40 citations as recorded by crossref.

1. Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al.
2. Ozone concentration prediction using machine learning models integrated with 115 volatile organic compound measurements and dimensionality reduction techniques M. Han et al.
3. Modelling ozone-induced changes in wheat amino acids and protein quality using a process-based crop model J. Cook et al.
4. Ozone exceedance forecasting with enhanced extreme instance augmentation: A case study in Germany T. Deng et al.
5. Investigating the response of China's surface ozone concentration to the future changes of multiple factors J. Yang et al.
6. Effects of 2010–2045 climate change on ozone levels in China under a carbon neutrality scenario: key meteorological parameters and processes L. Kang et al.
7. Analysis of Ozone pollution characteristics and collaborative control measures in a district of Chongqing J. Luo et al.
8. A global land daily 10-km-resolution surface ozone dataset from 2013–2022 R. Wang et al.
9. Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China M. Li et al.
10. Prediction of PM2.5 Concentrations in the Pearl River Delta by Integrating the PLUS and LUR Models X. Zhang et al.
11. Large differences of highly oxygenated organic molecules (HOMs) and low-volatile species in secondary organic aerosols (SOAs) formed from ozonolysis of β-pinene and limonene D. Liu et al.
12. Atmospheric Oxidation Capacity Dynamics Driven by Meteorology and Multiprecursor Interactions Modulate O3 Variation in Hangzhou, China W. Li et al.
13. Meteorological characteristics of extreme ozone pollution events in China and their future predictions Y. Yang et al.
14. 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.
15. Tracking surface ozone responses to clean air actions under a warming climate in China using machine learning J. Fang et al.
16. A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors Z. Gao & X. Zhou
17. Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model Y. Ni et al.
18. A novel approach to project future changes in meteorological factors affecting severe surface ozone over the Korean Peninsula under SSP scenarios D. Kim et al.
19. Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning V. Balamurugan et al.
20. Machine Learning Modeling Reveals Divergent Air Pollutant Responses to Stringent Emission Controls in the Yangtze River Delta Region Q. Yao et al.
21. Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China J. Yang et al.
22. The joint effect of long-term exposure to multiple air pollutants on non-accidental and cause-specific mortality: A longitudinal cohort study X. Wei et al.
23. Future trends and driving factors of ozone pollution in China under the carbon neutrality target using an ensemble machine learning approach S. Zhang et al.
24. Unraveling the spatiotemporal dynamics and drivers of surface and tropospheric ozone in China S. Yin et al.
25. Summer PM2.5 concentrations in the northern subtropics modulated by the Hadley circulation edge location S. Wang et al.
26. Rapid reduction of air pollution and short-term exposure risks in China H. Fan et al.
27. How NO2 effects the formation characteristics of ozone in α-pinene photooxidation process S. Liu et al.
28. The mechanisms of ozone formation in Shanghai based on explainable stacking ensemble machine learning: The role of OVOCs C. Shen et al.
29. Meteorological conditions and physicochemical processes amplifying ozone pollution during heatwaves in major city clusters of China Y. Song et al.
30. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al.
31. 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.
32. Impacts of projected changes in sea surface temperature on ozone pollution in China toward carbon neutrality J. Zhu et al.
33. Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan plateau with data-driven approach H. Zhong et al.
34. Explainable Machine Learning Reveals the Unknown Sources of Atmospheric HONO during COVID-19 Z. Gao et al.
35. Uncovering the evolution of ozone pollution in China: A spatiotemporal characteristics reconstruction from 1980 to 2021 S. Ding et al.
36. Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
37. Meteorological and socioeconomic impacts on ozone in China: Past and future analysis Y. Chen et al.
38. Analysis of potential sources and influencing factors of O3 pollution in the Hexi region based on XGBoost and SHAP models P. Wang et al.
39. Greenness may increase ozone-related mortality risk via BVOC emissions: A WRF-CMAQ modeling and population-based study J. Fu et al.
40. The impact of tropical cyclones on regional ozone pollution and its future trend in the Yangtze River Delta of China M. Xi et al.