the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Influencing factors analysis and prediction of near-surface ozone in Henan Province from 2015 to 2022
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.
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