Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2893-2024
© 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-16-2893-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
21 Jun 2024
Data description article |
| 21 Jun 2024
| 21 Jun 2024
Development of a high-resolution integrated emission inventory of air pollutants for China
Nana Wu
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Ruochong Xu
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Shigan Liu
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Xiaodong Liu
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
Qinren Shi
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
Ying Zhou
Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
Yu Zhao
State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Rd., Nanjing, Jiangsu 210023, China
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Yu Song
State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Junyu Zheng
Sustainable Energy and Environmental Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China
Qiang Zhang
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Kebin He
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
Institute for Carbon Neutrality, Tsinghua University, Beijing 100084, China
Related authors
No articles found.
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
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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.
Xinyue Shao, Yaman Liu, Xinyi Dong, Minghuai Wang, Ruochong Xu, Joel A. Thornton, Duseong S. Jo, Man Yue, Wenxiang Shen, Manish Shrivastava, Stephen R. Arnold, and Ken S. Carslaw
Atmos. Chem. Phys., 26, 6427–6448, https://doi.org/10.5194/acp-26-6427-2026, https://doi.org/10.5194/acp-26-6427-2026, 2026
Short summary
Short summary
Highly Oxygenated Organic Molecules (HOMs) are key precursors of secondary organic aerosols (SOA). Incorporating the HOMs chemical mechanism into a global climate model allows for a reasonable reproduction of observed HOM characteristics. HOM-SOA constitutes a significant fraction of global SOA, and its distribution and formation pathways exhibit strong sensitivity to uncertainties in autoxidation processes and peroxy radical branching ratios.
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.
Zeyu Yang, Fan Cheng, Jian Gao, Huan Liu, and Jing Wei
EGUsphere, https://doi.org/10.5194/egusphere-2026-891, https://doi.org/10.5194/egusphere-2026-891, 2026
Short summary
Short summary
We developed a machine learning approach to map daily air pollution across China at high resolution, covering six major pollutants. Our results reveal how different pollutants respond differently to changes in human activity and emissions, uncovering the underlying chemical and atmospheric processes. This study provides detailed evidence of air pollution patterns and interactions, offering insights that can guide more effective strategies to protect air quality and public health.
Qinren Shi, Philippe Ciais, Nicolas Megel, Xavier Bonnemaizon, Rohith Teja Mittakola, Richard Engelen, and Chuanlong Zhou
Earth Syst. Sci. Data, 18, 927–944, https://doi.org/10.5194/essd-18-927-2026, https://doi.org/10.5194/essd-18-927-2026, 2026
Short summary
Short summary
This study developed high-resolution maps (100 m × 100 m) showing how much carbon dioxide is emitted from cars and trucks in 20 European cities every hour. Using anonymous GPS signals from vehicles, we tracked traffic CO2 emission patterns throughout the year 2023. The results show that emissions vary significantly between cities and across different days and seasons. This method could help cities monitor on-road emissions in real time and design better strategies to reduce them.
Ruilin Wan, Guangjie Zheng, Yuyang Li, Xiaolin Duan, Jingkun Jiang, and Kebin He
Atmos. Chem. Phys., 26, 1795–1807, https://doi.org/10.5194/acp-26-1795-2026, https://doi.org/10.5194/acp-26-1795-2026, 2026
Short summary
Short summary
Semi-volatile NH4NO3 is a major component of atmospheric aerosols, and the activity coefficient of NH4NO3 is a key parameter in its estimations. Here we show different thermodynamic models differed in the NH4NO3 non-ideality representations, resulting in discrepancies in nitrate gas–particle partitioning predictions. The driving factors of chemical profiles and meteorological conditions are also investigated. This discrepancy can be an important source of uncertainty in air quality predictions.
Yijing Chen, Cheng Wu, Epameinondas Tsiligiannis, Ravi Kant Pathak, Jan B. C. Pettersson, Harsh Raj Mishra, Gazala Habib, Geetam Tiwari, Kebin He, Jingkun Jiang, and Mattias Hallquist
EGUsphere, https://doi.org/10.5194/egusphere-2026-73, https://doi.org/10.5194/egusphere-2026-73, 2026
Short summary
Short summary
This work extends current understanding of atmospheric chemistry across different NO regimes in Delhi. Field measurements during the early springtime of 2023 show enhanced nighttime production of N2O5 and ClNO2, driven by decreased NO and high particulate chloride concentrations. The inert particulate chloride can be efficiently activated to photolabile gases with the reduction of NO, and contribute to secondary pollution by enhancing atmospheric organics oxidation in Delhi.
Chen Gu, Yutong Wang, Yuan Ji, Lei Zhang, Shuanzhu Sun, Yuandong Bian, Zimeng Zhang, Jiewen Zhu, Wenxin Zhao, Sheng Zhong, and Yu Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2025-5605, https://doi.org/10.5194/egusphere-2025-5605, 2026
Short summary
Short summary
We developed a near-real-time approach that consistently estimates the daily emissions of air pollutants. Compared to previous emission inventory, the new emission estimates better supported air quality simulation and efficiently detected short-term emission change due to unexpected events at the provincial level. By combining machine learning, moreover, the major sources of temporal variability of air quality were identified for effective policy making of air pollution controls.
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.
Zhenyu Luo, Li Peng, Zhaofeng Lv, Junchao Zhao, Tingkun He, Wen Yi, Yongyue Wang, Kebin He, and Huan Liu
Atmos. Chem. Phys., 25, 13635–13649, https://doi.org/10.5194/acp-25-13635-2025, https://doi.org/10.5194/acp-25-13635-2025, 2025
Short summary
Short summary
This study explores how shipping emissions affect ozone pollution in China. By combining atmospheric simulation and machine learning, we show that shipping emissions increase ozone levels by an average of 3.5 ppb nationwide, with large differences depending on location and season. Our findings highlight that controlling shipping emissions together with land-based sources is critical for improving air quality.
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.
Tian Feng, Guohui Li, Shuyu Zhao, Naifang Bei, Xin Long, Yuepeng Pan, Yu Song, Ruonan Wang, Xuexi Tie, and Luisa T. Molina
Atmos. Chem. Phys., 25, 11703–11718, https://doi.org/10.5194/acp-25-11703-2025, https://doi.org/10.5194/acp-25-11703-2025, 2025
Short summary
Short summary
The impacts of agricultural fertilization on nitrogen oxide and air quality are becoming more pronounced with continuous reductions in fossil fuel sources in China. We report that atmospheric nitrogen dioxide pulses driven by agricultural fertilization largely complicate air pollution in the North China Plain, highlighting the necessity of agricultural emission control.
Jianlei Lang, Zekang Yang, Ying Zhou, Chaoyu Wen, and Xiaoqing Cheng
Earth Syst. Sci. Data, 17, 2489–2506, https://doi.org/10.5194/essd-17-2489-2025, https://doi.org/10.5194/essd-17-2489-2025, 2025
Short summary
Short summary
This study established a four-dimensional (hourly; 0.03° × 0.03° × 34 height layers) aircraft emission inventory dataset in the landing-and-takeoff cycle for China during 2019–2023 considering actual running time and flight trajectory. The dataset reflects unique horizontal and height spatial characteristics and hourly temporal variations of aircraft emissions and the impact of COVID-19 on these emissions, providing essential information for environmental analysis and policy decisions.
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.
Jinya Yang, Yutong Wang, Lei Zhang, and Yu Zhao
Atmos. Chem. Phys., 25, 2649–2666, https://doi.org/10.5194/acp-25-2649-2025, https://doi.org/10.5194/acp-25-2649-2025, 2025
Short summary
Short summary
We develop a modeling framework to predict future ozone concentrations (till the 2060s) in China following an IPCC scenario. We evaluate the contributions of climatic, anthropogenic, and biogenic factors by season and region. We find persistent emission controls will alter the nonlinear response of ozone to its precursors and dominate the declining ozone level. The outcomes highlight the importance of human actions, even with a climate penalty on air quality.
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.
Wen Yi, Xiaotong Wang, Tingkun He, Huan Liu, Zhenyu Luo, Zhaofeng Lv, and Kebin He
Earth Syst. Sci. Data, 17, 277–292, https://doi.org/10.5194/essd-17-277-2025, https://doi.org/10.5194/essd-17-277-2025, 2025
Short summary
Short summary
This study presents a detailed global dataset on ship emissions, covering the years 2013 and 2016–2021, using advanced modeling techniques. The dataset includes emissions data for four types of greenhouse gases and five types of air pollutants. The data, available for research, offer valuable insights into ship emission spatiotemporal patterns by vessel type and age, providing a solid data foundation for fine-scale scientific research and shipping emission mitigation.
Zhaojin An, Rujing Yin, Xinyan Zhao, Xiaoxiao Li, Yuyang Li, Yi Yuan, Junchen Guo, Yiqi Zhao, Xue Li, Dandan Li, Yaowei Li, Dongbin Wang, Chao Yan, Kebin He, Douglas R. Worsnop, Frank N. Keutsch, and Jingkun Jiang
Atmos. Chem. Phys., 24, 13793–13810, https://doi.org/10.5194/acp-24-13793-2024, https://doi.org/10.5194/acp-24-13793-2024, 2024
Short summary
Short summary
Online Vocus-PTR measurements show the compositions and seasonal variations in organic vapors in urban Beijing. With enhanced sensitivity and mass resolution, various species at a level of sub-parts per trillion (ppt) and organics with multiple oxygens (≥ 3) were observed. The fast photooxidation process in summer leads to an increase in both concentration and proportion of organics with multiple oxygens, while, in other seasons, the variations in them could be influenced by mixed sources.
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.
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.
Wenxin Zhao, Yu Zhao, Yu Zheng, Dong Chen, Jinyuan Xin, Kaitao Li, Huizheng Che, Zhengqiang Li, Mingrui Ma, and Yun Hang
Atmos. Chem. Phys., 24, 6593–6612, https://doi.org/10.5194/acp-24-6593-2024, https://doi.org/10.5194/acp-24-6593-2024, 2024
Short summary
Short summary
We evaluate the long-term (2000–2020) variabilities of aerosol absorption optical depth, black carbon emissions, and associated health risks in China with an integrated framework that combines multiple observations and modeling techniques. We demonstrate the remarkable emission abatement resulting from the implementation of national pollution controls and show how human activities affected the emissions with a spatiotemporal heterogeneity, thus supporting differentiated policy-making by region.
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.
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.
Xipeng Jin, Xuhui Cai, Xuesong Wang, Qianqian Huang, Yu Song, Ling Kang, Hongsheng Zhang, and Tong Zhu
Atmos. Chem. Phys., 24, 259–274, https://doi.org/10.5194/acp-24-259-2024, https://doi.org/10.5194/acp-24-259-2024, 2024
Short summary
Short summary
This work presents a climatology of water vapour exchange flux between the atmospheric boundary layer (ABL) and free troposphere (FT) over eastern China. The water vapour exchange maintains ABL humidity in cold months and moistens the FT in warm seasons, and its distribution has terrain-dependent features. The exchange flux is correlated with the El Niño–Southern Oscillation (ENSO) index and precipitation pattern. The study provides new insight into moisture transport and extreme weather.
Kaiyue Zhou, Wen Xu, Lin Zhang, Mingrui Ma, Xuejun Liu, and Yu Zhao
Atmos. Chem. Phys., 23, 8531–8551, https://doi.org/10.5194/acp-23-8531-2023, https://doi.org/10.5194/acp-23-8531-2023, 2023
Short summary
Short summary
We developed a dataset of the long-term (2005–2020) variabilities of China’s nitrogen and sulfur deposition, with multiple statistical models that combine available observations and chemistry transport modeling. We demonstrated the strong impact of human activities and national pollution control actions on the spatiotemporal changes in deposition and indicated a relatively small benefit of emission abatement on deposition (and thereby ecological risk) for China compared to Europe and the USA.
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.
Chen Gu, Lei Zhang, Zidie Xu, Sijia Xia, Yutong Wang, Li Li, Zeren Wang, Qiuyue Zhao, Hanying Wang, and Yu Zhao
Atmos. Chem. Phys., 23, 4247–4269, https://doi.org/10.5194/acp-23-4247-2023, https://doi.org/10.5194/acp-23-4247-2023, 2023
Short summary
Short summary
We demonstrated the development of a high-resolution emission inventory and its application to evaluate the effectiveness of emission control actions, by incorporating the improved methodology, the best available data, and air quality modeling. We show that substantial efforts for emission controls indeed played an important role in air quality improvement even with worsened meteorological conditions and that the contributions of individual measures to emission reduction were greatly changing.
Chuanhua Ren, Xin Huang, Tengyu Liu, Yu Song, Zhang Wen, Xuejun Liu, Aijun Ding, and Tong Zhu
Geosci. Model Dev., 16, 1641–1659, https://doi.org/10.5194/gmd-16-1641-2023, https://doi.org/10.5194/gmd-16-1641-2023, 2023
Short summary
Short summary
Ammonia in the atmosphere has wide impacts on the ecological environment and air quality, and its emission from soil volatilization is highly sensitive to meteorology, making it challenging to be well captured in models. We developed a dynamic emission model capable of calculating ammonia emission interactively with meteorological and soil conditions. Such a coupling of soil emission with meteorology provides a better understanding of ammonia emission and its contribution to atmospheric aerosol.
Jingyu An, Cheng Huang, Dandan Huang, Momei Qin, Huan Liu, Rusha Yan, Liping Qiao, Min Zhou, Yingjie Li, Shuhui Zhu, Qian Wang, and Hongli Wang
Atmos. Chem. Phys., 23, 323–344, https://doi.org/10.5194/acp-23-323-2023, https://doi.org/10.5194/acp-23-323-2023, 2023
Short summary
Short summary
This paper aims to build up an approach to establish a high-resolution emission inventory of intermediate-volatility and semi-volatile organic compounds in city-scale and detailed source categories and incorporate it into the CMAQ model. We believe this approach can be widely applied to improve the simulation of secondary organic aerosol and its source contributions.
Zhaofeng Lv, Zhenyu Luo, Fanyuan Deng, Xiaotong Wang, Junchao Zhao, Lucheng Xu, Tingkun He, Yingzhi Zhang, Huan Liu, and Kebin He
Atmos. Chem. Phys., 22, 15685–15702, https://doi.org/10.5194/acp-22-15685-2022, https://doi.org/10.5194/acp-22-15685-2022, 2022
Short summary
Short summary
This study developed a hybrid model, CMAQ-RLINE_URBAN, to predict the urban NO2 concentrations at a high spatial resolution. To estimate the influence of various street canyons on the dispersion of air pollutants, a new parameterization scheme was established based on computational fluid dynamics and machine learning methods. This work created a new method to identify the characteristics of vehicle-related air pollution at both city and street scales simultaneously and accurately.
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.
Xipeng Jin, Xuhui Cai, Mingyuan Yu, Yu Song, Xuesong Wang, Hongsheng Zhang, and Tong Zhu
Atmos. Chem. Phys., 22, 11409–11427, https://doi.org/10.5194/acp-22-11409-2022, https://doi.org/10.5194/acp-22-11409-2022, 2022
Short summary
Short summary
Meteorological discontinuities in the vertical direction define the lowest atmosphere as the boundary layer, while in the horizontal direction it identifies the contrast zone as the internal boundary. Both of them determine the polluted air mass dimension over the North China Plain. This study reveals the boundary layer structures under three categories of internal boundaries, modified by thermal, dynamical, and blending effects. It provides a new insight to understand regional pollution.
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.
Suxia Yang, Bin Yuan, Yuwen Peng, Shan Huang, Wei Chen, Weiwei Hu, Chenglei Pei, Jun Zhou, David D. Parrish, Wenjie Wang, Xianjun He, Chunlei Cheng, Xiao-Bing Li, Xiaoyun Yang, Yu Song, Haichao Wang, Jipeng Qi, Baolin Wang, Chen Wang, Chaomin Wang, Zelong Wang, Tiange Li, E Zheng, Sihang Wang, Caihong Wu, Mingfu Cai, Chenshuo Ye, Wei Song, Peng Cheng, Duohong Chen, Xinming Wang, Zhanyi Zhang, Xuemei Wang, Junyu Zheng, and Min Shao
Atmos. Chem. Phys., 22, 4539–4556, https://doi.org/10.5194/acp-22-4539-2022, https://doi.org/10.5194/acp-22-4539-2022, 2022
Short summary
Short summary
We use a model constrained using observations to study the formation of nitrate aerosol in and downwind of a representative megacity. We found different contributions of various chemical reactions to ground-level nitrate concentrations between urban and suburban regions. We also show that controlling VOC emissions are effective for decreasing nitrate formation in both urban and regional environments, although VOCs are not direct precursors of nitrate aerosol.
Ruili Wu, Christopher W. Tessum, Yang Zhang, Chaopeng Hong, Yixuan Zheng, Xinyin Qin, Shigan Liu, and Qiang Zhang
Geosci. Model Dev., 14, 7621–7638, https://doi.org/10.5194/gmd-14-7621-2021, https://doi.org/10.5194/gmd-14-7621-2021, 2021
Short summary
Short summary
Reduced-complexity air quality models are less computationally intensive and easier to use. We developed a reduced-complexity air quality Intervention Model for Air Pollution over China (InMAP-China) to rapidly predict the air quality and estimate the health impacts of emission sources in China. We believe that this work will be of great interest to a broad audience, including environmentalists in China and scientists in relevant fields at both national and local institutes.
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.
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.
Xiaotong Wang, Wen Yi, Zhaofeng Lv, Fanyuan Deng, Songxin Zheng, Hailian Xu, Junchao Zhao, Huan Liu, and Kebin He
Atmos. Chem. Phys., 21, 13835–13853, https://doi.org/10.5194/acp-21-13835-2021, https://doi.org/10.5194/acp-21-13835-2021, 2021
Short summary
Short summary
This study updates our previous Ship Emission Inventory Model to version 2.0 (SEIM v2.0) and develops high-spatiotemporal ship emission inventories of China’s inland rivers and a 200 nautical mile coastal zone in 2016–2019. The 4-year consecutive daily ship emissions and emission structure changes are analyzed from the national to port levels. The results of this study can provide high-quality datasets for air quality modeling and observation experiment verifications.
Gongda Lu, Eloise A. Marais, Tuan V. Vu, Jingsha Xu, Zongbo Shi, James D. Lee, Qiang Zhang, Lu Shen, Gan Luo, and Fangqun Yu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-428, https://doi.org/10.5194/acp-2021-428, 2021
Revised manuscript not accepted
Short summary
Short summary
Emission controls were imposed in Beijing-Tianjin-Hebei in northern China in autumn-winter 2017. We find that regional PM2.5 targets (15 % decrease relative to previous year) were exceeded. Our analysis shows that decline in precursor emissions only leads to less than half (43 %) the improved air quality. Most of the change (57 %) is due to interannual variability in meteorology. Stricter emission controls may be necessary in years with unfavourable meteorology.
Meng Gao, Yang Yang, Hong Liao, Bin Zhu, Yuxuan Zhang, Zirui Liu, Xiao Lu, Chen Wang, Qiming Zhou, Yuesi Wang, Qiang Zhang, Gregory R. Carmichael, and Jianlin Hu
Atmos. Chem. Phys., 21, 11405–11421, https://doi.org/10.5194/acp-21-11405-2021, https://doi.org/10.5194/acp-21-11405-2021, 2021
Short summary
Short summary
Light absorption and radiative forcing of black carbon (BC) is influenced by both BC itself and its interactions with other aerosol chemical compositions. In this study, we used the online coupled WRF-Chem model to examine how emission control measures during the Asian-Pacific Economic Cooperation (APEC) conference affect the mixing state and light absorption of BC and the associated implications for BC-PBL interactions.
Benjamin A. Nault, Duseong S. Jo, Brian C. McDonald, Pedro Campuzano-Jost, Douglas A. Day, Weiwei Hu, Jason C. Schroder, James Allan, Donald R. Blake, Manjula R. Canagaratna, Hugh Coe, Matthew M. Coggon, Peter F. DeCarlo, Glenn S. Diskin, Rachel Dunmore, Frank Flocke, Alan Fried, Jessica B. Gilman, Georgios Gkatzelis, Jacqui F. Hamilton, Thomas F. Hanisco, Patrick L. Hayes, Daven K. Henze, Alma Hodzic, James Hopkins, Min Hu, L. Greggory Huey, B. Thomas Jobson, William C. Kuster, Alastair Lewis, Meng Li, Jin Liao, M. Omar Nawaz, Ilana B. Pollack, Jeffrey Peischl, Bernhard Rappenglück, Claire E. Reeves, Dirk Richter, James M. Roberts, Thomas B. Ryerson, Min Shao, Jacob M. Sommers, James Walega, Carsten Warneke, Petter Weibring, Glenn M. Wolfe, Dominique E. Young, Bin Yuan, Qiang Zhang, Joost A. de Gouw, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 11201–11224, https://doi.org/10.5194/acp-21-11201-2021, https://doi.org/10.5194/acp-21-11201-2021, 2021
Short summary
Short summary
Secondary organic aerosol (SOA) is an important aspect of poor air quality for urban regions around the world, where a large fraction of the population lives. However, there is still large uncertainty in predicting SOA in urban regions. Here, we used data from 11 urban campaigns and show that the variability in SOA production in these regions is predictable and is explained by key emissions. These results are used to estimate the premature mortality associated with SOA in urban regions.
Qingyang Xiao, Yixuan Zheng, Guannan Geng, Cuihong Chen, Xiaomeng Huang, Huizheng Che, Xiaoye Zhang, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, https://doi.org/10.5194/acp-21-9475-2021, 2021
Short summary
Short summary
We used both statistical methods and a chemical transport model to assess the contribution of meteorology and emissions to PM2.5 during 2000–2018. Both methods revealed that emissions dominated the long-term PM2.5 trend with notable meteorological effects ranged up to 37.9 % of regional annual average PM2.5. The meteorological contribution became more beneficial to PM2.5 control in southern China but more unfavorable in northern China during the studied period.
Bo Zheng, Qiang Zhang, Guannan Geng, Cuihong Chen, Qinren Shi, Mengshi Cui, Yu Lei, and Kebin He
Earth Syst. Sci. Data, 13, 2895–2907, https://doi.org/10.5194/essd-13-2895-2021, https://doi.org/10.5194/essd-13-2895-2021, 2021
Short summary
Short summary
Here we report the monthly anthropogenic pollutant emissions in China during the COVID-19 pandemic by using a bottom-up approach based on near-real-time data. The COVID lockdowns were estimated to have reduced China's emissions substantially between January and March in 2020, with the largest reduction in February. With the spread of coronavirus controlled, China's anthropogenic emissions rebounded in April and since then returned to levels comparable to those of 2019 through December 2020.
Cited articles
An, J., Huang, Y., Huang, C., Wang, X., Yan, R., Wang, Q., Wang, H., Jing, S., Zhang, Y., Liu, Y., Chen, Y., Xu, C., Qiao, L., Zhou, M., Zhu, S., Hu, Q., Lu, J., and Chen, C.: Emission inventory of air pollutants and chemical speciation for specific anthropogenic sources based on local measurements in the Yangtze River Delta region, China, Atmos. Chem. Phys., 21, 2003–2025, https://doi.org/10.5194/acp-21-2003-2021, 2021.
Bo, X., Jia, M., Xue, X., Tang, L., Mi, Z., Wang, S., Cui, W., Chang, X., Ruan, J., Dong, G., Zhou, B., and Davis, S. J.: Effect of strengthened standards on Chinese ironmaking and steelmaking emissions, Nat. Sustain., 4, 811–820, https://doi.org/10.1038/s41893-021-00736-0, 2021.
Chen, H., Huang, Y., Shen, H., Chen, Y., Ru, M., Chen, Y., Lin, N., Su, S., Zhuo, S., Zhong, Q., Wang, X., Liu, J., Li, B., and Tao, S.: Modeling temporal variations in global residential energy consumption and pollutant emissions, Appl. Energ., 184, 820–829, https://doi.org/10.1016/j.apenergy.2015.10.185, 2016.
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.
Cheng, J., Tong, D., Liu, Y., Bo, Y., Zheng, B., Geng, G., He, K., and Zhang, Q.: Air quality and health benefits of China's current and upcoming clean air policies, Faraday Discuss., 226, 584–606, https://doi.org/10.1039/D0FD00090F, 2021a.
Cheng, J., Tong, D., Liu, Y., Yu, S., Yan, L., Zheng, B., Geng, G., He, K., and Zhang, Q.: Comparison of Current and Future PM2.5 Air Quality in China Under CMIP6 and DPEC Emission Scenarios, Geophys. Res. Lett., 48, e2021GL093197, https://doi.org/10.1029/2021GL093197, 2021b.
Cheng, Z., Wang, S., Fu, X., Watson, J. G., Jiang, J., Fu, Q., Chen, C., Xu, B., Yu, J., Chow, J. C., and Hao, J.: Impact of biomass burning on haze pollution in the Yangtze River delta, China: a case study in summer 2011, Atmos. Chem. Phys., 14, 4573–4585, https://doi.org/10.5194/acp-14-4573-2014, 2014.
Chou, M.-D., Suarez, M. J., Ho, C.-H., Yan, M. M. H., and Lee, K.-T.: Parameterizations for Cloud Overlapping and Shortwave Single-Scattering Properties for Use in General Circulation and Cloud Ensemble Models, J. Climate, 11, 202–214, https://doi.org/10.1175/1520-0442(1998)011<0202:PFCOAS>2.0.CO;2, 1998.
Crippa, M., Guizzardi, D., Butler, T., Keating, T., Wu, R., Kaminski, J., Kuenen, J., Kurokawa, J., Chatani, S., Morikawa, T., Pouliot, G., Racine, J., Moran, M. D., Klimont, Z., Manseau, P. M., Mashayekhi, R., Henderson, B. H., Smith, S. J., Suchyta, H., Muntean, M., Solazzo, E., Banja, M., Schaaf, E., Pagani, F., Woo, J.-H., Kim, J., Monforti-Ferrario, F., Pisoni, E., Zhang, J., Niemi, D., Sassi, M., Ansari, T., and Foley, K.: The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies, Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, 2023.
Deng, F., Lv, Z., Qi, L., Wang, X., Shi, M., and Liu, H.: A big data approach to improving the vehicle emission inventory in China, Nat. Commun., 11, 2801, https://doi.org/10.1038/s41467-020-16579-w, 2020.
Endresen, Ø., Sørgård, E., Behrens, H. L., Brett, P. O., and Isaksen, I. S. A.: A historical reconstruction of ships' fuel consumption and emissions, J. Geophys. Res.-Atmos., 112, D12301, https://doi.org/10.1029/2006JD007630, 2007.
Geng, G., Zhang, Q., Martin, R. V., Lin, J., Huo, H., Zheng, B., Wang, S., and He, K.: Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis, Atmos. Chem. Phys., 17, 4131–4145, https://doi.org/10.5194/acp-17-4131-2017, 2017.
Geng, G., Zheng, Y., Zhang, Q., Xue, T., Zhao, H., Tong, D., Zheng, B., Li, M., Liu, F., Hong, C., He, K., and Davis, S. J.: Drivers of PM2.5 air pollution deaths in China 2002–2017, Nat. Geosci., 14, 645–650, https://doi.org/10.1038/s41561-021-00792-3, 2021.
Gu, C., Zhang, L., Xu, Z., Xia, S., Wang, Y., Li, L., Wang, Z., Zhao, Q., Wang, H., and Zhao, Y.: High-resolution regional emission inventory contributes to the evaluation of policy effectiveness: a case study in Jiangsu Province, China, Atmos. Chem. Phys., 23, 4247–4269, https://doi.org/10.5194/acp-23-4247-2023, 2023.
Hong, S. Y. and Lim, J.-O. J.: The WRF Single-Moment 6-Class Microphysics Scheme (WSM6), Asia-Pac. J. Atmos. Sci., 42, 129–151, 2006.
Huang, C., Hu, Q., Wang, H., Qiao, L., Jing, S. a., Wang, H., Zhou, M., Zhu, S., Ma, Y., Lou, S., Li, L., Tao, S., Li, Y., and Lou, D.: Emission factors of particulate and gaseous compounds from a large cargo vessel operated under real-world conditions, Environ. Pollut., 242, 667–674, https://doi.org/10.1016/j.envpol.2018.07.036, 2018.
Huang, X., Li, M., Li, J., and Song, Y.: A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products, Atmos. Environ., 50, 9–15, https://doi.org/10.1016/j.atmosenv.2012.01.017, 2012a.
Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and Zhang, H.: A high-resolution ammonia emission inventory in China, Global Biogeochem. Cy., 26, GB1030, https://doi.org/10.1029/2011GB004161, 2012b.
Huang, Z., Zhong, Z., Sha, Q., Xu, Y., Zhang, Z., Wu, L., Wang, Y., Zhang, L., Cui, X., Tang, M., Shi, B., Zheng, C., Li, Z., Hu, M., Bi, L., Zheng, J., and Yan, M.: An updated model-ready emission inventory for Guangdong Province by incorporating big data and mapping onto multiple chemical mechanisms, Sci. Total Environ., 769, 144535, https://doi.org/10.1016/j.scitotenv.2020.144535, 2021.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015.
Kain, J. S.: The Kain–Fritsch Convective Parameterization: An Update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
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.
Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., and Akimoto, H.: Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019–11058, https://doi.org/10.5194/acp-13-11019-2013, 2013.
Li, B., Chen, L., Shen, W., Jin, J., Wang, T., Wang, P., Yang, Y., and Liao, H.: Improved gridded ammonia emission inventory in China, Atmos. Chem. Phys., 21, 15883–15900, https://doi.org/10.5194/acp-21-15883-2021, 2021.
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., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwx150, 2017a.
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, 2017b.
Li, M., Kurokawa, J., Zhang, Q., Woo, J.-H., Morikawa, T., Chatani, S., Lu, Z., Song, Y., Geng, G., Hu, H., Kim, J., Cooper, O. R., and McDonald, B. C.: MIXv2: a long-term mosaic emission inventory for Asia (2010–2017), Atmos. Chem. Phys., 24, 3925–3952, https://doi.org/10.5194/acp-24-3925-2024, 2024.
Li, S., Wang, S., Wu, Q., Zhang, Y., Ouyang, D., Zheng, H., Han, L., Qiu, X., Wen, Y., Liu, M., Jiang, Y., Yin, D., Liu, K., Zhao, B., Zhang, S., Wu, Y., and Hao, J.: Emission trends of air pollutants and CO2 in China from 2005 to 2021, Earth Syst. Sci. Data, 15, 2279–2294, https://doi.org/10.5194/essd-15-2279-2023, 2023.
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, F., Beirle, S., Zhang, Q., Dörner, S., He, K., and Wagner, T.: NOx lifetimes and emissions of cities and power plants in polluted background estimated by satellite observations, Atmos. Chem. Phys., 16, 5283–5298, https://doi.org/10.5194/acp-16-5283-2016, 2016.
Liu, H., Fu, M., Jin, X., Shang, Y., Shindell, D., Faluvegi, G., Shindell, C., and He, K.: Health and climate impacts of ocean-going vessels in East Asia, Nat. Clim. Change, 6, 1037–1041, https://doi.org/10.1038/nclimate3083, 2016.
Liu, H., Wu, B., Liu, S., Shao, P., Liu, X., Zhu, C., Wang, Y., Wu, Y., Xue, Y., Gao, J., Hao, Y., and Tian, H.: A regional high-resolution emission inventory of primary air pollutants in 2012 for Beijing and the surrounding five provinces of North China, Atmos. Environ., 181, 20–33, https://doi.org/10.1016/j.atmosenv.2018.03.013, 2018.
Liu, H., Meng, Z.-H., Lv, Z.-F., Wang, X.-T., Deng, F.-Y., Liu, Y., Zhang, Y.-N., Shi, M.-S., Zhang, Q., and He, K.-B.: Emissions and health impacts from global shipping embodied in US–China bilateral trade, Nat. Sustain., 2, 1027–1033, https://doi.org/10.1038/s41893-019-0414-z, 2019.
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., 20, 7783–7799, https://doi.org/10.5194/acp-20-7783-2020, 2020.
Liu, M., Song, Y., Yao, H., Kang, Y., Li, M., Huang, X., and Hu, M.: Estimating emissions from agricultural fires in the North China Plain based on MODIS fire radiative power, Atmos. Environ., 112, 326–334, https://doi.org/10.1016/j.atmosenv.2015.04.058, 2015.
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.
Lv, Z., Liu, H., Ying, Q., Fu, M., Meng, Z., Wang, Y., Wei, W., Gong, H., and He, K.: Impacts of shipping emissions on PM2.5 pollution in China, Atmos. Chem. Phys., 18, 15811–15824, https://doi.org/10.5194/acp-18-15811-2018, 2018.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Ni, H., Han, Y., Cao, J., Chen, L. W. A., Tian, J., Wang, X., Chow, J. C., Watson, J. G., Wang, Q., Wang, P., Li, H., and Huang, R.-J.: Emission characteristics of carbonaceous particles and trace gases from open burning of crop residues in China, Atmos. Environ., 123, 399–406, https://doi.org/10.1016/j.atmosenv.2015.05.007, 2015.
Ohara, T., Akimoto, H., Kurokawa, J., Horii, N., Yamaji, K., Yan, X., and Hayasaka, T.: An Asian emission inventory of anthropogenic emission sources for the period 1980–2020, Atmos. Chem. Phys., 7, 4419–4444, https://doi.org/10.5194/acp-7-4419-2007, 2007.
Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., and Henze, D. K.: Ammonia emissions in the United States, European Union, and China derived by high-resolution inversion of ammonium wet deposition data: Interpretation with a new agricultural emissions inventory (MASAGE_NH3), J. Geophys. Res.-Atmos., 119, 4343–4364, https://doi.org/10.1002/2013JD021130, 2014.
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. Energ., 235, 1169–1182, https://doi.org/10.1016/j.apenergy.2018.11.043, 2019.
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteorol. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007.
Reisen, F., Meyer, C. P., and Keywood, M. D.: Impact of biomass burning sources on seasonal aerosol air quality, Atmos. Environ., 67, 437–447, https://doi.org/10.1016/j.atmosenv.2012.11.004, 2013.
Schneider, A., Friedl, M. A., and Potere, D.: A new map of global urban extent from MODIS satellite data, Environ. Res. Lett., 4, 044003, https://doi.org/10.1088/1748-9326/4/4/044003, 2009.
Sha, Q., Zhu, M., Huang, H., Wang, Y., Huang, Z., Zhang, X., Tang, M., Lu, M., Chen, C., Shi, B., Chen, Z., Wu, L., Zhong, Z., Li, C., Xu, Y., Yu, F., Jia, G., Liao, S., Cui, X., Liu, J., and Zheng, J.: A newly integrated dataset of volatile organic compounds (VOCs) source profiles and implications for the future development of VOCs profiles in China, Sci. Total Environ., 793, 148348, https://doi.org/10.1016/j.scitotenv.2021.148348, 2021.
Song, Y., Liu, B., Miao, W., Chang, D., and Zhang, Y.: Spatiotemporal variation in nonagricultural open fire emissions in China from 2000 to 2007, Global Biogeochem. Cy., 23, GB2008, https://doi.org/10.1029/2008GB003344, 2009.
Streets, D. G., Bond, T. C., Carmichael, G. R., Fernandes, S. D., Fu, Q., He, D., Klimont, Z., Nelson, S. M., Tsai, N. Y., Wang, M. Q., Woo, J. H., and Yarber, K. F.: An inventory of gaseous and primary aerosol emissions in Asia in the year 2000, J. Geophys. Res.-Atmos., 108, 8809, https://doi.org/10.1029/2002JD003093, 2003.
Tang, L., Jia, M., Yang, J., Li, L., Bo, X., and Mi, Z.: Chinese industrial air pollution emissions based on the continuous emission monitoring systems network, Scientific Data, 10, 153, https://doi.org/10.1038/s41597-023-02054-w, 2023.
United Nations Conference on Trade and Development (UNCTAD): Review of Maritime Transport 2014, United Nations, https://doi.org/10.18356/5a566ab1-en, 2014.
Wang, W., Khanna, N., Lin, J., and Liu, X.: Black carbon emissions and reduction potential in China: 2015–2050, J. Environ. Manage., 329, 117087, https://doi.org/10.1016/j.jenvman.2022.117087, 2023.
Wang, X., Lei, Y., Yan, L., Liu, T., Zhang, Q., and He, K.: A unit-based emission inventory of SO2, NOx and PM for the Chinese iron and steel industry from 2010 to 2015, Sci. Total Environ., 676, 18–30, https://doi.org/10.1016/j.scitotenv.2019.04.241, 2019.
Wu, N., Geng, G., Yan, L., Bi, J., Li, Y., Tong, D., Zheng, B., and Zhang, Q.: Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements, Environ. Res. Lett., 16, 084056, https://doi.org/10.1088/1748-9326/ac175f, 2021.
Wu, N., Geng, G., Qin, X., Tong, D., Zheng, Y., Lei, Y., and Zhang, Q.: Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion, ACS Environ. Au., 2, 363–372, https://doi.org/10.1021/acsenvironau.2c00014, 2022.
Wu, N., Geng, G., Xu, R., Liu, S., Liu, X., Shi, Q., Zhou, Y., Zhao, Y., Liu, H., Song, Y., Zheng, J., and Zhang, Q.: INTAC: a high-resolution INTegrated emission inventory of Air pollutants for China in 2017 [Data set], Zenodo, https://doi.org/10.5281/zenodo.10459198, 2024.
Wu, Q., Han, L., Li, S., Wang, S., Cong, Y., Liu, K., Lei, Y., Zheng, H., Li, G., Cai, B., and Hao, J.: Facility-Level Emissions and Synergistic Control of Energy-Related Air Pollutants and Carbon Dioxide in China, Environ. Sci. Technol., 57, 4504–4512, https://doi.org/10.1021/acs.est.2c07704, 2023.
Xiao, Q., Geng, G., Xue, T., Liu, S., Cai, C., He, K., and Zhang, Q.: Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 2013–2020, Environ. Sci. Technol., 56, 6922–6932, https://doi.org/10.1021/acs.est.1c04548, 2022.
Xiu, A. and Pleim, J. E.: Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model, J. Appl. Meteorol., 40, 192–209, https://doi.org/10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2, 2001.
Yang, Y. and Zhao, Y.: Quantification and evaluation of atmospheric pollutant emissions from open biomass burning with multiple methods: a case study for the Yangtze River Delta region, China, Atmos. Chem. Phys., 19, 327–348, https://doi.org/10.5194/acp-19-327-2019, 2019.
Yin, L., Du, P., Zhang, M., Liu, M., Xu, T., and Song, Y.: Estimation of emissions from biomass burning in China (2003–2017) based on MODIS fire radiative energy data, Biogeosciences, 16, 1629–1640, https://doi.org/10.5194/bg-16-1629-2019, 2019.
Zhang, J., Liu, L., Zhao, Y., Li, H., Lian, Y., Zhang, Z., Huang, C., and Du, X.: Development of a high-resolution emission inventory of agricultural machinery with a novel methodology: A case study for Yangtze River Delta region, Environ. Pollut., 266, 115075, https://doi.org/10.1016/j.envpol.2020.115075, 2020.
Zhang, Q. and Geng, G.: Impact of clean air action on PM2.5 pollution in China, Science China Earth Sciences, 62, 1845–1846, https://doi.org/10.1007/s11430-019-9531-4, 2019.
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, 2019.
Zhang, Y., Bo, X., Zhao, Y., and Nielsen, C. P.: Benefits of current and future policies on emissions of China's coal-fired power sector indicated by continuous emission monitoring, Environ. Pollut., 251, 415–424, https://doi.org/10.1016/j.envpol.2019.05.021, 2019.
Zhang, Y., Zhao, Y., Gao, M., Bo, X., and Nielsen, C. P.: Air quality and health benefits from ultra-low emission control policy indicated by continuous emission monitoring: a case study in the Yangtze River Delta region, China, Atmos. Chem. Phys., 21, 6411–6430, https://doi.org/10.5194/acp-21-6411-2021, 2021.
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.
Zhao, Y., Qiu, L. P., Xu, R. Y., Xie, F. J., Zhang, Q., Yu, Y. Y., Nielsen, C. P., Qin, H. X., Wang, H. K., Wu, X. C., Li, W. Q., and Zhang, J.: Advantages of a city-scale emission inventory for urban air quality research and policy: the case of Nanjing, a typical industrial city in the Yangtze River Delta, China, Atmos. Chem. Phys., 15, 12623–12644, https://doi.org/10.5194/acp-15-12623-2015, 2015.
Zhao, Y., Mao, P., Zhou, Y., Yang, Y., Zhang, J., Wang, S., Dong, Y., Xie, F., Yu, Y., and Li, W.: Improved provincial emission inventory and speciation profiles of anthropogenic non-methane volatile organic compounds: a case study for Jiangsu, China, Atmos. Chem. Phys., 17, 7733–7756, https://doi.org/10.5194/acp-17-7733-2017, 2017a.
Zhao, Y., Zhou, Y., Qiu, L., and Zhang, J.: Quantifying the uncertainties of China's emission inventory for industrial sources: From national to provincial and city scales, Atmos. Environ., 165, 207–221, https://doi.org/10.1016/j.atmosenv.2017.06.045, 2017b.
Zhao, Y., Xia, Y., and Zhou, Y.: Assessment of a high-resolution NOX emission inventory using satellite observations: A case study of southern Jiangsu, China, Atmos. Environ., 190, 135–145, https://doi.org/10.1016/j.atmosenv.2018.07.029, 2018.
Zhao, Y., Yuan, M., Huang, X., Chen, F., and Zhang, J.: Quantification and evaluation of atmospheric ammonia emissions with different methods: a case study for the Yangtze River Delta region, China, Atmos. Chem. Phys., 20, 4275–4294, https://doi.org/10.5194/acp-20-4275-2020, 2020.
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., Tong, D., Chen, C., Hong, C., Li, M., Geng, G., Lei, Y., Huo, H., and He, K.: Resolution dependence of uncertainties in gridded emission inventories: a case study in Hebei, China, Atmos. Chem. Phys., 17, 921–933, https://doi.org/10.5194/acp-17-921-2017, 2017.
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, B., Cheng, J., Geng, G., Wang, X., Li, M., Shi, Q., Qi, J., Lei, Y., Zhang, Q., and He, K.: Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling, Sci. Bull., 66, 612–620, https://doi.org/10.1016/j.scib.2020.12.008, 2021.
Zheng, H., Cai, S., Wang, S., Zhao, B., Chang, X., and Hao, J.: Development of a unit-based industrial emission inventory in the Beijing–Tianjin–Hebei region and resulting improvement in air quality modeling, Atmos. Chem. Phys., 19, 3447–3462, https://doi.org/10.5194/acp-19-3447-2019, 2019.
Zheng, J., He, M., Shen, X., Yin, S., and Yuan, Z.: High resolution of black carbon and organic carbon emissions in the Pearl River Delta region, China, Sci. Total Environ., 438, 189–200, https://doi.org/10.1016/j.scitotenv.2012.08.068, 2012.
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, 2017a.
Zhou, Y., Zhao, Y., Mao, P., Zhang, Q., Zhang, J., Qiu, L., and Yang, Y.: Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China, Atmos. Chem. Phys., 17, 211–233, https://doi.org/10.5194/acp-17-211-2017, 2017b.
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.
The commonly used method for developing large-scale air pollutant emission datasets for China...
Altmetrics
Final-revised paper
Preprint