Abstract
Over the past few years, surface ozone (O3) pollution has dominated China’s air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O3 from 2015 to 2020 regularly increased from 57.32 to 62.72 μg/m3, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O3 levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O3. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO2 stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O3 levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R2) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O3 concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O3 guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O3 was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O3 and highlights the importance of future research toward devising an optimum strategy to combat growing O3 pollution in urban areas.
Graphical Abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The authors confirm that the data supporting the findings of this study are available within the article and extra information can be obtained by emailing the corresponding author, upon reasonable request.
References
Akritidis D, Zanis P, Georgoulias AK, Papakosta E, Tzoumaka P, Kelessis A (2021) Implications of COVID-19 restriction measures in urban air quality of Thessaloniki, Greece: a machine learning approach. Atmosphere 12(11):1500. https://doi.org/10.3390/atmos12111500
Amir Siddique M, Wang Y, Xu N, Ullah N, Zeng P (2021) The spatiotemporal implications of urbanization for urban heat islands in Beijing: a predictive approach based on CA–Markov modeling (2004–2050). Remote Sens 13(22):4697. https://doi.org/10.3390/rs13224697
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Chen S, Wang H, Lu K, Zeng L, Hu M, Zhang Y (2020) The trend of surface ozone in Beijing from 2013 to 2019: indications of the persisting strong atmospheric oxidation capacity. Atmos Environ 242:117801. https://doi.org/10.1016/j.atmosenv.2020.117801
Chen Z, Zhuang Y, Xie X, Chen D, Cheng N, Yang L, Li R (2019) Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing during 2006–2016. Environ Pollut 245:29–37. https://doi.org/10.1016/j.envpol.2018.10.117
Crilley LR, Iranpour YE, Young CJ (2021) Importance of meteorology and chemistry in determining air pollutant levels during COVID-19 lockdown in Indian cities. Environ Sci: Process Impacts 23(11):1718–1728. https://doi.org/10.1039/d1em00187f
Dantas G, Siciliano B, Franca BB, da Silva CM, Arbilla G (2020) The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil. Sci Total Environ 729:139085. https://doi.org/10.1016/j.scitotenv.2020.139085
Feng Y, Ning M, Lei Y, Sun Y, Liu W, Wang J (2019) Defending blue sky in China: effectiveness of the “Air Pollution Prevention and Control Action Plan” on air quality improvements from 2013 to 2017. J Environ Manage 252:109603. https://doi.org/10.1016/j.jenvman.2019.109603
Grange SK, Lee JD, Drysdale WS, Lewis AC, Hueglin C, Emmenegger L, Carslaw DC (2021) COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas. Atmos Chem Phys 21(5):4169–4185. https://doi.org/10.5194/acp-21-4169-2021
Guo B, Wu H, Pei L, Zhu X, Zhang D, Wang Y, Luo P (2022) Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign. Environ Int 170:107606. https://doi.org/10.1016/j.envint.2022.107606
Hassan MA, Dong Z (2018) Analysis of tropospheric ozone by artificial neural network approach in Beijing. J Geosci Environ Prot 06(11):8–17. https://doi.org/10.4236/gep.2018.611002
Hu C, Kang P, Jaffe DA, Li C, Zhang X, Wu K, Zhou M (2021a) Understanding the impact of meteorology on ozone in 334 cities of China. Atmos Environ 248:118221. https://doi.org/10.1016/j.atmosenv.2021.118221
Hu JB, Pan YP, He YX, Chi XY, Zhang QQ, Song T, Shen WS (2021b) Changes in air pollutants during the COVID-19 lockdown in Beijing: insights from a machine-learning technique and implications for future control policy. Atmos Ocean Sci Lett 14(4):100060. https://doi.org/10.1016/j.aosl.2021.100060
Huang X et al (2021) Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl Sci Rev 8(2):nwaa137. https://doi.org/10.1093/nsr/nwaa137
Jiang SY, Zhao CF, Fan H (2021) Toward understanding the variation of air quality based on a comprehensive analysis in Hebei Province under the influence of COVID-19 lockdown. Atmosphere 12(2):267. https://doi.org/10.3390/atmos12020267
Ju MJ, Oh J, Choi YH (2021) Changes in air pollution levels after COVID-19 outbreak in Korea. Sci Total Environ 750:141521. https://doi.org/10.1016/j.scitotenv.2020.141521
Keller CA, Evans MJ, Knowland KE, Hasenkopf CA, Modekurty S, Lucchesi RA, Oda T, Franca BB, Mandarino FC, Díaz Suárez MV, Ryan RG, Fakes LH, Pawson S (2021) Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone. Atmos Chem Phys 21(5):3555–3592. https://doi.org/10.5194/acp-21-3555-2021
Kroll JH, Heald CL, Cappa CD, Farmer DK, Fry JL, Murphy JG, Steiner AL (2020) The complex chemical effects of COVID-19 shutdowns on air quality. Nat Chem 12(9):777–779. https://doi.org/10.1038/s41557-020-0535-z
Kumari P, Toshniwal D (2020) Impact of lockdown on air quality over major cities across the globe during COVID-19 pandemic. Urban Clim 34:100719. https://doi.org/10.1016/j.uclim.2020.100719
Le T, Wang Y, Liu L, Yang J, Yung YL, Li G, Seinfeld JH (2020) Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369(6504):702–706. https://doi.org/10.1126/science.abb7431
Li K, Jacob DJ, Liao H, Qiu Y, Shen L, Zhai S, Bates KH, Sulprizio MP, Song S, Lu X, Zhang Q, Zheng B, Zhang Y, Zhang J, Lee HC, Kuk SK (2021) Ozone pollution in the North China Plain spreading into the late-winter haze season. Proc Natl Acad Sci U S A 118(10):e2015797118. https://doi.org/10.1073/pnas.2015797118
Li L et al (2020) Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: an insight into the impact of human activity pattern changes on air pollution variation. Sci Total Environ 732:139282. https://doi.org/10.1016/j.scitotenv.2020.139282
Li X, Chen W, Zhang H, Xue T, Zhong Y, Qi M, Shen X, Yao Z (2022) Emissions of biogenic volatile organic compounds from urban green spaces in the six core districts of Beijing based on a new satellite dataset. Environ Pollut 308:119672. https://doi.org/10.1016/j.envpol.2022.119672
Lian X, Huang J, Huang R, Liu C, Wang L, Zhang T (2020) Impact of city lockdown on the air quality of COVID-19-hit of Wuhan City. Sci Total Environ 742:140556. https://doi.org/10.1016/j.scitotenv.2020.140556
Lovric M, Pavlovic K, Vukovic M, Grange SK, Haberl M, Kern R (2021) Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning. Environ Pollut 274:115900. https://doi.org/10.1016/j.envpol.2020.115900
Lu X, Hong J, Zhang L, Cooper OR, Schultz MG, Xu X, Wang T, Gao M, Zhao Y, Zhang Y (2018) Severe surface ozone pollution in China: a global perspective. Environ Sci Technol Lett 5(8):487–494. https://doi.org/10.1021/acs.estlett.8b00366
Lyu Y, Ju Q, Lv F, Feng J, Pang X, Li X (2022) Spatiotemporal variations of air pollutants and ozone prediction using machine learning algorithms in the Beijing-Tianjin-Hebei region from 2014 to 2021. Environ Pollut 306:119420. https://doi.org/10.1016/j.envpol.2022.119420
Ma R, Ban J, Wang Q, Zhang Y, Yang Y, He MZ, Li S, Shi W, Li T (2021) Random forest model based fine scale spatiotemporal O(3) trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017. Environ Pollut 276:116635. https://doi.org/10.1016/j.envpol.2021.116635
Mahato S, Pal S, Ghosh KG (2020) Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci Total Environ 730:139086. https://doi.org/10.1016/j.scitotenv.2020.139086
Meng X, Jiang J, Chen T, Zhang Z, Lu B, Liu C, Xue L, Chen J, Herrmann H, Li X (2023) Chemical drivers of ozone change in extreme temperatures in eastern China. Sci Total Environ 874:162424. https://doi.org/10.1016/j.scitotenv.2023.162424
Munoz Sabater J (2019) Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.e2161bac
Nakada LYK, Urban RC (2020) COVID-19 pandemic: impacts on the air quality during the partial lockdown in Sao Paulo state, Brazil. Sci Total Environ 730:139087. https://doi.org/10.1016/j.scitotenv.2020.139087
Pei Z, Han G, Ma X, Su H, Gong W (2020) Response of major air pollutants to COVID-19 lockdowns in China. Sci Total Environ 743:140879. https://doi.org/10.1016/j.scitotenv.2020.140879
Qiu M, Zigler C, Selin NE (2022) Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions. Atmos Chem Phys 22(16):10551–10566. https://doi.org/10.5194/acp-22-10551-2022
Sadeghi B, Ghahremanloo M, Mousavinezhad S, Lops Y, Pouyaei A, Choi Y (2022) Contributions of meteorology to ozone variations: application of deep learning and the Kolmogorov-Zurbenko filter. Environ Pollut 310:119863. https://doi.org/10.1016/j.envpol.2022.119863
Shi X, Brasseur GP (2020) The response in air quality to the reduction of Chinese economic activities during the COVID-19 outbreak. Geophys Res Lett 47(11):e2020GL088070. https://doi.org/10.1029/2020GL088070
Shi Z, Song C, Liu B, Lu G, Xu J, Van Vu T, Elliott RJR, Li W, Bloss WJ, Harrison RM (2021) Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Sci Adv 7(3):eabd6696. https://doi.org/10.1126/sciadv.abd6696
Sicard P, De Marco A, Agathokleous E, Feng Z, Xu X, Paoletti E, Rodriguez JJD, Calatayud V (2020) Amplified ozone pollution in cities during the COVID-19 lockdown. Sci Total Environ 735:139542. https://doi.org/10.1016/j.scitotenv.2020.139542
Tian JQ, Fang CS, Qiu JX, Wang J (2021) Analysis of ozone pollution characteristics and influencing factors in Northeast Economic Cooperation Region, China. Atmosphere 12(7):843. https://doi.org/10.3390/atmos12070843
Tobias A, Carnerero C, Reche C, Massague J, Via M, Minguillon MC, Alastuey A, Querol X (2020) Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Sci Total Environ 726:138540. https://doi.org/10.1016/j.scitotenv.2020.138540
Toh YY, Lim SF, von Glasow R (2013) The influence of meteorological factors and biomass burning on surface ozone concentrations at Tanah Rata, Malaysia. Atmos Environ 70:435–446. https://doi.org/10.1016/j.atmosenv.2013.01.018
Wang L, Zhao B, Zhang Y, Hu H (2023) Correlation between surface PM2.5 and O3 in eastern China during 2015–2019: spatiotemporal variations and meteorological impacts. Atmos Environ 294:119520. https://doi.org/10.1016/j.atmosenv.2022.119520
Wang N, Lyu X, Deng X, Huang X, Jiang F, Ding A (2019) Aggravating O(3) pollution due to NO(x) emission control in eastern China. Sci Total Environ 677:732–744. https://doi.org/10.1016/j.scitotenv.2019.04.388
Wang T, Xue L, Brimblecombe P, Lam YF, Li L, Zhang L (2017) 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
Wang W, Primbs T, Tao S, Simonich SL (2009) Atmospheric particulate matter pollution during the 2008 Beijing Olympics. Environ Sci Technol 43(14):5314–5320. https://doi.org/10.1021/es9007504
Wang YJ, Wen YF, Wang Y, Zhang SJ, Zhang KM, Zheng HT, Xing J, Wu Y, Hao JM (2020a) Four-month changes in air quality during and after the COVID-19 lockdown in six megacities in China. Environ Sci Technol Lett 7(11):802–808. https://doi.org/10.1021/acs.estlett.0c00605
Wang ZB, Li JX, Liang LW (2020b) Spatio-temporal evolution of ozone pollution and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration. Environ Pollut 256:113419. https://doi.org/10.1016/j.envpol.2019.113419
Wu J, Guo S, Huang H, Liu W, Xiang Y (2018) Information and communications technologies for sustainable development goals: state-of-the-art, needs and perspectives. IEEE Commun Surv Tutorials 20(3):2389–2406. https://doi.org/10.1109/COMST.2018.2812301
Wu XY, Xin JY, Zhang WY, Gao WK, Ma YN, Ma YJ, Wen TX, Liu ZR, Hu B, Wang YS, Wang LL (2022) Variation characteristics of air combined pollution in Beijing City. Atmos Res 274:106197. https://doi.org/10.1016/j.atmosres.2022.106197
Xiao C, Chang M, Guo P, Gu M, Li Y (2020) Analysis of air quality characteristics of Beijing-Tianjin-Hebei and its surrounding air pollution transport channel cities in China. J Environ Sci (China) 87:213–227. https://doi.org/10.1016/j.jes.2019.05.024
Yang JY, Zhao Y (2023) 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
Yang X, Zeng G, Iyakaremye V, Zhu B (2022) Effects of different types of heat wave days on ozone pollution over Beijing-Tianjin-Hebei and its future projection. Sci Total Environ 837:155762. https://doi.org/10.1016/j.scitotenv.2022.155762
Yin H, Lu X, Sun YW, Li K, Gao M, Zheng B, Liu C (2021) Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology. Environ Res Lett 16(12):124069. https://doi.org/10.1088/1748-9326/ac3e22
Zangari S, Hill DT, Charette AT, Mirowsky JE (2020) Air quality changes in New York City during the COVID-19 pandemic. Sci Total Environ 742:140496. https://doi.org/10.1016/j.scitotenv.2020.140496
Zhang ZN, Man HY, Duan FK, Lv ZF, Zheng SX, Zhao JC, Huang FF, Luo ZY, He KB, Liu H (2022) Evaluation of the VOC pollution pattern and emission characteristics during the Beijing resurgence of COVID-19 in summer 2020 based on the measurement of PTR-ToF-MS. Environ Res Lett 17(2):024002. https://doi.org/10.1088/1748-9326/ac3e99
Zhao N, Elshareef H, Li B, Wang B, Jia Z, Zhou L, Liu Y, Sultan M, Dong R, Zhou Y (2022) The efforts of China to combat air pollution during the period of 2015–2018: a case study assessing the environmental, health and economic benefits in the Beijing-Tianjin-Hebei and surrounding “2 + 26” regions. Sci Total Environ 853:158437. https://doi.org/10.1016/j.scitotenv.2022.158437
Zhao YB, Zhang K, Xu XT, Shen HZ, Zhu X, Zhang YX, Hu YT, Shen GF (2020) Substantial changes in nitrogen dioxide and ozone after excluding meteorological impacts during the COVID-19 outbreak in mainland China. Environ Sci Technol Lett 7(6):402–408. https://doi.org/10.1021/acs.estlett.0c00304
Acknowledgements
The authors would like to thank ECMWF for their efforts and data availability.
Funding
This research was supported by Nature Science Foundation of Tianjin, grant number (21JCZDJC00560). Author Junjie Liu has received research support from Nature Science Foundation of Tianjin.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design.
Muhammad Azher Hassan: conceptualization; data curation; formal analysis; methodology; visualization; writing — original draft; writing — review and editing.
Muhammad Faheem: methodology; writing — review and editing.
Tariq Mehmood: formal analysis; writing — review and editing.
Yihui Yin: writing — review and editing.
Junjie Liu: conceptualization; visualization; writing — review and editing.
Corresponding author
Ethics declarations
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Marcus Schulz
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hassan, M.A., Faheem, M., Mehmood, T. et al. Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach. Environ Sci Pollut Res 30, 104086–104099 (2023). https://doi.org/10.1007/s11356-023-29665-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-29665-5