Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6117-2026
© Author(s) 2026. 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-26-6117-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-machine-learning approaches to modeling small-scale source attribution of ozone formation
Zheng Xiao
State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management Of Chemical Pollutants, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
Yifeng Lu
Shanghai Chemical Industry Park Administration Committee, Shanghai 201507, China
State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management Of Chemical Pollutants, School of Resources & Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
Related authors
No articles found.
Meng Wang, Yusen Duan, Wei Xu, Qiyuan Wang, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Haijie Tong, Juntao Huo, Jia Chen, Shan Gao, Zhongbiao Wu, Long Cui, Yu Huang, Guangli Xiu, Junji Cao, Qingyan Fu, and Shun-cheng Lee
Atmos. Chem. Phys., 22, 12789–12802, https://doi.org/10.5194/acp-22-12789-2022, https://doi.org/10.5194/acp-22-12789-2022, 2022
Short summary
Short summary
In this study, we report the long-term measurement of organic carbon (OC) and elementary carbon (EC) in PM2.5 with hourly time resolution conducted at a regional site in Shanghai from 2016 to 2020. The results from this study provide critical information about the long-term trend of carbonaceous aerosol, in particular secondary OC, in one of the largest megacities in the world and are helpful for developing pollution control measures from a long-term planning perspective.
Cited articles
Baklanov, A. and Korsholm, U.: On-line integrated meteorological and chemical transport modelling: advantages and prospectives, Air Pollution Modeling and Its Application XIX, Springer Netherlands, 3–17, https://doi.org/10.1007/978-1-4020-8453-9_1, 2008.
Burdett, I. D. and Eisinger, R. S.: Ethylene polymerization processes and manufacture of polyethylene, Handbook of Industrial Polyethylene and Technology: Definitive Guide to Manufacturing, Properties, Processing, Applications and Markets, Wiley, 61–103, https://doi.org/10.1002/9781119159797, 2017.
Cao, X., Yi, J., Li, Y., Zhao, M., Duan, Y., Zhang, F., and Duan, L.: Characteristics and Source Apportionment of Volatile Organic Compounds in an Industrial Area at the Zhejiang–Shanghai Boundary, China, Atmosphere, 15, 237, https://doi.org/10.3390/atmos15020237, 2024.
Carter, W. P.: Development of the SAPRC-07 chemical mechanism, Atmos. Environ., 44, 5324–5335, https://doi.org/10.1016/j.atmosenv.2010.01.026, 2010.
Chang, L., He, F., Tie, X., Xu, J., and Gao, W.: Meteorology driving the highest ozone level occurred during mid-spring to early summer in Shanghai, China, Sci. Total Environ., 785, 147253, https://doi.org/10.1016/j.scitotenv.2021.147253, 2021.
Chen, D., Zhou, L., Wang, C., Liu, H., Qiu, Y., Shi, G., Song, D., Tan, Q., and Yang, F.: Characteristics of ambient volatile organic compounds during spring O3 pollution episode in Chengdu, China, J. Environ. Sci., 114, 115–125, https://doi.org/10.1016/j.jes.2021.08.014, 2022.
Chen, W., Xu, X., and Liu, W.: Combined PMF modelling and machine learning to identify sources and meteorological influencers of volatile organic compound pollution in an industrial city in eastern China, Atmos. Environ., 334, 120714, https://doi.org/10.1016/j.atmosenv.2024.120714, 2024.
Cheng, N., Jing, D., Gu, Z., Cai, X., Shi, Z., Li, S., Chen, L., Li, W., and Wang, Q.: Observation-Based Ozone Formation Rules by Gradient Boosting Decision Trees Model in Typical Chemical Industrial Parks, Atmosphere, 15, 600, https://doi.org/10.3390/atmos15050600, 2024.
Cheng, Y., Huang, X.-F., Peng, Y., Tang, M.-X., Zhu, B., Xia, S.-Y., and He, L.-Y.: A novel machine learning method for evaluating the impact of emission sources on ozone formation, Environ. Pollut., 316, 120685, https://doi.org/10.1016/j.envpol.2022.120685, 2023.
Choi, M. S., Qiu, X., Zhang, J., Wang, S., Li, X., Sun, Y., Chen, J., and Ying, Q.: Study of secondary organic aerosol formation from chlorine radical-initiated oxidation of volatile organic compounds in a polluted atmosphere using a 3D chemical transport model, Environ. Sci. Technol., 54, 13409–13418, https://doi.org/10.1021/acs.est.0c02958, 2020.
Essamlali, I., Nhaila, H., and El Khaili, M.: Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review, Sustainability, 16, 976, https://doi.org/10.3390/su16030976, 2024.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D.: A survey of methods for explaining black box models, ACM. Comput. Surv, 51, 1–42, https://doi.org/10.1145/3236009, 2018.
Guo, W., Yang, Y., Chen, Q., Zhu, Y., Zhang, Y., Zhang, Y., Liu, Y., Li, G., Sun, W., and She, J.: Chemical reactivity of volatile organic compounds and their effects on ozone formation in a petrochemical industrial area of Lanzhou, Western China, Sci. Total Environ., 839, 155901, https://doi.org/10.1016/j.scitotenv.2022.155901, 2022.
He, L., Duan, Y., Zhang, Y., Yu, Q., Huo, J., Chen, J., Cui, H., Li, Y., and Ma, W.: Effects of VOC emissions from chemical industrial parks on regional O3-PM2.5 compound pollution in the Yangtze River Delta, Sci. Total Environ., 906, 167503, https://doi.org/10.1016/j.scitotenv.2023.167503, 2024.
Huang, B., Lei, C., Wei, C., and Zeng, G.: Chlorinated volatile organic compounds (Cl-VOCs) in environment – sources, potential human health impacts, and current remediation technologies, Environ. Int., 71, 118–138, https://doi.org/10.1016/j.envint.2014.06.013, 2014.
Hui, L., Liu, X., Tan, Q., Feng, M., An, J., Qu, Y., Zhang, Y., and Cheng, N.: VOC characteristics, sources and contributions to SOA formation during haze events in Wuhan, Central China, Sci. Total Environ., 650, 2624–2639, https://doi.org/10.1016/j.scitotenv.2018.10.029, 2019.
Hui, L., Liu, X., Tan, Q., Feng, M., An, J., Qu, Y., Zhang, Y., Deng, Y., Zhai, R., and Wang, Z.: VOC characteristics, chemical reactivity and sources in urban Wuhan, central China, Atmos. Environ., 224, 117340, https://doi.org/10.1016/j.atmosenv.2020.117340, 2020.
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., and Wortman Vaughan, J.: Interpreting interpretability: understanding data scientists' use of interpretability tools for machine learning, Proceedings of the 2020 CHI conference on human factors in computing systems, 2020, Honolulu, HI, USA, 1–14, https://doi.org/10.1145/3313831.3376219, 2020.
Kim, S.-J., Lee, H.-Y., Lee, S.-J., and Choi, S.-D.: Passive air sampling of VOCs, O3, NO2, and SO2 in the large industrial city of Ulsan, South Korea: spatial–temporal variations, source identification, and ozone formation potential, Environ. Sci. Pollut. Res., 30, 125478–125491, https://doi.org/10.1007/s11356-023-31109-z, 2023.
Kuo, C.-P. and Fu, J. S.: Ozone response modeling to NOx and VOC emissions: Examining machine learning models, Environ. Int., 176, 107969, https://doi.org/10.1016/j.envint.2023.107969, 2023.
Li, M., Zhang, Q., Streets, D. G., He, K. B., Cheng, Y. F., Emmons, L. K., Huo, H., Kang, S. C., Lu, Z., Shao, M., Su, H., Yu, X., and Zhang, Y.: Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical mechanisms, Atmos. Chem. Phys., 14, 5617–5638, https://doi.org/10.5194/acp-14-5617-2014, 2014.
Li, M., Sun, H., Huang, Y., and Chen, H.: Shapley value: from cooperative game to explainable artificial intelligence, Auton. Intell. Syst., 4, 2, https://doi.org/10.1007/s43684-023-00060-8, 2024.
Liu, B., Liang, D., Yang, J., Dai, Q., Bi, X., Feng, Y., Yuan, J., Xiao, Z., Zhang, Y., and Xu, H.: Characterization and source apportionment of volatile organic compounds based on 1-year of observational data in Tianjin, China, Environ. Pollut., 218, 757–769, https://doi.org/10.1016/j.envpol.2016.07.072, 2016.
Liu, X., Lu, D., Zhang, A., Liu, Q., and Jiang, G.: Data-driven machine learning in environmental pollution: gains and problems, Environ. Sci. Technol., 56, 2124–2133, https://doi.org/10.1021/acs.est.1c06157, 2022.
Liu, Y., Wang, H., Jing, S., Peng, Y., Gao, Y., Yan, R., Wang, Q., Lou, S., Cheng, T., and Huang, C.: Strong regional transport of volatile organic compounds (VOCs) during wintertime in Shanghai megacity of China, Atmos. Environ., 244, 117940, https://doi.org/10.1016/j.atmosenv.2020.117940, 2021.
Long, Y., Wu, Y., Xie, Y., Huang, L., Wang, W., Liu, X., Zhou, Z., Zhang, Y., Hanaoka, T., and Ju, Y.: PM2.5 and ozone pollution-related health challenges in Japan with regards to climate change, Global Environ. Change, 79, 102640, https://doi.org/10.1016/j.gloenvcha.2023.102640, 2023.
Louhichi, M., Nesmaoui, R., Mbarek, M., and Lazaar, M.: Shapley values for explaining the black box nature of machine learning model clustering, Procedia Comput. Sci., 220, 806–811, https://doi.org/10.1016/j.procs.2023.03.107, 2023.
Lu, B., Zhang, Z., Jiang, J., Meng, X., Liu, C., Herrmann, H., Chen, J., Xue, L., and Li, X.: Unraveling the O3-NOx-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai, Atmos. Environ., 308, 119864, https://doi.org/10.1016/j.atmosenv.2023.119864, 2023.
Lu, X., Zhang, D., Wang, L., Wang, S., Zhang, X., Liu, Y., Chen, K., Song, X., Yin, S., and Zhang, R.: Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China, J. Environ. Sci., 149, 406–418, https://doi.org/10.1016/j.jes.2024.01.033, 2025.
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, arXiv [preprint], 30, https://doi.org/10.48550/arXiv.1705.07874, 2017.
Masui, N., Shiojiri, K., Agathokleous, E., Tani, A., and Koike, T.: Elevated O3 threatens biological communications mediated by plant volatiles: A review focusing on the urban environment, Crit. Rev. Environ. Sci. Technol., 53, 1982–2001, https://doi.org/10.1080/10643389.2023.2202105, 2023.
Mu, J., Zhang, Y., Xia, Z., Fan, G., Zhao, M., Sun, X., Liu, Y., Chen, T., Shen, H., Zhang, Z., Zhang, H., Pan, G., Wang, W., and Xue, L.: Two-year online measurements of volatile organic compounds (VOCs) at four sites in a Chinese city: Significant impact of petrochemical industry, Sci. Total Environ., 858, 159951, https://doi.org/10.1016/j.scitotenv.2022.159951, 2023.
Mukhamatdinov, I. I., Salih, I. S., Khelkhal, M. A., and Vakhin, A. V.: Application of aromatic and industrial solvents for enhancing heavy oil recovery from the Ashalcha field, Energy Fuels, 35, 374–385, https://doi.org/10.1021/acs.energyfuels.0c03090, 2020.
Nelson, D., Choi, Y., Sadeghi, B., Yeganeh, A. K., Ghahremanloo, M., and Park, J.: A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas, Environ. Pollut., 334, 122223, https://doi.org/10.1016/j.envpol.2023.122223, 2023.
Ning, Z., Gao, S., Gu, Z., Ni, C., Fang, F., Nie, Y., Jiao, Z., and Wang, C.: Prediction and explanation for ozone variability using cross-stacked ensemble learning model, Sci. Total Environ., 935, 173382, https://doi.org/10.1016/j.scitotenv.2024.173382, 2024.
Paatero, P.: Least squares formulation of robust non-negative factor analysis, Chemometrics Intell. Lab. Syst., 37, 1, https://doi.org/10.1016/S0169-7439(96)00044-5, 1997.
Pichler, M. and Hartig, F.: Machine learning and deep learning – A review for ecologists, Methods Ecol. Evol., 14, 994–1016, https://doi.org/10.1111/2041-210X.14061, 2023.
Pinthong, N., Thepanondh, S., Kultan, V., and Keawboonchu, J.: Characteristics and impact of VOCs on ozone formation potential in a petrochemical industrial area, Thailand, Atmosphere, 13, 732, https://doi.org/10.3390/atmos13050732, 2022.
Ragothaman, A. and Anderson, W. A.: Air quality impacts of petroleum refining and petrochemical industries, Environments, 4, 66, https://doi.org/10.3390/environments4030066, 2017.
Ren, H., Xia, Z., Yao, L., Qin, G., Zhang, Y., Xu, H., Wang, Z., and Cheng, J.: Investigation on ozone formation mechanism and control strategy of VOCs in petrochemical region: insights from chemical reactivity and photochemical loss, Sci. Total Environ., 914, 169891, https://doi.org/10.1016/j.scitotenv.2024.169891, 2024.
Robin, Y., Amann, J., Baur, T., Goodarzi, P., Schultealbert, C., Schneider, T., and Schütze, A.: High-performance VOC quantification for IAQ monitoring using advanced sensor systems and deep learning, Atmosphere, 12, 1487, https://doi.org/10.3390/atmos12111487, 2021.
Salcedo-Sanz, S., Pérez-Aracil, J., Ascenso, G., Del Ser, J., Casillas-Pérez, D., Kadow, C., Fister, D., Barriopedro, D., García-Herrera, R., and Giuliani, M.: Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review, Theor. Appl. Climatol., 155, 1–44, https://doi.org/10.1007/s00704-023-04571-5, 2024.
Sharma, A. K., Sharma, M., Sharma, A. K., and Sharma, M.: Mapping the impact of environmental pollutants on human health and environment: A systematic review and meta-analysis, J. Geochem. Explor., 255, 107325, https://doi.org/10.1016/j.gexplo.2023.107325, 2023.
Sharma, S., Sharma, P., and Khare, M.: Photo-chemical transport modelling of tropospheric ozone: A review, Atmos. Environ., 159, 34–54, https://doi.org/10.1016/j.atmosenv.2017.03.047, 2017.
Sharma, S., Singhal, A., Venkatramanan, V., Verma, P., and Pandey, M.: Variability in air quality, ozone formation potential by VOCs, and associated air pollution attributable health risks for Delhi's inhabitants, Environ. Sci.-Atmos., 4, 897–910, https://doi.org/10.1039/d4ea00064a, 2024.
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments, Atmos. Environ., 33, 1821–1845, https://doi.org/10.1016/S1352-2310(98)00345-8, 1999.
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.
Tan, Y., Han, S., Chen, Y., Zhang, Z., Li, H., Li, W., Yuan, Q., Li, X., Wang, T., and Lee, S.-C.: Characteristics and source apportionment of volatile organic compounds (VOCs) at a coastal site in Hong Kong, Sci. Total Environ., 777, 146241, https://doi.org/10.1016/j.scitotenv.2021.146241, 2021.
Venecek, M. A., Carter, W. P., and Kleeman, M. J.: Updating the SAPRC Maximum Incremental Reactivity (MIR) scale for the United States from 1988 to 2010, J. Air Waste Manage. Assoc., 68, 1301–1316, https://doi.org/10.1080/10962247.2018.1498410, 2018.
Wang, H., Lyu, X., Guo, H., Wang, Y., Zou, S., Ling, Z., Wang, X., Jiang, F., Zeren, Y., Pan, W., Huang, X., and Shen, J.: Ozone pollution around a coastal region of South China Sea: interaction between marine and continental air, Atmos. Chem. Phys., 18, 4277–4295, https://doi.org/10.5194/acp-18-4277-2018, 2018.
Wang, S., Zhao, Y., Han, Y., Li, R., Fu, H., Gao, S., Duan, Y., Zhang, L., and Chen, J.: Spatiotemporal variation, source and secondary transformation potential of volatile organic compounds (VOCs) during the winter days in Shanghai, China, Atmos. Environ., 286, 119203, https://doi.org/10.1016/j.atmosenv.2022.119203, 2022.
Wang, Y., Jiang, S., Huang, L., Lu, G., Kasemsan, M., Yaluk, E. A., Liu, H., Liao, J., Bian, J., and Zhang, K.: Differences between VOCs and NOx transport contributions, their impacts on O3, and implications for O3 pollution mitigation based on CMAQ simulation over the Yangtze River Delta, China, Sci. Total Environ., 872, 162118, https://doi.org/10.1016/j.scitotenv.2023.162118, 2023.
Washenfelder, R., Trainer, M., Frost, G., Ryerson, T., Atlas, E., De Gouw, J., Flocke, F., Fried, A., Holloway, J., and Parrish, D.: Characterization of NOx, SO2, ethene, and propene from industrial emission sources in Houston, Texas, J. Geophys. Res.-Atmos., 115, D16311, https://doi.org/10.1029/2009JD013645, 2010.
Weiss, K. D.: Paint and coatings: A mature industry in transition, Prog. Polym. Sci., 22, 203–245, https://doi.org/10.1016/S0079-6700(96)00019-6, 1997.
White, W. C.: Butadiene production process overview, Chem. Biol. Interact., 166, 10–14, https://doi.org/10.1016/j.cbi.2007.01.009, 2007.
Wu, Y., Fan, X., Liu, Y., Zhang, J., Wang, H., Sun, L., Fang, T., Mao, H., Hu, J., and Wu, L.: Source apportionment of VOCs based on photochemical loss in summer at a suburban site in Beijing, Atmos. Environ., 293, 119459, https://doi.org/10.1016/j.atmosenv.2022.119459, 2023.
Xiao, Z., Yang, X., Gu, H., Hu, J., Zhang, T., Chen, J., Pan, X., Xiu, G., Zhang, W., and Lin, M.: Characterization and sources of volatile organic compounds (VOCs) during 2022 summer ozone pollution control in Shanghai, China, Atmos. Environ., 327, 120464, https://doi.org/10.1016/j.atmosenv.2024.120464, 2024.
Xu, Z., Zou, Q., Jin, L., Shen, Y., Shen, J., Xu, B., Qu, F., Zhang, F., Xu, J., and Pei, X.: Characteristics and sources of ambient Volatile Organic Compounds (VOCs) at a regional background site, YRD region, China: Significant influence of solvent evaporation during hot months, Sci. Total Environ., 857, 159674, https://doi.org/10.1016/j.scitotenv.2022.159674, 2023.
Yang, M., Li, F., Huang, C., Tong, L., Dai, X., and Xiao, H.: VOC characteristics and their source apportionment in a coastal industrial area in the Yangtze River Delta, China, J. Environ. Sci., 127, 483–494, https://doi.org/10.1016/j.jes.2022.05.041, 2023.
Yang, Y., Meng, X., Chen, Q., Xue, Q., Wang, L., Sun, J., Guo, W., Tao, H., Yang, L., and Chen, F.: Characteristics of volatile organic compounds under different operating conditions in a petrochemical industrial zone and their effects on ozone formation, Environ. Pollut., 363, 125254, https://doi.org/10.1016/j.envpol.2024.125254, 2024.
Yao, D., Tang, G., Wang, Y., Yang, Y., Wang, L., Chen, T., He, H., and Wang, Y.: Significant contribution of spring northwest transport to volatile organic compounds in Beijing, J. Environ. Sci., 104, 169–181, https://doi.org/10.1016/j.jes.2020.11.023, 2021.
Zhang, M., Liu, Y., Xu, X., He, J., Ji, D., Qu, K., Xu, Y., Cong, C., and Wang, Y.: A Systematic Review on Atmospheric Ozone Pollution in a Typical Peninsula Region of North China: Formation Mechanism, Spatiotemporal Distribution, Source Apportionment, and Health and Ecological Effects, Curr. Pollution Rep., 11, 9, https://doi.org/10.1007/s40726-024-00338-2, 2025.
Zhang, Y., Xue, L., Carter, W. P. L., Pei, C., Chen, T., Mu, J., Wang, Y., Zhang, Q., and Wang, W.: Development of ozone reactivity scales for volatile organic compounds in a Chinese megacity, Atmos. Chem. Phys., 21, 11053–11068, https://doi.org/10.5194/acp-21-11053-2021, 2021.
Zhang, Y., Fu, Q., Wang, T., Huo, J., Cui, H., Mu, J., Tan, Y., Chen, T., Shen, H., and Li, Q.: A quantitative analysis of causes for increasing ozone pollution in Shanghai during the 2022 lockdown and implications for control policy, Atmos. Environ., 326, 120469, https://doi.org/10.1016/j.atmosenv.2024.120469, 2024.
Zhang, Z., Xu, J., Ye, T., Chen, L., Chen, H., and Yao, J.: Distributions and temporal changes of benzene, toluene, ethylbenzene, and xylene concentrations in newly decorated rooms in southeastern China, and the health risks posed, Atmos. Environ., 246, 118071, https://doi.org/10.1016/j.atmosenv.2020.118071, 2021.
Zhao, D., Xin, J., Wang, W., Jia, D., Wang, Z., Xiao, H., Liu, C., Zhou, J., Tong, L., and Ma, Y.: Effects of the sea-land breeze on coastal ozone pollution in the Yangtze River Delta, China, Sci. Total Environ., 807, 150306, https://doi.org/10.1016/j.scitotenv.2021.150306, 2022.
Zhou, X., Sun, Z., Yan, H., Feng, X., Zhao, H., Liu, Y., Chen, X., and Yang, C.: Produce petrochemicals directly from crude oil catalytic cracking, a techno-economic analysis and life cycle society-environment assessment, J. Cleaner Prod., 308, 127283, https://doi.org/10.1016/j.jclepro.2021.127283, 2021.
Short summary
This study innovates air pollution tracking in industry by combining AI with traditional methods. By analyzing 3 years of data from a chemical park in Shanghai, we identified sources of ozone pollution and seasonal variations, revealing chemical solvents and fuel vapor as key factors. Our approach identifies sources of pollution faster and more accurately, helping to make better air quality decisions in rapidly developing areas.
This study innovates air pollution tracking in industry by combining AI with traditional...
Altmetrics
Final-revised paper
Preprint