Articles | Volume 24, issue 15
https://doi.org/10.5194/acp-24-8847-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-8847-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2024
Research article |
| 13 Aug 2024
| 13 Aug 2024
Spatiotemporal source apportionment of ozone pollution over the Greater Bay Area
Yiang Chen
Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
Department of Geography and Resource Management, Chinese University of Hong Kong, Sha Tin, New Territory, Hong Kong SAR, China
Jimmy C. H. Fung
Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
Related authors
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
Short summary
Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Xueying Liu, Yeqi Huang, Yao Chen, Xin Feng, Yang Xu, Yi Chen, Dasa Gu, Hao Sun, Zhi Ning, Jianzhen Yu, Wing Sze Chow, Changqing Lin, Yan Xiang, Tianshu Zhang, Claire Granier, Guy Brasseur, Zhe Wang, and Jimmy C. H. Fung
EGUsphere, https://doi.org/10.5194/egusphere-2025-3227, https://doi.org/10.5194/egusphere-2025-3227, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Volatile organic compounds (VOCs) affect ozone formation and air quality. However, our understanding is limited due to insufficient measurements, especially for oxygenated VOCs. This study combines land, ship, and satellite data in Hong Kong, showing that oxygenated VOCs make up a significant portion of total VOCs. Despite their importance, many are underestimated in current models. These findings highlight the need to improve VOC representation in models to enhance air quality management.
Wanliang Zhang, Chao Ren, Edward Yan Yung Ng, Michael Mau Fung Wong, and Jimmy Chi Hung Fung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-205, https://doi.org/10.5194/gmd-2024-205, 2024
Revised manuscript under review for GMD
Short summary
Short summary
This study focuses on improving the accuracy of numerical weather prediction (NWP) model particularly in urbanized areas. We coupled a recently validated boundary layer model with a building effect model within an NWP. Validation has been performed under idealized atmospheric conditions by benchmarking the coupled model with a fine-scale numerical model. Subsequently, the improvements and limitations are investigated aided by observations in real case simulations.
Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Tianshu Zhang, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao
Atmos. Chem. Phys., 24, 9645–9665, https://doi.org/10.5194/acp-24-9645-2024, https://doi.org/10.5194/acp-24-9645-2024, 2024
Short summary
Short summary
This study developed a nested machine learning model to convert the GEMS NO2 column measurements into ground-level concentrations across China. The model directly incorporates the NO2 mixing height (NMH) into the methodological framework. The study underscores the importance of considering NMH when estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of new-generation geostationary satellites in air quality monitoring.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
Short summary
Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Yun Fat Lam, Chi Chiu Cheung, Xuguo Zhang, Joshua S. Fu, and Jimmy Chi Hung Fung
Atmos. Chem. Phys., 21, 12895–12908, https://doi.org/10.5194/acp-21-12895-2021, https://doi.org/10.5194/acp-21-12895-2021, 2021
Short summary
Short summary
In recent years, air pollution forecasting has become an important municipal service of the government. In this study, a new spatial allocation method based on satellite remote sensing and GIS techniques was developed to address the spatial deficiency of industrial source emissions in China, providing a substantial improvement on NO2 and PM2.5 forecast for the Pearl River Delta/Greater Bay Area.
Cited articles
Bian, Y., Huang, Z., Ou, J., Zhong, Z., Xu, Y., Zhang, Z., Xiao, X., Ye, X., Wu, Y., Yin, X., Li, C., Chen, L., Shao, M., and Zheng, J.: Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015, Atmos. Chem. Phys., 19, 11701–11719, https://doi.org/10.5194/acp-19-11701-2019, 2019.
CAMx: Comprehensive Air Quality Model with Extensions, Ramboll Environ US Corporation [code], https://www.camx.com/download/, last access: 24 December 2023.
Cao, M., Fan, S., Jin, C., Cai, Q., and He, Y.: O3 pollution characteristics, weather classifications and local meteorological conditions in Guangdong from 2015 to 2020, Acta Scientiae Circumstantiae, 43, 19–31, https://doi.org/10.13671/j.hjkxxb.2022.0416, 2023 (in Chinese).
Cao, T., Wang, H., Li, L., Lu, X., Liu, Y., and Fan, S.: Fast spreading of surface ozone in both temporal and spatial scale in Pearl River Delta, J. Environ. Sci., 137, 540–552, https://doi.org/10.1016/j.jes.2023.02.025, 2024.
Chen, W., Chen, Y., Chu, Y., Zhang, J., Xian, C., Lin, C., Fung, Z., and Lu, X.: Numerical simulation of ozone source characteristics in the Pearl River Delta region, Acta Scientiae Circumstantiae, 42, 293–308, https://doi.org/10.13671/j.hjkxxb.2021.0328, 2022 (in Chinese).
Chen, X., Wang, N., Wang, G., Wang, Z., Chen, H., Cheng, C., Li, M., Zheng, L., Wu, L., Zhang, Q., Tang, M., Huang, B., Wang, X., and Zhou, Z.: The Influence of Synoptic Weather Patterns on Spatiotemporal Characteristics of Ozone Pollution Across Pearl River Delta of Southern China, J. Geophys. Res.-Atmos., 127, e2022JD037121, https://doi.org/10.1029/2022jd037121, 2022.
Chen, Y., Fung, J. C. H., Huang, Y., Lu, X., Wang, Z., Louie, P. K. K., Chen, W., Yu, C. W., Yu, R., and Lau, A. K. H.: Temporal Source Apportionment of PM2.5 Over the Pearl River Delta Region in Southern China, J. Geophys. Res.-Atmos., 127, e2021JD035271, https://doi.org/10.1029/2021jd035271, 2022.
Chen, Y., Fung, J. C. H., Yuan, D., Chen, W., Fung, T., and Lu, X.: Development of an integrated machine-learning and data assimilation framework for NOx emission inversion, Sci. Total Environ., 871, 161951, https://doi.org/10.1016/j.scitotenv.2023.161951, 2023.
Cho, D., Yoo, C., Im, J., and Cha, D. H.: Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas, Earth Space Sci., 7, e2019EA000740, https://doi.org/10.1029/2019ea000740, 2020.
Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017, 2017.
CNEMC: Real-time National Air Quality, China National Environmental Monitoring Centre [data set], https://quotsoft.net/air/, last access: 24 December 2023.
Coffel, E. D., Horton, R. M., and de Sherbinin, A.: Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century, Environ. Res. Lett., 13, 014001, https://doi.org/10.1088/1748-9326/aaa00e, 2018.
Deng, T., Wang, T., Wang, S., Zou, Y., Yin, C., Li, F., Liu, L., Wang, N., Song, L., Wu, C., and Wu, D.: Impact of typhoon periphery on high ozone and high aerosol pollution in the Pearl River Delta region, Sci. Total Environ., 668, 617–630, https://doi.org/10.1016/j.scitotenv.2019.02.450, 2019.
Dong, W., Jia, X., Qian, Q., and Li, X.: Rapid Acceleration of Dangerous Compound Heatwaves and Their Impacts in a Warmer China, Geophys. Res. Lett., 50, e2023GL104850, https://doi.org/10.1029/2023gl104850, 2023.
East, J. D., Henderson, B. H., Napelenok, S. L., Koplitz, S. N., Sarwar, G., Gilliam, R., Lenzen, A., Tong, D. Q., Pierce, R. B., and Garcia-Menendez, F.: Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations, Atmos. Chem. Phys., 22, 15981–16001, https://doi.org/10.5194/acp-22-15981-2022, 2022.
Emery, C. and Tai, E.: Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes, Texas Natural Resource Conservation Commission, ENVIRON International Corp, https://api.semanticscholar.org/CorpusID:127579774 (last access: 31 July 2024), 2001.
ERA5: ECMWF Reanalysis v5 data, ERA5 [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 (last access: 17 May 2024), 2024.
Fang, T., Zhu, Y., Wang, S., Xing, J., Zhao, B., Fan, S., Li, M., Yang, W., Chen, Y., and Huang, R.: Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China, Environ. Pollut., 289, 117860, https://doi.org/10.1016/j.envpol.2021.117860, 2021.
Feng, X., Guo, J., Wang, Z., Gu, D., Ho, K.-F., Chen, Y., Liao, K., Cheung, V. T. F., Louie, P. K. K., Leung, K. K. M., Yu, J. Z., Fung, J. C. H., and Lau, A. K. H.: Investigation of the multi-year trend of surface ozone and ozone-precursor relationship in Hong Kong, Atmos. Environ., 315, 120139, https://doi.org/10.1016/j.atmosenv.2023.120139, 2023.
Gao, X., Deng, X., Tan, H., Wang, C., Wang, N., and Yue, D.: Characteristics and analysis on regional pollution process and circulation weather types over Guangdong Province, Acta Scientiae Circumstantiae, 38, 1708–1716, https://doi.org/10.13671/j.hjkxxb.2017.0473, 2018 (in Chinese).
Gilliam, R. C., Hogrefe, C., Godowitch, J. M., Napelenok, S., Mathur, R., and Rao, S. T.: Impact of inherent meteorology uncertainty on air quality model predictions, J. Geophys. Res.-Atmos., 120, 12259–12280, https://doi.org/10.1002/2015jd023674, 2015.
Gong, C., Liao, H., Yue, X., Ma, Y., and Lei, Y.: Impacts of Ozone-Vegetation Interactions on Ozone Pollution Episodes in North China and the Yangtze River Delta, Geophys. Res. Lett., 48, e2021GL093814, https://doi.org/10.1029/2021gl093814, 2021.
Gong, S., Zhang, L., Liu, C., Lu, S., Pan, W., and Zhang, Y.: Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 2. Key weather elements and emissions, Sci. Total Environ., 824, 153847, https://doi.org/10.1016/j.scitotenv.2022.153847, 2022.
Han, H., Liu, J., Shu, L., Wang, T., and Yuan, H.: Local and synoptic meteorological influences on daily variability in summertime surface ozone in eastern China, Atmos. Chem. Phys., 20, 203–222, https://doi.org/10.5194/acp-20-203-2020, 2020.
He, Z., Wang, X., Ling, Z., Zhao, J., Guo, H., Shao, M., and Wang, Z.: Contributions of different anthropogenic volatile organic compound sources to ozone formation at a receptor site in the Pearl River Delta region and its policy implications, Atmos. Chem. Phys., 19, 8801–8816, https://doi.org/10.5194/acp-19-8801-2019, 2019.
HKEPD: Hong Kong Air Quality Data, Hong Kong Environmental Protection Department [data set], https://cd.epic.epd.gov.hk/EPICDI/air/station/?lang=en, last access: 24 December 2023.
Kwok, R. H. F., Baker, K. R., Napelenok, S. L., and Tonnesen, G. S.: Photochemical grid model implementation and application of VOC, NOx, and O3 source apportionment, Geosci. Model Dev., 8, 99–114, https://doi.org/10.5194/gmd-8-99-2015, 2015.
Kwon, I.-H., English, S., Bell, W., Potthast, R., Collard, A., and Ruston, B.: Assessment of Progress and Status of Data Assimilation in Numerical Weather Prediction, B. Am. Meteorol. Soc., 99, ES75–ES79, https://doi.org/10.1175/bams-d-17-0266.1, 2018.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwx150, 2017.
Li, M., Zhang, Q., Zheng, B., Tong, D., Lei, Y., Liu, F., Hong, C., Kang, S., Yan, L., Zhang, Y., Bo, Y., Su, H., Cheng, Y., and He, K.: Persistent growth of anthropogenic non-methane volatile organic compound (NMVOC) emissions in China during 1990–2017: drivers, speciation and ozone formation potential, Atmos. Chem. Phys., 19, 8897–8913, https://doi.org/10.5194/acp-19-8897-2019, 2019.
Li, T., Chen, J., Weng, J., Shen, J., and Gong, Y.: Ozone pollution synoptic patterns and their variation characteristics in Guangdong Province, China Environmental Science, 42, 2015–2024, https://doi.org/10.19674/j.cnki.issn1000-6923.2022.0102, 2022 (in Chinese).
Li, Y., Lau, A. K. H., Fung, J. C. H., Zheng, J. Y., Zhong, L. J., and Louie, P. K. K.: Ozone source apportionment (OSAT) to differentiate local regional and super-regional source contributions in the Pearl River Delta region, China, J. Geophys. Res.-Atmos., 117, 1–18, https://doi.org/10.1029/2011jd017340, 2012.
Li, Y., Lau, A. K. H., Fung, J. C. H., Ma, H., and Tse, Y.: Systematic evaluation of ozone control policies using an Ozone Source Apportionment method, Atmos. Environ., 76, 136–146, https://doi.org/10.1016/j.atmosenv.2013.02.033, 2013.
Li, Y., Zhao, X., Deng, X., and Gao, J.: The impact of peripheral circulation characteristics of typhoon on sustained ozone episodes over the Pearl River Delta region, China, Atmos. Chem. Phys., 22, 3861–3873, https://doi.org/10.5194/acp-22-3861-2022, 2022.
Lin, X., Yuan, Z., Yang, L., Luo, H., and Li, W.: Impact of Extreme Meteorological Events on Ozone in the Pearl River Delta, China, Aerosol Air Qual. Res., 19, 1307–1324, https://doi.org/10.4209/aaqr.2019.01.0027, 2019.
Liu, H., Zhang, M., and Han, X.: A review of surface ozone source apportionment in China, Atmospheric and Oceanic Science Letters, 13, 470–484, https://doi.org/10.1080/16742834.2020.1768025, 2020.
Liu, Y. and Wang, T.: Worsening urban ozone pollution in China from 2013 to 2017 – Part 1: The complex and varying roles of meteorology, Atmos. Chem. Phys., 20, 6305–6321, https://doi.org/10.5194/acp-20-6305-2020, 2020.
Liu, Y., Geng, G., Cheng, J., Liu, Y., Xiao, Q., Liu, L., Shi, Q., Tong, D., He, K., and Zhang, Q.: Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013–2020, Environ. Sci. Technol., 57, 8954–8964, https://doi.org/10.1021/acs.est.3c00054, 2023.
Liu, Z., Deng, Z., He, G., Wang, H., Zhang, X., Lin, J., Qi, Y., and Liang, X.: Challenges and opportunities for carbon neutrality in China, Nature Reviews Earth & Environment, 3, 141–155, https://doi.org/10.1038/s43017-021-00244-x, 2021.
Lu, X., Yao, T., Li, Y., Fung, J. C. H., and Lau, A. K. H.: Source apportionment and health effect of NOx over the Pearl River Delta region in southern China, Environ. Pollut., 212, 135–146, https://doi.org/10.1016/j.envpol.2016.01.056, 2016.
Lu, X., Zhang, L., and Shen, L.: Meteorology and Climate Influences on Tropospheric Ozone: a Review of Natural Sources, Chemistry, and Transport Patterns, Current Pollution Reports, 5, 238–260, https://doi.org/10.1007/s40726-019-00118-3, 2019.
Maji, K. J., Ye, W.-F., Arora, M., and Nagendra, S. M. S.: Ozone pollution in Chinese cities: Assessment of seasonal variation, health effects and economic burden, Environ. Pollut., 247, 792–801, https://doi.org/10.1016/j.envpol.2019.01.049, 2019.
Ouyang, S., Deng, T., Liu, R., Chen, J., He, G., Leung, J. C.-H., Wang, N., and Liu, S. C.: Impact of a subtropical high and a typhoon on a severe ozone pollution episode in the Pearl River Delta, China, Atmos. Chem. Phys., 22, 10751–10767, https://doi.org/10.5194/acp-22-10751-2022, 2022.
Qu, K., Wang, X., Yan, Y., Shen, J., Xiao, T., Dong, H., Zeng, L., and Zhang, Y.: A comparative study to reveal the influence of typhoons on the transport, production and accumulation of O3 in the Pearl River Delta, China, Atmos. Chem. Phys., 21, 11593–11612, https://doi.org/10.5194/acp-21-11593-2021, 2021.
Sahu, S. K., Liu, S., Liu, S., Ding, D., and Xing, J.: Ozone pollution in China: Background and transboundary contributions to ozone concentration & related health effects across the country, Sci. Total Environ., 761, https://doi.org/10.1016/j.scitotenv.2020.144131, 2021.
Scher, S. and Messori, G.: Predicting weather forecast uncertainty with machine learning, Q. J. Roy. Meteor. Soc., 144, 2830–2841, https://doi.org/10.1002/qj.3410, 2018.
US EPA: Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina, USA, EPA-454/B-07-002, 262 pp., 2007.
Wang, N., Huang, X., Xu, J., Wang, T., Tan, Z.-m., and Ding, A.: Typhoon-boosted biogenic emission aggravates cross-regional ozone pollution in China, Science Advances, 8, eabl6166, https://doi.org/10.1126/sciadv.abl6166, 2022.
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, https://doi.org/10.1016/j.scitotenv.2016.10.081, 2017.
Wang, T., Xue, L., Feng, Z., Dai, J., Zhang, Y., and Tan, Y.: Ground-level ozone pollution in China: a synthesis of recent findings on influencing factors and impacts, Environ. Res. Lett., 17, 063003, https://doi.org/10.1088/1748-9326/ac69fe, 2022.
Wang, Y., Wild, O., Ashworth, K., Chen, X., Wu, Q., Qi, Y., and Wang, Z.: Reductions in crop yields across China from elevated ozone, Environ. Pollut., 292, 118218, https://doi.org/10.1016/j.envpol.2021.118218, 2022.
Wang, W., Parrish, D. D., Wang, S., Bao, F., Ni, R., Li, X., Yang, S., Wang, H., Cheng, Y., and Su, H.: Long-term trend of ozone pollution in China during 2014–2020: distinct seasonal and spatial characteristics and ozone sensitivity, Atmos. Chem. Phys., 22, 8935–8949, https://doi.org/10.5194/acp-22-8935-2022, 2022.
Wu, Y., Chen, W., You, Y., Xie, Q., Jia, S., and Wang, X.: Quantitative impacts of vertical transport on the long-term trend of nocturnal ozone increase over the Pearl River Delta region during 2006–2019, Atmos. Chem. Phys., 23, 453–469, https://doi.org/10.5194/acp-23-453-2023, 2023.
Xie, X., Shi, Z., Ying, Q., Zhang, H., and Hu, J.: Age-Resolved Source and Region Contributions to Fine Particulate Matter During an Extreme Haze Episode in China, Geophys. Res. Lett., 48, e2021GL095388, https://doi.org/10.1029/2021gl095388, 2021.
Xie, X., Hu, J., Qin, M., Guo, S., Hu, M., Ji, D., Wang, H., Lou, S., Huang, C., Liu, C., Zhang, H., Ying, Q., Liao, H., and Zhang, Y.: Evolution of atmospheric age of particles and its implications for the formation of a severe haze event in eastern China, Atmos. Chem. Phys., 23, 10563–10578, https://doi.org/10.5194/acp-23-10563-2023, 2023.
Xu, J., Zhao, Z., Wu, Y., Zhang, Y., Wang, Y., Su, B., Liang, Y., Hu, T., and Liu, R.: Impacts of Meteorological Conditions on Autumn Surface Ozone During 2014–2020 in the Pearl River Delta, China, Earth Space Sci., 10, e2022EA002742, https://doi.org/10.1029/2022ea002742, 2023.
Xu, Y., Shen, A., Jin, Y., Liu, Y., Lu, X., Fan, S., Hong, Y., and Fan, Q.: A quantitative assessment and process analysis of the contribution from meteorological conditions in an O3 pollution episode in Guangzhou, China, Atmos. Environ., 303, 119757, https://doi.org/10.1016/j.atmosenv.2023.119757, 2023.
Yang, J. and Zhao, Y.: Performance and application of air quality models on ozone simulation in China – A review, Atmos. Environ., 293, 119446, https://doi.org/10.1016/j.atmosenv.2022.119446, 2023.
Yang, L., Luo, H., Yuan, Z., Zheng, J., Huang, Z., Li, C., Lin, X., Louie, P. K. K., Chen, D., and Bian, Y.: Quantitative impacts of meteorology and precursor emission changes on the long-term trend of ambient ozone over the Pearl River Delta, China, and implications for ozone control strategy, Atmos. Chem. Phys., 19, 12901–12916, https://doi.org/10.5194/acp-19-12901-2019, 2019.
Yang, W., Chen, H., Wang, W., Wu, J., Li, J., Wang, Z., Zheng, J., and Chen, D.: Modeling study of ozone source apportionment over the Pearl River Delta in 2015, Environ. Pollut., 253, 393–402, https://doi.org/10.1016/j.envpol.2019.06.091, 2019.
Yin, P., Chen, R., Wang, L., Meng, X., Liu, C., Niu, Y., Lin, Z., Liu, Y., Liu, J., Qi, J., You, J., Zhou, M., and Kan, H.: Ambient Ozone Pollution and Daily Mortality: A Nationwide Study in 272 Chinese Cities, Environ. Health Persp., 125, https://doi.org/10.1289/ehp1849, 2017.
Ying, Q., Zhang, J., Zhang, H., Hu, J., and Kleeman, M. J.: Atmospheric Age Distribution of Primary and Secondary Inorganic Aerosols in a Polluted Atmosphere, Environ. Sci. Technol., 55, 5668–5676, https://doi.org/10.1021/acs.est.0c07334, 2021.
Zeren, Y., Zhou, B., Zheng, Y., Jiang, F., Lyu, X., Xue, L., Wang, H., Liu, X., and Guo, H.: Does Ozone Pollution Share the Same Formation Mechanisms in the Bay Areas of China?, Environ. Sci. Technol., 56, 14326–14337, https://doi.org/10.1021/acs.est.2c05126, 2022.
Zhan, C., Xie, M., Huang, C., Liu, J., Wang, T., Xu, M., Ma, C., Yu, J., Jiao, Y., Li, M., Li, S., Zhuang, B., Zhao, M., and Nie, D.: Ozone affected by a succession of four landfall typhoons in the Yangtze River Delta, China: major processes and health impacts, Atmos. Chem. Phys., 20, 13781–13799, https://doi.org/10.5194/acp-20-13781-2020, 2020.
Zhang, R. and Hanaoka, T.: Deployment of electric vehicles in China to meet the carbon neutral target by 2060: Provincial disparities in energy systems, CO2 emissions, and cost effectiveness, Resour. Conserv. Recy., 170, 105622, https://doi.org/10.1016/j.resconrec.2021.105622, 2021..
Zheng, H., Kong, S., He, Y., Song, C., Cheng, Y., Yao, L., Chen, N., and Zhu, B.: Enhanced ozone pollution in the summer of 2022 in China: The roles of meteorology and emission variations, Atmos. Environ., 301, 119701, https://doi.org/10.1016/j.atmosenv.2023.119701, 2023.
Short summary
This study investigates the contribution of pollutants from different emitting periods to ozone episodes over the Greater Bay Area. The analysis reveals the variation in major spatiotemporal contributors to the O3 pollution under the influence of typhoons and subtropical high pressure. Through temporal contribution analysis, our work offers a new perspective on the evolution of O3 pollution and can aid in developing effective and timely control policies under unfavorable weather conditions.
This study investigates the contribution of pollutants from different emitting periods to ozone...
Altmetrics
Final-revised paper
Preprint