Spatiotemporal Analysis of XCO2 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

Monitoring CO2 concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate sparse and uneven spatial distribution ground observation stations, allowing collection a wide range concentration data. However, satellite monitoring’s coverage remains limited. This study fills knowledge gaps column-averaged dry-air mole fraction (XCO2) products retrieved from Greenhouse Gases Observing (GOSAT) Orbiting Carbon Observatory (OCO-2) based on normalized output atmospheric chemical models, WRF-Chem, Southern China during 2010–2019. Hefei (HF)/Total Column Network (TCCON), Lulin (LLN)/World Data Centre (WDCGG) station observations were used validate results void filling with acceptable accuracy spatiotemporal analysis (R = 0.96, R2 0.92, RMSE 2.44 ppm). Compared IDW (inverse distance weighting) Kriging (ordinary Kriging) interpolation methods, this method has higher validation accuracy. In addition, distributions CO2, as well sensitivity urban built-up areas green space China’s major southern cities 2010–2019, are discussed. The approximate annual average have gradually increased 388.56 414.72 ppm, growth rate 6.73%, seasonal cycle presents maximum spring minimum summer or autumn 2010 2019. strong positive correlation impervious area city ratio, while anomaly values ratio occurred individual cities. experimental findings demonstrate viability hypothesis that combines remote sensing data WRF-Chem model produce local dataset high resolution extracted unit statistical