Articles | Volume 24, issue 7
https://doi.org/10.5194/acp-24-4177-2024
© 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-24-4177-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2024
Research article |
| 08 Apr 2024
| 08 Apr 2024
Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
Chenliang Tao
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Yanbo Peng
CORRESPONDING AUTHOR
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Shandong Academy for Environmental Planning, Jinan 250101, PR China
Qingzhu Zhang
CORRESPONDING AUTHOR
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Yuqiang Zhang
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Bing Gong
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Qiao Wang
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
Wenxing Wang
Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
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Cited articles
Action Plan on Air Pollution Prevention and Control (in Chinese): http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (last access: 1 February 2023), 2023.
Bertasius, G., Wang, H., and Torresani, L.: Is Space-Time Attention All You Need for Video Understanding?, arXiv [preprint], https://doi.org/10.48550/arXiv.2102.05095, 9 June 2021.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA, 785–794, https://doi.org/10/gdp84q, 2016.
Chu, W., Li, H., Ji, Y., Zhang, X., Xue, L., Gao, J., and An, C.: Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China, J. Environ. Sci., 138, 543–560, https://doi.org/10/gr4qzk, 2023.
Cooper, M. J., Martin, R. V., Hammer, M. S., Levelt, P. F., Veefkind, P., Lamsal, L. N., Krotkov, N. A., Brook, J. R., and McLinden, C. A.: Global fine-scale changes in ambient NO2 during COVID-19 lockdowns, Nature, 601, 380–387, https://doi.org/10.1038/s41586-021-04229-0, 2022.
Copernicus Sentinel-5P (processed by ESA): TROPOMI Level 2 Ozone Total Column products (Version 02), European Space Agency [data set], https://doi.org/10.5270/S5P-ft13p57, 2020.
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., and Schwartz, J.: Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States, Environ. Sci. Technol., 50, 4712–4721, https://doi.org/10.1021/acs.est.5b06121, 2016.
Dias, D. and Tchepel, O.: Spatial and Temporal Dynamics in Air Pollution Exposure Assessment, IJERPH, 15, 558, https://doi.org/10.3390/ijerph15030558, 2018.
Didan, K.: MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD13Q1.061, 2021.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.11929, 3 June 2021.
Dyson, J. E., Whalley, L. K., Slater, E. J., Woodward-Massey, R., Ye, C., Lee, J. D., Squires, F., Hopkins, J. R., Dunmore, R. E., Shaw, M., Hamilton, J. F., Lewis, A. C., Worrall, S. D., Bacak, A., Mehra, A., Bannan, T. J., Coe, H., Percival, C. J., Ouyang, B., Hewitt, C. N., Jones, R. L., Crilley, L. R., Kramer, L. J., Acton, W. J. F., Bloss, W. J., Saksakulkrai, S., Xu, J., Shi, Z., Harrison, R. M., Kotthaus, S., Grimmond, S., Sun, Y., Xu, W., Yue, S., Wei, L., Fu, P., Wang, X., Arnold, S. R., and Heard, D. E.: Impact of HO2 aerosol uptake on radical levels and O3 production during summertime in Beijing, Atmos. Chem. Phys., 23, 5679–5697, https://doi.org/10.5194/acp-23-5679-2023, 2023.
Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., Xue, T., Tong, D., Zheng, B., Peng, Y., Huang, X., He, K., and Zhang, Q.: Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion, Environ. Sci. Technol., 55, 12106–12115, https://doi.org/10.1021/acs.est.1c01863, 2021.
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst3_2d_gas_Nx: 2d, 3-Hourly, Instantaneous, Single-Level, Assimilation, Aerosol Optical Depth Analysis V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/HNGA0EWW0R09, 2015.
Han, H., Zhang, L., Liu, Z., Yue, X., Shu, L., Wang, X., and Zhang, Y.: Narrowing Differences in Urban and Nonurban Surface Ozone in the Northern Hemisphere Over 1990–2020, Environ. Sci. Technol. Lett., 10, 410–417, https://doi.org/10/gsd5gk, 2023.
Han, X. and Naeher, L. P.: A review of traffic-related air pollution exposure assessment studies in the developing world, Environ. Int., 32, 106–120, https://doi.org/10.1016/j.envint.2005.05.020, 2006.
Hersbach, H., Bell, B., Berrisford, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, C., Hu, J., Xue, T., Xu, H., and Wang, M.: High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019, Environ. Sci. Technol., 55, 2152–2162, https://doi.org/10.1021/acs.est.0c05815, 2021.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Ivatt, P. D., Evans, M. J., and Lewis, A. C.: Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime, Nat. Geosci., 15, 536–540, https://doi.org/10.1038/s41561-022-00972-9, 2022.
Jerrett, M., Arain, A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., Morrison, J., and Giovis, C.: A review and evaluation of intraurban air pollution exposure models, J. Expo. Sci. Env. Epid., 15, 185–204, https://doi.org/10.1038/sj.jea.7500388, 2005.
Jin, X., Fiore, A. M., Murray, L. T., Valin, L. C., Lamsal, L. N., Duncan, B., Folkert Boersma, K., De Smedt, I., Abad, G. G., Chance, K., and Tonnesen, G. S.: Evaluating a Space-Based Indicator of Surface Ozone-NOx-VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends: Space-Based Indicator of O3 Sensitivity, J. Geophys. Res.-Atmos., 122, 10439–10461, https://doi.org/10.1002/2017JD026720, 2017.
Jin, X., Fiore, A., Boersma, K. F., Smedt, I. D., and Valin, L.: Inferring Changes in Summertime Surface Ozone-NOx-VOC Chemistry over U.S. Urban Areas from Two Decades of Satellite and Ground-Based Observations, Environ. Sci. Technol., 54, 6518–6529, https://doi.org/10.1021/acs.est.9b07785, 2020.
Jin, X., Fiore, A. M., and Cohen, R. C.: Space-Based Observations of Ozone Precursors within California Wildfire Plumes and the Impacts on Ozone-NOx-VOC Chemistry, Environ. Sci. Technol., 57, 14648–14660, https://doi.org/10.1021/acs.est.3c04411, 2023.
Jun, C., Ban, Y., and Li, S.: China: Open access to Earth land-cover map, Nature, 514, 434–434, https://doi.org/10.1038/514434c, 2014.
Jung, J., Choi, Y., Souri, A. H., Mousavinezhad, S., Sayeed, A., and Lee, K.: The Impact of Springtime-Transported Air Pollutants on Local Air Quality With Satellite-Constrained NOx Emission Adjustments Over East Asia, J. Geophys. Res.-Atmos., 127, e2021JD035251, https://doi.org/10.1029/2021JD035251, 2022.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, event-place: Long Beach, California, USA, 3149–3157, 2017.
Kohno, N., Zhou, J., Li, J., Takemura, M., Ono, N., Sadanaga, Y., Nakashima, Y., Sato, K., Kato, S., Sakamoto, Y., and Kajii, Y.: Impacts of missing OH reactivity and aerosol uptake of HO2 radicals on tropospheric O3 production during the AQUAS-Kyoto summer campaign in 2018, Atmos. Environ., 281, 119130, https://doi.org/10/gshfc4, 2022.
Lamsal, L. N., Krotkov, N. A., Marchenko, S. V., Joiner, J., Oman, L., Vasilkov, A., Fisher, B., Qin, W., Yang, E.-S., Fasnacht, Z., Choi, S., Leonard, P., and Haffner, D.: TROPOMI/S5P NO2 Tropospheric, Stratospheric and Total Columns MINDS 1-Orbit L2 Swath 5.5 km × 3.5 km, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/MEASURES/MINDS/DATA203, 2022.
Lee, H. J., Kuwayama, T., and FitzGibbon, M.: Trends of ambient O3 levels associated with O3 precursor gases and meteorology in California: Synergies from ground and satellite observations, Remote Sens. Environ., 284, 113358, https://doi.org/10.1016/j.rse.2022.113358, 2023.
Li, C., Zhu, Q., Jin, X., and Cohen, R. C.: Elucidating Contributions of Anthropogenic Volatile Organic Compounds and Particulate Matter to Ozone Trends over China, Environ. Sci. Technol., 56, 12906–12916, https://doi.org/10.1021/acs.est.2c03315, 2022.
Li, D., Wang, S., Xue, R., Zhu, J., Zhang, S., Sun, Z., and Zhou, B.: OMI-observed HCHO in Shanghai, China, during 2010–2019 and ozone sensitivity inferred by an improved HCHO/NO2 ratio, Atmos. Chem. Phys., 21, 15447–15460, https://doi.org/10.5194/acp-21-15447-2021, 2021.
Li, K., Jacob, D. J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K. H., Zhang, Q., and Zhai, S.: A two-pollutant strategy for improving ozone and particulate air quality in China, Nat. Geosci., 12, 906–910, https://doi.org/10.1038/s41561-019-0464-x, 2019.
Li, K., Wang, Y., Peng, G., Song, G., Liu, Y., Li, H., and Qiao, Y.: UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning, International Conference on Learning Representations, Virtual, 25–29 April 2022, 2021.
Li, L. and Wu, J.: Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks, Remote Sens. Environ., 254, 112257, https://doi.org/10.1016/j.rse.2020.112257, 2021.
Li, M., Wang, T., Xie, M., Zhuang, B., Li, S., Han, Y., and Chen, P.: Impacts of aerosol-radiation feedback on local air quality during a severe haze episode in Nanjing megacity, eastern China, Tellus B, 69, 1339548, https://doi.org/10/gsfjz3, 2017.
Li, M., Yang, Q., Yuan, Q., and Zhu, L.: Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model, Chemosphere, 301, 134817, https://doi.org/10.1016/j.chemosphere.2022.134817, 2022.
Lin, C., Huang, R.-J., Zhong, H., Duan, J., Wang, Z., Huang, W., and Xu, W.: Elucidating ozone and PM2.5 pollution in the Fenwei Plain reveals the co-benefits of controlling precursor gas emissions in winter haze, Atmos. Chem. Phys., 23, 3595–3607, https://doi.org/10.5194/acp-23-3595-2023, 2023.
Liu, M., Huang, Y., Ma, Z., Jin, Z., Liu, X., Wang, H., Liu, Y., Wang, J., Jantunen, M., Bi, J., and Kinney, P. L.: Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012, Environ. Int., 98, 75–81, https://doi.org/10.1016/j.envint.2016.10.003, 2017.
Liu, X., Shi, X., Lei, Y., and Xue, W.: Path of coordinated control of PM2.5 and ozone in China, Chin. Sci. Bull., 67, 2089–2099, https://doi.org/10.1360/TB-2021-0832, 2022.
Lu, D., Mao, W., Zheng, L., Xiao, W., Zhang, L., and Wei, J.: Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data, Remote Sens., 13, 1423, https://doi.org/10.3390/rs13081423, 2021.
Lu, X., Hong, J., Zhang, L., Cooper, O. R., Schultz, M. G., Xu, X., Wang, T., Gao, M., Zhao, Y., and Zhang, Y.: Severe Surface Ozone Pollution in China: A Global Perspective, Environ. Sci. Technol. Lett., 5, 487–494, https://doi.org/10.1021/acs.estlett.8b00366, 2018.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 4–9 December 2017, Red Hook, NY, USA, 4768–4777, 2017.
Lyapustin, A. and Wang, Y.: MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1 km SIN Grid V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD19A2.061, 2022.
Miller, D. F., Alkezweeny, A. J., Hales, J. M., and Lee, R. N.: Ozone Formation Related to Power Plant Emissions, Science, 202, 1186–1188, https://doi.org/10/b5kgjr, 1978.
Mitchell, R., Frank, E., and Holmes, G.: GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles, PeerJ Comput. Sci., 8, e880, https://doi.org/10.7717/peerj-cs.880, 2020.
Myers, S. L.: The Worst Dust Storm in a Decade Shrouds Beijing and Northern China, The New York Times, https://www.nytimes.com/2021/03/15/world/asia/china-sandstorm.html (last access: 12 March 2023), 15 March 2021.
Pusede, S. E., Steiner, A. L., and Cohen, R. C.: Temperature and Recent Trends in the Chemistry of Continental Surface Ozone, Chem. Rev., 115, 3898–3918, https://doi.org/10.1021/cr5006815, 2015.
Ren, J., Guo, F., and Xie, S.: Diagnosing ozone–NOx–VOC sensitivity and revealing causes of ozone increases in China based on 2013–2021 satellite retrievals, Atmos. Chem. Phys., 22, 15035–15047, https://doi.org/10.5194/acp-22-15035-2022, 2022.
Ren, X., Mi, Z., Cai, T., Nolte, C. G., and Georgopoulos, P. G.: Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations, Environ. Sci. Technol., 56, 3871–3883, https://doi.org/10.1021/acs.est.1c04076, 2022.
Requia, W. J., Di, Q., Silvern, R., Kelly, J. T., Koutrakis, P., Mickley, L. J., Sulprizio, M. P., Amini, H., Shi, L., and Schwartz, J.: An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States, Environ. Sci. Technol., 54, 11037–11047, https://doi.org/10.1021/acs.est.0c01791, 2020.
Román, M. O., Wang, Z., Sun, Q., Kalb, V., Miller, S. D., Molthan, A., Schultz, L., Bell, J., Stokes, E. C., Pandey, B., Seto, K. C., Hall, D., Oda, T., Wolfe, R. E., Lin, G., Golpayegani, N., Devadiga, S., Davidson, C., Sarkar, S., Praderas, C., Schmaltz, J., Boller, R., Stevens, J., Ramos González, O. M., Padilla, E., Alonso, J., Detrés, Y., Armstrong, R., Miranda, I., Conte, Y., Marrero, N., MacManus, K., Esch, T., and Masuoka, E. J.: NASA's Black Marble nighttime lights product suite, Remote Sens. Environ., 210, 113–143, https://doi.org/10/ghqpjh, 2018.
Shapley, L. S.: A value for n-person games, in: The Shapley Value: Essays in Honor of Lloyd S. Shapley, edited by: Roth, A. E., Cambridge University Press, Cambridge, 31–40, https://doi.org/10.1017/CBO9780511528446.003, 1988.
Shrikumar, A., Greenside, P., and Kundaje, A.: Learning Important Features Through Propagating Activation Differences, in: International conference on machine learning, Sydney NSW Australia, 6–11 August 2017, 3145–3153, 2017.
Sicard, P., Serra, R., and Rossello, P.: Spatiotemporal trends in ground-level ozone concentrations and metrics in France over the time period 1999–2012, Environ. Res., 149, 122–144, https://doi.org/10.1016/j.envres.2016.05.014, 2016.
Sicard, P., De Marco, A., Agathokleous, E., Feng, Z., Xu, X., Paoletti, E., Rodriguez, J. J. D., and Calatayud, V.: Amplified ozone pollution in cities during the COVID-19 lockdown, Sci. Total Environ., 735, 139542, https://doi.org/10/gg5w8h, 2020.
Sillman, S.: The use of NOy, H2O2, and HNO3 as indicators for ozone-NOx-hydrocarbon sensitivity in urban locations, J. Geophys. Res., 100, 14175, https://doi.org/10.1029/94JD02953, 1995.
Song, H., Lu, K., Dong, H., Tan, Z., Chen, S., Zeng, L., and Zhang, Y.: Reduced Aerosol Uptake of Hydroperoxyl Radical May Increase the Sensitivity of Ozone Production to Volatile Organic Compounds, Environ. Sci. Technol. Lett., 9, 22–29, https://doi.org/10/gnqqb9, 2022.
Song, K., Liu, R., Wang, Y., Liu, T., Wei, L., Wu, Y., Zheng, J., Wang, B., and Liu, S. C.: Observation-based analysis of ozone production sensitivity for two persistent ozone episodes in Guangdong, China, Atmos. Chem. Phys., 22, 8403–8416, https://doi.org/10.5194/acp-22-8403-2022, 2022.
Souri, A. H., Johnson, M. S., Wolfe, G. M., Crawford, J. H., Fried, A., Wisthaler, A., Brune, W. H., Blake, D. R., Weinheimer, A. J., Verhoelst, T., Compernolle, S., Pinardi, G., Vigouroux, C., Langerock, B., Choi, S., Lamsal, L., Zhu, L., Sun, S., Cohen, R. C., Min, K.-E., Cho, C., Philip, S., Liu, X., and Chance, K.: Characterization of errors in satellite-based HCHO/NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties, Atmos. Chem. Phys., 23, 1963–1986, https://doi.org/10.5194/acp-23-1963-2023, 2023.
Su, W., Hu, Q., Chen, Y., Lin, J., Zhang, C., and Liu, C.: Inferring global surface HCHO concentrations from multisource hyperspectral satellites and their application to HCHO-related global cancer burden estimation, Environ. Int., 170, 107600, https://doi.org/10.1016/j.envint.2022.107600, 2022.
Sun, H., Shin, Y. M., Xia, M., Ke, S., Wan, M., Yuan, L., Guo, Y., and Archibald, A. T.: Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space – Time Bayesian Neural Network Downscaler, Environ. Sci. Technol., 56, 7337–7349, https://doi.org/10.1021/acs.est.1c04797, 2022.
Tan, Z., Lu, K., Ma, X., Chen, S., He, L., Huang, X., Li, X., Lin, X., Tang, M., Yu, D., Wahner, A., and Zhang, Y.: Multiple Impacts of Aerosols on O3 Production Are Largely Compensated: A Case Study Shenzhen, China, Environ. Sci. Technol., 56, 17569–17580, https://doi.org/10/gsgp79, 2022.
Tang, L., Xue, X., Qu, J., Mi, Z., Bo, X., Chang, X., Wang, S., Li, S., Cui, W., and Dong, G.: Air pollution emissions from Chinese power plants based on the continuous emission monitoring systems network, Sci. Data, 7, 325, https://doi.org/10/ghfqqf, 2020.
Tao, C.: Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data, Zenodo [data set], https://doi.org/10.5281/zenodo.10071408, 2023.
Tao, C.: myles-tcl/Air-Transformer: V1.0.0 (publish), Zenodo [code], https://doi.org/10.5281/zenodo.10889597, 2024.
Thongthammachart, T., Araki, S., Shimadera, H., Matsuo, T., and Kondo, A.: Incorporating Light Gradient Boosting Machine to land use regression model for estimating NO2 and PM2.5 levels in Kansai region, Japan, Environ. Modell. Softw., 155, 105447, https://doi.org/10.1016/j.envsoft.2022.105447, 2022.
van Donkelaar, A., Martin, R. V., Spurr, R. J. D., and Burnett, R. T.: High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America, Environ. Sci. Technol., 49, 10482–10491, https://doi.org/10.1021/acs.est.5b02076, 2015.
Wei, J., Li, Z., Li, K., Dickerson, R. R., Pinker, R. T., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M.: Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China, Remote Sens. Environ., 270, 112775, https://doi.org/10.1016/j.rse.2021.112775, 2022a.
Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R. T., Dickerson, R. R., Lin, J., Boersma, K. F., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J.: Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence, Environ. Sci. Technol., 56, 9988–9998, https://doi.org/10.1021/acs.est.2c03834, 2022b.
Wei, W., Wang, X., Wang, X., Li, R., Zhou, C., and Cheng, S.: Attenuated sensitivity of ozone to precursors in Beijing–Tianjin–Hebei region with the continuous NOx reduction within 2014–2018, Sci. Total Environ., 813, 152589, https://doi.org/10/gq7ngn, 2022.
WorldPop: Global High Resolution Population Denominators Project – Funded by The Bill and Melinda Gates Foundation (OPP1134076) [data set], https://doi.org/10.5258/SOTON/WP00675, 2018.
Xiao, Q., Chang, H. H., Geng, G., and Liu, Y.: An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data, Environ. Sci. Technol., 52, 13260–13269, https://doi.org/10.1021/acs.est.8b02917, 2018.
Yue, X., Unger, N., Harper, K., Xia, X., Liao, H., Zhu, T., Xiao, J., Feng, Z., and Li, J.: Ozone and haze pollution weakens net primary productivity in China, Atmos. Chem. Phys., 17, 6073–6089, https://doi.org/10.5194/acp-17-6073-2017, 2017.
Zhang, J., Wang, J., Sun, Y., Li, J., Ninneman, M., Ye, J., Li, K., Crandall, B., Mao, J., Xu, W., Schwab, M. J., Li, W., Ge, X., Chen, M., Ying, Q., Zhang, Q., and Schwab, J. J.: Insights from ozone and particulate matter pollution control in New York City applied to Beijing, Clim. Atmos. Sci., 5, 85, https://doi.org/10.1038/s41612-022-00309-8, 2022.
Zhang, R., Lei, W., Tie, X., and Hess, P.: Industrial emissions cause extreme urban ozone diurnal variability, P. Natl. Acad. Sci. USA, 101, 6346–6350, https://doi.org/10.1073/pnas.0401484101, 2004.
Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., and Song, C.: A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights, Earth Syst. Sci. Data, 14, 517–534, https://doi.org/10.5194/essd-14-517-2022, 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.
Short summary
We developed a novel transformer framework to bridge the sparse surface monitoring for inferring ozone–NOx–VOC–aerosol sensitivity and their urban–nonurban discrepancies at a finer scale with implications for improving our understanding of ozone variations. The change in urban–rural disparities in ozone was dominated by PM2.5 from 2019 to 2020. An aerosol-inhibited regime on top of the two traditional NOx- and VOC-limited regimes was identified in Jiaodong Peninsula, Shandong, China.
We developed a novel transformer framework to bridge the sparse surface monitoring for inferring...
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