Preprints
https://doi.org/10.5194/egusphere-2024-3495
https://doi.org/10.5194/egusphere-2024-3495
05 Feb 2025
 | 05 Feb 2025
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Influencing factors analysis and prediction of near-surface ozone in Henan Province from 2015 to 2022

Haoming Bao, Jiandong Shang, Jinzhu Li, Gang Wu, Haitao Wei, Lingling Wang, Nan Wang, Jingye Shi, Wenge Zhou, Feng Chen, Jiahui Guo, Jinyang Wang, Dujuan Zhang, and Hengliang Guo

Abstract. This study analyzed factors influencing near-surface ozone (O3) in Henan Province from 2015 to 2022 using real-time pollutant data from the China National Environmental Monitoring Centre and daily meteorological data from the Henan Provincial Ecological Environment Monitoring and Safety Center. Regression and machine learning models (including multiple linear regression (MLR), support vector machine (SVM), random forest (RF), ridge regression (RR), BP neural network, and extreme gradient boosting (XGBoost)) were used to predict O3 concentrations. The results showed that among the major pollutants (CO, NO2, SO2, PM2.5, and PM10), there was a consistent negative correlation with O3. Notably, NO2 had the strongest negative correlation (r = -0.825), while PM10 showed the weakest (r = -0.687). From the perspective of meteorological factors, temperature showed a strong positive correlation with O3, while wind speed, relative humidity, and precipitation showed weak negative correlations, influencing regional variations in O3 concentrations. Among the six prediction models constructed to predict O3 concentrations, the most accurate model for predicting concentrations for the next day was the extreme gradient boosting (XGBoost) model (R2 = 0.883). For the next 3 days, the random forest (RF) model demonstrated the highest accuracy (R2 = 0.704). Similarly, the random forest model (RF) also exhibited the highest accuracy for predicting the next 7 days (R2 = 0.651). In summary, over the past 7 years, there has been a strong correlation observed between O3 concentration and other major pollutants, as well as meteorological factors in Henan Province. Therefore, it is essential to implement targeted measures for O3 pollution prevention and control based on specific weather conditions.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Haoming Bao, Jiandong Shang, Jinzhu Li, Gang Wu, Haitao Wei, Lingling Wang, Nan Wang, Jingye Shi, Wenge Zhou, Feng Chen, Jiahui Guo, Jinyang Wang, Dujuan Zhang, and Hengliang Guo

Status: open (until 12 Mar 2025)

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Haoming Bao, Jiandong Shang, Jinzhu Li, Gang Wu, Haitao Wei, Lingling Wang, Nan Wang, Jingye Shi, Wenge Zhou, Feng Chen, Jiahui Guo, Jinyang Wang, Dujuan Zhang, and Hengliang Guo
Haoming Bao, Jiandong Shang, Jinzhu Li, Gang Wu, Haitao Wei, Lingling Wang, Nan Wang, Jingye Shi, Wenge Zhou, Feng Chen, Jiahui Guo, Jinyang Wang, Dujuan Zhang, and Hengliang Guo

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Short summary
An analysis of ozone pollution in Henan Province, China, from 2015 to 2022 was conducted. The spatiotemporal distribution patterns of ozone pollution in Henan Province during this period and its driving factors were examined from the perspectives of pollutant concentrations, meteorological conditions, and socioeconomic factors. Time-series analysis and machine learning techniques were employed to predict both short-term and long-term ozone concentrations in the region.
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