Articles | Volume 22, issue 18
https://doi.org/10.5194/acp-22-11931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-11931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report: Ambient volatile organic compound (VOC) pollution in urban Beijing: characteristics, sources, and implications for pollution control
Lulu Cui
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
Di Wu
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Qingcheng Xu
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
Ruolan Hu
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
Jiming Hao
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Related authors
No articles found.
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, 17, 5113–5135, https://doi.org/10.5194/essd-17-5113-2025, https://doi.org/10.5194/essd-17-5113-2025, 2025
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Jingsha Xu, Shaojie Song, Roy M. Harrison, Congbo Song, Lianfang Wei, Qiang Zhang, Yele Sun, Lu Lei, Chao Zhang, Xiaohong Yao, Dihui Chen, Weijun Li, Miaomiao Wu, Hezhong Tian, Lining Luo, Shengrui Tong, Weiran Li, Junling Wang, Guoliang Shi, Yanqi Huangfu, Yingze Tian, Baozhu Ge, Shaoli Su, Chao Peng, Yang Chen, Fumo Yang, Aleksandra Mihajlidi-Zelić, Dragana Đorđević, Stefan J. Swift, Imogen Andrews, Jacqueline F. Hamilton, Ye Sun, Agung Kramawijaya, Jinxiu Han, Supattarachai Saksakulkrai, Clarissa Baldo, Siqi Hou, Feixue Zheng, Kaspar R. Daellenbach, Chao Yan, Yongchun Liu, Markku Kulmala, Pingqing Fu, and Zongbo Shi
Atmos. Meas. Tech., 13, 6325–6341, https://doi.org/10.5194/amt-13-6325-2020, https://doi.org/10.5194/amt-13-6325-2020, 2020
Short summary
Short summary
An interlaboratory comparison was conducted for the first time to examine differences in water-soluble inorganic ions (WSIIs) measured by 10 labs using ion chromatography (IC) and by two online aerosol chemical speciation monitor (ACSM) methods. Major ions including SO42−, NO3− and NH4+ agreed well in 10 IC labs and correlated well with ACSM data. WSII interlab variability strongly affected aerosol acidity results based on ion balance, but aerosol pH computed by ISORROPIA II was very similar.
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
Abeleira, A., Pollack, I. B., Sive, B., Zhou, Y., Fischer, E. V., and
Farmer, D. K.: Source characterization of volatile organic compounds in the
Colorado Northern Front Range Metropolitan Area during spring and summer
2015, J. Geophys. Res.-Atmos., 122, 3595–3613, https://doi.org/10.1002/2016jd026227, 2017
Ahmad, W., Coeur, C., Tomas, A., Fagniez, T., Brubach, J. B., and Cuisset,
A.: Infrared spectroscopy of secondary organic aerosol precursors and
investigation of the hygroscopicity of SOA formed from the OH reaction with
guaiacol and syringol, Appl. Optics, 56, E116, https://doi.org/10.1364/AO.56.00E116, 2017.
Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011.
An, J. L., Wang, Y. S., Wu, F. K., and Zhu, B.: Characterizations of volatile
organic compounds during high ozone episodes in Beijing, China, Environ.
Monit. Assess., 184, 1879–1889, 2012.
Atkinson, R.: Atmospheric chemistry of VOCs and NOx, Atmos. Environ.,
34, 2063–2101, 2000.
Carter, W. P. L.: Development of the SAPRC-07 chemical mechanism, Atmos.
Environ., 44, 5324–5335, https://doi.org/10.1016/j.atmosenv.2010.01.026, 2010.
Carter, W. P. L. and Atkinson, R.: Computer modeling study of incremental
hydrocarbon reactivity, Environ. Sci. Technol., 23, 864–880, https://doi.org/10.1021/es00065a017, 1989.
Chang, T. Y. and Rudy, S. J.: Ozone-forming potential of organic emissions
from alternative-fueled vehicles, Atmos. Environ., 24, 2421–2430,
https://doi.org/10.1016/0960-1686(90)90335-K, 1990.
Chen, L., Zhu, J., Liao, H., Yang, Y., and Yue, X.: Meteorological
influences on PM2.5 and O3 trends and associated health
burden since China's clean air actions, Sci. Total Environ., 744, 140837,
https://doi.org/10.1016/j.scitotenv.2020.140837, 2020.
Cui, L., Wu, D., Wang, S., Xu, Q., Hu, R., and Hao, J.: Daily mixing ratio of individual VOCs species, Zenodo [data set], https://doi.org/10.5281/zenodo.6888723, 2022.
Dai, P., Ge, Y., Lin, Y., Su, S., and Liang, B.: Investigation on
characteristics of exhaust and evaporative emissions from passenger cars
fueled with gasoline/methanol blends, Fuel, 113, 10–16, 2013.
Deng, C. X., Jin, Y. J., Zhang, M., Liu, X. W., and Yu, Z. M.: Emission
Characteristics of VOCs from On-Road Vehicles in an Urban Tunnel in Eastern
China and Predictions for 2017–2026, Aerosol Air Qual. Res., 18,
3025–3034, 2018.
Doumbia, T., Granier, C., Elguindi, N., Bouarar, I., Darras, S., Brasseur, G., Gaubert, B., Liu, Y., Shi, X., Stavrakou, T., Tilmes, S., Lacey, F., Deroubaix, A., and Wang, T.: Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling, Earth Syst. Sci. Data, 13, 4191–4206, https://doi.org/10.5194/essd-13-4191-2021, 2021.
Fan, H., Zhao, C., and Yang, Y.: A Comprehensive Analysis of the
Spatio-Temporal Variation of Urban Air Pollution in China During 2014–2018,
Atmos. Environ., 220, 117066, https://doi.org/10.1016/j.atmosenv.2019.117066, 2020.
Feng, J., Liao, H., Li, Y., Zhang, Z., and Tang, Y.: Long-term trends and
variations in haze-related weather conditions in north China during
1980–2018 based on emission-weighted stagnation intensity, Atmos. Environ.,
240, 117830, https://doi.org/10.1016/j.atmosenv.2020.117830,
2020.
Fu, Y., Liao, H., and Yang, Y.: Interannual and Decadal Changes in
Tropospheric Ozone in China and the Associated Chemistry Climate
Interactions: A Review, Adv. Atmos. Sci., 36, 975–993, 2019.
Grosjean, D. and Seinfeld, J. H.: Parameterization of the formation
potential of secondary organic aerosols, Atmos. Environ., 23, 1733–1747,
https://doi.org/10.1016/0004-6981(89)90058-9, 1989.
Guenther, A. B., Zimmerman, P. R., Harley, P. C., Monson, R. K., and Fall, R.:
Isoprene and monoterpene emission rate variability: Model evaluations and
sensitivity analyses, J. Geophys. Res.-Atmos., 98, 12609–12617, https://doi.org/10.1029/93JD00527, 1993.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Han, S., Zhao, Q., Zhang, R., Liu, Y., Li, C., Zhang, Y., Li, Y., Yin, S.,
and Yan, Q.: Emission characteristic and environmental impact of
process-based VOCs from prebaked anode manufacturing industry in Zhengzhou,
China, Atmos. Pollut. Res., 627, 67–77, https://doi.org/10.1016/j.apr.2019.09.016, 2020.
Hanna, S. R., Moore, G. E., and Fernau, M.: Evaluation of photochemical grid
models (UAM-IV, UAM-V, and the ROM/UAMIV couple) using data from the Lake
Michigan Ozone Study (LMOS), Atmos. Environ., 30, 3265–3279, 1996.
Huang, R. J., Zhang, Y., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y.,
Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P.,
Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G.,
Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J.,
Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., El Haddad, I., and
Prevot, A. S. H.: High secondary aerosol contribution to particulate pollution
during haze events in China, Nature, 514, 218–222, https://doi.org/10.1038/nature13774,
2014.
Jobson, B. T., Berkowitz, C. M., Kuster, W. C., Goldan,P. D., Williams, E.
J., Fesenfeld, F. C., Apel, E. C., Karl, T., Lonneman, W. A., and Riemer, D.:
Hydrocarbon source signatures in Houston, Texas: Influence of the
petrochemical industry, J. Geophys. Res.-Atmos., 109, D24305, https://doi.org/10.1029/2004jd004887, 2004.
Kuang, Y., He, Y., Xu, W. Y., Yuan, B., Zhang, G., Ma, Z. Q., Wu, C. H., Wang,
C. M., Wang, S. H., Zhang, H. Y., Tao, J. C., Ma, N., Su, H., Cheng, Y. F., Shao,
M., and Sun, Y. L.: Photochemical aqueous-phase reactions induce rapid
daytime formation of oxygenated organic aerosol on the North China Plain,
Environ. Sci. Technol., 54, 3849–3860, 2020.
Li, B., Ho, S. S. H., Gong, S., Ni, J., Li, H., Han, L., Yang, Y., Qi, Y., and Zhao, D.: Characterization of VOCs and their related atmospheric processes in a central Chinese city during severe ozone pollution periods, Atmos. Chem. Phys., 19, 617–638, https://doi.org/10.5194/acp-19-617-2019, 2019.
Li, J., Xie, S. D., Zeng, L. M., Li, L. Y., Li, Y. Q., and Wu, R. R.: Characterization of ambient volatile organic compounds and their sources in Beijing, before, during, and after Asia-Pacific Economic Cooperation China 2014, Atmos. Chem. Phys., 15, 7945–7959, https://doi.org/10.5194/acp-15-7945-2015, 2015.
Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., and Bates, K. H.:
Anthropogenic Drivers of 2013–2017 Trends in Summer Surface Ozone in China,
P. Natl. Acad. Sci. USA, 116, 422–427, 2019a.
Li, K., Li, J., Tong, S., Wang, W., Huang, R.-J., and Ge, M.: Characteristics of wintertime VOCs in suburban and urban Beijing: concentrations, emission ratios, and festival effects, Atmos. Chem. Phys., 19, 8021–8036, https://doi.org/10.5194/acp-19-8021-2019, 2019b.
Li, K., Jacob, D. J., Shen, L., Lu, X., De Smedt, I., and Liao, H.: Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences, Atmos. Chem. Phys., 20, 11423–11433, https://doi.org/10.5194/acp-20-11423-2020, 2020.
Li, L., Chen, Y., Zeng, L., Shao, M., Xie, S., Chen, W., Lu, S., Wu, Y., and
Cao, W.: Biomass burning contribution to ambient volatile organic compounds
(VOCs) in the Chengdu–Chongqing Region (CCR), China, Atmos. Environ., 99,
403–410, 2014.
Li, Y. J., Sun, Y., Zhang, Q., Li, X., Li, M., Zhou, Z., and Chan, C. K.:
Real-time chemical characterization of atmospheric particulate matter in
China: A review, Atmos. Environ., 158, 270–304, 2017.
Liang, Y., Liu, X., Wu, F., Guo, Y., and Xiao, H.: The year-round variations
of VOC mixing ratios and their sources in Kuytun City (northwestern China),
near oilfields, Atmos. Pollut. Res., 11, 1513–1523, https://doi.org/10.1016/j.apr.2020.05.022,
2020.
Ling, Z. H., Guo, H., Cheng, H. R., and Yu, Y. F.: Sources of ambient volatile organic compounds and their contributions to photochemical ozone formation at a site in the Pearl River Delta, southern China, Environ. Pollut., 159, 2310–2319, 2011.
Liu, Y. and Wang, T.: Worsening urban ozone pollution in China from 2013 to 2017 – Part 2: The effects of emission changes and implications for multi-pollutant control, Atmos. Chem. Phys., 20, 6323–6337, https://doi.org/10.5194/acp-20-6323-2020, 2020.
Liu, Y. F., Song, M. D., Liu, X. G., Zhang, Y. P., Hui, L. R., Kong, L. W., Zhang,
Y. Y., Zhang, C., Qu, Y., An, J. L., Ma, D. P., Tan, Q. W., and Feng, M.:
Characterization and sources of volatile organic compounds (VOCs) and their
related changes during ozone pollution days in 2016 in Beijing, China,
Environ Pollut., 257, 113599, https://doi.org/10.1016/j.envpol.2019.113599, 2020.
McDonald, B. C., de Gouw, J. A., Gilman, J. B., Jathar, S. H., Akherati, A.,
Cappa, C. D., Jimenez, J. L., Lee-Taylor, J., Hayes, P. L., McKeen, S. A., Cui,
Y. Y., Kim, S. W., Gentner, D. R., Isaacman-VanWertz, G., Goldstein, A. H.,
Harley, R. A., Frost, G. J., Roberts, J. M., Ryerson, T. B., and Trainer, M.:
Volatile chemical products emerging as largest petrochemical source of urban
organic emissions, Science, 359, 760–764,
https://doi.org/10.1126/science.aaq0524, 2018.
McGaughey, G. R., Desai, N. R., Allen, D. T., Seila, R.L., Lonneman, W. A.,
Fraser, M. P., Harley, R. A., Pollack, A. K., Ivy, J. M., and Price, J. H.:
Analysis of motor vehicle emissions in a Houston tunnel during the TexasAir
Quality Study 2000, Atmos. Environ., 38, 3363–3372, 2004.
Miller, L., Xu, X., Grgicak-Mannion, A., Brook, J., and Wheeler, A.:
Multi-season, multiyear concentrations and correlations amongst the BTEX
group of VOCs in an urbanized industrial city, Atmos. Environ., 61,
305–315, 2012.
Mo, Z., Shao, M., Lu, S., Qu, H., Zhou, M., Sun, J., and Gou, B.:
Process-specific emission characteristics of volatile organic compounds
(VOCs) from petrochemical facilities in the Yangtze River Delta, China, Sci.
Total Environ., 533, 422–431, 2015.
Mo, Z., Shao, M., and Lu, S.: Compilation of a source profile database for
hydrocarbon and OVOC emissions in China, Atmos. Environ., 143, 209–217,
2016.
Odum, J. R., Jungkamp, T. P. W., Griffifin, R. J., Flagan, R. C., and Seinfeld,
J. H.: The atmospheric aerosol-forming potential of whole gasoline vapor,
Science, 276, 96–99, 1997.
Pallavi, Sinha, B., and Sinha, V.: Source apportionment of volatile organic compounds in the northwest Indo-Gangetic Plain using a positive matrix factorization model, Atmos. Chem. Phys., 19, 15467–15482, https://doi.org/10.5194/acp-19-15467-2019, 2019.
Qiao, Y. Z., Wang, H. L., Huang, C., Chen, C. H., Su, L. Y., Zhou, M., Xu, H.,
Zhang, G. F., Chen, Y. R., Li, L., Chen, M. H., and Huang, H. Y.: Source Profile
and Chemical Reactivity of VolatileOrganic Compounds from Vehicle Exhaust,
Huanjing Kexue, 33, 1071–1079, 2012.
Raysoni, A. U., Stock, T. H., Sarnat, J. A., Chavez, M. C., Sarnat, S. E.,
Montoya, T., Holguin, F., and Li, W. W.: Evaluation of VOC concentrations in
indoor and outdoor microenvironments at near-road schools, Environ. Pollut.,
231, 681–693, 2017.
Russo, R. S., Zhou, Y., White, M. L., Mao, H., Talbot, R., and Sive, B. C.: Multi-year (2004–2008) record of nonmethane hydrocarbons and halocarbons in New England: seasonal variations and regional sources, Atmos. Chem. Phys., 10, 4909–4929, https://doi.org/10.5194/acp-10-4909-2010, 2010.
Sato, K., Takami, A., Isozaki, T., Hikida, T., Shimono, A., and Imamura, T.:
Mass spectrometric study of secondary organic aerosol formed from the
photo-oxidation of aromatic hydrocarbons, Atmos. Environ., 44, 1080–1087,
https://doi.org/10.1016/j.atmosenv.2009.12.013, 2010.
Shao, M., Zhang, Y., Zeng, L., Tang, X., Zhang, J., Zhong, L., and Wang, B.:
Ground-level ozone in the Pearl River Delta and the roles of VOC and NOx in
its production, J. Environ. Manage., 90, 512–518, 2009.
She, Q., Choi, M., Belle, J. H., Xiao, Q., Bi, J., Huang, K., Meng, X.,
Geng, G., Kim, J., He, K., Liu, M., and Liu, Y.: Satellite-based estimation
of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze
River Delta, China, Chemosphere, 239, 124678,
https://doi.org/10.1016/j.chemosphere.2019.124678, 2020.
Shen, L., Jacob, D. J., Liu, X., Huang, G., Li, K., Liao, H., and Wang, T.: An evaluation of the ability of the Ozone Monitoring Instrument (OMI) to observe boundary layer ozone pollution across China: application to 2005–2017 ozone trends, Atmos. Chem. Phys., 19, 6551–6560, https://doi.org/10.5194/acp-19-6551-2019, 2019.
Shen, L., Wang, Z., Cheng, H., Liang, S., Xiang, P., Hu, K., Yin, T., and
Yu, J.: A Spatial-Temporal Resolved Validation of Source Apportionment by
Measurements of Ambient VOCs in Central China, Int. J. Env. Res. Pub. He.,
17, 791, https://doi.org/10.3390/ijerph17030791, 2020.
Shi, J., Deng, H., Bai, Z., Kong, S., Wang, X., Hao, J., Han, X., and Ning,
P.: Emission and profile characteristic of volatile organic compounds
emitted from coke production, iron smelt, heating station and power plant in
Liaoning Province, China Sci. Total Environ., 515, 101–108, 2015.
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and
polluted rural environments, Atmos. Environ., 33, 1821–1845, 1999.
Sindelarova, K., Markova, J., Simpson, D., Huszar, P., Karlicky, J., Darras, S., and Granier, C.: High-resolution biogenic global emission inventory for the time period 2000–2019 for air quality modelling, Earth Syst. Sci. Data, 14, 251–270, https://doi.org/10.5194/essd-14-251-2022, 2022.
Song, M., Li, X., Yang, S., Yu, X., Zhou, S., Yang, Y., Chen, S., Dong, H., Liao, K., Chen, Q., Lu, K., Zhang, N., Cao, J., Zeng, L., and Zhang, Y.: Spatiotemporal variation, sources, and secondary transformation potential of volatile organic compounds in Xi'an, China, Atmos. Chem. Phys., 21, 4939–4958, https://doi.org/10.5194/acp-21-4939-2021, 2021.
Stavrakou, T., Müller, J.-F., Bauwens, M., De Smedt, I., Van Roozendael, M., Guenther, A., Wild, M., and Xia, X.: Isoprene emissions over Asia 1979–2012: impact of climate and land-use changes, Atmos. Chem. Phys., 14, 4587–4605, https://doi.org/10.5194/acp-14-4587-2014, 2014.
Sun, Y. L., He, Y., Kuang, Y.. Xu, W. Y., Song, S. J., Ma, N., Tao, J. C.,
Cheng, P., Wu, C., Su, H., Cheng, Y. F., Xie, C. H., Chen, C., Lei, L., Qiu,
Y. M., Fu, P. Q., Croteau, P., and Worsnop, D. R.: Chemical Differences
Between PM1 and PM2.5 in Highly Polluted Environment and
Implications in Air Pollution Studies, Geophys. Res. Lett., 47,
e2019GL086288, https://doi.org/10.1029/2019GL086288, 2020.
Tsai, S. M., Zhang, J. J., Smith, K. R., Ma, Y., Rasmussen, R. A., and
Khalil, M. A. K.: Characterization of Non-methane Hydrocarbons Emitted from
Various Cookstoves Used in China, Environ. Sci. Technol., 37, 2869–2877,
2003.
Tong, Y., Pospisilova, V., Qi, L., Duan, J., Gu, Y., Kumar, V., Rai, P., Stefenelli, G., Wang, L., Wang, Y., Zhong, H., Baltensperger, U., Cao, J., Huang, R.-J., Prévôt, A. S. H., and Slowik, J. G.: Quantification of solid fuel combustion and aqueous chemistry contributions to secondary organic aerosol during wintertime haze events in Beijing, Atmos. Chem. Phys., 21, 9859–9886, https://doi.org/10.5194/acp-21-9859-2021, 2021.
US Environmental Protection Agency: Compendium of Method for the Determination of Toxic Organic Compounds in ambient air: Method TO-15, 2nd Edn., EPA 600/625R-96/010b, 1999.
Wang, H., Qiao, Y., Chen, C., Lu, J., Dai, H., Qiao, L., Lou, S., Huang, C.,
Li, L., Jing, S., and Wu, J.: Source Profiles and Chemical Reactivity of
Volatile Organic Compounds from Solvent Use in Shanghai, China, Aerosol Air
Qual. Res., 14, 301–310, 2014.
Wang, H. L., Chen, C. H., Wang, Q., Huang, C., Su, L. Y., Huang, H. Y., Lou,
S. R., Zhou, M., Li, L., Qiao, L. P., and Wang, Y. H.: Chemical loss of
volatile organic compounds and its impact on the source analysis through a
two-year continuous measurement, Atmos. Environ., 80, 488–498, 2013.
Wang, J., Jin, L., Gao, J., Shi, J., Zhao, Y., Liu, S., Jin, T., Bai, Z.,
and Wu, C. Y.: Investigation of speciated VOC in gasoline vehicular exhaust
under ECE and EUDC test cycles, Sci. Total Environ., 445, 110–116, 2013.
Wang, M., Shao, M., Lu, S. H., Yang, Y. D., and Chen, W. T.: Evidence of coal
combustion contribution to ambient VOCs during winter in Beijing, Chinese
Chem. Lett., 24, 829–832, 2013.
Wang, M. L., Li, S. Y., Zhu, R. C., Zhang, R. Q., Zu, L., Wang, Y. J., and Bao,
X. F.: On-road tailpipe emission characteristics and ozone formation
potentials of VOCs from gasoline, diesel and liquefied petroleum gas fueled
vehicles, Atmos. Environ., 223, 117294, https://doi.org/10.1016/j.atmosenv.2020.117294, 2020.
Wang, T., Xue, L., Brimblecombe, P., Lam, Y. F., Li, L., and Zhang, L.: Ozone
Pollution in China: A Review of Concentrations, Meteorological Influences,
Chemical Precursors, and Effects, Sci. Total Environ., 575, 1582–1596, 2017.
Wang, Y. S., Yao, L., Wang, L. L., Liu, Z. R., Ji, D. S., Tang, G. Q., Zhang,
J. K., Sun, Y., Hu, B., and Xin, J. Y.: Mechanism for the formation of the
January 2013 heavy haze pollution episode over central and eastern China,
Sci. China Earth Sci., 57, 14–25,
https://doi.org/10.1007/s11430-013-4773-4, 2014.
Warneke, C., McKeen, S. A., de Gouw, J. A., Goldan, P. D., Kuster, W. C.,
Holloway, J. S., Williams, E. J., Lerner, B. M., Parrish, D. D., Trainer,
M., Fehsenfeld, F. C., Kato, S., Atlas, E. L., Baker, A., and Blake, D. R.:
Determination of urban volatile organic compound emission ratios and
comparison with an emissions database, J. Geophys. Res., 112, D10S47,
https://doi.org/10.1029/2006jd007930, 2007.
Wu, R. and Xie, S.: Spatial Distribution of Secondary Organic Aerosol
Formation Potential in China Derived from Speciated Anthropogenic Volatile
Organic Compound Emissions, Environ. Sci. Technol., 52, 8146–8156,
https://doi.org/10.1021/acs.est.8b01269, 2018.
Wu, R. R., Li, J., Hao, Y. F., Li, Y. Q., Zeng, L. M., and Xie, S. D.: Evolution
process and sources of ambient volatile organic compounds during a severe
haze event in Beijing, China, Sci. Total. Environ., 560–561, 62–72, 2016.
Xing, J., Wang, S. X., Jang, C., Zhu, Y., and Hao, J. M.: Nonlinear response of ozone to precursor emission changes in China: a modeling study using response surface methodology, Atmos. Chem. Phys., 11, 5027–5044, https://doi.org/10.5194/acp-11-5027-2011, 2011.
Xu, Q., Wang, S., and Jiang, J.: Nitrate dominates the chemical composition
of PM2.5 during haze event in Beijing, China, Sci. Total. Environ.,
689, 1293–1303, 2019.
Xu, W., Sun, Y., Wang, Q., Zhao, J., Wang, J., Ge, X., Xie, C., Zhou, W.,
Du, W., Li, J., Fu, P., Wang, Z., Worsnop, D. R., and Coe, H.: Changes in
Aerosol Chemistry From 2014 to 2016 in Winter in Beijing: Insights From
High-Resolution Aerosol Mass Spectrometry, J. Geophys. Res.-Atmos., 124,
1132–1147, 2019.
Xue, Y., Ho, S. S. H., Huang, Y., Li, B., Wang, L., Dai, W., Cao, J., and
Lee, S.: Source apportionment of VOCs and their impacts on surface ozone in
an industry city of Baoji, Northwestern China, Sci. Rep.-UK, 7, 9979,
https://doi.org/10.1038/s41598-017-10631-4, 2017.
Yan, Y., Peng, L., Li, R., Li, Y., Li, L., and Bai, H.: Concentration, ozone
formation potential and source analysis of volatile organic compounds (VOCs)
in a thermal power station centralized area: A study in Shuozhou, China,
Environ. Pollut., 223, 295–304, 2017.
Yang, W., Zhang, Y., Wang, X., Li, S., Zhu, M., Yu, Q., Li, G., Huang, Z., Zhang, H., Wu, Z., Song, W., Tan, J., and Shao, M.: Volatile organic compounds at a rural site in Beijing: influence of temporary emission control and wintertime heating, Atmos. Chem. Phys., 18, 12663–12682, https://doi.org/10.5194/acp-18-12663-2018, 2018.
Yao, Y. C., Tsai, J. H., Wang, I. T.: Emissionsof gaseous pollutant from
motorcycle powered byethanol-gasoline blend, Appl. Energy., 102, 93–100,
2013.
Yao, Z., Wu, B., Shen, X., Cao, X., Jiang, X., Ye, Y., and He, K.: On-road
emission characteristics of VOCs from rural vehicles and their ozone
formation potential in Beijing, China, Atmos. Environ., 105, 91–96, 2015.
Yuan, B., Shao, M., Lu, S., and Wang, B.: Source profiles of volatile
organic compounds associated with solvent use in Beijing, China, Atmos.
Environ., 44, 1919–1926, 2010.
Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, https://doi.org/10.5194/acp-19-11031-2019, 2019.
Zhang, Y., Wang, X., Zhang, Z., Lu, S., Shao, M., Lee, F. S. C., and Yu, J.:
Species profiles and normalized re activity of volatile organic
compounds from gasoline evaporation in China, Atmos. Environ., 79, 110–118,
2013.
Zhang, X., Xue, Z., Li, H., Yan, L., Yang, Y., Wang, Y., Duan, J., Li, L.,
Chai, F., Cheng, M., and Zhang, W.: Ambient volatile organic compounds
pollution in China, J. Environ. Sci., 55, 69–75, https://doi.org/10.1016/j.jes.2016.05.036,
2017.
Zhao, D., Liu, G., Xin, J., Quan, J., Wang, Y., Wang, X., Dai, L., Gao, W., Tang, G., Hu, B., Ma, Y., Wu, X., Wang, L., Liu, Z., and Wu, F.: Haze pollution under a high atmospheric oxidization capacity in summer in Beijing: insights into formation mechanism of atmospheric physicochemical processes, Atmos. Chem. Phys., 20, 4575–4592, https://doi.org/10.5194/acp-20-4575-2020, 2020.
Zhao, Q., Bi, J., Liu, Q., Ling, Z., Shen, G., Chen, F., Qiao, Y., Li, C., and Ma, Z.: Sources of volatile organic compounds and policy implications for regional ozone pollution control in an urban location of Nanjing, East China, Atmos. Chem. Phys., 20, 3905–3919, https://doi.org/10.5194/acp-20-3905-2020, 2020.
Zheng, J., Yu, Y., Mo, Z., Zhang, Z., Wang, X., Yin, S., Peng, K., Yang, Y.,
Feng, X., and Cai, H.: Industrial sector-based volatile organic compound (VOC)
source profiles measured in manufacturing facilities in the Pearl River
Delta, China, Sci. Total Environ., 456, 127–136, 2013.
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.
A 1-year campaign was conducted to characterize VOCs at a Beijing urban site during different...
Altmetrics
Final-revised paper
Preprint