Articles | Volume 26, issue 15
https://doi.org/10.5194/acp-26-10789-2026
© 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-26-10789-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Aug 2026
Research article |
| 03 Aug 2026
| 03 Aug 2026
Direct thermal enhancement dominates over emission-mediated pathways in heatwave-induced O3 and SOA increases across China
Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
Institute of Eco-Chongming (IEC), Shanghai 202151, China
Shanghai Key Laboratory of Ocean-land-atmosphere Boundary Dynamics and Climate Change, Shanghai 200438, China
Wenxuan Yu
Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
Zhaolei Zhang
Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
Jianyan Lu
Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction, Shanghai 200438, China
Shenxin Li
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Institute of Eco-Chongming (IEC), Shanghai 202151, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
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Cited articles
An, N., Chen, Y., Zhai, P., Li, J., and Wei, Y.: Compound hot and ozone extremes in urban China, Urban Climate, 52, 101689, https://doi.org/10.1016/j.uclim.2023.101689, 2023.
An, Z., Huang, R. J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z., Han, Y., Gu, Z., and Ji, Y.: Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes, P. Natl. Acad. Sci. USA, 116, 8657–8666, https://doi.org/10.1073/pnas.1900125116, 2019.
Bai, Y., Zhao, T., Hu, W., Zhou, Y., Xiong, J., Wang, Y., Liu, L., Shen, L., Kong, S., Meng, K., and Zheng, H.: Meteorological mechanism of regional PM2.5 transport building a receptor region for heavy air pollution over Central China, Sci. Total Environ., 808, 151951, https://doi.org/10.1016/j.scitotenv.2021.151951, 2022.
Byun, D. and Schere, K. L.: Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Cao, J., Situ, S., Hao, Y., Xie, S., and Li, L.: Enhanced summertime ozone and SOA from biogenic volatile organic compound (BVOC) emissions due to vegetation biomass variability during 1981–2018 in China, Atmos. Chem. Phys., 22, 2351–2364, https://doi.org/10.5194/acp-22-2351-2022, 2022.
Chang, K.-H., Yu, J.-Y., Chen, T.-F., and Lin, Y.-P.: Estimating Taiwan biogenic VOC emission: Leaf energy balance consideration, Atmos. Environ., 43, 5092—5100, https://doi.org/10.1016/j.atmosenv.2009.06.038, 2009.
Chen, T., Wang, T., Xue, L., and Brasseur, G.: Heatwave exacerbates air pollution in China through intertwined climate-energy-environment interactions, Sci. Bull., 69, 2765–2775, https://doi.org/10.1016/j.scib.2024.05.018, 2024.
Clinton, N. and Gong, P.: MODIS detected surface urban heat islands and sinks: Global locations and controls, Remote Sens. Environ., 134, 294–304, https://doi.org/10.1016/j.rse.2013.03.008, 2013.
Coates, J., Mar, K. A., Ojha, N., and Butler, T. M.: The influence of temperature on ozone production under varying NOx conditions – a modelling study, Atmos. Chem. Phys., 16, 11601–11615, https://doi.org/10.5194/acp-16-11601-2016, 2016.
Conti, S., Meli, P., Minelli, G., Solimini, R., Toccaceli, V., Vichi, M., Beltrano, C., and Perini, L.: Epidemiologic study of mortality during the Summer 2003 heat wave in Italy, Environ. Res., 98, 390–399, https://doi.org/10.1016/j.envres.2004.10.009, 2005.
Delcloo, A., Duchêne, F., Hamdi, R., Berckmans, J., Deckmyn, A., and Termonia, P.: The impact of heat waves and urban heat island on the production of ozone concentrations under present and future climate conditions for the Belgian domain, Air Pollution Modeling and its Application XXV, 35, 189–193, 2018.
Ding, H., Cao, L., Jiang, H., Jia, W., Chen, Y., and An, J.: Influence on the temperature estimation of the planetary boundary layer scheme with different minimum eddy diffusivity in WRF v3.9.1.1, Geosci. Model Dev., 14, 6135–6153, https://doi.org/10.5194/gmd-14-6135-2021, 2021.
Donahue, N. M., Robinson, A. L., Stanier, C. O., and Pandis, S. N.: Coupled Partitioning, Dilution, and Chemical Aging of Semivolatile Organics, Environ. Sci. Technol., 40, 2635–2643, https://doi.org/10.1021/es052297c, 2006.
Duan, J., Huang, R.-J., Li, Y., Chen, Q., Zheng, Y., Chen, Y., Lin, C., Ni, H., Wang, M., Ovadnevaite, J., Ceburnis, D., Chen, C., Worsnop, D. R., Hoffmann, T., O'Dowd, C., and Cao, J.: Summertime and wintertime atmospheric processes of secondary aerosol in Beijing, Atmos. Chem. Phys., 20, 3793–3807, https://doi.org/10.5194/acp-20-3793-2020, 2020.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar, N.: Recommendations on statistics and benchmarks to assess photochemical model performance, J. Air Waste Manage., 67, 582–598, https://doi.org/10.1080/10962247.2016.1265027, 2017.
Eremenko, M., Dufour, G., Foret, G., Keim, C., Orphal, J., Beekmann, M., Bergametti, G., and Flaud, J. M.: Tropospheric ozone distributions over Europe during the heat wave in July 2007 observed from infrared nadir spectra recorded by IASI, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL034803, 2008.
Filleul, L., Cassadou, S., Médina, S., Fabres, P., Lefranc, A., Eilstein, D., Le Tertre, A., Pascal, L., Chardon, B., Blanchard, M., Declercq, C., Jusot, J.-F., Prouvost, H., and Ledrans, M.: The Relation Between Temperature, Ozone, and Mortality in Nine French Cities During the Heat Wave of 2003, Environ. Health Persp., 114, 1344–1347, https://doi.org/10.1289/ehp.8328, 2006.
García-García, A., Cuesta-Valero, F. J., Miralles, D. G., Mahecha, M. D., Quaas, J., Reichstein, M., Zscheischler, J., and Peng, J.: Soil heat extremes can outpace air temperature extremes, Nat. Clim. Change, 13, 1237–1241, https://doi.org/10.1038/s41558-023-01812-3, 2023.
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.
Hu, F. and Guo, Y.: Health impacts of air pollution in China, Front. Env. Sci. Eng., 15, 74, https://doi.org/10.1007/s11783-020-1367-1, 2020.
Huang, J., Pan, X., Guo, X., and Li, G.: Health impact of China's Air Pollution Prevention and Control Action Plan: an analysis of national air quality monitoring and mortality data, Lancet – Planetary Health, 2, e313–e323, https://doi.org/10.1016/s2542-5196(18)30141-4, 2018.
Huang, L., Fang, J., Liao, J., Yarwood, G., Chen, H., Wang, Y., and Li, L.: Insights into soil NO emissions and the contribution to surface ozone formation in China, Atmos. Chem. Phys., 23, 14919–14932, https://doi.org/10.5194/acp-23-14919-2023, 2023.
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., Haddad, I. E., and Prévôt, 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.
Huang, X., Ding, A., Wang, Z., Ding, K., Gao, J., Chai, F., and Fu, C.: Amplified transboundary transport of haze by aerosol–boundary layer interaction in China, Nat. Geosci., 13, 428–434, https://doi.org/10.1038/s41561-020-0583-4, 2020.
Huang, X., Tang, G., Zhang, J., Liu, B., Liu, C., Zhang, J., Cong, L., Cheng, M., Yan, G., Gao, W., Wang, Y., and Wang, Y.: Characteristics of PM2.5 pollution in Beijing after the improvement of air quality, J. Environ. Sci., 100, 1–10, https://doi.org/10.1016/j.jes.2020.06.004, 2021.
Ji, X., Chen, G., Chen, J., Xu, L., Lin, Z., Zhang, K., Fan, X., Li, M., Zhang, F., Wang, H., Huang, Z., and Hong, Y.: Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022, Sci. Total Environ., 914, 170035, https://doi.org/10.1016/j.scitotenv.2024.170035, 2024.
Li, D., Shindell, D., Ding, D., Lu, X., Zhang, L., and Zhang, Y.: Surface ozone impacts on major crop production in China from 2010 to 2017, Atmos. Chem. Phys., 22, 2625–2638, https://doi.org/10.5194/acp-22-2625-2022, 2022.
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, M., Huang, X., Yan, D., Lai, S., Zhang, Z., Zhu, L., Lu, Y., Jiang, X., Wang, N., Wang, T., Song, Y., and Ding, A.: Coping with the concurrent heatwaves and ozone extremes in China under a warming climate, Sci. Bull., https://doi.org/10.1016/j.scib.2024.05.034, 2024.
Li, Y., Qin, M., Hu, W., Zhao, B., Li, Y., Pye, H. O. T., Li, J., Zeng, L., Guo, S., Hu, M., and Hu, J.: Evaluating simulations of organic aerosol volatility and degree of oxygenation in eastern China , Atmos. Chem. Phys., 26, 1001–1020, https://doi.org/10.5194/acp-26-1001-2026, 2026.
Lin, H., Ding, K., Huang, X., Lou, S., Xue, L., Wang, Z., Ma, Y., and Ding, A.: Impacts of Northward Typhoons on Autumn Haze Pollution Over North China Plain, J. Geophys. Res.-Atmos., 129, e2023JD040465, https://doi.org/10.1029/2023JD040465, 2024.
Liu, T., Hong, Y., Li, M., Xu, L., Chen, J., Bian, Y., Yang, C., Dan, Y., Zhang, Y., Xue, L., Zhao, M., Huang, Z., and Wang, H.: Atmospheric oxidation capacity and ozone pollution mechanism in a coastal city of southeastern China: analysis of a typical photochemical episode by an observation-based model, Atmos. Chem. Phys., 22, 2173–2190, https://doi.org/10.5194/acp-22-2173-2022, 2022.
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, Z., Wang, Y., Gu, D., Zhao, C., Huey, L. G., Stickel, R., Liao, J., Shao, M., Zhu, T., Zeng, L., Amoroso, A., Costabile, F., Chang, C.-C., and Liu, S.-C.: Summertime photochemistry during CAREBeijing-2007: ROx budgets and O3 formation, Atmos. Chem. Phys., 12, 7737–7752, https://doi.org/10.5194/acp-12-7737-2012, 2012.
Lu, R., Xu, K., Chen, R., Chen, W., Li, F., and Lv, C.: Heat waves in summer 2022 and increasing concern regarding heat waves in general, Atmos. Ocean. Sc. Lett., 16, 100290, https://doi.org/10.1016/j.aosl.2022.100290, 2023.
Luo, M. and Lau, N.-C.: Heat Waves in Southern China: Synoptic Behavior, Long-Term Change, and Urbanization Effects, Environ. Sci. Technol., 30, 703–720, https://doi.org/10.1175/JCLI-D-16-0269.1, 2017.
Lyu, X., Li, K., Guo, H., Morawska, L., Zhou, B., Zeren, Y., Jiang, F., Chen, C., Goldstein, A. H., Xu, X., Wang, T., Lu, X., Zhu, T., Querol, X., Chatani, S., Latif, M. T., Schuch, D., Sinha, V., Kumar, P., Mullins, B., Seguel, R., Shao, M., Xue, L., Wang, N., Chen, J., Gao, J., Chai, F., Simpson, I., Sinha, B., and Blake, D. R.: A synergistic ozone-climate control to address emerging ozone pollution challenges, One Earth, 6, 964–977, https://doi.org/10.1016/j.oneear.2023.07.004, 2023.
Ma, M., Gao, Y., Wang, Y., Zhang, S., Leung, L. R., Liu, C., Wang, S., Zhao, B., Chang, X., Su, H., Zhang, T., Sheng, L., Yao, X., and Gao, H.: Substantial ozone enhancement over the North China Plain from increased biogenic emissions due to heat waves and land cover in summer 2017, Atmos. Chem. Phys., 19, 12195–12207, https://doi.org/10.5194/acp-19-12195-2019, 2019.
Megaritis, A. G., Fountoukis, C., Charalampidis, P. E., Pilinis, C., and Pandis, S. N.: Response of fine particulate matter concentrations to changes of emissions and temperature in Europe, Atmos. Chem. Phys., 13, 3423–3443, https://doi.org/10.5194/acp-13-3423-2013, 2013.
Meng, X., Jiang, J., Chen, T., Zhang, Z., Lu, B., Liu, C., Xue, L., Chen, J., Herrmann, H., and Li, X.: 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, 2023.
Mousavinezhad, S., Choi, Y., Pouyaei, A., Ghahremanloo, M., and Nelson, D. L.: A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions, Atmos. Res., 257, 105599, https://doi.org/10.1016/j.atmosres.2021.105599, 2021.
Nasong, D., Zhou, S., Kornhuber, K., and Yu, B.: Concurrent Heat Extremes in Relation to Global Warming, High Atmospheric Pressure and Low Soil Moisture in the Northern Hemisphere, Earth's Future, 13, e2024EF005256, https://doi.org/10.1029/2024EF005256, 2025.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, updated daily, NSF National Center for Atmospheric Research [data set], https://doi.org/10.5065/D6M043C6, 2000.
Oh, H.-J., Chen, Y., and Kim, H.: Deposition of secondary organic aerosol in human lung model: Effect of photochemically aged aerosol on human respiratory system, Ecotox. Environ. Safe., 265, 115497, https://doi.org/10.1016/j.ecoenv.2023.115497, 2023.
Oikawa, P. Y., Ge, C., Wang, J., Eberwein, J. R., Liang, L. L., Allsman, L. A., Grantz, D. A., and Jenerette, G. D.: Unusually high soil nitrogen oxide emissions influence air quality in a high-temperature agricultural region, Nat. Commun., 6, 8753, https://doi.org/10.1038/ncomms9753, 2015.
Oke, T. R.: The energetic basis of the urban heat island, Q. J. Roy. Meteor. Soc., 108, 1–24, https://doi.org/10.1002/qj.49710845502, 1982.
Otero, N., Sillmann, J., Schnell, J. L., Rust, H. W., and Butler, T.: Synoptic and meteorological drivers of extreme ozone concentrations over Europe, Environ. Res. Lett., 11, 024005, https://doi.org/10.1088/1748-9326/11/2/024005, 2016.
Ou, J., Yuan, Z., Zheng, J., Huang, Z., Shao, M., Li, Z., Huang, X., Guo, H., and Louie, P. K. K.: Ambient Ozone Control in a Photochemically Active Region: Short-Term Despiking or Long-Term Attainment?, Environ. Sci. Technol., 50, 5720–5728, https://doi.org/10.1021/acs.est.6b00345, 2016.
Seco, R., Holst, T., Davie-Martin, C. L., Simin, T., Guenther, A., Pirk, N., Rinne, J., and Rinnan, R.: Strong isoprene emission response to temperature in tundra vegetation, P. Natl. Acad. Sci., 119, e2118014119, https://doi.org/10.1073/pnas.2118014119, 2022.
Shao, T., Wang, P., Yu, W., Gao, Y., Zhu, S., Zhang, Y., Hu, D., Zhang, B., and Zhang, H.: Drivers of alleviated PM2.5 and O3 concentrations in China from 2013 to 2020, Resour. Conserv. Recy., 197, 107110, https://doi.org/10.1016/j.resconrec.2023.107110, 2023.
Sillman, S.: The use of NOy, H2O2, and HNO3 as indicators for ozone-NOx-hydrocarbon sensitivity in urban locations, J. Geophys. Res.-Atmos., 100, 14175–14188, https://doi.org/10.1029/94JD02953, 1995.
Su, X., Yan, X., and Tsai, C.-L.: Linear regression, WIREs Computational Statistics, 4, 275–294, https://doi.org/10.1002/wics.1198, 2012.
Sun, Y., Zhang, X., Zwiers, F. W., Song, L., Wan, H., Hu, T., Yin, H., and Ren, G.: Rapid increase in the risk of extreme summer heat in Eastern China, Nat. Clim. Change, 4, 1082–1085, https://doi.org/10.1038/nclimate2410, 2014.
Tian, Y., Kleidon, A., Lesk, C., Zhou, S., Luo, X., Ghausi, S. A., Wang, G., Zhong, D., and Zscheischler, J.: Characterizing heatwaves based on land surface energy budget, Commun. Earth Environ., 5, 617, https://doi.org/10.1038/s43247-024-01784-y, 2024.
Wang, H., Wu, K., Liu, Y., Sheng, B., Lu, X., He, Y., Xie, J., Wang, H., and Fan, S.: Role of Heat Wave-Induced Biogenic VOC Enhancements in Persistent Ozone Episodes Formation in Pearl River Delta, J. Geophys. Res.-Atmos., 126, e2020JD034317, https://doi.org/10.1029/2020JD034317, 2021a.
Wang, J., Ye, J., Zhang, Q., Zhao, J., Wu, Y., Li, J., Liu, D., Li, W., Zhang, Y., Wu, C., Xie, C., Qin, Y., Lei, Y., Huang, X., Guo, J., Liu, P., Fu, P., Li, Y., Lee, H. C., Choi, H., Zhang, J., Liao, H., Chen, M., Sun, Y., Ge, X., Martin, S. T., and Jacob, D. J.: Aqueous production of secondary organic aerosol from fossil-fuel emissions in winter Beijing haze, P. Natl. Acad. Sci., 118, e2022179118, https://doi.org/10.1073/pnas.2022179118, 2021b.
Wang, J., Wang, D., Ge, B., Lin, W., Ji, D., Pan, X., Li, J., and Wang, Z.: Increase in daytime ozone exposure due to nighttime accumulation in a typical city in eastern China during 2014–2020, Atmos. Pollut. Res., 13, 101387, https://doi.org/10.1016/j.apr.2022.101387, 2022.
Wang, N., Lyu, X., Deng, X., Huang, X., Jiang, F., and Ding, A.: Aggravating O3 pollution due to NOx emission control in eastern China, Sci. Total Environ., 677, 732–744, https://doi.org/10.1016/j.scitotenv.2019.04.388, 2019.
Wang, N., Du, Y., Chen, D., Meng, H., Chen, X., Zhou, L., Shi, G., Zhan, Y., Feng, M., Li, W., Chen, M., Li, Z., and Yang, F.: Spatial disparities of ozone pollution in the Sichuan Basin spurred by extreme, hot weather, Atmos. Chem. Phys., 24, 3029–3042, https://doi.org/10.5194/acp-24-3029-2024, 2024.
Wang, Y., Du, H., Xu, Y., Lu, D., Wang, X., and Guo, Z.: Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China, Sci. Total Environ., 631–632, 921–933, https://doi.org/10.1016/j.scitotenv.2018.03.050, 2018.
Weng, Z., Tong, D., Wu, S., and Xie, Y.: Improved air quality from China's clean air actions alleviates health expenditure inequality, Environ. Int., 173, 107831, https://doi.org/10.1016/j.envint.2023.107831, 2023.
Xing, J., Wang, J., Mathur, R., Wang, S., Sarwar, G., Pleim, J., Hogrefe, C., Zhang, Y., Jiang, J., Wong, D. C., and Hao, J.: Impacts of aerosol direct effects on tropospheric ozone through changes in atmospheric dynamics and photolysis rates, Atmos. Chem. Phys., 17, 9869–9883, https://doi.org/10.5194/acp-17-9869-2017, 2017.
Yuan, Y., Liao, Z., Zhou, B., and Zhai, P.: Unprecedented Hot Extremes Observed in City Clusters in China during Summer 2022, J. Meteorol. Res., 37, 141–148, https://doi.org/10.1007/s13351-023-2184-9, 2023.
Zhang, H., Yee, L. D., Lee, B. H., Curtis, M. P., Worton, D. R., Isaacman-VanWertz, G., Offenberg, J. H., Lewandowski, M., Kleindienst, T. E., Beaver, M. R., Holder, A. L., Lonneman, W. A., Docherty, K. S., Jaoui, M., Pye, H. O. T., Hu, W., Day, D. A., Campuzano-Jost, P., Jimenez, J. L., Guo, H., Weber, R. J., de Gouw, J., Koss, A. R., Edgerton, E. S., Brune, W., Mohr, C., Lopez-Hilfiker, F. D., Lutz, A., Kreisberg, N. M., Spielman, S. R., Hering, S. V., Wilson, K. R., Thornton, J. A., and Goldstein, A. H.: Monoterpenes are the largest source of summertime organic aerosol in the southeastern United States, P. Natl. Acad. Sci., 115, 2038–2043, https://doi.org/10.1073/pnas.1717513115, 2018.
Zhang, J., Su, Y., Chen, C., Guo, W., Tan, Q., Feng, M., Song, D., Jiang, T., Chen, Q., Li, Y., Li, W., Wang, Y., Huang, X., Han, L., Wu, W., and Wang, G.: Chemical composition, sources and formation mechanism of urban PM2.5 in Southwest China: a case study at the beginning of 2023, Atmos. Chem. Phys., 24, 2803–2820, https://doi.org/10.5194/acp-24-2803-2024, 2024.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang, J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang, Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu, F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu, Z., Yang, F., He, K., and Hao, J.: Drivers of improved PM2.5 air quality in China from 2013 to 2017, P. Natl. Acad. Sci., 116, 24463–24469, https://doi.org/10.1073/pnas.1907956116, 2019.
Zhang, Y., Dai, J., Li, Q., Chen, T., Mu, J., Brasseur, G., Wang, T., and Xue, L.: Biogenic volatile organic compounds enhance ozone production and complicate control efforts: Insights from long-term observations in Hong Kong, Atmos. Environ., 309, 119917, https://doi.org/10.1016/j.atmosenv.2023.119917, 2023.
Zhang, Y., Ran, H., Guenther, A., Zhang, Q., George, C., Mellouki, W., Sheng, G., Peng, P. A., and Wang, X.: Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas, Nat. Commun., 16, 8064, https://doi.org/10.1038/s41467-025-63437-8, 2025.
Zhao, B., Zheng, H., Wang, S., Smith, K. R., Lu, X., Aunan, K., Gu, Y., Wang, Y., Ding, D., Xing, J., Fu, X., Yang, X., Liou, K.-N., and Hao, J.: Change in household fuels dominates the decrease in PM2.5 exposure and premature mortality in China in 2005–2015, P. Natl. Acad. Sci., 115, 12401–12406, https://doi.org/10.1073/pnas.1812955115, 2018.
Zhao, J., Lv, Z., Qi, L., Zhao, B., Deng, F., Chang, X., Wang, X., Luo, Z., Zhang, Z., Xu, H., Ying, Q., Wang, S., He, K., and Liu, H.: Comprehensive Assessment for the Impacts of S/IVOC Emissions from Mobile Sources on SOA Formation in China, Environ. Sci. Technol., 56, 16695–16706, https://doi.org/10.1021/acs.est.2c07265, 2022.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zheng, Y., Xue, T., Zhang, Q., Geng, G., Tong, D., Li, X., and He, K.: Air quality improvements and health benefits from China's clean air action since 2013, Environ. Res. Lett., 12, 114020, https://doi.org/10.1088/1748-9326/aa8a32, 2017.
Zhu, S., Poetzscher, J., Shen, J., Wang, S., Wang, P., and Zhang, H.: Comprehensive Insights Into O3 Changes During the COVID-19 From O3 Formation Regime and Atmospheric Oxidation Capacity, Geophys. Res. Lett., 48, e2021GL093668, https://doi.org/10.1029/2021GL093668, 2021.
Zhu, S., Ma, J., Wang, S., Sun, S., Wang, P., and Zhang, H.: Shifts of Formation Regimes and Increases of Atmospheric Oxidation Led to Ozone Increase in North China Plain and Yangtze River Delta From 2016 to 2019, J. Geophys. Res.-Atmos., 128, e2022JD038373, https://doi.org/10.1029/2022JD038373, 2023.
Zhu, X., Ma, Z., Li, Z., Wu, J., Guo, H., Yin, X., Ma, X., and Qiao, L.: Impacts of meteorological conditions on nocturnal surface ozone enhancement during the summertime in Beijing, Atmos. Environ., 225, 117368, https://doi.org/10.1016/j.atmosenv.2020.117368, 2020.
Short summary
Heatwaves worsen ozone and secondary organic aerosol pollution. We quantified how much comes from heat speeding up chemical reactions versus increasing natural emissions in China. High temperatures drive over 80 % of the pollution increase nationwide by making the atmosphere more chemically reactive. The effect was strongest in the Yangtze River Delta and Pearl River Delta. Limiting climate change is essential for controlling future pollution and protecting health.
Heatwaves worsen ozone and secondary organic aerosol pollution. We quantified how much comes...
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