Articles | Volume 20, issue 9
https://doi.org/10.5194/acp-20-5729-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-5729-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
14 May 2020
Research article | Highlight paper |
| 14 May 2020
| 14 May 2020
Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Jing Cheng
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Sha Yu
Joint Global Change Research Institute, Pacific Northwest National
Laboratory, University Research Court, College Park, MD 20742, USA
Liu Yan
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Chaopeng Hong
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Yu Qin
State Key Joint Laboratory of Environment Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084,
People's Republic of China
Hongyan Zhao
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Yixuan Zheng
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Guannan Geng
State Key Joint Laboratory of Environment Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084,
People's Republic of China
Meng Li
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
State Key Joint Laboratory of Environment Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084,
People's Republic of China
Yuxuan Zhang
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
State Key Joint Laboratory of Environment Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084,
People's Republic of China
Leon Clarke
Center for Global Sustainability, School of Public Policy, University of Maryland, College Park, MD 20742, USA
Qiang Zhang
CORRESPONDING AUTHOR
Ministry of Education Key Laboratory for Earth System Modelling,
Department of Earth System Science, Tsinghua University, Beijing 100084,
People's Republic of China
Related authors
No articles found.
Hui Li, Philippe Ciais, Frédéric Chevallier, Bo Zheng, Paul I. Palmer, Frank Hase, Morgan Lopez, Elsa M. Ordway, Shushi Peng, Danielle Monteverde, Michel Ramonet, Jason M. St. Clair, Le Bienfaiteur Sagang, and Benjamin Poulter
Atmos. Chem. Phys., 26, 11893–11908, https://doi.org/10.5194/acp-26-11893-2026, https://doi.org/10.5194/acp-26-11893-2026, 2026
Short summary
Short summary
We studied how to best place methane monitoring instruments across Africa to better understand where emissions come from. Because observations can overlap in what they detect, adding more sites does not always improve results equally. Using a statistical approach, we identified both the ideal number and locations of sites (ten new sites can reduce emission uncertainty by over 65 % for total African CH4). Our results help guide cost-effective monitoring strategies in regions with limited data.
Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Douglas I. Kelley, Maria L. F. Barbosa, Joe McNorton, Robert Potts, Carlota Segura-Garcia, John T. Abatzoglou, Iulian Alin Rosu, Niels Andela, Liana O. Anderson, Sally Archibald, Esther Brambleby, Emilio Chuvieco, Andy Ciavarella, Hamish Clarke, Francisco de la Barrera, Carlos M. Di Bella, Enza Di Tomaso, Kebonye Dintwe, Tim S. Doherty, Victoria M. Donovan, Jonathan M. Eden, Paulo M. Fernandes, Lukas Fiedler, Louis Giglio, Olivia Haas, Douglas Hamilton, Stijn Hantson, Sarah Harris, Andrew J. Hartley, Theodore R. Keeping, Crystal A. Kolden, Jean-Luc Kouassi, Elena Kukavskaya, Seppe Lampe, Zhongwei Liu, Guilherme Mataveli, Sarah Meier, Duarte Oom, Mark Parrington, Yuquan Qu, Victor Resco de Dios, Julien Ruffault, Mojtaba Sadegh, Cristina Santín, Fernando Sedano, Jacquelyn K. Shuman, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Veerachai Tanpipat, Miguel Ángel Torres-Vazquez, Marco Turco, Guido R. van der Werf, Dave van Wees, Sander Veraverbeke, Jakob B. Wessel, and Bo Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-587, https://doi.org/10.5194/essd-2026-587, 2026
Preprint under review for ESSD
Short summary
Short summary
The third State of Wildfires report examines extreme wildfire events from 2025 to early 2026. It analyses key regional events in Midwestern Canadian Shield Forests, Northwest Iberia, and Chilean Temperate Forests and Matorral, assessing their impacts, drivers, and attributing them to climate change. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Ruqian Miao, Ruochong Xu, Shan Huang, Sihan Xiao, Hao Wang, Yan Zheng, Siyi Liu, Jingxian Li, Guannan Geng, Manish Shrivastava, Xu Dao, Claire Granier, Guy Brasseur, Vlassis A. Karydis, Alexandra P. Tsimpidi, Jianhui Jiang, Kaspar R. Daellenbach, Imad El Haddad, André S. H. Prévôt, and Qi Chen
EGUsphere, https://doi.org/10.5194/egusphere-2026-3160, https://doi.org/10.5194/egusphere-2026-3160, 2026
Short summary
Short summary
Primary and secondary organic aerosols (POA and SOA) have distinct climate and human health impacts. We develop an OA simulation framework resolving the full volatility spectrum of organic precursor emissions, which improves global POA and SOA characterization. Reassessing their global distributions and budgets, this study shows greater POA and SOA burdens than previous estimates and presents region-dependent OA sources and variability to guide health-oriented pollution mitigation strategies.
Christopher R. Oxford, Haihui Zhu, Maya Mehrotra, Xuan Liu, Yuxuan Ren, Maya Arnott, Isaac Abionum Adimula, Taiye Benjamin Ajibolataiye, Clement Akoshile, Omar Amador-Munoz, Araya Asfaw, Rachel Ying-Wen Chang, Sagnik Dey, Ann M. Dillner, David J. Diner, Connor J. Flynn, Diana Francis, Paterne Gahungu, Rebecca M. Garland, Michel Grutter, Sina Hasheminassab, Fahad Imam, Jhoon Kim, Kristy Langerman, Pei-Chen Lee, Puji Lestari, Po-Hsiung Lin, S. Marcela Loria-Salazar, Tesfaye Mamo, Olga L. Mayol-Bracero, Mogesh Naidoo, Narendra Nelli, Sang Seo Park, Abdus Salam, Bighnaraj Sarangi, Trailokya Saud, Robyn Schofield, Yoav Schechner, Sachchida N. Tripathi, Emily K. West, Eli Windwer, Ming-Tsang Wu, Qiang Zhang, Michael Brauer, Yinon Rudich, Jay R. Turner, and Randall V. Martin
EGUsphere, https://doi.org/10.5194/egusphere-2026-3224, https://doi.org/10.5194/egusphere-2026-3224, 2026
Short summary
Short summary
A high-sensitivity balance, controlled temperature and relative humidity chamber, and filter samples collected around the world were used to develop a mass as a function of relative humidity relationship. We subsequently measured the chemical composition of these same samples. A relationship between the chemical composition and water mass was created and used to calculate aerosol water content showing how composition and water content varies worldwide.
Weiwei Zhang, Zhaofeng Lv, Wen Yi, Tingkun He, Bensheng Xiao, Qiang Zhang, and Huan Liu
EGUsphere, https://doi.org/10.5194/egusphere-2026-1935, https://doi.org/10.5194/egusphere-2026-1935, 2026
Short summary
Short summary
We developed a model to track near-real-time global ship emissions. Previous methods failed to include newly-built ship activities or account for different compliance strategies ships used to meet environmental laws. Our model uncovered significant underestimated carbon dioxide and particular matter emissions, especially in the Indian Ocean and South China Sea. The findings provide a more accurate foundation for understanding how shipping affects our air quality and climate.
Shuling Xu, Yang Liu, Sha Yu, Weidong Jia, Yongye Jiang, Yuqin Li, Zeyuan Liu, Andy Miller, Jianxiang Shen, Jingyang Song, Can Wang, Huaxuan Wang, Rui Wang, Fan Wu, Hongzhi Zhang, Mengting Zhu, Rongqi Zhu, Jenna Behrendt, Matthew Binsted, Jing Cheng, Robert Dahowski, Casie Davidson, Shiyu Deng, Jay Fuhrman, Matthew Gidden, Chaoyi Guo, Jill Horing, Hanwoong Kim, Chaojun Li, Bo Liu, Xunzhang Pan, Pralit Patel, Meiyin Qian, Yang Qiu, Tianming Shao, Yisheng Sun, Lining Wang, Yuyao Yang, Brinda Yarlagadda, Zihua Yin, Jian Zang, Qianzhi Zhang, Wenjia Cai, Wenying Chen, Leon Clarke, Ryna Cui, Nathan Hultman, Bengang Li, Jiashuo Li, Xi Lu, Haewon McJeon, Bo Wang, Can Wang, Peng Wang, Shuxiao Wang, Zhaohua Wang, Lixiao Zhang, Qiang Zhang, and Yang Ou
EGUsphere, https://doi.org/10.5194/egusphere-2026-1919, https://doi.org/10.5194/egusphere-2026-1919, 2026
Short summary
Short summary
This study introduces a new open-source model GCAM-China-v8, developed by the community to better understand China’s energy and emissions at both national and provincial levels. The model links detailed regional systems with global trends, allowing users to explore how economic growth, technology, and policy choices shape future pathways. It provides a transparent and flexible tool to support research and decision making on energy and climate issues.
Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David J. Beerling, Dmitry A. Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison M. Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Yi Xi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 18, 3507–3524, https://doi.org/10.5194/essd-18-3507-2026, https://doi.org/10.5194/essd-18-3507-2026, 2026
Short summary
Short summary
We proposed a framework that combines machine-learning and climate data to predict global natural vegetated wetland methane emissions for 2000–2025. We found that although total global emissions remained stable in the post-2020s, Northern Hemisphere emissions surged whilst tropical emissions fell. This approach allows us to rapidly monitor emissions and provides early warnings for climate impacts.
Yifan Li, Drew Pendergrass, Daniel Jacob, Yunxiao Tang, Jiaxin Qiu, and Bo Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-6284, https://doi.org/10.5194/egusphere-2025-6284, 2026
Short summary
Short summary
We tracked China’s weekly methane emissions from 2019 to 2024 by constructing a satellite-based regional atmospheric inversion framework. Over the period, annual methane emissions in China rose from 61.1 to 66.8 million tonnes, with nearly half of the growth driven by livestock, alongside additional contributions from waste and oil and gas. Emissions growth shifted toward previously low-emitting northwest and northeast China, revealing new priorities for targeted methane mitigation.
Detlef P. Van Vuuren, Brian C. O'Neill, Claudia Tebaldi, Benjamin M. Sanderson, Louise P. Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh M. N. Fall, Katja Frieler, Matthew J. Gidden, Laila K. Gohar, Annika Högner, Andrew D. Jones, Jarmo Kikstra, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camilla Mathison, Shahbaz Mehmood, Zebedee Nicholls, Luciana F. Prado, Qiang Zhang, Steven K. Rose, Alex C. Ruane, Marit Sandstad, Carl-Friedrich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna A. Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha S. Vishwanathan, Tokuta Yokohata, Marco Zecchetto, and Tilo Ziehn
Geosci. Model Dev., 19, 2627–2656, https://doi.org/10.5194/gmd-19-2627-2026, https://doi.org/10.5194/gmd-19-2627-2026, 2026
Short summary
Short summary
We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Jie Fang, Yunjiang Zhang, Didier Hauglustaine, Bo Zheng, Ming Wang, Jingyi Li, Yong Sun, Haiwei Li, Junfeng Wang, Yun Wu, Bin Yuan, Mindong Chen, and Xinlei Ge
Atmos. Chem. Phys., 26, 851–867, https://doi.org/10.5194/acp-26-851-2026, https://doi.org/10.5194/acp-26-851-2026, 2026
Short summary
Short summary
Surface ozone pollution is a pressing global challenge driven by human activities and a warming climate. Using nationwide observations (2013–2023) across China together with satellite data, we developed a new machine learning approach to decouple the impacts of emission controls and weather changes. Our results show that while emission reductions improved ozone in some regions, climate change is increasingly offsetting these gains, underscoring the need for joint air quality and climate actions.
Hui Li, Philippe Ciais, Pramod Kumar, Didier A. Hauglustaine, Frédéric Chevallier, Grégoire Broquet, Dylan B. Millet, Kelley C. Wells, Jinghui Lian, and Bo Zheng
Earth Syst. Sci. Data, 17, 7035–7054, https://doi.org/10.5194/essd-17-7035-2025, https://doi.org/10.5194/essd-17-7035-2025, 2025
Short summary
Short summary
We present the first global, multi-year maps of monthly isoprene emissions (2013–2020) derived from satellite isoprene observations, averaging 456 TgC yr-1. The dataset reveals two emission peaks linked to 2015–2016 El Niño and 2019–2020 extreme heat events, driven mainly by tropical regions such as the Amazon. It highlights the region-specific sensitivity of biogenic isoprene emissions to temperature anomalies, providing new insights into their roles in air quality and climate feedbacks.
Diego Guizzardi, Monica Crippa, 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, Rachel Hoesly, Marilena Muntean, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Annie Duhamel, Tabish Ansari, Kristen Foley, Guannan Geng, Yifei Chen, and Qiang Zhang
Earth Syst. Sci. Data, 17, 5915–5950, https://doi.org/10.5194/essd-17-5915-2025, https://doi.org/10.5194/essd-17-5915-2025, 2025
Short summary
Short summary
The global air pollution emission mosaic HTAP_v3.2 is the state-of-the-art inventory to address the evolution of a set of policy-relevant pollutants over the past 2 decades. The mosaic is made harmonising and blending seven regional inventories, gapfilled with the most recent release of the Emissions Database for Global Atmospheric Research. By incorporating the best available local information, the HTAP_v3.2 emission mosaic can be used for policy-relevant studies at regional and global level.
Zachary Watson, Can Li, Fei Liu, Sean W. Freeman, Huanxin Zhang, Jun Wang, and Shan-Hu Lee
Atmos. Chem. Phys., 25, 13527–13545, https://doi.org/10.5194/acp-25-13527-2025, https://doi.org/10.5194/acp-25-13527-2025, 2025
Short summary
Short summary
Air pollutants like sulfur dioxide impact human health and the environment. Our work estimated surface sulfur dioxide concentrations from satellite data over eastern China. One method used atmospheric models, and another method used machine learning. We found that compared to measurements from an air quality monitoring network, both methods accurately captured the locations of sulfur dioxide, but the machine learning method was generally much more accurate in the estimated concentrations.
Zhige Wang, Ce Zhang, Kejian Shi, Yulin Shangguan, Bifeng Hu, Xueyao Chen, Danqing Wei, Songchao Chen, Peter M. Atkinson, and Qiang Zhang
Earth Syst. Sci. Data, 17, 5355–5375, https://doi.org/10.5194/essd-17-5355-2025, https://doi.org/10.5194/essd-17-5355-2025, 2025
Short summary
Short summary
The irreversible trend in global warming underscores the necessity for accurate monitoring of atmospheric carbon dynamics on a global scale. This study generated a global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite data and a deep learning model. The dataset accurately depicts global and regional XCO2 patterns, advancing the monitoring of carbon emissions and understanding of global carbon dynamics.
Ruochong Xu, Hanchen Ma, Jingxian Li, Dan Tong, Liu Yan, Lanyuan Wang, Xinying Qin, Qingyang Xiao, Xizhe Yan, Hanwen Hu, Yujia Fu, Nana Wu, Huaxuan Wang, Yuexuanzi Wang, Xiaodong Liu, Guannan Geng, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 25, 12675–12700, https://doi.org/10.5194/acp-25-12675-2025, https://doi.org/10.5194/acp-25-12675-2025, 2025
Short summary
Short summary
In this study, we developed a new global emission inventory for non-methane volatile organic compounds (NMVOC) for the period of 1970–2020, with a focus on improving the representation of NMVOC-emission-related technologies. Our analysis revealed that activity growth, technology advancements, and policy-driven emission controls were key driving forces of NMVOC emission changes, but their roles were different across sectors and regions.
Pramod Kumar, Grégoire Broquet, Didier Hauglustaine, Maureen Beaudor, Lieven Clarisse, Martin Van Damme, Pierre Coheur, Anne Cozic, Bo Zheng, Beatriz Revilla Romero, Antony Delavois, and Philippe Ciais
Atmos. Chem. Phys., 25, 12379–12407, https://doi.org/10.5194/acp-25-12379-2025, https://doi.org/10.5194/acp-25-12379-2025, 2025
Short summary
Short summary
Global maps of the NH3 emissions over 2019–2022 are derived using IASI NH3 spaceborne observations, the LMDZ-INCA chemistry transport model at 1.27°×2.5° resolution, and the mass-balance approach. The average global NH3 emissions over the period are ~97 Tg NH3 yr-1, which is significantly higher than three reference inventories. The analysis provides confidence in the seasonal variability and regional budgets and provides new insights into NH3 emissions at global and regional scales.
Rimal Abeed, Audrey Fortems-Cheiney, Grégoire Broquet, Robin Plauchu, Isabelle Pison, Antoine Berchet, Elise Potier, Bo Zheng, Gaëlle Dufour, Adriana Coman, Dilek Savas, Guillaume Siour, Henk Eskes, Beatriz Revilla-Romero, Antony Delavois, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2025-3329, https://doi.org/10.5194/egusphere-2025-3329, 2025
Preprint archived
Short summary
Short summary
We investigate changes in air pollution from nitrogen oxides NOx (=NO+NO2) across Eastern China from 2019 to 2021, using a satellite-based modelling approach. Our results show a drop in pollution in 2020 in most provinces, and along the China-Mongolia-Russia Economic Corridor. The analysis also captures emission variations during the Lunar New Year. By estimating emissions at the provincial level, the study provides insights into how major events and policy measures influence local air quality.
Yawen Kong, Bo Zheng, and Yuxi Liu
Atmos. Chem. Phys., 25, 5959–5976, https://doi.org/10.5194/acp-25-5959-2025, https://doi.org/10.5194/acp-25-5959-2025, 2025
Short summary
Short summary
Current high-resolution satellite remote sensing technologies provide a unique opportunity to derive timely high-resolution emission data. We developed an emission inversion system to assimilate satellite NO2 data to obtain daily kilometer-scale NOx emission inventories. Our results enhance inventory accuracy, allowing us to capture the effects of pollution control policies on daily emissions (e.g., during COVID-19 lockdowns) and improve fine-scale air quality modeling.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Xiaolin Duan, Guangjie Zheng, Chuchu Chen, Qiang Zhang, and Kebin He
Atmos. Chem. Phys., 25, 3919–3928, https://doi.org/10.5194/acp-25-3919-2025, https://doi.org/10.5194/acp-25-3919-2025, 2025
Short summary
Short summary
Aerosol acidity is an important parameter in atmospheric chemistry, while its driving factors, especially chemical profiles versus meteorological conditions, are not yet fully understood. Here, we established a hierarchical quantitative analysis framework to understand the driving factors of aerosol acidity on different timescales. Its application in Changzhou, China, revealed distinct driving factors and corresponding mechanisms of aerosol acidity from annual trends to random residuals.
Zhu Deng, Philippe Ciais, Liting Hu, Adrien Martinez, Marielle Saunois, Rona L. Thompson, Kushal Tibrewal, Wouter Peters, Brendan Byrne, Giacomo Grassi, Paul I. Palmer, Ingrid T. Luijkx, Zhu Liu, Junjie Liu, Xuekun Fang, Tengjiao Wang, Hanqin Tian, Katsumasa Tanaka, Ana Bastos, Stephen Sitch, Benjamin Poulter, Clément Albergel, Aki Tsuruta, Shamil Maksyutov, Rajesh Janardanan, Yosuke Niwa, Bo Zheng, Joël Thanwerdas, Dmitry Belikov, Arjo Segers, and Frédéric Chevallier
Earth Syst. Sci. Data, 17, 1121–1152, https://doi.org/10.5194/essd-17-1121-2025, https://doi.org/10.5194/essd-17-1121-2025, 2025
Short summary
Short summary
This study reconciles national greenhouse gas (GHG) inventories with updated atmospheric inversion results to evaluate discrepancies for three principal GHG fluxes at the national level. Compared to our previous study, new satellite-based CO2 inversions were included and an updated mask of managed lands was used, improving agreement for Brazil and Canada. The proposed methodology can be regularly applied as a check to assess the gap between top-down inversions and bottom-up inventories.
Mingrui Ma, Jiachen Cao, Dan Tong, Bo Zheng, and Yu Zhao
Atmos. Chem. Phys., 25, 2147–2166, https://doi.org/10.5194/acp-25-2147-2025, https://doi.org/10.5194/acp-25-2147-2025, 2025
Short summary
Short summary
We combined two global climate change pathways and three national emission control scenarios to analyze the future evolution of reactive nitrogen (Nr) deposition till the 2060s in China with air quality modeling. We show China’s clean air and carbon neutrality policies would overcome the adverse effects of climate change and efficiently reduce Nr deposition. The outflow of Nr fluxes from mainland China to the west Pacific would also be clearly reduced from continuous stringent emission controls.
Hui Li, Jiaxin Qiu, and Bo Zheng
Atmos. Chem. Phys., 25, 1949–1963, https://doi.org/10.5194/acp-25-1949-2025, https://doi.org/10.5194/acp-25-1949-2025, 2025
Short summary
Short summary
We conduct a sensitivity analysis with 31 tests on various factors including prior emissions, model resolution, satellite constraint, and other system configurations to assess the vulnerability of emission estimates across temporal, sectoral, and regional dimensions. This reveals the robustness of emissions estimated by this air-pollution-satellite-based CO2 emission inversion system, with relative change between tests and base inversion below 4.0 % for national annual NOx and CO2 emissions.
Liu Yan, Qiang Zhang, Bo Zheng, and Kebin He
Earth Syst. Sci. Data, 16, 4497–4509, https://doi.org/10.5194/essd-16-4497-2024, https://doi.org/10.5194/essd-16-4497-2024, 2024
Short summary
Short summary
A new database of fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles from 1970 to 2020 is developed with the fleet turnover model built in this study. Based on this database, the evolution of the global vehicle stock over 50 years is analyzed, the dominant emission contributors by vehicle and fuel type are identified, and the age distribution of on-road CO2 emissions is characterized further.
Jinbo Wang, Jiaping Wang, Yuxuan Zhang, Tengyu Liu, Xuguang Chi, Xin Huang, Dafeng Ge, Shiyi Lai, Caijun Zhu, Lei Wang, Qiaozhi Zha, Ximeng Qi, Wei Nie, Congbin Fu, and Aijun Ding
Atmos. Chem. Phys., 24, 11063–11080, https://doi.org/10.5194/acp-24-11063-2024, https://doi.org/10.5194/acp-24-11063-2024, 2024
Short summary
Short summary
In this study, we found large spatial discrepancies in the physical and chemical properties of black carbon over the Tibetan Plateau (TP). Elevated anthropogenic emissions from low-altitude regions can significantly change the mass concentration, mixing state and chemical composition of black-carbon-containing aerosol in the TP region, further altering its light absorption ability. Our study emphasizes the vulnerability of remote plateau regions to intense anthropogenic influences.
Yuan Cheng, Xu-bing Cao, Sheng-qiang Zhu, Zhi-qing Zhang, Jiu-meng Liu, Hong-liang Zhang, Qiang Zhang, and Ke-bin He
Atmos. Chem. Phys., 24, 9869–9883, https://doi.org/10.5194/acp-24-9869-2024, https://doi.org/10.5194/acp-24-9869-2024, 2024
Short summary
Short summary
The agreement between observational and modeling results is essential for the development of efficient air pollution control strategies. Here we constrained the modeling results of carbonaceous aerosols by field observation in Northeast China, a historically overlooked but recently targeted region of national clean-air actions. Our study suggested that the simulation of agricultural fire emissions and secondary organic aerosols remains challenging.
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.
Meng Li, Junichi Kurokawa, Qiang Zhang, Jung-Hun Woo, Tazuko Morikawa, Satoru Chatani, Zifeng Lu, Yu Song, Guannan Geng, Hanwen Hu, Jinseok Kim, Owen R. Cooper, and Brian C. McDonald
Atmos. Chem. Phys., 24, 3925–3952, https://doi.org/10.5194/acp-24-3925-2024, https://doi.org/10.5194/acp-24-3925-2024, 2024
Short summary
Short summary
In this work, we developed MIXv2, a mosaic Asian emission inventory for 2010–2017. With high spatial (0.1°) and monthly temporal resolution, MIXv2 integrates anthropogenic and open biomass burning emissions across seven sectors following a mosaic methodology. It provides CO2 emissions data alongside nine key pollutants and three chemical mechanisms. Our publicly accessible gridded monthly emissions data can facilitate long-term atmospheric and climate model analyses.
Fei Liu, Steffen Beirle, Joanna Joiner, Sungyeon Choi, Zhining Tao, K. Emma Knowland, Steven J. Smith, Daniel Q. Tong, Siqi Ma, Zachary T. Fasnacht, and Thomas Wagner
Atmos. Chem. Phys., 24, 3717–3728, https://doi.org/10.5194/acp-24-3717-2024, https://doi.org/10.5194/acp-24-3717-2024, 2024
Short summary
Short summary
Using satellite data, we developed a coupled method independent of the chemical transport model to map NOx emissions across US cities. After validating our technique with synthetic data, we charted NOx emissions from 2018–2021 in 39 cities. Our results closely matched EPA estimates but also highlighted some inconsistencies in both magnitude and spatial distribution. This research can help refine strategies for monitoring and managing air quality.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song
EGUsphere, https://doi.org/10.22541/essoar.168771101.14987378/v1, https://doi.org/10.22541/essoar.168771101.14987378/v1, 2023
Preprint archived
Short summary
Short summary
We train and apply a state-of-the-art deep learning model to detect NO2 plumes emitted by ships using NO2 retrievals from TROPOMI. By applying the model, we can detect individual plumes with excellent fidelity. The aggregated data show major shipping routes, but miss other routes. The missing routes are due to high cloudiness. Our method can be potentially useful for monitoring ship emissions of NOx and verifying compliance of emission standards.
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.
Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao
Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
Short summary
Short summary
Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Peng Wang, Ruhan Zhang, Shida Sun, Meng Gao, Bo Zheng, Dan Zhang, Yanli Zhang, Gregory R. Carmichael, and Hongliang Zhang
Atmos. Chem. Phys., 23, 2983–2996, https://doi.org/10.5194/acp-23-2983-2023, https://doi.org/10.5194/acp-23-2983-2023, 2023
Short summary
Short summary
In China, the number of vehicles has jumped significantly in the last decade. This caused severe traffic congestion and aggravated air pollution. In this study, we developed a new temporal allocation approach to quantify the impacts of traffic congestion. We found that traffic congestion worsens air quality and the health burden across China, especially in the urban clusters. More effective and comprehensive vehicle emission control policies should be implemented to improve air quality in China.
Yuanhong Zhao, Marielle Saunois, Philippe Bousquet, Xin Lin, Michaela I. Hegglin, Josep G. Canadell, Robert B. Jackson, and Bo Zheng
Atmos. Chem. Phys., 23, 789–807, https://doi.org/10.5194/acp-23-789-2023, https://doi.org/10.5194/acp-23-789-2023, 2023
Short summary
Short summary
The large uncertainties in OH simulated by atmospheric chemistry models hinder accurate estimates of CH4 chemical loss through the bottom-up method. This study presents a new approach based on OH precursor observations and a chemical box model to improve the tropospheric OH distributions simulated by atmospheric chemistry models. Through this approach, both the global OH burden and the corresponding methane chemical loss reach consistency with the top-down method based on MCF inversions.
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
Short summary
Short summary
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.
Qingyang Xiao, Guannan Geng, Shigan Liu, Jiajun Liu, Xia Meng, and Qiang Zhang
Atmos. Chem. Phys., 22, 13229–13242, https://doi.org/10.5194/acp-22-13229-2022, https://doi.org/10.5194/acp-22-13229-2022, 2022
Short summary
Short summary
We provided complete coverage PM2.5 concentrations at a 1-km resolution from 2000 to the present, carefully considering the significant changes in land use characteristics in China. This high-resolution PM2.5 data successfully revealed the local-scale PM2.5 variations. We noticed changes in PM2.5 spatial patterns in association with the clean air policies, with the pollution hotspots having transferred from urban centers to rural regions with limited air quality monitoring.
Can Li, Joanna Joiner, Fei Liu, Nickolay A. Krotkov, Vitali Fioletov, and Chris McLinden
Atmos. Meas. Tech., 15, 5497–5514, https://doi.org/10.5194/amt-15-5497-2022, https://doi.org/10.5194/amt-15-5497-2022, 2022
Short summary
Short summary
Satellite observations provide information on the sources of SO2, an important pollutant that affects both air quality and climate. However, these observations suffer from relatively poor data quality due to weak signals of SO2. Here, we use a machine learning technique to analyze satellite SO2 observations in order to reduce the noise and artifacts over relatively clean areas while keeping the signals near pollution sources. This leads to significant improvement in satellite SO2 data.
Yawen Kong, Bo Zheng, Qiang Zhang, and Kebin He
Atmos. Chem. Phys., 22, 10769–10788, https://doi.org/10.5194/acp-22-10769-2022, https://doi.org/10.5194/acp-22-10769-2022, 2022
Short summary
Short summary
We developed a Bayesian atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) algorithm coupled with GEOS-Chem from the latest Orbiting Carbon Observatory-2 (OCO-2) V10r XCO2 retrievals. This is the first adaptation of 4D-LETKF to an OCO-2-based global carbon inversion system. We inferred global gridded carbon fluxes and investigated their magnitudes, variations, and partitioning schemes to understand the global and regional carbon budgets for 2015–2020.
Le Yuan, Olalekan A. M. Popoola, Christina Hood, David Carruthers, Roderic L. Jones, Haitong Zhe Sun, Huan Liu, Qiang Zhang, and Alexander T. Archibald
Atmos. Chem. Phys., 22, 8617–8637, https://doi.org/10.5194/acp-22-8617-2022, https://doi.org/10.5194/acp-22-8617-2022, 2022
Short summary
Short summary
Emission estimates represent a major source of uncertainty in air quality modelling. We developed a novel approach to improve emission estimates from existing inventories using air quality models and routine in situ observations. Using this approach, we derived improved estimates of NOx emissions from the transport sector in Beijing in 2016. This approach has great potential in deriving timely updates of emissions for other pollutants, particularly in regions undergoing rapid emission changes.
Marios Panagi, Roberto Sommariva, Zoë L. Fleming, Paul S. Monks, Gongda Lu, Eloise A. Marais, James R. Hopkins, Alastair C. Lewis, Qiang Zhang, James D. Lee, Freya A. Squires, Lisa K. Whalley, Eloise J. Slater, Dwayne E. Heard, Robert Woodward-Massey, Chunxiang Ye, and Joshua D. Vande Hey
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-379, https://doi.org/10.5194/acp-2022-379, 2022
Revised manuscript not accepted
Short summary
Short summary
A dispersion model and a box model were combined to investigate the evolution of VOCs in Beijing once they are emitted from anthropogenic sources. It was determined that during the winter time the VOC concentrations in Beijing are driven predominantly by sources within Beijing and by a combination of transport and chemistry during the summer. Furthermore, the results in the paper highlight the need for a season specific policy.
Vitali Fioletov, Chris A. McLinden, Debora Griffin, Nickolay Krotkov, Fei Liu, and Henk Eskes
Atmos. Chem. Phys., 22, 4201–4236, https://doi.org/10.5194/acp-22-4201-2022, https://doi.org/10.5194/acp-22-4201-2022, 2022
Short summary
Short summary
The COVID-19 lockdown had a large impact on anthropogenic emissions and particularly on nitrogen dioxide (NO2). A new method of isolation of background, urban, and industrial components in NO2 is applied to estimate the lockdown impact on each of them. From 16 March to 15 June 2020, urban NO2 declined by −18 % to −28 % in most regions of the world, while background NO2 typically declined by less than −10 %.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
Short summary
Short summary
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Yaqing Zhou, Nan Ma, Qiaoqiao Wang, Zhibin Wang, Chunrong Chen, Jiangchuan Tao, Juan Hong, Long Peng, Yao He, Linhong Xie, Shaowen Zhu, Yuxuan Zhang, Guo Li, Wanyun Xu, Peng Cheng, Uwe Kuhn, Guangsheng Zhou, Pingqing Fu, Qiang Zhang, Hang Su, and Yafang Cheng
Atmos. Chem. Phys., 22, 2029–2047, https://doi.org/10.5194/acp-22-2029-2022, https://doi.org/10.5194/acp-22-2029-2022, 2022
Short summary
Short summary
This study characterizes size-resolved particle effective densities and their evolution associated with emissions and aging processes in a rural area of the North China Plain. Particle effective density exhibits a high-frequency bimodal distribution, and two density modes exhibit opposite trends with increasing particle size. SIA and BC mass fractions are key factors of particle effective density, and a value of 0.6 g cm−3 is appropriate to represent BC effective density in bulk particles.
Fei Liu, Zhining Tao, Steffen Beirle, Joanna Joiner, Yasuko Yoshida, Steven J. Smith, K. Emma Knowland, and Thomas Wagner
Atmos. Chem. Phys., 22, 1333–1349, https://doi.org/10.5194/acp-22-1333-2022, https://doi.org/10.5194/acp-22-1333-2022, 2022
Short summary
Short summary
In this work, we present a novel method to infer NOx emissions and lifetimes based on tropospheric NO2 observations together with reanalysis wind fields for cities located in polluted backgrounds. We evaluate the accuracy of the method using synthetic NO2 observations derived from a high-resolution model simulation. Our work provides an estimate for uncertainties in satellite-derived emissions inferred from chemical transport model (CTM)-independent approaches.
Youwen Sun, Hao Yin, Xiao Lu, Justus Notholt, Mathias Palm, Cheng Liu, Yuan Tian, and Bo Zheng
Atmos. Chem. Phys., 21, 18589–18608, https://doi.org/10.5194/acp-21-18589-2021, https://doi.org/10.5194/acp-21-18589-2021, 2021
Short summary
Short summary
This study uses high-resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method, and the exposure–response relationship to determine the drivers and evaluate the health risks of the unexpected surface O3 enhancements over the Sichuan Basin in 2020. These unexpected O3 enhancements were induced by meteorological anomalies and caused dramatically high health risks.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
Short summary
Short summary
Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Yuqiang Zhang, Drew Shindell, Karl Seltzer, Lu Shen, Jean-Francois Lamarque, Qiang Zhang, Bo Zheng, Jia Xing, Zhe Jiang, and Lei Zhang
Atmos. Chem. Phys., 21, 16051–16065, https://doi.org/10.5194/acp-21-16051-2021, https://doi.org/10.5194/acp-21-16051-2021, 2021
Short summary
Short summary
In this study, we use a global chemical transport model to simulate the effects on global air quality and human health due to emission changes in China from 2010 to 2017. By performing sensitivity analysis, we found that the air pollution control policies not only decrease the air pollutant concentration but also bring significant co-benefits in air quality to downwind regions. The benefits for the improved air pollution are dominated by PM2.5.
Gaëlle Dufour, Didier Hauglustaine, Yunjiang Zhang, Maxim Eremenko, Yann Cohen, Audrey Gaudel, Guillaume Siour, Mathieu Lachatre, Axel Bense, Bertrand Bessagnet, Juan Cuesta, Jerry Ziemke, Valérie Thouret, and Bo Zheng
Atmos. Chem. Phys., 21, 16001–16025, https://doi.org/10.5194/acp-21-16001-2021, https://doi.org/10.5194/acp-21-16001-2021, 2021
Short summary
Short summary
The IASI observations and the LMDZ-OR-INCA model simulations show negative ozone trends in the Central East China region in the lower free (3–6 km column) and the upper free (6–9 km column) troposphere. Sensitivity studies from the model show that the Chinese anthropogenic emissions contribute to more than 50 % in the trend. The reduction in NOx emissions that has occurred since 2013 in China seems to lead to a decrease in ozone in the free troposphere, contrary to the increase at the surface.
Yuan Cheng, Qin-qin Yu, Jiu-meng Liu, Xu-bing Cao, Ying-jie Zhong, Zhen-yu Du, Lin-lin Liang, Guan-nan Geng, Wan-li Ma, Hong Qi, Qiang Zhang, and Ke-bin He
Atmos. Chem. Phys., 21, 15199–15211, https://doi.org/10.5194/acp-21-15199-2021, https://doi.org/10.5194/acp-21-15199-2021, 2021
Short summary
Short summary
Open burning policies in Heilongjiang Province experienced a rapid transition during 2018 to 2020. This study evaluated the responses of PM2.5 pollution to this transition and suggested that neither of the policies could be considered successful. In addition, heterogeneous reactions were found to be at play in secondary aerosol formation, even in the frigid atmosphere in Heilongjiang. The unique haze in northeast China deserves more attention.
Cited articles
Alcamo, J. and Kreileman, E.: Emission scenarios and global climate
protection, Global Environ. Chang., 6, 305–334,
https://doi.org/10.1016/S0959-3780(96)00030-1, 1996.
Amann, M., Klimont, Z. and Wagner, F.: Regional and Global Emissions of Air
Pollutants: Recent Trends and Future Scenarios, Annu. Rev. Env. Resour., 38,
31–55, https://doi.org/10.1146/annurev-environ-052912-173303, 2013.
Belete, G. F., Voinov, A., Arto, I., Dhavala, K., Bulavskaya, T., Niamir,
L., Moghayer, S., and Filatova, T.: Exploring Low-Carbon Futures: A Web
Service Approach to Linking Diverse Climate-Energy-Economy Models, Energies,
12, 2880, https://doi.org/10.3390/en12152880, 2019.
Belaissaoui, B., Le Moullec, Y., and Favre, E.: Energy efficiency of a hybrid
membrane/condensation process for VOC (Volatile Organic Compounds) recovery
from air: A generic approach, Energy, 95, 291–302,
https://doi.org/10.1016/j.energy.2015.12.006, 2016.
Braspenning Radu, O., van den Berg, M., Klimont, Z., Deetman, S.,
Janssens-Maenhout, G., Muntean, M., Heyes, C., Dentener, F., and van Vuuren,
D. P.: Exploring synergies between climate and air quality policies using
long-term global and regional emission scenarios, Atmos. Environ., 140,
577–591, https://doi.org/10.1016/j.atmosenv.2016.05.021, 2016.
Cao, Z., Shen, L., Liu, L., and Zhong, S.: Analysis on major drivers of
cement consumption during the urbanization process in China, J. Clean.
Prod., 133, 304–313, https://doi.org/10.1016/j.jclepro.2016.05.130, 2016.
Calvin, K., Bond-Lamberty, B., Clarke, L., Edmonds, J., Eom, J., Hartin, C.,
Kim, S., Kyle, P., Link, R., Moss, R., McJeon, H., Patel, P., Smith, S.,
Waldhoff, S., and Wise, M.: The SSP4: A world of deepening inequality, Global
Environ. Chang., 42, 284–296, https://doi.org/10.1016/j.gloenvcha.2016.06.010, 2017.
Calvin, K., Bond-Lamberty, B., Jones, A., Shi, X., Di Vittorio, A., and
Thornton, P.: Characteristics of human-climate feedbacks differ at different
radiative forcing levels, Glob. Planet. Change, 180, 126–135,
https://doi.org/10.1016/j.gloplacha.2019.06.003, 2019.
Cheng, J., Su, J., Cui, T., Li, X., Dong, X., Sun, F., Yang, Y., Tong, D., Zheng, Y., Li, Y., Li, J., Zhang, Q., and He, K.: Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis, Atmos. Chem. Phys., 19, 6125–6146, https://doi.org/10.5194/acp-19-6125-2019, 2019.
China State Council: Action Plan on Prevention and Control of Air Pollution,
China State Council, Beijing, China, available at:
http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (last
access: 10 August 2018), 2013.
China State Council: Three-Year Action Plan for Winning the Blue Sky Defense
Battle, China State Council, Beijing, China, available at:
http://www.gov.cn/zhengce/content/2018-07/03/content_5303158.htm (last access: 10 September 2019), 2018.
Clarke, L., Kyle, P.,
Wise, M., Calvin, K., Edmonds, J., Kim, S., Placet, M., and Smith, S.:
CO2 emissions mitigation and technological advance: an updated analysis of advanced technology scenarios, PNNL Report Pacific Northwest National Laboratory, Richmond, 2008.
Clarke, L., Eom, J., Marten, E. H., Horowitz, R., Kyle, P., Link, R.,
Mignone, B. K., Mundra, A., and Zhou, Y.: Effects of long-term climate change
on global building energy expenditures, Energy Econ., 72, 667–677,
https://doi.org/10.1016/j.eneco.2018.01.003, 2018.
Cofala, J., Amann, M., Klimont, Z., Kupiainen, K., and Höglund-Isaksson,
L.: Scenarios of global anthropogenic emissions of air pollutants and
methane until 2030, Atmos. Environ., 41, 8486–8499,
https://doi.org/10.1016/j.atmosenv.2007.07.010, 2007.
Collins, W. D., Craig, A. P., Truesdale, J. E., Di Vittorio, A. V., Jones, A. D., Bond-Lamberty, B., Calvin, K. V., Edmonds, J. A., Kim, S. H., Thomson, A. M., Patel, P., Zhou, Y., Mao, J., Shi, X., Thornton, P. E., Chini, L. P., and Hurtt, G. C.: The integrated Earth system model version 1: formulation and functionality, Geosci. Model Dev., 8, 2203–2219, https://doi.org/10.5194/gmd-8-2203-2015, 2015.
Dong, N., You, L., Cai, W., Li, G., and Lin, H.: Land use projections in
China under global socioeconomic and emission scenarios: Utilizing a
scenario-based land-use change assessment framework, Global Environ. Chang.,
50, 164–177, https://doi.org/10.1016/j.gloenvcha.2018.04.001, 2018.
Edmonds, J. and Reilly, J.: Global energy and CO2 to the year 2050,
IEA/ORAU Working Paper Contribution No. 82-6, 1982.
Edmonds, J. and Reilly, J.: Global Energy and CO2 to the Year 2050, The Energy Journal, 4, 21–47, 1983.
Edmonds, J., Reilly, J., Trabalka, J. R., and Reichle, D. E.: An Analysis of
Possible Future Atmospheric Retention of Fossil Fuel CO2, TR013,
DOE/OR/21400-1, National Technical Information Service, U.S. Department of
Commerce, Springfield Virginia 22161, 1984.
European Commission, Joint Research Centre: Common Waste Water and Waste Gas
Treatment/Management Systems in the Chemical Sector, available at:
https://eippcb.jrc.ec.europa.eu/reference/cww.html (last
access: 11 October 2019), 2016.
Fawcett, A. A., Iyer, G. C., Clarke, L. E., Edmonds, J. A., Hultman, N. E.,
McJeon, H. C., Rogelj, J., Schuler, R., Alsalam, J., Asrar, G. R., Creason,
J., Jeong, M., McFarland, J., Mundra, A., and Shi, W.: Can Paris pledges
avert severe climate change?, Science, 350, 1168–1169,
https://doi.org/10.1126/science.aad5761, 2015.
Geng, G., Zhang, Q., Tong, D., Li, M., Zheng, Y., Wang, S., and He, K.: Chemical composition of ambient PM2.5 over China and relationship to precursor emissions during 2005–2012, Atmos. Chem. Phys., 17, 9187–9203, https://doi.org/10.5194/acp-17-9187-2017, 2017.
Geng, G., Xiao, Q., Zheng, Y., Tong, D., Zhang, Y., Zhang, X., Zhang, Q.,
He, K., and Liu, Y.: Impact of China's Air Pollution Prevention and Control
Action Plan on PM2.5 chemical composition over eastern China, Sci. China
Earth Sci., 62, 1872–1884, https://doi.org/10.1007/s11430-018-9353-x, 2019.
Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.
Hong, C., Zhang, Q., He, K., Guan, D., Li, M., Liu, F., and Zheng, B.: Variations of China's emission estimates: response to uncertainties in energy statistics, Atmos. Chem. Phys., 17, 1227–1239, https://doi.org/10.5194/acp-17-1227-2017, 2017.
Huo, H., Lei, Y., Zhang, Q., Zhao, L., and He, K.: China's coke industry:
Recent policies, technology shift, and implication for energy and the
environment, Energy Policy, 51, 397–404, https://doi.org/10.1016/j.enpol.2012.08.041,
2012.
Huo, H., Zheng, B., Wang, M., Zhang, Q., and He, K.-B.: Vehicular air
pollutant emissions in China: evaluation of past control policies and future
perspectives, Mitig. Adapt. Strateg. Glob. Change., 20, 719–733,
https://doi.org/10.1007/s11027-014-9613-0, 2015.
International Energy Agency (IEA): Energy balances & global energy
statistics, 2011.
Jiang, K., He, C., Dai, H., Liu, J., and Xu, X.: Emission scenario analysis
for China under the global 1.5 ∘C target, Carbon Manag., 9,
481–491, https://doi.org/10.1080/17583004.2018.1477835, 2018.
Ju, X.-T., Xing, G.-X., Chen, X.-P., Zhang, S.-L., Zhang, L.-J., Liu, X.-J.,
Cui, Z.-L., Yin, B., Christie, P., Zhu, Z.-L., and Zhang, F.-S.: Reducing
environmental risk by improving N management in intensive Chinese
agricultural systems, P. Natl. Acad. Sci. USA, 106, 3041–3046,
https://doi.org/10.1073/pnas.0813417106, 2009.
Kang, Y., Liu, M., Song, Y., Huang, X., Yao, H., Cai, X., Zhang, H., Kang, L., Liu, X., Yan, X., He, H., Zhang, Q., Shao, M., and Zhu, T.: High-resolution ammonia emissions inventories in China from 1980 to 2012, Atmos. Chem. Phys., 16, 2043–2058, https://doi.org/10.5194/acp-16-2043-2016, 2016.
Klimont, Z., Streets, D. G., Gupta, S., Cofala, J., Lixin, F., and Ichikawa,
Y.: Anthropogenic emissions of non-methane volatile organic compounds in
China, Atmos. Environ., 36, 1309–1322,
https://doi.org/10.1016/S1352-2310(01)00529-5, 2002.
Kramer, G. J. and Haigh, M.: No quick switch to low-carbon energy, Nature,
462, 568–569, https://doi.org/10.1038/462568a, 2009.
Lasseter, R. H. and Piagi, P.: Microgrid: a conceptual solution, in: PESC,
Aachen, Germany, 20–25, 2004.
Lei, Y., Zhang, Q., He, K. B., and Streets, D. G.: Primary anthropogenic aerosol emission trends for China, 1990–2005, Atmos. Chem. Phys., 11, 931–954, https://doi.org/10.5194/acp-11-931-2011, 2011a.
Lei, Y., Zhang, Q., Nielsen, C., and He, K.: An inventory of primary air
pollutants and CO2 emissions from cement production in China,
1990–2020, Atmos. Environ., 45, 147–154, 2011b.
Li, M., Zhang, Q., Streets, D. G., He, K. B., Cheng, Y. F., Emmons, L. K., Huo, H., Kang, S. C., Lu, Z., Shao, M., Su, H., Yu, X., and Zhang, Y.: Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms, Atmos. Chem. Phys., 14, 5617–5638, https://doi.org/10.5194/acp-14-5617-2014, 2014.
Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017.
Li, M., Zhang, Q., Zheng, B., Tong, D., Lei, Y., Liu, F., Hong, C., Kang, S., Yan, L., Zhang, Y., Bo, Y., Su, H., Cheng, Y., and He, K.: Persistent growth of anthropogenic non-methane volatile organic compound (NMVOC) emissions in China during 1990–2017: drivers, speciation and ozone formation potential, Atmos. Chem. Phys., 19, 8897–8913, https://doi.org/10.5194/acp-19-8897-2019, 2019.
Li, N., Chen, W., Rafaj, P., Kiesewetter, G., Schöpp, W., Wang, H.,
Zhang, H., Krey, V., and Riahi, K.: Air Quality Improvement Co-benefits of
Low-Carbon Pathways toward Well Below the 2 ∘C Climate Target in
China, Environ. Sci. Technol., 53, 5576–5584, https://doi.org/10.1021/acs.est.8b06948,
2019.
Liang, X., Zhang, S., Wu, Y., Xing, J., He, X., Zhang, K. M., Wang, S., and
Hao, J.: Air quality and health benefits from fleet electrification in
China, Nat. Sustain., 2, 962–971, https://doi.org/10.1038/s41893-019-0398-8, 2019.
Liu, F., Zhang, Q., Tong, D., Zheng, B., Li, M., Huo, H., and He, K. B.: High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010, Atmos. Chem. Phys., 15, 13299–13317, https://doi.org/10.5194/acp-15-13299-2015, 2015.
Liu, J., Zheng, Y., Geng, G., Hong, C., Li, M., Li, X., Liu, F., Tong, D., Wu, R., Zheng, B., He, K., and Zhang, Q.: Decadal changes in anthropogenic source contribution of PM2.5 pollution and related health impacts in China, 1990–2015, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-895, in review, 2019.
Lu, Z., Zhang, Q., and Streets, D. G.: Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010, Atmos. Chem. Phys., 11, 9839–9864, https://doi.org/10.5194/acp-11-9839-2011, 2011.
Miketa, A. and Schrattenholzer, L.: Equity implications of two
burden-sharing rules for stabilizing greenhouse-gas concentrations, Energ.
Policy, 34, 877–891, https://doi.org/10.1016/j.enpol.2004.08.050, 2006.
Ministry of Ecology and Environment, the People's Republic of China: 2013
Report on the State of the Ecology and Environment in China, Beijing, China,
available at:
http://english.mee.gov.cn/Resources/Reports/soe/soe2011/201606/P020160601591756378883.pdf
(last access: 10 September 2019), 2014.
Ministry of Ecology and Environment, the People's Republic of China: 2017
Report on the State of the Ecology and Environment in China, Beijing, China,
available at:
http://english.mee.gov.cn/Resources/Reports/soe/SOEE2017/201808/P020180801597738742758.pdf
(last access: 10 September 2019), 2018.
National Bureau of Statistics (NBS): China Energy Statistical Yearbook (2006
editions), China Statistics Press, Beijing, China, 2006.
National Bureau of Statistics (NBS): China Energy Statistical Yearbook (2007
editions), China Statistics Press, Beijing, China, 2007.
National Bureau of Statistics (NBS): China Energy Statistical Yearbook (2016
editions), China Statistics Press, Beijing, China, 2016.
National Development and Reform Commission: 13th FYP development plan for
renewable energy, available at:
http://www.ndrc.gov.cn/zcfb/zcfbghwb/201612/W020161216661816762488.pdf
(access at: 26 May 2019), 2016 (in Chinese).
O'Neill, B. C., Dalton, M., Fuchs, R., Jiang, L., Pachauri, S., and Zigova,
K.: Global demographic trends and future carbon emissions, P. Natl. Acad.
Sci. USA, 107, 17521–17526, https://doi.org/10.1073/pnas.1004581107, 2010.
O'Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter,
T. R., Mathur, R., and van Vuuren, D. P.: A new scenario framework for
climate change research: the concept of shared socioeconomic pathways,
Clim. Change, 122, 387–400, https://doi.org/10.1007/s10584-013-0905-2, 2014.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ,
R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta, P., and Dubash, N. K.:
Climate change 2014: synthesis report, Contribution of Working Groups I, II
and III to the fifth assessment report of the Intergovernmental Panel on
Climate Change, IPCC, p. 151, 2014.
Pan, B., Lam, S. K., Mosier, A., Luo, Y., and Chen, D.: Ammonia
volatilization from synthetic fertilizers and its mitigation strategies: A
global synthesis, Agr. Ecosyst. Environ., 232, 283–289,
https://doi.org/10.1016/j.agee.2016.08.019, 2016.
Peng, L., Zhang, Q., Yao, Z., Mauzerall, D. L., Kang, S., Du, Z., Zheng, Y.,
Xue, T., and He, K.: Underreported coal in statistics: A survey-based solid
fuel consumption and emission inventory for the rural residential sector in
China, Appl. Energy, 235, 1169–1182, https://doi.org/10.1016/j.apenergy.2018.11.043,
2019.
Rao, S., Klimont, Z., Smith, S. J., Van Dingenen, R., Dentener, F., Bouwman,
L., Riahi, K., Amann, M., Bodirsky, B. L., van Vuuren, D. P., Aleluia Reis,
L., Calvin, K., Drouet, L., Fricko, O., Fujimori, S., Gernaat, D., Havlik,
P., Harmsen, M., Hasegawa, T., Heyes, C., Hilaire, J., Luderer, G., Masui,
T., Stehfest, E., Strefler, J., van der Sluis, S., and Tavoni, M.: Future air
pollution in the Shared Socio-economic Pathways, Global Environ. Chang., 42,
346–358, https://doi.org/10.1016/j.gloenvcha.2016.05.012, 2017.
Ray, D. K., Mueller, N. D., West, P. C., and Foley, J. A.: Yield Trends Are
Insufficient to Double Global Crop Production by 2050, PLOS ONE, 8,
e66428, https://doi.org/10.1371/journal.pone.0066428, 2013.
Reilly, J. M., Edmonds, J. A., Gardner, R. H., and Brenkert, A. L.: Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model, The Energy Journal, 8, 1–29, 1987.
Rezaie, B. and Rosen, M. A.: District heating and cooling: Review of
technology and potential enhancements, Appl. Energy, 93, 2–10,
https://doi.org/10.1016/j.apenergy.2011.04.020, 2012.
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W.,
Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao,
S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da
Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: An overview, Global Environ.
Chang., 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Shi, J., Chen, W., and Yin, X.: Modelling building's decarbonization with
application of China TIMES model, Appl. Energy, 162, 1303–1312,
https://doi.org/10.1016/j.apenergy.2015.06.056, 2016.
Shen, G., Ru, M., Du, W., Zhu, X., Zhong, Q., Chen, Y., Shen, H., Yun, X.,
Meng, W., Liu, J., Cheng, H., Hu, J., Guan, D., and Tao, S.: Impacts of air
pollutants from rural Chinese households under the rapid residential energy
transition, Nat. Commun., 10, 3405, https://doi.org/10.1038/s41467-019-11453-w, 2019.
Silva Herran, D., Tachiiri, K., and Matsumoto, K.: Global energy system
transformations in mitigation scenarios considering climate uncertainties,
Appl. Energy, 243, 119–131, https://doi.org/10.1016/j.apenergy.2019.03.069, 2019.
Sinha, E., Michalak, A. M., Calvin, K. V., and Lawrence, P. J.: Societal
decisions about climate mitigation will have dramatic impacts on
eutrophication in the 21st century, Nat. Commun., 10, 939,
https://doi.org/10.1038/s41467-019-08884-w, 2019.
Tian, H., Liu, K., Hao, J., Wang, Y., Gao, J., Qiu, P., and Zhu, C.: Nitrogen
Oxides Emissions from Thermal Power Plants in China: Current Status and
Future Predictions, Environ. Sci. Technol., 47, 11350–11357,
https://doi.org/10.1021/es402202d, 2013.
Tong, D., Zhang, Q., Liu, F., Geng, G., Zheng, Y., Xue, T., Hong, C., Wu,
R., Qin, Y., Zhao, H., Yan, L., and He, K.: Current Emissions and Future
Mitigation Pathways of Coal-Fired Power Plants in China from 2010 to 2030,
Environ. Sci. Technol., 52, 12905–12914, https://doi.org/10.1021/acs.est.8b02919,
2018a.
Tong, D., Zhang, Q., Davis, S. J., Liu, F., Zheng, B., Geng, G., Xue, T.,
Li, M., Hong, C., Lu, Z., Streets, D. G., Guan, D., and He, K.: Targeted
emission reductions from global super-polluting power plant units, Nat.
Sustain., 1, 59–68, https://doi.org/10.1038/s41893-017-0003-y, 2018b.
Tong, D., Zhang, Q., Zheng, Y., Caldeira, K., Shearer, C., Hong, C., Qin, Y.,
and Davis, S. J.: Committed emissions from existing energy infrastructure
jeopardize 1.5 ∘C climate target, Nature, 572, 373–377,
https://doi.org/10.1038/s41586-019-1364-3, 2019.
The Ministry of Public Security of the People's Republic of China: China's
private cars exceed 200 million for first time, available at:
https://www.mps.gov.cn/n2254314/n6409334/c6852472/content.html, last access: 4 March 2020 (in Chinese).
Turner, P. A., Field, C. B., Lobell, D. B., Sanchez, D. L., and Mach, K. J.:
Unprecedented rates of land-use transformation in modelled climate change
mitigation pathways, Nat. Sustain., 1, 240–245,
https://doi.org/10.1038/s41893-018-0063-7, 2018.
United Nations Framework Convention on Climate Change: China's Nationally
Determined Contribution, available at:
https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/China First/China's First NDC Submission.pdf
(last access: 9 May 2019), 2015.
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A.,
Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Clim. Change, 109, 5,
https://doi.org/10.1007/s10584-011-0148-z, 2011.
Vittorio, A. V. D., Mao, J., Shi, X., Chini, L., Hurtt, G., and Collins, W.
D.: Quantifying the Effects of Historical Land Cover Conversion Uncertainty
on Global Carbon and Climate Estimates, Geophys. Res. Lett., 45,
974–982, https://doi.org/10.1002/2017GL075124, 2018.
Wang, H. and Chen, W.: Modelling deep decarbonization of industrial energy
consumption under 2-degree target: Comparing China, India and Western
Europe, Appl. Energy, 238, 1563–1572, https://doi.org/10.1016/j.apenergy.2019.01.131,
2019.
Wang, K., Tian, H., Hua, S., Zhu, C., Gao, J., Xue, Y., Hao, J., Wang, Y.,
and Zhou, J.: A comprehensive emission inventory of multiple air pollutants
from iron and steel industry in China: Temporal trends and spatial variation
characteristics, Sci. Total Environ., 559, 7–14,
https://doi.org/10.1016/j.scitotenv.2016.03.125, 2016.
Wang, S. X., Zhao, B., Cai, S. Y., Klimont, Z., Nielsen, C. P., Morikawa, T., Woo, J. H., Kim, Y., Fu, X., Xu, J. Y., Hao, J. M., and He, K. B.: Emission trends and mitigation options for air pollutants in East Asia, Atmos. Chem. Phys., 14, 6571–6603, https://doi.org/10.5194/acp-14-6571-2014, 2014.
Wei, W., Wang, S., Hao, J., and Cheng, S.: Projection of anthropogenic
volatile organic compounds (VOCs) emissions in China for the period
2010–2020, Atmos. Environ., 45, 6863–6871,
https://doi.org/10.1016/j.atmosenv.2011.01.013, 2011.
World Health Organization: WHO Air Quality Guidelines for Particulate
Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide, Global Update 2005:
Summary of Risk Assessment. Geneva, WHO, available:
http://whqlibdoc.who.int/hq/2006/WHO_SDE_PHE_OEH_06.02_eng.pdf (last
access: 11 October 2019), 2006.
Xing, J., Wang, S. X., Chatani, S., Zhang, C. Y., Wei, W., Hao, J. M., Klimont, Z., Cofala, J., and Amann, M.: Projections of air pollutant emissions and its impacts on regional air quality in China in 2020, Atmos. Chem. Phys., 11, 3119–3136, https://doi.org/10.5194/acp-11-3119-2011, 2011.
Xu, P., Koloutsou-Vakakis, S., Rood, M. J., and Luan, S.: Projections of
NH3 emissions from manure generated by livestock production in China to 2030 under six mitigation scenarios, Sci. Total. Environ., 607–608, 78–86, https://doi.org/10.1016/j.scitotenv.2017.06.258, 2017.
Xue, T., Zheng, Y., Tong, D., Zheng, B., Li, X., Zhu, T., and Zhang, Q.:
Spatiotemporal continuous estimates of PM2.5 concentrations in China,
2000–2016: A machine learning method with inputs from satellites, chemical
transport model, and ground observations, Environ. Int., 123, 345–357,
https://doi.org/10.1016/j.envint.2018.11.075, 2019a.
Xue, T., Liu, J., Zhang, Q., Geng, G., Zheng, Y., Tong, D., Liu, Z., Guan,
D., Bo, Y., Zhu, T., He, K., and Hao, J.: Rapid improvement of PM2.5
pollution and associated health benefits in China during 2013–2017, Sci.
China Earth Sci., 62, 1847–1856, https://doi.org/10.1007/s11430-018-9348-2, 2019b.
Yu, S., Horing, J., Liu, Q., Dahowski, R., Davidson, C., Edmonds, J., Liu,
B., Mcjeon, H., McLeod, J., Patel, P., and Clarke, L.: CCUS in China's
mitigation strategy: insights from integrated assessment modeling, Int. J.
Greenh. Gas Con., 84, 204–218, https://doi.org/10.1016/j.ijggc.2019.03.004, 2019.
Zhang, J. and Smith, K. R.: Household Air Pollution from Coal and Biomass
Fuels in China: Measurements, Health Impacts, and Interventions, Environ.
Health Perspect., 115, 848–855, https://doi.org/10.1289/ehp.9479, 2007.
Zhang, J., Xiao, J., Chen, X., Liang, X., Fan, L., and Ye, D.: Allowance and
allocation of industrial volatile organic compounds emission in China for
year 2020 and 2030, J. Environ. Sci., 69, 155–165,
https://doi.org/10.1016/j.jes.2017.10.003, 2018.
Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z., Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L. T., and Yao, Z. L.: Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153, https://doi.org/10.5194/acp-9-5131-2009, 2009.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang,
J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang,
Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu,
F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu,
Z., Yang, F., He, K., and Hao, J.: Drivers of improved PM2.5 air quality in China from 2013 to 2017, P. Natl. Acad. Sci. USA, 116, 24463–24469,
https://doi.org/10.1073/pnas.1907956116, 2019a.
Zhang, S., Yi, B.-W., Worrell, E., Wagner, F., Crijns-Graus, W., Purohit,
P., Wada, Y. and Varis, O.: Integrated assessment of
resource-energy-environment nexus in China's iron and steel industry, J.
Clean. Prod., 232, 235–249, https://doi.org/10.1016/j.jclepro.2019.05.392, 2019b.
Zhao, B., Wang, S. X., Liu, H., Xu, J. Y., Fu, K., Klimont, Z., Hao, J. M., He, K. B., Cofala, J., and Amann, M.: NOx emissions in China: historical trends and future perspectives, Atmos. Chem. Phys., 13, 9869–9897, https://doi.org/10.5194/acp-13-9869-2013, 2013.
Zhao, Y., Nielsen, C. P., Lei, Y., McElroy, M. B., and Hao, J.: Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China, Atmos. Chem. Phys., 11, 2295–2308, https://doi.org/10.5194/acp-11-2295-2011, 2011.
Zheng, B., Huo, H., Zhang, Q., Yao, Z. L., Wang, X. T., Yang, X. F., Liu, H., and He, K. B.: High-resolution mapping of vehicle emissions in China in 2008, Atmos. Chem. Phys., 14, 9787–9805, https://doi.org/10.5194/acp-14-9787-2014, 2014.
Zheng, B., Zhang, Q., Borken-Kleefeld, J., Huo, H., Guan, D., Klimont, Z.,
Peters, G. P., and He, K.: How will greenhouse gas emissions from motor
vehicles be constrained in China around 2030?, Appl. Energy, 156, 230–240,
https://doi.org/10.1016/j.apenergy.2015.07.018, 2015.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zheng, H., Zhao, B., Wang, S., Wang, T., Ding, D., Chang, X., Liu, K., Xing,
J., Dong, Z., Aunan, K., Liu, T., Wu, X., Zhang, S., and Wu, Y.: Transition
in source contributions of PM2.5 exposure and associated premature mortality
in China during 2005–2015, Environ. Int., 132, 105111,
https://doi.org/10.1016/j.envint.2019.105111, 2019.
Zhou, S., Kyle, G. P., Yu, S., Clarke, L. E., Eom, J., Luckow, P.,
Chaturvedi, V., Zhang, X., and Edmonds, J. A.: Energy use and CO2 emissions
of China's industrial sector from a global perspective, Energ. Policy, 58,
284–294, https://doi.org/10.1016/j.enpol.2013.03.014, 2013.
Short summary
Future trends in air pollution and greenhouse gas emissions in China are of great concern to the community. Here we developed a sophisticated dynamic projection model to understand 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios. By coupling strong low-carbon transitions and clean air policy, emissions of major air pollutants in China will be reduced by 58–87 % during 2015–2050. This work can support future co-governance policy design.
Future trends in air pollution and greenhouse gas emissions in China are of great concern to the...
Altmetrics
Final-revised paper
Preprint