Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2279-2023
© Author(s) 2023. 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-15-2279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 Jun 2023
Data description paper |
| 06 Jun 2023
| 06 Jun 2023
Emission trends of air pollutants and CO2 in China from 2005 to 2021
Shengyue Li
State Key Joint Laboratory of Environmental 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
State Key Joint Laboratory of Environmental 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
Qingru Wu
State Key Joint Laboratory of Environmental 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
Yanning Zhang
State Key Joint Laboratory of Environmental 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
Daiwei Ouyang
State Key Joint Laboratory of Environmental 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
Haotian Zheng
State Key Joint Laboratory of Environmental 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
Licong Han
State Key Joint Laboratory of Environmental 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
Xionghui Qiu
School of Environment, Beijing Jiaotong University, Beijing 100044,
China
Yifan Wen
State Key Joint Laboratory of Environmental 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
Min Liu
State Key Joint Laboratory of Environmental 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
Yueqi Jiang
State Key Joint Laboratory of Environmental 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
Dejia Yin
State Key Joint Laboratory of Environmental 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
Kaiyun Liu
State Key Joint Laboratory of Environmental 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
State Key Joint Laboratory of Environmental 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
Shaojun Zhang
State Key Joint Laboratory of Environmental 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
Ye Wu
State Key Joint Laboratory of Environmental 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
Jiming Hao
State Key Joint Laboratory of Environmental 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
Related authors
Da Gao, Bin Zhao, Shuxiao Wang, Yuan Wang, Brian Gaudet, Yun Zhu, Xiaochun Wang, Jiewen Shen, Shengyue Li, Yicong He, Dejia Yin, and Zhaoxin Dong
Atmos. Chem. Phys., 23, 14359–14373, https://doi.org/10.5194/acp-23-14359-2023, https://doi.org/10.5194/acp-23-14359-2023, 2023
Short summary
Short summary
Surface PM2.5 concentrations can be enhanced by aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs). In this study, we found PM2.5 enhancement induced by ACIs shows a significantly smaller decrease ratio than that induced by ARIs in China with anthropogenic emission reduction from 2013 to 2021, making ACIs more important for enhancing PM2.5 concentrations. ACI-induced PM2.5 enhancement needs to be emphatically considered to meet the national PM2.5 air quality standard.
Yu Li, Momei Qin, Weiwei Hu, Bin Zhao, Ying Li, Havala O. T. Pye, Jingyi Li, Linghan Zeng, Song Guo, Min Hu, and Jianlin Hu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2879, https://doi.org/10.5194/egusphere-2025-2879, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We evaluated how well a widely used air quality model simulates key properties of organic particles in the atmosphere, such as volatility and oxygen content, which influence how particles age, spread, and affect both air quality and climate. Using observations from eastern China, we found the model underestimated particle mass and misrepresented their chemical makeup. Our results highlight the need for improved emissions and chemical treatments to better predict air quality and climate impacts.
Yuying Cui, Qingru Wu, Shuxiao Wang, Kaiyun Liu, Shengyue Li, Zhezhe Shi, Daiwei Ouyang, Zhongyan Li, Qinqin Chen, Changwei Lü, Fei Xie, Yi Tang, Yan Wang, and Jiming Hao
Earth Syst. Sci. Data, 17, 3315–3328, https://doi.org/10.5194/essd-17-3315-2025, https://doi.org/10.5194/essd-17-3315-2025, 2025
Short summary
Short summary
We develop P-CAME, a long-term gridded emission inventory for China spanning from 1978 to 2021. P-CAME enhances the accuracy of emissions mapping, identifies potential pollution hotspots, and aligns with observed Hg0 concentration trends. With its improved spatial resolution and reliable long-term trends, P-CAME offers valuable support for global emissions modeling, legacy impact studies, and evaluations of the Minamata Convention.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Zeqi Li, Bin Zhao, Shengyue Li, Zhezhe Shi, Dejia Yin, Qingru Wu, Fenfen Zhang, Xiao Yun, Guanghan Huang, Yun Zhu, and Shuxiao Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-104, https://doi.org/10.5194/essd-2025-104, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study uses an ensemble machine learning model to predict long-term, high-resolution cooking activity data, establishing China’s first county-level cooking emission inventory spanning 1990–2021. It covers key pollutants such as polycyclic aromatic hydrocarbons. It reveals emissions’ long-term spatiotemporal trends and driving factors, such as population migration and economic growth, offering efficient control strategies. This dataset is crucial for air pollution and health impact studies.
Qianqian Zhang, K. Folkert Boersma, Chiel van der Laan, Alba Mols, Bin Zhao, Shengyue Li, and Yuepeng Pan
Atmos. Chem. Phys., 25, 3313–3326, https://doi.org/10.5194/acp-25-3313-2025, https://doi.org/10.5194/acp-25-3313-2025, 2025
Short summary
Short summary
Accurate NOx emission estimates are required to better understand air pollution. This study investigates and demonstrates the ability of the superposition column model in combination with TROPOMI tropospheric NO2 column data to estimate city-scale NOx emissions and lifetimes and their variabilities. The results of this work nevertheless confirm the strength of the superposition column model in estimating urban NOx emissions with reasonable accuracy.
Shuwen Guo, Xuan Zheng, Xiao He, Lewei Zeng, Liqiang He, Xian Wu, Yifei Dai, Zihao Huang, Ting Chen, Shupei Xiao, Yan You, Sheng Xiang, Shaojun Zhang, Jingkun Jiang, and Ye Wu
Atmos. Chem. Phys., 25, 2695–2705, https://doi.org/10.5194/acp-25-2695-2025, https://doi.org/10.5194/acp-25-2695-2025, 2025
Short summary
Short summary
We considered two potential influencing factors of heavy-duty diesel vehicle emissions that are rarely mentioned in the literature: cumulative mileage and ambient temperatures. The results suggest that prolonged use of heavy-duty diesel vehicles and low ambient temperatures leads to reduced engine combustion efficiency, which in turn increases tailpipe emissions significantly.
Yuzhi Jin, Jiandong Wang, Chao Liu, David C. Wong, Golam Sarwar, Kathleen M. Fahey, Shang Wu, Jiaping Wang, Jing Cai, Zeyuan Tian, Zhouyang Zhang, Jia Xing, Aijun Ding, and Shuxiao Wang
Atmos. Chem. Phys., 25, 2613–2630, https://doi.org/10.5194/acp-25-2613-2025, https://doi.org/10.5194/acp-25-2613-2025, 2025
Short summary
Short summary
Black carbon (BC) affects climate and the environment, and its aging process alters its properties. Current models, like WRF-CMAQ, lack full accounting for it. We developed the WRF-CMAQ-BCG model to better represent BC aging by introducing bare and coated BC species and their conversion. The WRF-CMAQ-BCG model introduces the capability to simulate BC mixing states and bare and coated BC wet deposition, and it improves the accuracy of BC mass concentration and aerosol optics.
Zhouyang Zhang, Jiandong Wang, Jiaping Wang, Nicole Riemer, Chao Liu, Yuzhi Jin, Zeyuan Tian, Jing Cai, Yueyue Cheng, Ganzhen Chen, Bin Wang, Shuxiao Wang, and Aijun Ding
Atmos. Chem. Phys., 25, 1869–1881, https://doi.org/10.5194/acp-25-1869-2025, https://doi.org/10.5194/acp-25-1869-2025, 2025
Short summary
Short summary
Black carbon (BC) exerts notable warming effects. We use a particle-resolved model to investigate the long-term behavior of the BC mixing state, revealing its compositions, coating thickness distribution, and optical properties all stabilize with a characteristic time of less than 1 d. This study can effectively simplify the description of the BC mixing state, which facilitates the precise assessment of the optical properties of BC aerosols in global and chemical transport models.
Xiao He, Xuan Zheng, Shuwen Guo, Lewei Zeng, Ting Chen, Bohan Yang, Shupei Xiao, Qiongqiong Wang, Zhiyuan Li, Yan You, Shaojun Zhang, and Ye Wu
Atmos. Chem. Phys., 24, 10655–10666, https://doi.org/10.5194/acp-24-10655-2024, https://doi.org/10.5194/acp-24-10655-2024, 2024
Short summary
Short summary
This study introduces an innovative method for identifying and quantifying complex organic vapors and aerosols. By combining advanced analytical techniques and new algorithms, we categorized thousands of compounds from heavy-duty diesel vehicles and ambient air and highlighted specific tracers for emission sources. The innovative approach enhances peak identification, reduces quantification uncertainties, and offers new insights for air quality management and atmospheric chemistry.
Jiewen Shen, Bin Zhao, Shuxiao Wang, An Ning, Yuyang Li, Runlong Cai, Da Gao, Biwu Chu, Yang Gao, Manish Shrivastava, Jingkun Jiang, Xiuhui Zhang, and Hong He
Atmos. Chem. Phys., 24, 10261–10278, https://doi.org/10.5194/acp-24-10261-2024, https://doi.org/10.5194/acp-24-10261-2024, 2024
Short summary
Short summary
We extensively compare various cluster-dynamics-based parameterizations for sulfuric acid–dimethylamine nucleation and identify a newly developed parameterization derived from Atmospheric Cluster Dynamic Code (ACDC) simulations as being the most reliable one. This study offers a valuable reference for developing parameterizations of other nucleation systems and is meaningful for the accurate quantification of the environmental and climate impacts of new particle formation.
Elyse A. Pennington, Yuan Wang, Benjamin C. Schulze, Karl M. Seltzer, Jiani Yang, Bin Zhao, Zhe Jiang, Hongru Shi, Melissa Venecek, Daniel Chau, Benjamin N. Murphy, Christopher M. Kenseth, Ryan X. Ward, Havala O. T. Pye, and John H. Seinfeld
Atmos. Chem. Phys., 24, 2345–2363, https://doi.org/10.5194/acp-24-2345-2024, https://doi.org/10.5194/acp-24-2345-2024, 2024
Short summary
Short summary
To assess the air quality in Los Angeles (LA), we improved the CMAQ model by using dynamic traffic emissions and new secondary organic aerosol schemes to represent volatile chemical products. Source apportionment demonstrates that the urban areas of the LA Basin and vicinity are NOx-saturated, with the largest sensitivity of O3 to changes in volatile organic compounds in the urban core. The improvement and remaining issues shed light on the future direction of the model development.
Da Gao, Bin Zhao, Shuxiao Wang, Yuan Wang, Brian Gaudet, Yun Zhu, Xiaochun Wang, Jiewen Shen, Shengyue Li, Yicong He, Dejia Yin, and Zhaoxin Dong
Atmos. Chem. Phys., 23, 14359–14373, https://doi.org/10.5194/acp-23-14359-2023, https://doi.org/10.5194/acp-23-14359-2023, 2023
Short summary
Short summary
Surface PM2.5 concentrations can be enhanced by aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs). In this study, we found PM2.5 enhancement induced by ACIs shows a significantly smaller decrease ratio than that induced by ARIs in China with anthropogenic emission reduction from 2013 to 2021, making ACIs more important for enhancing PM2.5 concentrations. ACI-induced PM2.5 enhancement needs to be emphatically considered to meet the national PM2.5 air quality standard.
Zeqi Li, Shuxiao Wang, Shengyue Li, Xiaochun Wang, Guanghan Huang, Xing Chang, Lyuyin Huang, Chengrui Liang, Yun Zhu, Haotian Zheng, Qian Song, Qingru Wu, Fenfen Zhang, and Bin Zhao
Earth Syst. Sci. Data, 15, 5017–5037, https://doi.org/10.5194/essd-15-5017-2023, https://doi.org/10.5194/essd-15-5017-2023, 2023
Short summary
Short summary
This study developed the first full-volatility organic emission inventory for cooking sources in China, presenting high-resolution cooking emissions during 2015–2021. It identified the key subsectors and hotspots of cooking emissions, analyzed emission trends and drivers, and proposed future control strategies. The dataset is valuable for accurately simulating organic aerosol formation and evolution and for understanding the impact of organic emissions on air pollution and climate change.
Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 23, 10713–10730, https://doi.org/10.5194/acp-23-10713-2023, https://doi.org/10.5194/acp-23-10713-2023, 2023
Short summary
Short summary
New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
Short summary
Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Yuyang Li, Jiewen Shen, Bin Zhao, Runlong Cai, Shuxiao Wang, Yang Gao, Manish Shrivastava, Da Gao, Jun Zheng, Markku Kulmala, and Jingkun Jiang
Atmos. Chem. Phys., 23, 8789–8804, https://doi.org/10.5194/acp-23-8789-2023, https://doi.org/10.5194/acp-23-8789-2023, 2023
Short summary
Short summary
We set up a new parameterization for 1.4 nm particle formation rates from sulfuric acid–dimethylamine (SA–DMA) nucleation, fully including the effects of coagulation scavenging and cluster stability. Incorporating the new parameterization into 3-D chemical transport models, we achieved better consistencies between simulation results and observation data. This new parameterization provides new insights into atmospheric nucleation simulations and its effects on atmospheric pollution or health.
Rui Li, Yining Gao, Yubao Chen, Meng Peng, Weidong Zhao, Gehui Wang, and Jiming Hao
Atmos. Chem. Phys., 23, 4709–4726, https://doi.org/10.5194/acp-23-4709-2023, https://doi.org/10.5194/acp-23-4709-2023, 2023
Short summary
Short summary
A random forest model was used to isolate the effects of emission and meteorology to trace elements in PM2.5 in Tangshan. The results suggested that control measures facilitated decreases of Ga, Co, Pb, Zn, and As, due to the strict implementation of coal-to-gas strategies and optimisation of industrial structure and layout. However, the deweathered levels of Ca, Cr, and Fe only displayed minor decreases, indicating that ferrous metal smelting and vehicle emission controls should be enhanced.
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.
Yifan Wen, Shaojun Zhang, Ye Wu, and Jiming Hao
Atmos. Chem. Phys., 23, 3819–3828, https://doi.org/10.5194/acp-23-3819-2023, https://doi.org/10.5194/acp-23-3819-2023, 2023
Short summary
Short summary
This study established a high-resolution vehicular NH3 emission inventory for mainland China to quantify the absolute value and relative importance of on-road NH3 emissions for different regions, seasons and population densities. Our results indicate that the significant role of on-road NH3 emissions in populated urban areas may have been underappreciated, suggesting the control of vehicular NH3 emission can be a feasible and cost-effective way of mitigating haze pollution in urban areas.
Qianqian Zhang, K. Folkert Boersma, Bin Zhao, Henk Eskes, Cuihong Chen, Haotian Zheng, and Xingying Zhang
Atmos. Chem. Phys., 23, 551–563, https://doi.org/10.5194/acp-23-551-2023, https://doi.org/10.5194/acp-23-551-2023, 2023
Short summary
Short summary
We developed an improved superposition column model and used the latest released (v2.3.1) TROPOMI satellite NO2 observations to estimate daily city-scale NOx and CO2 emissions. The results are verified against bottom-up emissions and OCO-2 XCO2 observations. We obtained the day-to-day variation of city NOx and CO2 emissions, allowing policymakers to gain real-time information on spatial–temporal emission patterns and the effectiveness of carbon and nitrogen regulation in urban environments.
Xiao He, Xuan Zheng, Shaojun Zhang, Xuan Wang, Ting Chen, Xiao Zhang, Guanghan Huang, Yihuan Cao, Liqiang He, Xubing Cao, Yuan Cheng, Shuxiao Wang, and Ye Wu
Atmos. Chem. Phys., 22, 13935–13947, https://doi.org/10.5194/acp-22-13935-2022, https://doi.org/10.5194/acp-22-13935-2022, 2022
Short summary
Short summary
With the use of two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC ToF-MS), we successfully give a comprehensive characterization of particulate intermediate-volatility and semi-volatile organic compounds (I/SVOCs) emitted from heavy-duty diesel vehicles. I/SVOCs are speciated, identified, and quantified based on the patterns of the mass spectrum, and the gas–particle partitioning is fully addressed.
Yi Cheng, Shaofei Kong, Liquan Yao, Huang Zheng, Jian Wu, Qin Yan, Shurui Zheng, Yao Hu, Zhenzhen Niu, Yingying Yan, Zhenxing Shen, Guofeng Shen, Dantong Liu, Shuxiao Wang, and Shihua Qi
Earth Syst. Sci. Data, 14, 4757–4775, https://doi.org/10.5194/essd-14-4757-2022, https://doi.org/10.5194/essd-14-4757-2022, 2022
Short summary
Short summary
This work establishes the first emission inventory of carbonaceous aerosols from cooking, fireworks, sacrificial incense, joss paper burning, and barbecue, using multi-source datasets and tested emission factors. These emissions were concentrated in specific periods and areas. Positive and negative correlations between income and emissions were revealed in urban and rural regions. The dataset will be helpful for improving modeling studies and modifying corresponding emission control policies.
Chao Yan, Yicheng Shen, Dominik Stolzenburg, Lubna Dada, Ximeng Qi, Simo Hakala, Anu-Maija Sundström, Yishuo Guo, Antti Lipponen, Tom V. Kokkonen, Jenni Kontkanen, Runlong Cai, Jing Cai, Tommy Chan, Liangduo Chen, Biwu Chu, Chenjuan Deng, Wei Du, Xiaolong Fan, Xu-Cheng He, Juha Kangasluoma, Joni Kujansuu, Mona Kurppa, Chang Li, Yiran Li, Zhuohui Lin, Yiliang Liu, Yuliang Liu, Yiqun Lu, Wei Nie, Jouni Pulliainen, Xiaohui Qiao, Yonghong Wang, Yifan Wen, Ye Wu, Gan Yang, Lei Yao, Rujing Yin, Gen Zhang, Shaojun Zhang, Feixue Zheng, Ying Zhou, Antti Arola, Johanna Tamminen, Pauli Paasonen, Yele Sun, Lin Wang, Neil M. Donahue, Yongchun Liu, Federico Bianchi, Kaspar R. Daellenbach, Douglas R. Worsnop, Veli-Matti Kerminen, Tuukka Petäjä, Aijun Ding, Jingkun Jiang, and Markku Kulmala
Atmos. Chem. Phys., 22, 12207–12220, https://doi.org/10.5194/acp-22-12207-2022, https://doi.org/10.5194/acp-22-12207-2022, 2022
Short summary
Short summary
Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles. In urban environments, traffic emissions are a major source of primary pollutants, but their contribution to NPF remains under debate. During the COVID-19 lockdown, traffic emissions were significantly reduced, providing a unique chance to examine their relevance to NPF. Based on our comprehensive measurements, we demonstrate that traffic emissions alone are not able to explain the NPF in Beijing.
Lulu Cui, Di Wu, Shuxiao Wang, Qingcheng Xu, Ruolan Hu, and Jiming Hao
Atmos. Chem. Phys., 22, 11931–11944, https://doi.org/10.5194/acp-22-11931-2022, https://doi.org/10.5194/acp-22-11931-2022, 2022
Short summary
Short summary
A 1-year campaign was conducted to characterize VOCs at a Beijing urban site during different episodes. VOCs from fuel evaporation and diesel exhaust, particularly toluene, xylenes, trans-2-butene, acrolein, methyl methacrylate, vinyl acetate, 1-butene, and 1-hexene, were the main contributors. VOCs from diesel exhaust as well as coal and biomass combustion were found to be the dominant contributors for SOAFP, particularly the VOC species toluene, 1-hexene, xylenes, ethylbenzene, and styrene.
Mengying Li, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Zhe Song, Weiping Liu, Pengfei Li, Xiaoye Zhang, Meigen Zhang, Yele Sun, Zirui Liu, Caiping Sun, Jingkun Jiang, Shuxiao Wang, Benjamin N. Murphy, Kiran Alapaty, Rohit Mathur, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 22, 11845–11866, https://doi.org/10.5194/acp-22-11845-2022, https://doi.org/10.5194/acp-22-11845-2022, 2022
Short summary
Short summary
This study constructed an emission inventory of condensable particulate matter (CPM) in China with a focus on organic aerosols (OAs), based on collected CPM emission information. The results show that OA emissions are enhanced twofold for the years 2014 and 2017 after the inclusion of CPM in the new inventory. Sensitivity cases demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to primary, secondary, and total OA concentrations.
Jiandong Wang, Jia Xing, Shuxiao Wang, Rohit Mathur, Jiaping Wang, Yuqiang Zhang, Chao Liu, Jonathan Pleim, Dian Ding, Xing Chang, Jingkun Jiang, Peng Zhao, Shovan Kumar Sahu, Yuzhi Jin, David C. Wong, and Jiming Hao
Atmos. Chem. Phys., 22, 5147–5156, https://doi.org/10.5194/acp-22-5147-2022, https://doi.org/10.5194/acp-22-5147-2022, 2022
Short summary
Short summary
Aerosols reduce surface solar radiation and change the photolysis rate and planetary boundary layer stability. In this study, the online coupled meteorological and chemistry model was used to explore the detailed pathway of how aerosol direct effects affect secondary inorganic aerosol. The effects through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter.
Xiaomeng Wu, Daoyuan Yang, Ruoxi Wu, Jiajun Gu, Yifan Wen, Shaojun Zhang, Rui Wu, Renjie Wang, Honglei Xu, K. Max Zhang, Ye Wu, and Jiming Hao
Atmos. Chem. Phys., 22, 1939–1950, https://doi.org/10.5194/acp-22-1939-2022, https://doi.org/10.5194/acp-22-1939-2022, 2022
Short summary
Short summary
Our work pioneered land-use machine learning methods for developing link-level emission inventories, utilizing hourly traffic profiles, including volume, speed, and fleet mix, obtained from the governmental intercity highway monitoring network in the "capital circles" of China. This research provides a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
Shuping Zhang, Golam Sarwar, Jia Xing, Biwu Chu, Chaoyang Xue, Arunachalam Sarav, Dian Ding, Haotian Zheng, Yujing Mu, Fengkui Duan, Tao Ma, and Hong He
Atmos. Chem. Phys., 21, 15809–15826, https://doi.org/10.5194/acp-21-15809-2021, https://doi.org/10.5194/acp-21-15809-2021, 2021
Short summary
Short summary
Six heterogeneous HONO chemistry updates in CMAQ significantly improve HONO concentration. HONO production is primarily controlled by the heterogeneous reactions on ground and aerosol surfaces during haze. Additional HONO chemistry updates increase OH and production of secondary aerosols: sulfate, nitrate, and SOA.
Lin Huang, Song Liu, Zeyuan Yang, Jia Xing, Jia Zhang, Jiang Bian, Siwei Li, Shovan Kumar Sahu, Shuxiao Wang, and Tie-Yan Liu
Geosci. Model Dev., 14, 4641–4654, https://doi.org/10.5194/gmd-14-4641-2021, https://doi.org/10.5194/gmd-14-4641-2021, 2021
Short summary
Short summary
Accurate estimation of emissions is a prerequisite for effectively controlling air pollution, but current methods lack either sufficient data or a representation of nonlinearity. Here, we proposed a novel deep learning method to model the dual relationship between emissions and pollutant concentrations. Emissions can be updated by back-propagating the gradient of the loss function measuring the deviation between simulations and observations, resulting in better model performance.
Zhe Jiang, Hongrong Shi, Bin Zhao, Yu Gu, Yifang Zhu, Kazuyuki Miyazaki, Xin Lu, Yuqiang Zhang, Kevin W. Bowman, Takashi Sekiya, and Kuo-Nan Liou
Atmos. Chem. Phys., 21, 8693–8708, https://doi.org/10.5194/acp-21-8693-2021, https://doi.org/10.5194/acp-21-8693-2021, 2021
Short summary
Short summary
We use the COVID-19 pandemic as a unique natural experiment to obtain a more robust understanding of the effectiveness of emission reductions toward air quality improvement by combining chemical transport simulations and observations. Our findings imply a shift from current control policies in California: a strengthened control on primary PM2.5 emissions and a well-balanced control on NOx and volatile organic compounds are needed to effectively and sustainably alleviate PM2.5 and O3 pollution.
Sunling Gong, Hongli Liu, Bihui Zhang, Jianjun He, Hengde Zhang, Yaqiang Wang, Shuxiao Wang, Lei Zhang, and Jie Wang
Atmos. Chem. Phys., 21, 2999–3013, https://doi.org/10.5194/acp-21-2999-2021, https://doi.org/10.5194/acp-21-2999-2021, 2021
Short summary
Short summary
Surface concentrations of PM2.5 in China have had a declining trend since 2013 across the country. This research found that the control measures of emission reduction are the dominant factors in the PM2.5 declining trends in various regions. The contribution by the meteorology to the surface PM2.5 concentrations from 2013 to 2019 was not found to show a consistent trend, fluctuating positively or negatively by about 5% on the annual average and 10–20% for the fall–winter heavy-pollution seasons.
Runlong Cai, Chao Yan, Dongsen Yang, Rujing Yin, Yiqun Lu, Chenjuan Deng, Yueyun Fu, Jiaxin Ruan, Xiaoxiao Li, Jenni Kontkanen, Qiang Zhang, Juha Kangasluoma, Yan Ma, Jiming Hao, Douglas R. Worsnop, Federico Bianchi, Pauli Paasonen, Veli-Matti Kerminen, Yongchun Liu, Lin Wang, Jun Zheng, Markku Kulmala, and Jingkun Jiang
Atmos. Chem. Phys., 21, 2457–2468, https://doi.org/10.5194/acp-21-2457-2021, https://doi.org/10.5194/acp-21-2457-2021, 2021
Short summary
Short summary
Based on long-term measurements, we discovered that the collision of H2SO4–amine clusters is the governing mechanism that initializes fast new particle formation in the polluted atmospheric environment of urban Beijing. The mechanism and the governing factors for H2SO4–amine nucleation in the polluted atmosphere are quantitatively investigated in this study.
Brigitte Rooney, Yuan Wang, Jonathan H. Jiang, Bin Zhao, Zhao-Cheng Zeng, and John H. Seinfeld
Atmos. Chem. Phys., 20, 14597–14616, https://doi.org/10.5194/acp-20-14597-2020, https://doi.org/10.5194/acp-20-14597-2020, 2020
Short summary
Short summary
Wildfires have become increasingly prevalent. Intense smoke consisting of particulate matter (PM) leads to an increased risk of morbidity and mortality. The record-breaking Camp Fire ravaged Northern California for two weeks in 2018. Here, we employ a comprehensive chemical transport model along with ground-based and satellite observations to characterize the PM concentrations across Northern California and to investigate the pollution sensitivity predictions to key parameters of the model.
Jia Xing, Siwei Li, Yueqi Jiang, Shuxiao Wang, Dian Ding, Zhaoxin Dong, Yun Zhu, and Jiming Hao
Atmos. Chem. Phys., 20, 14347–14359, https://doi.org/10.5194/acp-20-14347-2020, https://doi.org/10.5194/acp-20-14347-2020, 2020
Short summary
Short summary
Quantifying emission changes is a prerequisite for assessment of control effectiveness in improving air quality. However, traditional bottom-up methods usually take months to perform and limit timely assessments. A novel method was developed by using a response model that provides real-time estimation of emission changes based on air quality observations. It was successfully applied to quantify emission changes on the North China Plain due to the COVID-19 pandemic shutdown.
Jing Cai, Biwu Chu, Lei Yao, Chao Yan, Liine M. Heikkinen, Feixue Zheng, Chang Li, Xiaolong Fan, Shaojun Zhang, Daoyuan Yang, Yonghong Wang, Tom V. Kokkonen, Tommy Chan, Ying Zhou, Lubna Dada, Yongchun Liu, Hong He, Pauli Paasonen, Joni T. Kujansuu, Tuukka Petäjä, Claudia Mohr, Juha Kangasluoma, Federico Bianchi, Yele Sun, Philip L. Croteau, Douglas R. Worsnop, Veli-Matti Kerminen, Wei Du, Markku Kulmala, and Kaspar R. Daellenbach
Atmos. Chem. Phys., 20, 12721–12740, https://doi.org/10.5194/acp-20-12721-2020, https://doi.org/10.5194/acp-20-12721-2020, 2020
Short summary
Short summary
By applying both OA PMF and size PMF at the same urban measurement site in Beijing, similar particle source types, including vehicular emissions, cooking emissions and secondary formation-related sources, were resolved by both frameworks and agreed well. It is also found that in the absence of new particle formation, vehicular and cooking emissions dominate the particle number concentration, while secondary particulate matter governed PM2.5 mass during spring and summer in Beijing.
Cited articles
Air Benefit and Cost and Attainment Assessment System-Emission Inventory
(ABaCAS-EI), http://www.abacas-dss.com/abacas/Software.aspx, last access: 14 December 2022.
Bai, D., Mei, J., Li, H., and Xu, Y.: Calculation and analysis of
influencing factors of SO2 emission in cement plant, Chin. Cem., 11, 78–80,
2019.
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.
Cai, B., Li, Q., and Zhang, X.: China Carbon Dioxide Capture, Use and
Storage (CCUS) Annual Report (2021)-China CCUS Pathway Study, Institute of
Environmental Planning of Ministry of Ecology and Environment, Institute of
Rock and Soil Mechanics of Chinese Academy of Sciences, The Administrative
Center for China's Agenda 21, 2021.
Cheng, J., Tong, D., Zhang, Q., Liu, Y., Lei, Y., Yan, G., Yan, L., Yu, S.,
Cui, R. Y., Clarke, L., Geng, G., Zheng, B., Zhang, X., Davis, S. J., and
He, K.: Pathways of China's PM2.5 air quality 2015–2060 in the context of
carbon neutrality, Natl. Sci. Rev., 8, 1–11, https://doi.org/10.1093/nsr/nwab078,
2021.
Choulga, M., Janssens-Maenhout, G., Super, I., Solazzo, E., Agusti-Panareda, A., Balsamo, G., Bousserez, N., Crippa, M., Denier van der Gon, H., Engelen, R., Guizzardi, D., Kuenen, J., McNorton, J., Oreggioni, G., and Visschedijk, A.: Global anthropogenic CO2 emissions and uncertainties as a prior for Earth system modelling and data assimilation, Earth Syst. Sci. Data, 13, 5311–5335, https://doi.org/10.5194/essd-13-5311-2021, 2021.
CPGPRC (The Central People's Government of the People's Republic of China):
National Climate Change Program,
http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm
(last access: 14 December 2022), 2007.
CPGPRC (The Central People's Government of the People's Republic of China):
The CPC Central Committee's proposal on the formulation of the Eleventh
Five-Year Plan, http://www.gov.cn/test/2008-08/20/content_1075373.htm (last access: 14 December 2022), 2008.
CPGPRC (The Central People's Government of the People's Republic of China):
Outline of the Twelfth Five-Year Plan for National Economic and Social
Development, http://www.gov.cn/zhuanti/2011-03/16/content_2623428_2.htm (last access: 14 December 2022), 2011.
CPGPRC (The Central People's Government of the People's Republic of China):
Action plan for the prevention and control of air pollutants,
http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm
(last access: 14 December 2022), 2013.
CPGPRC (The Central People's Government of the People's Republic of China):
Three-year action plan to win the battle against the blue sky,
http://www.gov.cn/zhengce/content/2018-07/03/content_5303158.htm (last access: 14 December 2022), 2018.
CPGPRC (The Central People's Government of the People's Republic of China):
China's policies and actions on climate change,
http://www.gov.cn/zhengce/2021-10/27/content_5646697.htm (last access: 14 December 2022),
2021.
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E. N., Muntean,
M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: Towards time
varying emissions: development of high resolution temporal profiles in the
Emissions Database for Global Atmospheric Research, Nat. Sci. Data,
https://doi.org/10.6084/m9.figshare.12052887, 2020.
Cui, Y., Wu, Q., Liu, K., Wang, S., Wang, X., Jiang, T., Meng, B., Wu, Y.,
and Guo, J.: Source apportionment of speciated mercury in Chinese rice grain
using a high-resolution model, ACS Environmental Au., 2, 324–335,
https://doi.org/10.1021/acsenvironau.1c00061, 2022.
EBCEPY (Editorial board of China Electric Power Yearbook):
China Electric Power Yearbook, China electric power press, edited by: Yu, C., Beijing, ISBN 9787519864477, 2021.
Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K.: 2006
IPCC Guidelines for National Greenhouse Gas Inventories, Institute for
Global Environmental Strategies, Japan, 2006.
Gao, M., Liu, W., Wang, H., Shao, X., Shi, A., An, X., Li, G., and Nie, L.:
Emission factors and characteristics of volatile organic compounds (VOCs)
from adhesive application in indoor decoration in China, Sci. Total
Environ., 779, 145169, https://doi.org/10.1016/j.scitotenv.2021.145169, 2021.
Gao, Y., Zhang, L., Huang, A., Kou, W., Bo, X., Cai, B., and Qu, J.:
Unveiling the spatial and sectoral characteristics of a high-resolution
emission inventory of CO2 and air pollutants in China, Sci. Total Environ.,
847, 157623, https://doi.org/10.1016/j.scitotenv.2022.157623, 2022.
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.
Goodwin, J., Gillenwater, M., Romano, D., and Radunsky, K.: 2019 Refinement
to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories,
Intergovernmental Panel on Climate Change, Switzerland, edited by: Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize S.,
Osako, A., Pyrozhenko, Y., Shermanau, P., and Federici, S., IPCC, Switzerland, ISBN 978-4-88788-232-4, 2019.
Huang, C., An, J., Wang, H., Liu, Q., Tian, J., Wang, Q., Hu, Q., Yan, R.,
Shen, Y., Duan, Y., Fu, Q., Shen, J., Ye, H., Wang, M., Wei, C., Cheng, Y.,
and Su, H.: Highly resolved dynamic emissions of air pollutants and
greenhouse gas CO2 during COVID-19 Pandemic in east China, Environ. Sci.
Technol. Lett., 8, 853–860, https://doi.org/10.1021/acs.estlett.1c00600,
2021.
IEA: Global Energy Review: CO2 Emissions in 2021, IEA, Paris,
https://www.iea.org/reports/global-energy-review-co2-emissions-in-2021-2 (last access: 14 December 2022),
2022.
Jiang, Y., Wang, S., Xing, J., Zhao, B., Li, S., Chang, X., Zhang, S., and
Dong, Z.: Ambient fine particulate matter and ozone pollution in China:
synergy in anthropogenic emissions and atmospheric processes, Environ.
Res. Lett., 17, 123001, https://doi.org/10.1088/1748-9326/aca16a, 2022.
Lang, J., Cheng, S., Zhou, Y., Zhang, Y., and Wang, G.: Air pollutant
emissions from on-road vehicles in China, 1999–2011, Sci. Total Environ.,
496, 1–10, https://doi.org/10.1016/j.scitotenv.2014.07.021, 2014.
Lang, J., Tian, J., Zhou, Y., Li, K., Chen, D., Huang, Q., Xing, X., Zhang,
Y., and Cheng, S.: A high temporal-spatial resolution air pollutant emission
inventory for agricultural machinery in China, J. Clean.
Prod., 183, 1110–1121, https://doi.org/10.1016/j.jclepro.2018.02.120,
2018.
Li, C., Hammer, M. S., Zheng, B., and Cohen, R. C.: Accelerated reduction of
air pollutants in China, 2017–2020, Sci. Total Environ., 803, 150011,
https://doi.org/10.1016/j.scitotenv.2021.150011, 2022.
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, figshare [data set], https://doi.org/10.6084/m9.figshare.21777005.v1, 2022.
Li, S. Y., Lang, J. L., Zhou, Y., Liang, X., Chen, D., and Wei, P.: Trends
in ammonia emissions from light-duty gasoline vehicles in China, 1999–2017,
Sci. Total Environ., 700, 1–9,
https://doi.org/10.1016/j.scitotenv.2019.134359, 2020.
Liu, J., Tong, D., Zheng, Y., Cheng, J., Qin, X., Shi, Q., Yan, L., Lei, Y., and Zhang, Q.: Carbon and air pollutant emissions from China's cement industry 1990–2015: trends, evolution of technologies, and drivers, Atmos. Chem. Phys., 21, 1627–1647, https://doi.org/10.5194/acp-21-1627-2021, 2021.
Liu, K., Wang, S., Wu, Q., Wang, L., Ma, Q., Zhang, L., Li, G., Tian, H.,
Duan, L., and Hao, J.: A highly resolved mercury emission inventory of
Chinese coal-fired power plants, Environ. Sci. Technol., 52, 2400–2408,
https://doi.org/10.1021/acs.est.7b06209, 2018.
Liu, K., Wu, Q., Wang, L., Wang, S., Liu, T., Ding, D., Tang, Y., Li, G.,
Tian, H., Duan, L., Wang, X., Fu, X., Feng, X., and Hao, J.:
Measure-specific effectiveness of air pollution control on China's
atmospheric mercury concentration and deposition during 2013–2017, Environ.
Sci. Technol., 53, 8938–8946, https://doi.org/10.1021/acs.est.9b02428, 2019.
Liu, S., Liu, K., Wang, K., Chen, X., and Wu, K.: Fossil-fuel and food
systems equally dominate anthropogenic methane emissions in China, Environ.
Sci. Technol., 57, 2495–2505, https://doi.org/10.1021/acs.est.2c07933, 2023.
Lu, X., Zhang, S., Xing, J., Wang, Y., Chen, W., Ding, D., Wu, Y., Wang, S.,
Duan, L., and Hao, J.: Progress of Air Pollution Control in China and Its
Challenges and Opportunities in the Ecological Civilization Era,
Engineering, 6, 1423–1431, https://doi.org/10.1016/j.eng.2020.03.014, 2020.
MEE (Ministry of Ecological Environment): Emission standard of air
pollutants for thermal power plants (GB 13223-2003), Ministry of Ecological Environment, China Environmental Science Press, ISBN 978-5123-4422-8, 2003a.
MEE (Ministry of Ecological Environment): Announcement on the implementation of the national emission standards for motor vehicles in the second stage, https://www.mee.gov.cn/gkml/zj/wj/200910/t20091022_172223.htm (last access: 3 June 2023), 2003b.
MEE (Ministry of Ecological Environment): Emission standard of air
pollutants for cement industry (GB 4915-2004), Ministry of Ecological Environment, China Environmental Science Press, ISSN 1671-8321, 2004.
MEE (Ministry of Ecological Environment), National Development and Reform Commission, Ministry of Industry and Information Technology, Ministry of Finance, Ministry of Transport: Opinions on promoting the implementation of ultra-low emissions in the iron and steel industry, https://www.gov.cn/zhengce/zhengceku/2019-10/10/content_5438149.htm (last access: 3 June 2023), 2019.
MEE (Ministry of Ecological Environment): Bulletin on China’s Ecological Environment 2013–2021, https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/ (last access: 3 June 2023), 2021.
MEE (Ministry of Ecological Environment), National Development and Reform
Commission, Ministry of Industry and Information Technology, Ministry of
Housing and Urban-Rural Development, Ministry of Transport, Ministry of
Agriculture and Rural Affairs, China National Energy Administration:
mplementation Plan for Synergistic Effectiveness in Reducing Pollution and Carbon, https://www.gov.cn/zhengce/zhengceku/2022-06/17/content_5696364.htm (last access: 3 June 2023), 2022.
National Bureau of Statistics (NBS): China Energy Statistical
Yearbook 2021, edited by: Hu, H., China Statistics Press, Beijing, ISBN 978-7-5230-0106-6, 2021.
NDRC (National Development and Reform Commission): Guidelines for compiling
provincial greenhouse gas inventories (Trial), http://www.cbcsd.org.cn/sjk/nengyuan/standard/home/20140113/download/shengjiwenshiqiti.pdf (last access: 3 June 2023), 2011.
Ning, L. and Qin, J.: Analysis of nitrogen oxide emission from pulverized
coal injection in blast furnace ironmaking, Metall. Environ. Prot., 6,
44–46, 2006.
OECD: Sustainable development: indicators to measure decoupling of
environmental pressure from economic growth, OECD Secretariat, OECD, Paris, SG/SD(2022)1/FINAL, 2002, 2002.
O'Rourke, P. R, Smith, S. J., Mott, A., Ahsan, H., McDuffie, E. E., Crippa,
M., Klimont, S., McDonald, B., Z., Wang, Nicholson, M. B, Feng, L., and
Hoesly, R. M.: CEDS v-2021-02-05 Emission Data 1975–2019, Zenodo [data set], https://doi.org/10.5281/zenodo.4509372,
2021.
Shi, Q., Zheng, B., Zheng, Y., Tong, D., Liu, Y., Ma, H., Hong, C., Geng,
G., Guan, D., He, K., and Zhang, Q.: Co-benefits of CO2 emission reduction
from China's clean air actions between 2013-2020, Nat. Commun., 13, 5061,
https://doi.org/10.1038/s41467-022-32656-8, 2022.
Sun, C., Shi, A., Bai, H., Chen, X., Zhai, Y., and Li, G.: Emission
characteristics of the catering industry in Beijing, Environ. Sci.,
41, 2596–2601, 2020.
Tang, L., Ruan, J., Bo, X., Mi, Z., Wang, S., Dong, G., and Davis, S. J.:
Plant-level real-time monitoring data reveal substantial abatement potential
of air pollution and CO2 in China's cement sector, One Earth, 5, 892–906,
https://doi.org/10.1016/j.oneear.2022.07.003, 2022.
Tao, S., Shen, G., Cheng, H., and Ma, J.: Toward clean residential energy:
Challenges and priorities in research, Environ. Sci. Technol., 55,
13602–13613, https://doi.org/10.1021/acs.est.1c02283, 2021.
Wang, G., Deng, J., Zhang, Y., Zhang, Q., Duan, L., Hao, J., and Jiang, J.:
Air pollutant emissions from coal-fired power plants in China over the past
two decades, Sci. Total Environ., 741, 140326,
https://doi.org/10.1016/j.scitotenv.2020.140326, 2020.
Wang, X., Yan, L., and Lei, Y.: Discussion on particulate matter emission
and control in China's iron and steel industry, Environ. Sust. Dev.,
39, 21–25, https://doi.org/10.19758/j.cnki.issn1673-288x.2014.05.006, 2014.
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.
Wang, Z., Hu, B., Zhang, C., Atkinson, P. M., Wang, Z., Xu, K., Chang, J.,
Fang, X., Jiang, Y., and Shi, Z.: How the Air Clean Plan and carbon
mitigation measures co-benefited China in PM2.5 reduction and health from
2014 to 2020, Environ. Int., 169, 107510,
https://doi.org/10.1016/j.envint.2022.107510, 2022.
Wei, Y., Yu, B., Tang, B., Liu, L., Liao, H., Chen, J., Sun, F., An, R., Wu,
X., and Tan, J.: Study on the schedule and roadmap of carbon peaking carbon
neutralization in China, J. Beijing Inst. Technol., 24, 14,
https://doi.org/10.15918/j.jbitss1009-3370.2022.1165, 2022.
Wen, Y., Zhang, S., He, L., Yang, S., Wu, X., and Wu, Y.: Characterizing
start emissions of gasoline vehicles and the seasonal, diurnal and spatial
variabilities in China, Atmos. Environ., 245, 118040,
https://doi.org/10.1016/j.atmosenv.2020.118040, 2021.
Wu, Y., Zhang, S., Hao, J., Liu, H., Wu, X., Hu, J., Walsh, M. P.,
Wallington, T. J., Zhang, K. M., and Stevanovic, S.: On-road vehicle
emissions and their control in China: A review and outlook, Sci. Total
Environ., 574, 332–349, https://doi.org/10.1016/j.scitotenv.2016.09.040,
2017.
Xing, J., Lu, X., Wang, S., Wang, T., Ding, D., Yu, S., Shindell, D., Ou,
Y., Morawska, L., Li, S., Ren, L., Zhang, Y., Loughlin, D., Zheng, H., Zhao,
B., Liu, S., Smith, K. R., and Hao, J.: The quest for improved air quality
may push China to continue its CO2 reduction beyond the Paris Commitment,
P. Natl. Acad. Sci. USA, 117, 29535–29542, https://doi.org/10.1073/pnas.2013297117, 2020.
Xu, W., Zhao, Y., Wen, Z., Chang, Y., Pan, Y., Sun, Y., Ma, X., Sha, Z., Li,
Z., Kang, J., Liu, L., Tang, A., Wang, K., Zhang, Y., Guo, Y., Zhang, L.,
Sheng, L., Zhang, X., Gu, B., Song, Y., Van Damme, M., Clarisse, L., Coheur,
P. F., Collett, J. L., Jr., Goulding, K., Zhang, F., He, K., and Liu, X.:
Increasing importance of ammonia emission abatement in PM2.5 pollution
control, Sci. Bull. (Beijing), 67, 1745–1749, https://doi.org/10.1016/j.scib.2022.07.021,
2022.
You, Q., Li, H., Bo, X., Zheng, Y., and Chen, S.: Study on air pollution and
CO2 emission inventory of Chinese civil aviation airport, Chin. Environ.
Sci., 42, 4517–4524, https://doi.org/10.19674/j.cnki.issn1000-6923.20220706.001, 2022.
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, X., Wu, G., Wu, S., and Xiang, X.: Determination of carbon dioxide
emission factors in typical processes for large iron-steel companies, Acta
Scientiae Circumstantiae, 32, 2024–2027,
https://doi.org/10.13671/j.hjkxxb.2012.08.009, 2012.
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, 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., 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, H., Chang, X., Wang, S., Li, S., Yin, D., Zhao, B., Huang, G., Huang,
L., Jiang, Y., Dong, Z., He, Y., Huang, C., and Xing, J.: Trends of
full-volatility organic emissions in China from 2005 to 2019 and their
organic aerosol formation potentials, Environ. Sci. Technol.
Lett., 10, 137–144, https://doi.org/10.1021/acs.estlett.2c00944, 2023.
Zheng, J., Sha, Q., Bi, L., Chen, C., Chen, Z., Cui, X., Hu, J., Hu, M.,
HUang, Z., Li, Z., Liu, H., Lu, M., Ming, G., Rao, S., Tang, M., Junchi, W.,
Wang, Y., Wang, Y., Wu, L., Xu, Y., Zhang, L., Zhang, X., Zhang, X., and
Zhong, Z.: Emission inventory products of air pollutants, greenhouse gases
and heavy metals from the same sources in Guangdong Province in 2019 v1.0,
Jinan University institute for environmental and climate research,
Guangdong, 2021.
Zheng, X., Lu, Y., Yuan, J., Baninla, Y., Zhang, S., Stenseth, N. C.,
Hessen, D. O., Tian, H., Obersteiner, M., and Chen, D.: Drivers of change in
China's energy-related CO2 emissions, P. Natl. Acad. Sci. USA, 117,
29–36, https://doi.org/10.1073/pnas.1908513117, 2020.
Zhou, Y., Xia, X., Lang, J., Zhao, B., Chen, D., Mao, S., Zhang, Y., Liu,
J., and Li, J.: A coupled framework for estimating pollutant emissions from
open burning of specific crop residue: A case study for wheat, Sci. Total
Environ., 844, 156731, https://doi.org/10.1016/j.scitotenv.2022.156731,
2022.
Short summary
This study compiled China's emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0) based on unified emission-source framework. The emission trends and its drivers are analyzed. Key sectors and regions with higher synergistic reduction potential of air pollutants and CO2 are identified. Future control measures are suggested. The dataset and analyses provide insights into the synergistic reduction of air pollutants and CO2 emissions for China and other developing countries.
This study compiled China's emission inventory of air pollutants and CO2 during 2005–2021...
Altmetrics
Final-revised paper
Preprint