Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020
Satellite retrievals can capture the spatiotemporal variation of O<sub>3</sub> 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 mode...
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MDPI AG
2025-04-01
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| author | Shoutao Zhu Bin Zou Xinyu Huang Ning Liu Shenxin Li |
| author_facet | Shoutao Zhu Bin Zou Xinyu Huang Ning Liu Shenxin Li |
| author_sort | Shoutao Zhu |
| collection | DOAJ |
| description | Satellite retrievals can capture the spatiotemporal variation of O<sub>3</sub> 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 O<sub>3</sub> concentration datasets of 0.1° in China from 2005 to 2020 using O<sub>3</sub> precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O<sub>3</sub> 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 <i>R</i><sup>2</sup> and <i>RMSE</i> of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m<sup>3</sup>. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual <i>R</i><sup>2</sup> increasing from 0.59 to 0.8, and the <i>RMSE</i> decreasing from 17.24 μg/m<sup>3</sup> to 10.74 μg/m<sup>3</sup>, which is significantly better than that of the <i>RF</i> 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 O<sub>3</sub> 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. |
| format | Article |
| id | doaj-art-0ebe59237db643f988b91ef994474c56 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
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| series | Remote Sensing |
| spelling | doaj-art-0ebe59237db643f988b91ef994474c562025-08-20T02:31:16ZengMDPI AGRemote Sensing2072-42922025-04-01179153410.3390/rs17091534Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020Shoutao Zhu0Bin Zou1Xinyu Huang2Ning Liu3Shenxin Li4School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSatellite retrievals can capture the spatiotemporal variation of O<sub>3</sub> 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 O<sub>3</sub> concentration datasets of 0.1° in China from 2005 to 2020 using O<sub>3</sub> precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O<sub>3</sub> 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 <i>R</i><sup>2</sup> and <i>RMSE</i> of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m<sup>3</sup>. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual <i>R</i><sup>2</sup> increasing from 0.59 to 0.8, and the <i>RMSE</i> decreasing from 17.24 μg/m<sup>3</sup> to 10.74 μg/m<sup>3</sup>, which is significantly better than that of the <i>RF</i> 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 O<sub>3</sub> 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.https://www.mdpi.com/2072-4292/17/9/1534O<sub>3</sub>residualsoutlier samplessatellite mapping |
| spellingShingle | Shoutao Zhu Bin Zou Xinyu Huang Ning Liu Shenxin Li Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 Remote Sensing O<sub>3</sub> residuals outlier samples satellite mapping |
| title | Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 |
| title_full | Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 |
| title_fullStr | Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 |
| title_full_unstemmed | Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 |
| title_short | Time-Series Modeling of Ozone Concentrations Constrained by Residual Variance in China from 2005 to 2020 |
| title_sort | time series modeling of ozone concentrations constrained by residual variance in china from 2005 to 2020 |
| topic | O<sub>3</sub> residuals outlier samples satellite mapping |
| url | https://www.mdpi.com/2072-4292/17/9/1534 |
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