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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- Inequality in air pollution–attributable mortality by income level between and within countries C. Tao et al. https://doi.org/10.1073/pnas.2511394122
- Specific-Source Insights into Changes of O3 Concentrations and Health Risks in China Y. Wang et al. https://doi.org/10.1021/acs.est.6c01808
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- Improved global daily nitrogen dioxide concentrations from 2005 to 2023 derived using a deep learning approach J. Mu et al. https://doi.org/10.5194/essd-18-2999-2026
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- China’s air pollution: Remarkable progress and ongoing challenges under new standards from combined surface-satellite observations (2015–2024) Y. Chen et al. https://doi.org/10.1016/j.jhazmat.2026.142461
- Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration N. Wang et al. https://doi.org/10.1038/s41612-025-01181-y
- Vertical profiling of ozone concentrations over India in response to the atmospheric parameters using integrated geospatial- machine learning technique A. Mandal et al. https://doi.org/10.1007/s00704-026-06285-w
28 citations as recorded by crossref.
- Response Mechanisms of Air Quality Index (AQI) Spatiotemporal Dynamics in Shandong Province: A Perspective of Vegetation Greenness and Ecological Efficiency J. Sun et al. https://doi.org/10.3390/atmos17040349
- Machine learning integrated PMF model reveals influencing factors of ozone pollution in a coal chemical industry city at the Jiangsu-Shandong-Henan-Anhui boundary C. Wang et al. https://doi.org/10.1016/j.atmosenv.2024.120916
- Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning Q. He et al. https://doi.org/10.1021/acs.est.4c02926
- Discrepant Global Surface Ozone Responses to Emission- and Heatwave-Induced Regime Shifts C. Tao et al. https://doi.org/10.1021/acs.est.4c08422
- Drivers of ozone episodes during clean and polluted days in eastern China: Insights into precursor characterization, source apportionment, and associated health risks H. Zhang et al. https://doi.org/10.1016/j.envint.2026.110318
- Decoding the urban-suburban ozone disparities in Shandong, China: A machine learning-driven attribution analysis (2019–2023) L. Zhang et al. https://doi.org/10.1016/j.jes.2025.12.028
- Investigating the Role of Microclimate and Microorganisms in the Deterioration of Stone Heritage: The Case of Rupestrian Church from Jac, Romania D. Ilieș et al. https://doi.org/10.3390/app14188136
- 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 M. Zhang et al. https://doi.org/10.1007/s40726-024-00338-2
- Inequality in air pollution–attributable mortality by income level between and within countries C. Tao et al. https://doi.org/10.1073/pnas.2511394122
- Specific-Source Insights into Changes of O3 Concentrations and Health Risks in China Y. Wang et al. https://doi.org/10.1021/acs.est.6c01808
- Vertical ozone formation mechanisms resulting from increased oxidation on the mountainside of Mount Tai, China W. Wu et al. https://doi.org/10.1093/pnasnexus/pgae347
- The spatial diversity of secondary organic carbon aerosols at the city level: insights from explainable machine learning X. Xu et al. https://doi.org/10.1016/j.atmosenv.2025.121522
- Interpretable machine learning quantifies composition and size influences on aerosol spectral absorption W. Wang et al. https://doi.org/10.5194/acp-26-6471-2026
- Deep learning for air pollutant forecasting: opportunities, challenges, and future directions C. Tao et al. https://doi.org/10.1007/s11783-025-2092-6
- How do atmospheric pollutant changes driven by anthropogenic heat provide feedback on the urban thermal environment in Beijing? J. Qian et al. https://doi.org/10.1016/j.scs.2025.106907
- Deep Learning Insights into Seamless Reconstruction of XCO2 in China: Spatiotemporal Patterns and Driving Mechanisms W. Wang et al. https://doi.org/10.3390/rs18091366
- The multi-scale insights into summer ozone sources and their impacts on maize yield over the North China Plain H. Du et al. https://doi.org/10.1016/j.atmosres.2026.108833
- Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning J. Gou et al. https://doi.org/10.1007/s10712-025-09919-2
- Global Estimates of Daily Gapless Atmospheric XCH4 Concentrations From Satellite and Reanalysis Data During 2003–2020 Y. Qu et al. https://doi.org/10.1109/TGRS.2025.3593486
- Exploring the impact of vertical exchanges on surface ozone based on interpretable deep learning Y. Chen et al. https://doi.org/10.1016/j.atmosres.2026.108879
- Improved global daily nitrogen dioxide concentrations from 2005 to 2023 derived using a deep learning approach J. Mu et al. https://doi.org/10.5194/essd-18-2999-2026
- Drivers of the Changing Urban and Rural Surface Ozone Differences in China: Reducing Emissions or Warming Cities J. Zheng et al. https://doi.org/10.1021/acsestair.6c00009
- Climate-Chemistry Synergy Is Reversing Global Urban–Rural Ozone Differences C. Tao et al. https://doi.org/10.1021/acs.est.6c07924
- Characterization of volatile organic compounds emissions and health risk assessment in coking industry: A case study in East China X. Zhao et al. https://doi.org/10.1016/j.jes.2025.10.054
- VOCs-driven ozone extremes during dry and wet heatwaves in the Jiangsu–Shandong–Henan–Anhui Boundary: Integrating meteorological forcings and SHAP interpretation C. Wang et al. https://doi.org/10.1016/j.atmosres.2025.108396
- China’s air pollution: Remarkable progress and ongoing challenges under new standards from combined surface-satellite observations (2015–2024) Y. Chen et al. https://doi.org/10.1016/j.jhazmat.2026.142461
- Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration N. Wang et al. https://doi.org/10.1038/s41612-025-01181-y
- Vertical profiling of ozone concentrations over India in response to the atmospheric parameters using integrated geospatial- machine learning technique A. Mandal et al. https://doi.org/10.1007/s00704-026-06285-w
Saved (final revised paper)
Latest update: 14 Jul 2026
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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