the year 2000, J. Geophys. Res.-Atmos., 109, D14S05, https://doi.org/10.1029/2003JD004423, 2004.
Jegasothy, E., Hanigan, I. C., Van Buskirk, J., Morgan, G. G., Jalaludin, B., Johnston, F. H., Guo, Y., and Broome, R. A.: Acute health effects of bushfire smoke on mortality in Sydney, Australia, Environ. Int., 171, 107684, https://doi.org/10.1016/j.envint.2022.107684, 2023.
Jones, M. W., Abatzoglou, J. T., Veraverbeke, S., Andela, N., Lasslop, G., Forkel, M., Smith, A. J. P., Burton, C., Betts, R. A., van der Werf, G. R., Sitch, S., Canadell, J. G., Santín, C., Kolden, C., Doerr, S. H., and Le Quéré, C.: Global and Regional Trends and Drivers of Fire Under Climate Change, Rev. Geophys., 60, e2020RG000726, https://doi.org/10.1029/2020RG000726, 2022.
Jurdao, S., Chuvieco, E., and Arevalillo, J. M.: Modelling Fire Ignition Probability from Satellite Estimates of Live Fuel Moisture Content, Fire Ecol., 8, 77–97, https://doi.org/10.4996/fireecology.0801077, 2012.
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.
Kanabkaew, T. and Kim Oanh, N. T.: Development of Spatial and Temporal Emission Inventory for Crop Residue Field Burning, Environ. Model. Assess., 16, 453–464, https://doi.org/10.1007/s10666-010-9244-0, 2011.
Keeley, J. E. and Syphard, A. D.: Large California wildfires: 2020 fires in historical context, Fire Ecol., 17, 22, https://doi.org/10.1186/s42408-021-00110-7, 2021.
Kolden, C. A., Abatzoglou, J. T., Jones, M. W., and Jain, P.: Wildfires in 2023, Nat. Rev. Earth Environ., 5, 238–240, https://doi.org/10.1038/s43017-024-00544-y, 2024.
Kröger, M. and Nygren, A.: Shifting frontier dynamics in Latin America, J. Agrar. Change, 20, 364–386, https://doi.org/10.1111/joac.12354, 2020.
Li, F., Zhu, Q., Riley, W. J., Zhao, L., Xu, L., Yuan, K., Chen, M., Wu, H., Gui, Z., Gong, J., and Randerson, J. T.: AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics, Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, 2023.
Li, W., Li, M., Shi, C., Fang, R., Zhao, Q., Meng, X., Yang, G., and Bai, W.: GPS and BeiDou Differential Code Bias Estimation Using Fengyun-3C Satellite Onboard GNSS Observations, Remote Sens.-Basel, 9, 1239, https://doi.org/10.3390/rs9121239, 2017.
Li, X., Wang, S., Duan, L., Hao, J., Li, C., Chen, Y., and Yang, L.: Particulate and Trace Gas Emissions from Open Burning of Wheat Straw and Corn Stover in China, Environ. Sci. Technol., 41, 6052–6058, https://doi.org/10.1021/es0705137, 2007.
Liu, X., Zhang, Y., Huey, L. G., Yokelson, R. J., Wang, Y., Jimenez, J. L., Campuzano-Jost, P., Beyersdorf, A. J., Blake, D. R., Choi, Y., St. Clair, J. M., Crounse, J. D., Day, D. A., Diskin, G. S., Fried, A., Hall, S. R., Hanisco, T. F., King, L. E., Meinardi, S., Mikoviny, T., Palm, B. B., Peischl, J., Perring, A. E., Pollack, I. B., Ryerson, T. B., Sachse, G., Schwarz, J. P., Simpson, I. J., Tanner, D. J., Thornhill, K. L., Ullmann, K., Weber, R. J., Wennberg, P. O., Wisthaler, A., Wolfe, G. M., and Ziemba, L. D.: Agricultural fires in the southeastern U. S. during SEAC4RS: Emissions of trace gases and particles and evolution of ozone, reactive nitrogen, and organic aerosol, J. Geophys. Res.-Atmos., 121, 7383–7414, https://doi.org/10.1002/2016JD025040, 2016.
Liu, Y. and Shi, Y.: Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product, Atmosphere, 14, 1575, https://doi.org/10.3390/atmos14101575, 2023.
Liu, Y., Goodrick, S., and Heilman, W.: Wildland fire emissions, carbon, and climate: Wildfire–climate interactions, Forest Ecol. Manag., 317, 80–96, https://doi.org/10.1016/j.foreco.2013.02.020, 2014.
Liu, Y., Jie, C., Shi, Y., Zheng, W., Shan, T., and Wang, G.: Global Emissions Inventory from Open Biomass Burning (GEIOBB): Utilizing Fengyun-3D global fire spot monitoring data, Figshare [data set], https://doi.org/10.6084/m9.figshare.24793623.v2, 2023.
Lourenco, M., Woodborne, S., and Fitchett, J. M.: Fire regime of peatlands in the Angolan Highlands, Environ. Monit. Assess., 195, 78, https://doi.org/10.1007/s10661-022-10704-6, 2022.
Machete, R. L. and Dintwe, K.: Cyclic Trends of Wildfires over Sub-Saharan Africa, Fire, 6, 71, https://doi.org/10.3390/fire6020071, 2023.
Martin, E. R. and Thorncroft, C. D.: The impact of the AMO on the West African monsoon annual cycle, Q. J. Roy. Meteor. Soc., 140, 31–46, https://doi.org/10.1002/qj.2107, 2014.
Mehmood, K., Bao, Y., Saifullah, Bibi, S., Dahlawi, S., Yaseen, M., Abrar, M. M., Srivastava, P., Fahad, S., and Faraj, T. K.: Contributions of Open Biomass Burning and Crop Straw Burning to Air Quality: Current Research Paradigm and Future Outlooks, Front. Environ. Sci., 10, 852492, https://doi.org/10.3389/fenvs.2022.852492, 2022.
Moritz, M. A., Parisien, M.-A., Batllori, E., Krawchuk, M. A., Van Dorn, J., Ganz, D. J., and Hayhoe, K.: Climate change and disruptions to global fire activity, Ecosphere, 3, art49, https://doi.org/10.1890/ES11-00345.1, 2012.
Murdiyarso, D. and Lebel, L.: Local to global perspectives on forest and land fires in Southeast Asia, Mitig. Adapt. Strat. Gl., 12, 3–11, https://doi.org/10.1007/s11027-006-9055-4, 2007.
Nepstad, D. C., Verssimo, A., Alencar, A., Nobre, C., Lima, E., Lefebvre, P., Schlesinger, P., Potter, C., Moutinho, P., Mendoza, E., Cochrane, M., and Brooks, V.: Large-scale impoverishment of Amazonian forests by logging and fire, Nature, 398, 505–508, https://doi.org/10.1038/19066, 1999.
Nguyen, H. M., He, J., and Wooster, M. J.: Biomass burning CO, PM and fuel consumption per unit burned area estimates derived across Africa using geostationary SEVIRI fire radiative power and Sentinel-5P CO data, Atmos. Chem. Phys., 23, 2089–2118, https://doi.org/10.5194/acp-23-2089-2023, 2023.
Pivello, V. R.: The Use of Fire in the Cerrado and Amazonian Rainforests of Brazil: Past and Present, Fire Ecol., 7, 24–39, https://doi.org/10.4996/fireecology.0701024, 2011.
Pletcher, E., Staver, C., and Schwartz, N. B.: The environmental drivers of tree cover and forest–savanna mosaics in Southeast Asia, Ecography, 2022, e06280, https://doi.org/10.1111/ecog.06280, 2022.
Ponomarev, E., Zabrodin, A., and Ponomareva, T.: Classification of Fire Damage to Boreal Forests of Siberia in 2021 Based on the dNBR Index, Fire, 5, 19, https://doi.org/10.3390/fire5010019, 2022.
Puliafito, S. E., Bolaño-Ortiz, T., Berná, L., and Pascual Flores, R.: High resolution inventory of atmospheric emissions from livestock production, agriculture, and biomass burning sectors of Argentina, Atmos. Environ., 223, 117248, https://doi.org/10.1016/j.atmosenv.2019.117248, 2020.
Qiu, X., Duan, L., Chai, F., Wang, S., Yu, Q., and Wang, S.: Deriving High-Resolution Emission Inventory of Open Biomass Burning in China based on Satellite Observations, Environ. Sci. Technol., 50, 11779–11786, https://doi.org/10.1021/acs.est.6b02705, 2016.
Richardson, D., Black, A. S., Irving, D., Matear, R. J., Monselesan, D. P., Risbey, J. S., Squire, D. T., and Tozer, C. R.: Global increase in wildfire potential from compound fire weather and drought, npj Clim. Atmos. Sci., 5, 1–12, https://doi.org/10.1038/s41612-022-00248-4, 2022.
Roberts, G., Wooster, M. J., and Lagoudakis, E.: Annual and diurnal african biomass burning temporal dynamics, Biogeosciences, 6, 849–866, https://doi.org/10.5194/bg-6-849-2009, 2009.
Roy, P. S., Ramachandran, R. M., Paul, O., Thakur, P. K., Ravan, S., Behera, M. D., Sarangi, C., and Kanawade, V. P.: Anthropogenic land use and land cover changes – a review on its environmental consequences and climate change, J Indian Soc. Remot., 50, 1615–1640, https://doi.org/10.1007/s12524-022-01569-w, 2022.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Safford, H. D., Paulson, A. K., Steel, Z. L., Young, D. J. N., and Wayman, R. B.: The 2020 California fire season: A year like no other, a return to the past or a harbinger of the future?, Global Ecol. Biogeogr., 31, 2005–2025, https://doi.org/10.1111/geb.13498, 2022.
Sahu, L. K. and Sheel, V.: Spatio-temporal variation of biomass burning sources over South and Southeast Asia, J. Atmos. Chem., 71, 1–19, https://doi.org/10.1007/s10874-013-9275-4, 2014.
Santana, V. M., Alday, J. G., Lee, H., Allen, K. A., and Marrs, R. H.: Modelling Carbon Emissions in Calluna vulgaris–Dominated Ecosystems when Prescribed Burning and Wildfires Interact, PLOS ONE, 11, e0167137, https://doi.org/10.1371/journal.pone.0167137, 2016.
Santiago-De La Rosa, N., González-Cardoso, G., Figueroa-Lara, J. de J., Gutiérrez-Arzaluz, M., Octaviano-Villasana, C., Ramírez-Hernández, I. F., and Mugica-Álvarez, V.: Emission factors of atmospheric and climatic pollutants from crop residues burning, J. Air Waste Manage., 68, 849–865, https://doi.org/10.1080/10962247.2018.1459326, 2018.
Scholes, M. and Andreae, M. O.: Biogenic and Pyrogenic Emissions from Africa and Their Impact on the Global Atmosphere, Ambio, 29, 23–29, 2000.
Schroeder, W., Csiszar, I., and Morisette, J.: Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon, Remote Sens. Environ., 112, 456–470, https://doi.org/10.1016/j.rse.2007.05.004, 2008.
Senande-Rivera, M., Insua-Costa, D., and Miguez-Macho, G.: Spatial and temporal expansion of global wildland fire activity in response to climate change, Nat. Commun., 13, 1208, https://doi.org/10.1038/s41467-022-28835-2, 2022.
Serrani, D., Cocco, S., Cardelli, V., D'Ottavio, P., Rafael, R. B. A., Feniasse, D., Vilanculos, A., Fernández-Marcos, M. L., Giosué, C., Tittarelli, F., and Corti, G.: Soil fertility in slash and burn agricultural systems in central Mozambique, J. Environ. Manage., 322, 116031, https://doi.org/10.1016/j.jenvman.2022.116031, 2022.
Shan, T. and Zheng, W.: Extraction method of burned area using GF-1 WFV images and FY-3D MERSI fire point products, Natl. Remote Sens. Bull., 28, 375–384, https://doi.org/10.11834/jrs.20221552, 2022.
Shea, R. W., Shea, B. W., Kauffman, J. B., Ward, D. E., Haskins, C. I., and Scholes, M. C.: Fuel biomass and combustion factors associated with fires in savanna ecosystems of South Africa and Zambia, J. Geophys. Res.-Atmos., 101, 23551–23568, https://doi.org/10.1029/95JD02047, 1996.
Shi, Y., Sasai, T., and Yamaguchi, Y.: Spatio-temporal evaluation of carbon emissions from biomass burning in Southeast Asia during the period 2001–2010, Ecol. Model., 272, 98–115, https://doi.org/10.1016/j.ecolmodel.2013.09.021, 2014.
Shi, Y., Matsunaga, T., and Yamaguchi, Y.: High-Resolution Mapping of Biomass Burning Emissions in Three Tropical Regions, Environ. Sci. Technol., 49, 10806–10814, https://doi.org/10.1021/acs.est.5b01598, 2015.
Shi, Y., Matsunaga, T., Yamaguchi, Y., Li, Z., Gu, X., and Chen, X.: Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014, Sci. Total Environ., 615, 177–186, https://doi.org/10.1016/j.scitotenv.2017.09.241, 2018.
Shi, Y., Zhao, A., Matsunaga, T., Yamaguchi, Y., Zang, S., Li, Z., Yu, T., and Gu, X.: High-resolution inventory of mercury emissions from biomass burning in tropical continents during 2001–2017, Sci. Total Environ., 653, 638–648, https://doi.org/10.1016/j.scitotenv.2018.10.420, 2019.
Shi, Y., Zang, S., Matsunaga, T., and Yamaguchi, Y.: A multi-year and high-resolution inventory of biomass burning emissions in tropical continents from 2001–2017 based on satellite observations, J. Clean. Prod., 270, 122511, https://doi.org/10.1016/j.jclepro.2020.122511, 2020.
Spawn, S. A., and Gibbs, H. K.: Global Aboveground and Belowground Biomass Carbon Density Maps for the Year 2010, ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1763, 2020.
Stockwell, C. E., Veres, P. R., Williams, J., and Yokelson, R. J.: Characterization of biomass burning emissions from cooking fires, peat, crop residue, and other fuels with high-resolution proton-transfer-reaction time-of-flight mass spectrometry, Atmos. Chem. Phys., 15, 845–865, https://doi.org/10.5194/acp-15-845-2015, 2015.
Storey, M. A., Price, O. F., and Fox-Hughes, P.: The influence of regional wind patterns on air quality during forest fires near Sydney, Australia, Sci. Total Environ., 905, 167335, https://doi.org/10.1016/j.scitotenv.2023.167335, 2023.
Tedim, F., Leone, V., Lovreglio, R., Xanthopoulos, G., Chas-Amil, M.-L., Ganteaume, A., Efe, R., Royé, D., Fuerst-Bjeliš, B., Nikolov, N., Musa, S., Milenković, M., Correia, F., Conedera, M., and Pezzatti, G. B.: Forest Fire Causes and Motivations in the Southern and South-Eastern Europe through Experts' Perception and Applications to Current Policies, Forests, 13, 562, https://doi.org/10.3390/f13040562, 2022.
Thackeray, C. W., Hall, A., Norris, J., and Chen, D.: Constraining the increased frequency of global precipitation extremes under warming, Nat. Clim. Chang., 12, 441–448, https://doi.org/10.1038/s41558-022-01329-1, 2022.
Tsivlidou, M., Sauvage, B., Bennouna, Y., Blot, R., Boulanger, D., Clark, H., Le Flochmoën, E., Nédélec, P., Thouret, V., Wolff, P., and Barret, B.: Tropical tropospheric ozone and carbon monoxide distributions: characteristics, origins, and control factors, as seen by IAGOS and IASI, Atmos. Chem. Phys., 23, 14039–14063, https://doi.org/10.5194/acp-23-14039-2023, 2023.
Umunnakwe, A., Parvania, M., Nguyen, H., Horel, J. D., and Davis, K. R.: Data-driven spatio-temporal analysis of wildfire risk to power systems operation, IET Generation, Transm. Distrib., 16, 2531–2546, https://doi.org/10.1049/gtd2.12463, 2022.
Urbanski, S.: Wildland fire emissions, carbon, and climate: Emission factors, Forest Ecol. Manag., 317, 51–60, https://doi.org/10.1016/j.foreco.2013.05.045, 2014.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., and Arellano Jr., A. F.: Interannual variability in global biomass burning emissions from 1997 to 2004, Atmos. Chem. Phys., 6, 3423–3441, https://doi.org/10.5194/acp-6-3423-2006, 2006.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010.
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.
Varga, K., Jones, C., Trugman, A., Carvalho, L. M. V., McLoughlin, N., Seto, D., Thompson, C., and Daum, K.: Megafires in a Warming World: What Wildfire Risk Factors Led to California's Largest Recorded Wildfire, Fire, 5, 16, https://doi.org/10.3390/fire5010016, 2022.
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.
Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X., O'Neill, S., and Wynne, K. K.: Estimating emissions from fires in North America for air quality modeling, Atmos. Environ., 40, 3419–3432, https://doi.org/10.1016/j.atmosenv.2006.02.010, 2006.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
Wiedinmyer, C., Kimura, Y., McDonald-Buller, E. C., Emmons, L. K., Buchholz, R. R., Tang, W., Seto, K., Joseph, M. B., Barsanti, K. C., Carlton, A. G., and Yokelson, R.: The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications, Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, 2023.
Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman-Morales, J., Bishop, D. A., Balch, J. K., and Lettenmaier, D. P.: Observed Impacts of Anthropogenic Climate Change on Wildfire in California, Earths Future, 7, 892–910, https://doi.org/10.1029/2019EF001210, 2019.
Wollstein, K., Creutzburg, M. K., Dunn, C., Johnson, D. D., O'Connor, C., and Boyd, C. S.: Toward integrated fire management to promote ecosystem resilience, Rangelands, 44, 227–234, https://doi.org/10.1016/j.rala.2022.01.001, 2022.
Wu, J., Kong, S., Wu, F., Cheng, Y., Zheng, S., Yan, Q., Zheng, H., Yang, G., Zheng, M., Liu, D., Zhao, D., and Qi, S.: Estimating the open biomass burning emissions in central and eastern China from 2003 to 2015 based on satellite observation, Atmos. Chem. Phys., 18, 11623–11646, https://doi.org/10.5194/acp-18-11623-2018, 2018.
Wu, M., Luo, J., Huang, T., Lian, L., Chen, T., Song, S., Wang, Z., Ma, S., Xie, C., Zhao, Y., Mao, X., Gao, H., and Ma, J.: Effects of African BaP emission from wildfire biomass burning on regional and global environment and human health, Environ. Int., 162, 107162, https://doi.org/10.1016/j.envint.2022.107162, 2022.
Xian, D., Zhang, P., Gao, L., Sun, R., Zhang, H., and Jia, X.: Fengyun meteorological satellite products for earth system science applications, Adv. Atmos. Sci., 38, 1267–1284, https://doi.org/10.1007/s00376-021-0425-3, 2021.
Yao, X., Tiantian, Q. U., Wenjing, C., Jun, Y. I. N., Yongjin, L. I., Zhenzhong, S. U. N., and Hui, Z.: Estimation of grassland biomass using MODIS data and plant community characteristics, Chin. J. Eco-Agric., 25, 530–541, https://doi.org/10.13930/j.cnki.cjea.160931, 2017.
Ye, X., Cheng, T., Li, X., and Zhu, H.: Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission, Sci. Total Environ., 864, 161055, https://doi.org/10.1016/j.scitotenv.2022.161055, 2023.
Yin, Z., Chen, F., Lin, Z., Yang, A., and Li, B.: Active fire monitoring based on FY-3D MERSI satellite data, Remote Sens. Technol. Appl., 35, 1099–1108, http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.5.1099 (last access: 31 July 2024), 2020.
You, C. and Xu, C.: Delayed wildfires in 2020 promote snowpack melting in the western United States, P. Natl. Acad. Sci. USA, 120, e2218087120, https://doi.org/10.1073/pnas.2218087120, 2023.
Zerriffi, H., Reyes, R., and Maloney, A.: Pathways to sustainable land use and food systems in Canada, Sustain. Sci., 18, 389–406, https://doi.org/10.1007/s11625-022-01213-z, 2023.
Zhang, X., Kondragunta, S., Schmidt, C., and Kogan, F.: Near real time monitoring of biomass burning particulate emissions (PM2.5) across contiguous United States using multiple satellite instruments, Atmos. Environ., 42, 6959–6972, https://doi.org/10.1016/j.atmosenv.2008.04.060, 2008.
Zhang, X., Duan, J., Cherubini, F., and Ma, Z.: A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022, Sci. Data, 10, 824, https://doi.org/10.1038/s41597-023-02756-1, 2023.
Zhang, Z., Zhang, L., Xu, H., Creed, I. F., Blanco, J. A., Wei, X., Sun, G., Asbjornsen, H., and Bishop, K.: Forest water-use efficiency: Effects of climate change and management on the coupling of carbon and water processes, Forest Ecol. Manag., 534, 120853, https://doi.org/10.1016/j.foreco.2023.120853, 2023.
Zheng, B., Ciais, P., Chevallier, F., Chuvieco, E., Chen, Y., and Yang, H.: Increasing forest fire emissions despite the decline in global burned area, Science Advances, 7, eabh2646, https://doi.org/10.1126/sciadv.abh2646, 2021.
Zheng, W. and Chen, J.: Fire monitoring based on FY-3D/MERSI-II far-infrared data, J. Infrared Millim. W., 39, 120–127, https://doi.org/10.11972/j.issn.1001-9014.2020.01.016, 2020.
Zheng, W., Chen, J., Yan, H., Liu, C., and Tang, S. H.: Global fire monitoring products of FY-3D/MERSI-II and their applications, J. Remote Sens., 24, 521–530, https://doi.org/10.11834/jrs.20209177, 2020.
Zheng, W., Chen, J., Liu, C., Shan, T., and Yan, H.: Study of the Application of FY-3D/MERSI-II Far-Infrared Data in Wildfire Monitoring, Remote Sens.-Basel, 15, 4228, https://doi.org/10.3390/rs15174228, 2023.
Zheng, Y., Liu, J., Jian, H., Fan, X., and Yan, F.: Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia, Remote Sens.-Basel, 13, 2852, https://doi.org/10.3390/rs13152852, 2021.
Zhou, Y., Xing, X., Lang, J., Chen, D., Cheng, S., Wei, L., Wei, X., and Liu, C.: A comprehensive biomass burning emission inventory with high spatial and temporal resolution in China, Atmos. Chem. Phys., 17, 2839–2864, https://doi.org/10.5194/acp-17-2839-2017, 2017.
Short summary
Open biomass burning has a significant impact on regional and global air quality. To enhance the quantification of global emissions from open biomass burning, we have developed the Global Emissions Inventory from Open Biomass Burning (GEIOBB) dataset, which provides a global daily-scale database at 1 km resolution of multiple pollutant emissions. This database aids global-scale environmental analysis of biomass burning.
Open biomass burning has a significant impact on regional and global air quality. To enhance the...
Altmetrics
Final-revised paper
Preprint