Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China

Mingliang Ma (), Mengjiao Liu, Mengnan Liu, Huaqiao Xing, Yuqiang Wang and Fei Meng ()
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Mingliang Ma: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Mengjiao Liu: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Mengnan Liu: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Huaqiao Xing: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Yuqiang Wang: Disaster Reduction Center of Shandong Province, Jinan 250102, China
Fei Meng: School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

Sustainability, 2023, vol. 16, issue 1, 1-22

Abstract: Surface ozone pollution in China has been persistently becoming worse in recent years; therefore, it is of great importance to accurately estimate ozone pollution and explore the spatiotemporal variations in surface ozone in East China. By using S5P-TROPOMI-observed NO 2 , HCHO data (7 km × 3.5 km), and other surface-ozone-influencing factors, including VOCs, meteorological data, NO X emission inventory, NDVI, DEM, population, land use and land cover, and hourly in situ surface ozone observations, an extreme gradient boosting model was used to estimate the daily 0.05° × 0.05° gridded maximum daily average 8 h ozone (MDA8) in East China during 2019–2021. Four surface ozone estimation models were established by combining NO 2 and HCHO data from S5P-TROPOMI observations and CAMS reanalysis data. The sample-based validation R 2 values of these four models were all larger than 0.92, while their site-based validation R 2 values were larger than 0.82. The results revealed that the coverage ratio of the model using CAMS NO 2 and CAMS HCHO was the highest (100%), while the coverage ratio of the model using S5P-TROPOMI NO 2 and CAMS HCHO was the second highest (96.26%). Furthermore, the MDA8 estimation results of these two models were averaged to produce the final surface ozone estimation dataset. It indicated that O 3 pollution in East China during 2019–2021 was susceptible to anthropogenic precursors such as VOCs (22.55%) and NO X (8.97%), as well as meteorological factors (27.35%) such as wind direction, temperature, and wind speed. Subsequently, the spatiotemporal patterns of ozone pollution were analyzed. Ozone pollution in East China is mainly concentrated in the North China Plain (NCP), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD). Among these three regions, ozone pollution in the NCP mainly occurs in June (summer), ozone pollution in the YRD mainly occurs in May (spring), and ozone pollution in the PRD mainly occurs in April (spring) and September (autumn). In addition, surface O 3 concentration in East China decreased by 3.74% in 2020 compared to 2019, which may have been influenced by the COVID-19 epidemic and the implementation of the policy of synergistic management of PM 2.5 and O 3 pollution. The regions mostly affected by the COVID-19 epidemic and the policy of the synergistic management of PM 2.5 and O 3 pollution were the NCP (−2~−8%), the Middle and Lower of Yangtze Plain (−6~−10%), and the PRD (−4~−10%). Overall, the estimated 0.05° × 0.05° gridded surface ozone in East China from 2019 to 2021 provides a promising data source and data analysis basis for the related researchers. Meanwhile, it reveals the spatial and temporal patterns of O 3 pollution and the main influencing factors, which provides a good basis for the control and management of O 3 pollution, and also provides technical support for the sustainable development of the environment in East China.

Keywords: surface ozone; ozone pollution; East China; machine learning model; ozone estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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