Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-5017-2023
© 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-15-5017-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution emission inventory of full-volatility organic compounds from cooking in China during 2015–2021
Zeqi 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
Shuxiao Wang
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
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
Xiaochun Wang
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
Guanghan Huang
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
Xing Chang
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
Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
Lyuyin Huang
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
Chengrui Liang
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
Yun Zhu
Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou, 510006, 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
Qian Song
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
Fenfen 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
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
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.
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.
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.
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.
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.
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.
Shengyue Li, Shuxiao Wang, Qingru Wu, Yanning Zhang, Daiwei Ouyang, Haotian Zheng, Licong Han, Xionghui Qiu, Yifan Wen, Min Liu, Yueqi Jiang, Dejia Yin, Kaiyun Liu, Bin Zhao, Shaojun Zhang, Ye Wu, and Jiming Hao
Earth Syst. Sci. Data, 15, 2279–2294, https://doi.org/10.5194/essd-15-2279-2023, https://doi.org/10.5194/essd-15-2279-2023, 2023
Short summary
Short summary
This study compiled China's emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0) based on unified emission-source framework. The emission trends and its drivers are analyzed. Key sectors and regions with higher synergistic reduction potential of air pollutants and CO2 are identified. Future control measures are suggested. The dataset and analyses provide insights into the synergistic reduction of air pollutants and CO2 emissions for China and other developing countries.
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.
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.
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.
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.
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.
Cited articles
Abdullahi, K. L., Delgado-Saborit, J. M., and Harrison, R. M.: Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: A review, Atmos. Environ., 71, 260–294, https://doi.org/10.1016/j.atmosenv.2013.01.061, 2013.
Amouei Torkmahalleh, M., Gorjinezhad, S., Unluevcek, H. S., and Hopke, P. K.: Review of factors impacting emission/concentration of cooking generated particulate matter, Sci. Total Environ., 586, 1046–1056, https://doi.org/10.1016/j.scitotenv.2017.02.088, 2017.
An, J., Huang, C., Huang, D., Qin, M., Liu, H., Yan, R., Qiao, L., Zhou, M., Li, Y., Zhu, S., Wang, Q., and Wang, H.: Sources of organic aerosols in eastern China: a modeling study with high-resolution intermediate-volatility and semivolatile organic compound emissions, Atmos. Chem. Phys., 23, 323–344, https://doi.org/10.5194/acp-23-323-2023, 2023.
Beijing Environmental Protection Bureau: Emission standards of air pollutants for catering industry, Beijing Environmental Protection Bureau, China Environmental Science Press, DB 11/1488-2018, 2018.
Buonanno, G., Morawska, L., and Stabile, L.: Particle emission factors during cooking activities, Atmos. Environ., 43, 3235–3242, https://doi.org/10.1016/j.atmosenv.2009.03.044, 2009.
Chang, X., Zhao, B., Zheng, H., Wang, S., Cai, S., Guo, F., Gui, P., Huang, G., Wu, D., Han, L., Xing, J., Man, H., Hu, R., Liang, C., Xu, Q., Qiu, X., Ding, D., Liu, K., Han, R., Robinson, A. L., and Donahue, N. M.: Full-volatility emission framework corrects missing and underestimated secondary organic aerosol sources, One Earth, 5, 403–412, https://doi.org/10.1016/j.oneear.2022.03.015, 2022.
Chen, C., Zhao, Y., and Zhao, B.: Emission Rates of Multiple Air Pollutants Generated from Chinese Residential Cooking, Environ. Sci. Technol., 52, 1081–1087, https://doi.org/10.1021/acs.est.7b05600, 2018.
Cheng, S., Wang, G., Lang, J., Wen, W., Wang, X., and Yao, S.: Characterization of volatile organic compounds from different cooking emissions, Atmos. Environ., 145, 299–307, https://doi.org/10.1016/j.atmosenv.2016.09.037, 2016.
Cheng, Y., Kong, S., Yao, L., Zheng, H., Wu, J., Yan, Q., Zheng, S., Hu, Y., Niu, Z., Yan, Y., Shen, Z., Shen, G., Liu, D., Wang, S., and Qi, S.: Multiyear emissions of carbonaceous aerosols from cooking, fireworks, sacrificial incense, joss paper burning, and barbecue as well as their key driving forces in China, Earth Syst. Sci. Data, 14, 4757–4775, https://doi.org/10.5194/essd-14-4757-2022, 2022.
Donahue, N. M., Kroll, J. H., Pandis, S. N., and Robinson, A. L.: A two-dimensional volatility basis set – Part 2: Diagnostics of organic-aerosol evolution, Atmos. Chem. Phys., 12, 615–634, https://doi.org/10.5194/acp-12-615-2012, 2012.
He, L., Hu, M., Huang, X., Yu, B., Zhang, Y., and Liu, D.: Measurement of emissions of fine particulate organic matter from Chinese cooking, Atmos. Environ., 38, 6557–6564, https://doi.org/10.1016/j.atmosenv.2004.08.034, 2004.
He, W., Wang, T., Shao, X., Nie, L., and Shi, A.: Pollution Characteristics of Cooking Fumes, Particulates, and Non-methane Hydrocarbons in the Exhaust of Typical Beijing Restaurants, Environ. Sci., 41, 2050–2056, https://doi.org/10.13227/j.hjkx.201908063, 2020.
Huang, D. D., Zhu, S., An, J., Wang, Q., Qiao, L., Zhou, M., He, X., Ma, Y., Sun, Y., Huang, C., Yu, J. Z., and Zhang, Q.: Comparative Assessment of Cooking Emission Contributions to Urban Organic Aerosol Using Online Molecular Tracers and Aerosol Mass Spectrometry Measurements, Environ. Sci. Technol., 55, 14526–14535, https://doi.org/10.1021/acs.est.1c03280, 2021.
Huang, G.: Characterizations of Intermediate Volatile and Semi-volatile Organic Compounds Emissions from Typical Stationary Sources, Tsinghua University, 2023.
Huang, X., Han, D., Cheng, J., Chen, X., Zhou, Y., Liao, H., Dong, W., and Yuan, C.: Characteristics and health risk assessment of volatile organic compounds (VOCs) in restaurants in Shanghai, Environ. Sci. Pollut. Res., 27, 490–499, https://doi.org/10.1007/s11356-019-06881-6, 2020.
Jathar, S. H., Gordon, T. D., Hennigan, C. J., Pye, H. O. T., Pouliot, G., Adams, P. J., Donahue, N. M., and Robinson, A. L.: Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States, P. Natl. Acad. Sci. USA, 111, 10473–10478, https://doi.org/10.1073/pnas.1323740111, 2014.
Jiang, B., Sun, C., Bai, H., Chen, X., He, W., Nie, L., Shi, A., and Li, G.: Influence of fume purifier on VOCs emission characteristics and photochemical reaction of catering, China Environmental Science, 41, 2040–2047, https://doi.org/10.19674/j.cnki.issn1000-6923.2021.0214, 2021.
Jin, W., Zhi, G., Zhang, Y., Wang, L., Guo, S., Zhang, Y., Xue, Z., Zhang, X., Du, J., Zhang, H., Ren, Y., Xu, P., Ma, J., Zhao, W., Wang, L., and Fu, R.: Toward a national emission inventory for the catering industry in China, Sci. Total Environ., 754, 142184, https://doi.org/10.1016/j.scitotenv.2020.142184, 2021.
Lee, B. P., Li, Y. J., Yu, J. Z., Louie, P. K. K., and Chan, C. K.: Characteristics of submicron particulate matter at the urban roadside in downtown Hong Kong–Overview of 4 months of continuous high-resolution aerosol mass spectrometer measurements, J. Geophys. Res.-Atmos., 120, 7040–7058, https://doi.org/10.1002/2015JD023311, 2015.
Li, B., Wang, J., Wu, S., Jia, Z., Li, Y., Wang, T., and Zhou, S.: New Method for Improving Spatial Allocation Accuracy of Industrial Energy Consumption and Implications for Polycyclic Aromatic Hydrocarbon Emissions in China, Environ. Sci. Technol., 53, 4326–4334, https://doi.org/10.1021/acs.est.8b06915, 2019.
Li, J.: Study on the Construction of Air Pollutant Emission Inventory of Restaurant Enterprises in Nanjing, Technology Trend, https://doi.org/10.19392/j.cnki.1671-7341.202019109, 2020.
Li, L., Cheng, Y., Du, X., Dai, Q., Wu, J., Bi, X., and Feng, Y.: Chemical Compositions of PM2.5 Emitted from Six Types of Chinese Cooking, Res. Environ. Sci., 34, 71–78, https://doi.org/10.13198/j.issn.1001-6929.2020.11.11, 2021.
Li, N: Quantitative Uncertainty Analysis and Verification of Emission Inventory in Guangdong Province, 2012, Master, South China University of Technology, China, 2017.
Li, Y., Wu, A., Tong, M., Luan, S., Li, Z., and Hu, M.: Emission Characteristics of Particulate Organic Matter from Cooking, Environ. Sci., 41, 3467–3474, https://doi.org/10.13227/j.hjkx.202001027, 2020.
Li, Z., Wang, S., Li, S., Wang, X., Huang, G., Chang, X., Huang, L., Liang, C., Zhu, Y., Zheng, H., Song, Q., Wu, Q., Zhang, F., and Zhao, B.: High-resolution emission inventory of full-volatility organic from cooking souce in China during 2015–2021, figshare [data set], https://doi.org/10.6084/m9.figshare.23537673, 2023.
Liang, X., Chen, L., Liu, M., Lu, Q., Lu, H., Gao, B., Zhao, W., Sun, X., Xu, J., and Ye, D.: Carbonyls from commercial, canteen and residential cooking activities as crucial components of VOC emissions in China, Sci. Total Environ., 846, 157317, https://doi.org/10.1016/j.scitotenv.2022.157317, 2022.
Lin, L., He, X., Wu, J., Yu, P., and Guo, T.: Research of Shanghai Cooking Fume Pollution, Environ. Sci. Technol., 37, 546–549, 2014.
Lin, P., He, W., Nie, L., Schauer, J. J., Wang, Y., Yang, S., and Zhang, Y.: Comparison of PM2.5 emission rates and source profiles for traditional Chinese cooking styles, Environ. Sci. Pollut. Res., 26, 21239–21252, https://doi.org/10.1007/s11356-019-05193-z, 2019.
Lin, P., Gao, J., He, W., Nie, L., Schauer, J. J., Yang, S., Xu, Y., and Zhang, Y.: Estimation of commercial cooking emissions in real-world operation: Particulate and gaseous emission factors, activity influencing and modelling, Environ. Pollut., 289, 117847, https://doi.org/10.1016/j.envpol.2021.117847, 2021.
Lin, P., Gao, J., Xu, Y., Schauer, J. J., Wang, J., He, W., and Nie, L.: Enhanced commercial cooking inventories from the city scale through normalized emission factor dataset and big data, Environ. Pollut., 315, 120320, https://doi.org/10.1016/j.envpol.2022.120320, 2022.
Mohr, C., DeCarlo, P. F., Heringa, M. F., Chirico, R., Slowik, J. G., Richter, R., Reche, C., Alastuey, A., Querol, X., Seco, R., Peñuelas, J., Jiménez, J. L., Crippa, M., Zimmermann, R., Baltensperger, U., and Prévôt, A. S. H.: Identification and quantification of organic aerosol from cooking and other sources in Barcelona using aerosol mass spectrometer data, Atmos. Chem. Phys., 12, 1649–1665, https://doi.org/10.5194/acp-12-1649-2012, 2012.
National Bureau of Statistics of China: China Labor Statistical Yearbook, China Statistics Press, Beijing, ISBN 978-7-5230-0077-9, 2022a.
National Bureau of Statistics of China: China Population and Employment Statistics Yearbook, China Statistics Press, Beijing, ISBN 978-7-5037-9915-0, 2022b.
National Bureau of Statistics of China: China Statistical Yearbook, China Statistics Press, Beijing, ISBN 978-7-5037-9950-1, 2022c.
Pei, B., Cui, H., Liu, H., and Yan, N.: Chemical characteristics of fine particulate matter emitted from commercial cooking, Front. Environ. Sci. Eng., 10, 559–568, https://doi.org/10.1007/s11783-016-0829-y, 2016.
Qi, X., Qu, J., Liu, J., Wang, X., Guo, P., Zhang, Y., Jia, K., Zhang, Y., and Liu, Y.: Preliminary estimation of chemical compositions and emissions of particulate matters from domestic cooking in Beijing, IOP Conf. Ser.: Earth Environ. Sci., 508, 012140, https://doi.org/10.1088/1755-1315/508/1/012140, 2020.
Robinson, A. L., Donahue, N. M., Shrivastava, M. K., Weitkamp, E. A., Sage, A. M., Grieshop, A. P., Lane, T. E., Pierce, J. R., and Pandis, S. N.: Rethinking Organic Aerosols: Semivolatile Emissions and Photochemical Aging, Science, 315, 1259–1262, https://doi.org/10.1126/science.1133061, 2007.
Shu, M., Li, Y., and Cao, J.: Emission characteristics of PM2.5 from different restaurant sources, in: Proceedings of the Annual Meeting of the Chinese Academy of Environmental Sciences (2014), 2014 China Environmental Science Society Annual Academic Conference, Chengdu, Sichuan, China, 22 August 2014, 5147, 2014.
Song, K., Guo, S., Gong, Y., Lv, D., Zhang, Y., Wan, Z., Li, T., Zhu, W., Wang, H., Yu, Y., Tan, R., Shen, R., Lu, S., Li, S., Chen, Y., and Hu, M.: Impact of cooking style and oil on semi-volatile and intermediate volatility organic compound emissions from Chinese domestic cooking, Atmos. Chem. Phys., 22, 9827–9841, https://doi.org/10.5194/acp-22-9827-2022, 2022.
Song, K., Guo, S., Gong, Y., Lv, D., Wan, Z., Zhang, Y., Fu, Z., Hu, K., and Lu, S.: Non-target scanning of organics from cooking emissions using comprehensive two-dimensional gas chromatography-mass spectrometer (GCxGC-MS), Appl. Geochem., 151, 105601, https://doi.org/10.1016/j.apgeochem.2023.105601, 2023.
MEE (Ministry of Ecological Environment): Emission standards of catering oil fume, Ministry of Ecological Environment, China Environmental Science Press, GB 18483-2001, 2001.
Sun, C., Zhao, L., Chen, X., Nie, L., Shi, A., Bai, H., and Li, G.: A comprehensive study of volatile organic compounds from the actual emission of Chinese cooking, Environ. Sci. Pollut. Res., 29, 53821–53830, https://doi.org/10.1007/s11356-022-19342-4, 2022.
Tong, M.: Research on Emission Characteristics of Volatile Organic Compounds from Cooking Oil Fumes, Master, Dalian Polytechnic University, https://doi.org/10.26992/d.cnki.gdlqc.2019.000301, 2019.
Wang, G.: The Organic Pollution Characteristics in the Beijing Cooking Source Emissions of Atmospheric Particulate Matter, Master, China University of Geosciences (Beijing), 2013.
Wang, G., Cheng, S., Wei, W., Wen, W., Wang, X., and Yao, S.: Chemical Characteristics of Fine Particles Emitted from Different Chinese Cooking Styles, Aerosol Air Qual. Res., 15, 2357–2366, https://doi.org/10.4209/aaqr.2015.02.0079, 2015.
Wang, H., Xiang, Z., Wang, L., Jing, S., Lou, S., Tao, S., Liu, J., Yu, M., Li, L., Lin, L., Chen, Y., Wiedensohler, A., and Chen, C.: Emissions of volatile organic compounds (VOCs) from cooking and their speciation: A case study for Shanghai with implications for China, Sci. Total Environ., 621, 1300–1309, https://doi.org/10.1016/j.scitotenv.2017.10.098, 2018a.
Wang, H., Jing, S., Lou, S., Tao, S., Qiao, L., Li, L., Huang, C., Lin, L., and Cheng, C.: Estimation of Fine Particle (PM2.5) Emission Inventory from Cooking: Case Study for Shanghai, Environ. Sci., 39, 1971–1977, https://doi.org/10.13227/j.hjkx.201708228, 2018b.
Wu, J., Kong, S., Zeng, X., Cheng, Y., Yan, Q., Zheng, H., Yan, Y., Zheng, S., Liu, D., Zhang, X., Fu, P., Wang, S., and Qi, S.: First High-Resolution Emission Inventory of Levoglucosan for Biomass Burning and Non-Biomass Burning Sources in China, Environ. Sci. Technol., 55, 1497–1507, https://doi.org/10.1021/acs.est.0c06675, 2021.
Xu, M., He, W., Nie, L., Han, L., Pan, T., and Shi, A.: Atmospheric Pollutant Emission Characteristics from the Cooking Process of Traditional Beijing Roast Duck, Environ. Sci., 38, 3139–3145, https://doi.org/10.13227/j.hjkx.201701165, 2017.
Xu, T., Zhang, C., Liu, C., and Hu, Q.: Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018–2020, J. Environ. Sci., 124, 1–10, https://doi.org/10.1016/j.jes.2021.10.014, 2023.
Yu, Y., Guo, S., Wang, H., Shen, R., Zhu, W., Tan, R., Song, K., Zhang, Z., Li, S., Chen, Y., and Hu, M.: Importance of Semivolatile/Intermediate-Volatility Organic Compounds to Secondary Organic Aerosol Formation from Chinese Domestic Cooking Emissions, Environ. Sci. Technol. Lett., 9, 507–512, https://doi.org/10.1021/acs.estlett.2c00207, 2022.
Yuan, Y., Zhu, Y., Lin, C.-J., Wang, S., Xie, Y., Li, H., Xing, J., Zhao, B., Zhang, M., and You, Z.: Impact of commercial cooking on urban PM2.5 and O3 with online data-assisted emission inventory, Sci. Total Environ., 873, 162256, https://doi.org/10.1016/j.scitotenv.2023.162256, 2023.
Zhang, T., Peng, L., Li, Y., Liu, H., Wang, Y., and Wang, Y.: Chemical Characteristics of PM2.5 Emitted from Cooking Fumes, Res. Environ. Sci., 28, 190–197, https://doi.org/10.13198/j.issn.1001-6929.2016.02.04, 2016.
Zhang, Z., Zhu, W., Hu, M., Wang, H., Chen, Z., Shen, R., Yu, Y., Tan, R., and Guo, S.: Secondary Organic Aerosol from Typical Chinese Domestic Cooking Emissions, Environ. Sci. Technol. Lett., 8, 24–31, https://doi.org/10.1021/acs.estlett.0c00754, 2021.
Zhao, Y. and Zhao, B.: Emissions of air pollutants from Chinese cooking: A literature review, Build. Simul.-China, 11, 977–995, https://doi.org/10.1007/s12273-018-0456-6, 2018.
Zhao, Y., Hu, M., Slanina, S., and Zhang, Y.: Chemical Compositions of Fine Particulate Organic Matter Emitted from Chinese Cooking, Environ. Sci. Technol., 41, 99–105, https://doi.org/10.1021/es0614518, 2007.
Zhao, Y., Chen, C., and Zhao, B.: Is oil temperature a key factor influencing air pollutant emissions from Chinese cooking?, Atmos. Environ., 193, 190–197, https://doi.org/10.1016/j.atmosenv.2018.09.012, 2018.
Zhao, Z., Tong, M., Li, Y., Li, Q., Li, Z., Wu, A., and Xu, T.: Characteristics of particulate matters emitted from cooking in Shenzhen, Environ. Chem., 39, 1763–1773, https://doi.org/10.7524/j.issn.0254-6108.2019050804, 2020.
Zheng, H., Chang, X., Wang, S., Li, S., Zhao, B., Dong, Z., Ding, D., Jiang, Y., Huang, G., Huang, C., An, J., Zhou, M., Qiao, L., and Xing, J.: Sources of Organic Aerosol in China from 2005 to 2019: A Modeling Analysis, Environ. Sci. Technol., 57, 5957–5966, https://doi.org/10.1021/acs.est.2c08315, 2023a.
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, 2023b.
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.
This study developed the first full-volatility organic emission inventory for cooking sources in...
Altmetrics
Final-revised paper
Preprint