Work

Satellite-Based Estimation of Urban CO2 Emissions in Shandong Province, China, Using TROPOMI NO2 Observations and Differential Evolution Algorithm

Year: 2026

Type: article

Abstract: Since the Industrial Revolution, anthropogenic activities, primarily fossil fuel combustion, have driven a sharp increase in CO2 emissions, making them the principal driver of global climate change. Precise monitoring and quantification of CO2 emissions are essential for effective greenhouse gas mitigation. Traditional “bottom-up” inventories often suffer from limited timeliness, low spatial resolution, and significant uncertainties. Satellite remote sensing offers an alternative “top-down” approach for emission estimation. Compared to existing CO2 sensors, NO2 observation satellites provide higher spatiotemporal resolution. Given that NO2 and CO2 are co-emitted during combustion with a stable relationship, NO2 can serve as an effective proxy to indirectly derive CO2 emissions. In this study, an exploratory framework for city-scale CO2 estimation was developed using TROPOMI NO2 column concentrations, MERRA-2 wind fields, EDGAR inventory and the ODIAC inventory. The analysis focused on seven major cities in Shandong Province, China, from April to September 2022. By integrating a wind-rotation technique with a line density model and the differential evolution (DE) algorithm, we derived NO2 emissions and atmospheric lifetimes. The NO2-to-CO2 relationship was established based on sector-weighted inventory data to quantify fossil-fuel CO2 fluxes. The results identify Qingdao, Jinan, and Linyi as emission hotspots, followed by Rizhao, with lower emissions observed in Yantai, Liaocheng, and Jining. Comparison with the ODIAC inventory illustrates that this framework provides a top-down constraint for identifying localized emission characteristics and potential discrepancies in bottom-up datasets. This study offers a complementary tool for near-real-time urban carbon monitoring during the non-heating season.

Source: Remote Sensing

DOI: 10.3390/rs18101470

Authors: Yu Xie, Wei Wang, Bin Liang, Yongfei Wu, Chengyu Dai, Jun Gao