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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
Published on: June 13, 2020
Understanding the spatial and seasonal variation of the ground-level ozone in Southeast China with an interpretable
Haobin Zhong1, Ling Zhen2, Qiufang Yao3
1School of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Jiaxing key Laboratory of Preparation and Application of Advanced Materials for Energy Conservation and Emission Reduction, Jiaxing 314001, China.
Abstract:
Ground-level ozone (O3) pollution poses significant threats to both human health and air quality. This study uses ground observations and satellite retrievals to explore the spatiotemporal characteristics of ground-level O3 in Zhejiang Province, China. We created data-driven machine learning models that include meteorological, geographical and atmospheric parameters from multi-source remote sensing products, achieving good performance (Pearson's r of 0.81) in explaining regional O3 dynamics. Analyses revealed the crucial roles of temperature, relative humidity, total column O3, and the distributions and interactions of precursor (volatile organic compounds and nitrogen oxides) in driving the varied O3 patterns observed in Zhejiang. Furthermore, the interpretable modeling quantified multifactor interactions that sustain high O3 levels in spring and autumn, suppress O3 levels in summer, and inhibit O3 formation in winter. This work demonstrates the value of a combined approach using satellite and machine learning as an effective novel tool for regional air quality assessment and control.
