View in EDS HTML Full Text PDF Full Text

Satellite-Based Estimation of Urban CO 2 Emissions in Shandong Province, China, Using TROPOMI NO 2 Observations and Differential Evolution Algorithm.

Saved in:
Bibliographic Details
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.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194140995
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Satellite-Based Estimation of Urban CO 2 Emissions in Shandong Province, China, Using TROPOMI NO 2 Observations and Differential Evolution Algorithm.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xie%2C+Yu%22">Xie, Yu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Wei%22">Wang, Wei</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Bin%22">Liang, Bin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yongfei%22">Wu, Yongfei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Chengyu%22">Dai, Chengyu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Jun%22">Gao, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gaojun@nies.org</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1470. 26p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Greenhouse+gas+analysis%22">Greenhouse gas analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Provinces%22">Provinces</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Jinan+%28Shandong+Sheng%2C+China%29%22">Jinan (Shandong Sheng, China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Qingdao+%28China%29%22">Qingdao (China)</searchLink><br /><searchLink fieldCode="DE" term="%22Shandong+Sheng+%28China%29%22">Shandong Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194140995
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18101470
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1470
    Subjects:
      – SubjectFull: Carbon emissions
        Type: general
      – SubjectFull: Satellite-based remote sensing
        Type: general
      – SubjectFull: Differential evolution
        Type: general
      – SubjectFull: Greenhouse gas analysis
        Type: general
      – SubjectFull: Provinces
        Type: general
      – SubjectFull: Jinan (Shandong Sheng, China)
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: Qingdao (China)
        Type: general
      – SubjectFull: Shandong Sheng (China)
        Type: general
    Titles:
      – TitleFull: Satellite-Based Estimation of Urban CO 2 Emissions in Shandong Province, China, Using TROPOMI NO 2 Observations and Differential Evolution Algorithm.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Xie, Yu
      – PersonEntity:
          Name:
            NameFull: Wang, Wei
      – PersonEntity:
          Name:
            NameFull: Liang, Bin
      – PersonEntity:
          Name:
            NameFull: Wu, Yongfei
      – PersonEntity:
          Name:
            NameFull: Dai, Chengyu
      – PersonEntity:
          Name:
            NameFull: Gao, Jun
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1