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Spatiotemporal Analysis of XCO 2 and Its Relationship to Urban and Green Areas of China’s Major Southern Cities from Remote Sensing and WRF-Chem Modeling Data from 2010 to 2019

Author

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  • Zixuan Tan

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Jinnian Wang

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Zhenyu Yu

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

  • Yiyun Luo

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China)

Abstract

Monitoring CO 2 concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate for the sparse and uneven spatial distribution of ground observation stations, allowing for the collection of a wide range of CO 2 concentration data. However, satellite monitoring’s spatial coverage remains limited. This study fills the knowledge gaps of column-averaged dry-air mole fraction of CO 2 (XCO 2 ) products retrieved from the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory Satellite (OCO-2) based on the normalized output of atmospheric chemical models, WRF-Chem, in Southern China during 2010–2019. Hefei (HF)/Total Carbon Column Observing Network (TCCON), Lulin (LLN)/World Data Centre for Greenhouse Gases (WDCGG) station observations were used to validate the results of void filling with an acceptable accuracy for spatiotemporal analysis ( R = 0.96, R 2 = 0.92, RMSE = 2.44 ppm). Compared to the IDW (inverse distance weighting) and Kriging (ordinary Kriging) interpolation methods, this method has a higher validation accuracy. In addition, spatiotemporal distributions of CO 2 , as well as the sensitivity of CO 2 concentration to the urban built-up areas and urban green space areas in China’s major southern cities during 2010–2019, are discussed. The approximate annual average concentrations have gradually increased from 388.56 to 414.72 ppm, with an annual growth rate of 6.73%, and the seasonal cycle presents a maximum in spring and a minimum in summer or autumn from 2010 to 2019. CO 2 concentrations have a strong positive correlation with the impervious area to city area ratio, while anomaly values of the impervious area to urban green area ratio occurred in individual cities. The experimental findings demonstrate the viability of the study hypothesis that combines remote sensing data with the WRF-Chem model to produce a local area dataset with high spatial resolution and an extracted urban unit from statistical data.

Suggested Citation

  • Zixuan Tan & Jinnian Wang & Zhenyu Yu & Yiyun Luo, 2023. "Spatiotemporal Analysis of XCO 2 and Its Relationship to Urban and Green Areas of China’s Major Southern Cities from Remote Sensing and WRF-Chem Modeling Data from 2010 to 2019," Geographies, MDPI, vol. 3(2), pages 1-22, March.
    • Handle: RePEc:gam:jgeogr:v:3:y:2023:i:2:p:13-267:d:1112258
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    References listed on IDEAS

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    1. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
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    Cited by:

    1. Qiwen Sun & Xuesheng Zhao & Yiying Hua, 2024. "Dynamic Coupling Between Atmospheric CO 2 Concentration and Land Surface Temperature in Major Urban Agglomerations in China: Insights for Sustainable Urban Development," Sustainability, MDPI, vol. 16(21), pages 1-19, October.

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