Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 - Shoutao Zhu; Bin Zou; Xinyu Huang; Ning Liu; Shenxin Li (Remote Sensing)
Satellite retrievals can capture the spatiotemporal variation of O3 over a large area near the surface. However, due to the unstable functional relationships between variables across spatiotemporal scales, the outlier predictions will reduce the accuracy of the prediction model. Therefore, a validated residual constrained random forest model (RF-RVC) is proposed to estimate the monthly and annual O3 concentration datasets of 0.1° in China from 2005 to 2020 using O3 precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O3 concentrations in China and the four urban agglomerations (Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan–Chongqing (SC)) were calculated. The results show that the annual R2 and RMSE of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m3. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual R2 increasing from 0.59 to 0.8, and the RMSE decreasing from 17.24 μg/m3 to 10.74 μg/m3, which is significantly better than that of the RF model. The North China Plain is the focus of ozone pollution. Summer is the season of a high incidence of ozone pollution in China, YRD, PYD, and SC, while pollution in the PRD is delayed to October due to the monsoon. In addition, the trend of the O3 and its excess proportion in China and the four urban agglomerations is not satisfactory; targeted measures should be taken to reduce the risk of environmental ozone. The research findings confirm the effectiveness of the residual constraint approach in long-term time-series modeling. In the future, it can be further extended to the modeling of other pollutants, providing more accurate data support for health risk assessments.