Satellite-Based Estimation of Urban CO 2 Emissions in Shandong Province, China, Using TROPOMI NO 2 Observations and Differential Evolution Algorithm.
Saved in:
| Title: | Satellite-Based Estimation of Urban CO 2 Emissions in Shandong Province, China, Using TROPOMI NO 2 Observations and Differential Evolution Algorithm. |
|---|---|
| Authors: | Xie, Yu1,2 (AUTHOR), Wang, Wei2,3 (AUTHOR), Liang, Bin3 (AUTHOR), Wu, Yongfei2 (AUTHOR), Dai, Chengyu2 (AUTHOR), Gao, Jun1 (AUTHOR) gaojun@nies.org |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1470. 26p. |
| Subjects: | Carbon emissions, Satellite-based remote sensing, Differential evolution, Greenhouse gas analysis, Provinces |
| Geographic Terms: | Jinan (Shandong Sheng, China), China, Qingdao (China), Shandong Sheng (China) |
| Abstract: | Highlights: What are the main findings? An exploratory top-down framework was established using TROPOMI NO 2 and a differential evolution algorithm, exhibiting consistent city-scale CO 2 emission estimation performance ( R 2 = 0.95 ). Spatial heterogeneity was identified across Shandong Province, with Jinan, Linyi, and Qingdao identified as the primary anthropogenic CO 2 hotspots within this seasonal window. What are the implications of the main findings? The implementation of the NO 2 – CO 2 relationship as a proxy provides a complementary, high-frequency technical perspective for carbon accounting in complex urban environments. The refined city-scale emission maps offer useful data support for characterizing localized emission patterns and evaluating regional carbon mitigation strategies. Since the Industrial Revolution, anthropogenic activities, primarily fossil fuel combustion, have driven a sharp increase in CO 2 emissions, making them the principal driver of global climate change. Precise monitoring and quantification of CO 2 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 CO 2 sensors, NO 2 observation satellites provide higher spatiotemporal resolution. Given that NO 2 and CO 2 are co-emitted during combustion with a stable relationship, NO 2 can serve as an effective proxy to indirectly derive CO 2 emissions. In this study, an exploratory framework for city-scale CO 2 estimation was developed using TROPOMI NO 2 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 NO 2 emissions and atmospheric lifetimes. The NO 2 -to-CO 2 relationship was established based on sector-weighted inventory data to quantify fossil-fuel CO 2 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? An exploratory top-down framework was established using TROPOMI NO 2 and a differential evolution algorithm, exhibiting consistent city-scale CO 2 emission estimation performance ( R 2 = 0.95 ). Spatial heterogeneity was identified across Shandong Province, with Jinan, Linyi, and Qingdao identified as the primary anthropogenic CO 2 hotspots within this seasonal window. What are the implications of the main findings? The implementation of the NO 2 – CO 2 relationship as a proxy provides a complementary, high-frequency technical perspective for carbon accounting in complex urban environments. The refined city-scale emission maps offer useful data support for characterizing localized emission patterns and evaluating regional carbon mitigation strategies. Since the Industrial Revolution, anthropogenic activities, primarily fossil fuel combustion, have driven a sharp increase in CO 2 emissions, making them the principal driver of global climate change. Precise monitoring and quantification of CO 2 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 CO 2 sensors, NO 2 observation satellites provide higher spatiotemporal resolution. Given that NO 2 and CO 2 are co-emitted during combustion with a stable relationship, NO 2 can serve as an effective proxy to indirectly derive CO 2 emissions. In this study, an exploratory framework for city-scale CO 2 estimation was developed using TROPOMI NO 2 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 NO 2 emissions and atmospheric lifetimes. The NO 2 -to-CO 2 relationship was established based on sector-weighted inventory data to quantify fossil-fuel CO 2 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. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101470 |
