87 citations as recorded by crossref.

1. Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling Y. Guo et al.
2. Spatiotemporal dynamics and key drivers of resource recycling industry in China (1987–2024): A multisource big data approach and machine learning analysis L. Chen & M. Gao
3. Large Modeling Uncertainty in Projecting Decadal Surface Ozone Changes Over City Clusters of China X. Weng et al.
4. Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al.
5. Rapid increase in spring ozone in the Pearl River Delta, China during 2013-2022 T. Cao et al.
6. Revealing the sources of water-soluble PM2.5 oxidative potential with explainable machine learning L. Zhang et al.
7. Tracking surface ozone responses to clean air actions under a warming climate in China using machine learning J. Fang et al.
8. Impact of the strong wintertime East Asian trough on the concurrent PM2.5 and surface O3 in eastern China X. An et al.
9. Rapid increases in ozone concentrations over the Tibetan Plateau caused by local and non-local factors C. Xu et al.
10. Hybrid Deep Learning Framework for Forecasting Ground-Level Ozone in a North Texas Urban Region J. Kanayankottupoyil et al.
11. Changes in Urban–Nonurban Ozone Disparities across China during 2000–2024 L. Guo et al.
12. O3–precursor relationship over multiple patterns of timescale: a case study in Zibo, Shandong Province, China Z. Zheng et al.
13. Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion X. Zheng et al.
14. Scenario-dependent O3 driver identification and formation responses to VOCs/NO emission reduction pathways from integrated and sectoral sources in the Beijing-Tianjin-Hebei region, China H. Zhang et al.
15. Machine-Learning Models for Surface Ozone Forecast in Mexico City M. Ahmad et al.
16. Separation of meteorological and anthropogenic contributions to major ambient pollutants using an integrated explainable machine learning framework: RMT-PAS H. Ke et al.
17. Spatiotemporal heterogeneity of drivers and impacts of composite air pollution at China's urban scale: A multi-source data and integrated approach analysis for 2015–2023 X. Wang et al.
18. Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chemical transport models X. Lu et al.
19. Comprehensive analysis of prevailing weather patterns and high-impact typhoon tracks to reveal where and how tropical cyclone affects regional ozone pollution in the Yangtze River Delta region, China M. Xi et al.
20. Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China H. Fang et al.
21. Modelling of ground-level ozone concentrations in Kolkata, India, using machine learning techniques R. Moncy & A. Mathew
22. Urban ozone variability using automated machine learning: inference from different feature importance schemes S. Nath et al.
23. Sustained emission reductions have restrained the ozone pollution over China Y. Wang et al.
24. Distinct climate drivers governing the dominant interannual variability of co-occurrences of heat and ozone pollution extremes over northern and southern urban clusters in Eastern China X. Zhong et al.
25. PM2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine learning C. Tao et al.
26. Differences in ozone pollution and associated meteorological factors among the megacities of Beijing, Shanghai and Guangzhou during 2015–2022 W. Yu et al.
27. Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
28. Quantifying the influencing factors and predictive analysis of cotton defoliation and maturation based on machine learning Y. Wang et al.
29. Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data H. Li et al.
30. Short-term forecasting of ozone air pollution across Europe with transformers S. Hickman et al.
31. Spatiotemporal change of PM2.5 concentration in Beijing-Tianjin-Hebei and its prediction based on machine learning N. Liu et al.
32. Gas-phase reactions accelerate nitrate formation in spring and early summer in a megacity of the Sichuan Basin, China Y. Li et al.
33. Opposing trends in the peak and low ozone concentrations in eastern China: anthropogenic and meteorological influences Z. Wang et al.
34. 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 S. Zhu et al.
35. Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning Y. Lyu et al.
36. Ozone pollution in a typical region of southeastern Shandong Province, China: Spatio-temporal distribution, sensitivity regimes, and sources S. Liu et al.
37. Evaluating the impact of control measures on sulfate-nitrate-ammonium aerosol variations and their formation mechanism in northern China during 2022 Winter Olympic Games Z. Gui et al.
38. Factors driving changes in surface ozone in 44 coastal cities in China X. Liu et al.
39. Uncovering the evolution of ozone pollution in China: A spatiotemporal characteristics reconstruction from 1980 to 2021 S. Ding et al.
40. Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China M. Cheng et al.
41. The role of NOx in Co-occurrence of O3 and PM2.5 pollution driven by wintertime east Asian monsoon in Hainan J. Zhan et al.
42. Decoding ozone pollution in Beijing-Tianjin-Hebei: A machine learning approach to disentangling meteorological and anthropogenic drivers X. Liu et al.
43. Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions Y. Ni et al.
44. The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe W. Han et al.
45. Extreme heat exacerbates atmospheric ozone pollution: Holistic data-based causal investigation in China R. Xia et al.
46. Analysis of Driving Factors of Ozone in the Urban Area of Meizhou Based on Machine Learning L. LUO et al.
47. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al.
48. Investigating the response of China's surface ozone concentration to the future changes of multiple factors J. Yang et al.
49. Long-term trends and climatic drivers of air pollution in a coastal urbanizing district of south India: Insights from Thrissur (2000–2023) C. Samuel J S et al.
50. Multi-scale analysis of ozone pollution Shanghai based on meteorological normalization: A case study of multi-model collaborative machine learning framework and MCM coupling mechanism M. Wu et al.
51. Coastal ozone dynamics and formation regime in Eastern China: Integrating trend decomposition and machine learning techniques L. Tong et al.
52. Heatwave exacerbates air pollution in China through intertwined climate-energy-environment interactions T. Chen et al.
53. Meteorological influence on surface ozone trends in China: assessing uncertainties caused by multi-dataset and multi-method X. Wang et al.
54. Divergent Ozone Predictions in China Under Carbon Neutrality: Why Chemical Mechanisms Disagree X. Weng et al.
55. Spatiotemporal variations in meteorological influences on ambient ozone in China: A machine learning approach T. Li et al.
56. Elucidating contributions of meteorology and emissions to O3 variations in coastal city of China during 2019–2022: Insights from VOCs sources K. Zhang et al.
57. Impact of weather patterns and meteorological factors on PM2.5 and O3 responses to the COVID-19 lockdown in China F. Shen et al.
58. Evolution of surface ozone pollution in Yangtze Rriver Delta between 2019 and 2022: influences of meteorology and emissions Y. Zhang et al.
59. Hybrid machine learning model for hourly ozone concentrations prediction and exposure risk assessment W. Lingxia et al.
60. Evaluating Hangzhou’s urgent source-specific regulatory policies for the 2024 New Year haze: A receptor model and machine learning approach W. Liang et al.
61. Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022 X. Ji et al.
62. Attention mechanism based CNN-LSTM hybrid deep learning model for atmospheric ozone concentration prediction J. Yuan et al.
63. Assessing and explaining temporal deep learning models for wildfire danger prediction P. Becker et al.
64. Trends of peroxyacetyl nitrate and its impact on ozone over 2018–2022 in urban atmosphere Z. Lin et al.
65. Ozone exceedance forecasting with enhanced extreme instance augmentation: A case study in Germany T. Deng et al.
66. Ozone Formation in a Representative Urban Environment: Model Discrepancies and Critical Roles of Oxygenated Volatile Organic Compounds X. Huang et al.
67. Modeling Surface Ozone Concentrations in Iraq Using Principal Component Analysis and Stepwise Multiple Linear Regression Method F. Abed et al.
68. Ozone response to precursors changes in the Chengdu-Chongqing economic circle, China, from satellite and ground-based observations J. Ren et al.
69. Evidence for Coordinated Control of PM2.5 and O3: Long-Term Observational Study in a Typical City of Central Plains Urban Agglomeration C. Jia et al.
70. Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability Z. Mai et al.
71. Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China J. Yang et al.
72. Quantifying and correcting systematic discrepancies in the comparison between surface CO observations and simulations C. Fang et al.
73. An Unprecedented Ozone Reduction at a Coastal Background Site in South China: Drivers and Implications R. Zhou et al.
74. Influences of synoptic circulations on regional transport, local accumulation and chemical transformation for PM2.5 heavy pollution over Twain-Hu Basin, central China J. Yao et al.
75. Effects of heat waves on ozone pollution in a coastal industrial city: Meteorological impacts and photochemical mechanisms D. Liao et al.
76. An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al.
77. Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning Y. Qiu et al.
78. A hybrid deep learning model for O3 forecasting and explaining in the Yangtze River Delta Region of China L. Wu et al.
79. Prediction of respiratory diseases based on random forest model X. Yang et al.
80. Constructing the 3D Spatial Distribution of the HCHO/NO2 Ratio via Satellite Observation and Machine Learning Model Z. Jiang et al.
81. Carbonyl Compound Reduction Mitigates Ozone Pollution: Lessons from Pollution Control during the 19th Asian Games in Hangzhou, China G. Guo et al.
82. Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning K. Miyazaki et al.
83. Deep Learning‐Based Ensemble Forecasts and Predictability Assessments for Surface Ozone Pollution A. Zhang et al.
84. Greenness may increase ozone-related mortality risk via BVOC emissions: A WRF-CMAQ modeling and population-based study J. Fu et al.
85. Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration X. Liu et al.
86. Anomalous high ozone in the Pearl River Delta, China in 2019: A cause attribution analysis Y. Wu et al.
87. Identification of Local and Transboundary Sources and Mechanisms of PM2.5 and O3 Pollution on the Tibetan Plateau: Implications for Sustainable Air Quality Governance Y. Li et al.