Preprints
https://doi.org/10.5194/egusphere-2026-3926
https://doi.org/10.5194/egusphere-2026-3926
07 Sep 2026
 | 07 Sep 2026
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Monitoring Indian CO2 Emissions: An Integrated Satellite-Based Dual-Species Approach Using TROPOMI and CO2M Retrievals.

Jithin Sukumaran, Dhanyalekshmi Pillai, Gerrit Kuhlmann, Vishnu Thilakan, and Abhinav Dhiman

Abstract. Accurate quantification of CO2 emissions from large point sources is essential for tracking progress in emission-reduction strategies under the Paris Agreement. However, quantifying these emissions remains challenging across the Indian subcontinent due to dense clustering of co-located sources, spatially heterogeneous emissions, and a sparse observational network coupled with insufficient modeling capabilities. This study presents an NO2-guided CO2 plume detection and Cross-Sectional Flux (CSF) emission quantification methodology using high-resolution TROPOMI NO2 and synthetic CO2M XCO2 retrievals. The plume detection and quantification system is evaluated through an Observing System Simulation Experiment conducted across 14 coal-fired thermal power plants (TPPs) in India. CO2M XCO2 retrievals are generated using the Weather Research and Forecasting model integrated with the Greenhouse Gas module by sampling through simulated CO2M satellite tracks. Several scenarios are examined to assess the potential of the quantification system and the impact of background XCO2 estimation error on emission estimates. For the highest emission TPP in this study, Vindhyachal, our CSF approach estimates emissions with remarkable accuracy, with an uncertainty of 9 % in a background-error-free scenario. Since erroneous background XCO2 estimations can have a considerable impact on the above emission estimates, two different methods are implemented to better decouple the anthropogenic contribution from the total measured quantity amid CO2M-level pixel noise, and their effectiveness is assessed with and without the noise-filter algorithm. The assessment of multiple power plants shows that sources exceeding 20 Mt of annual CO2 are reliably quantified across multiple seasons, establishing the operational lower quantification bound of our methodology at ~ 20 Mt CO2 yr-1. By combining TROPOMI observations with future CO2M retrievals, this study evaluates the potential of these data to detect and quantify CO2 emissions from Indian thermal power plants under real observational conditions. Overall, the study demonstrates the operational readiness of the quantification methodology for upcoming satellite missions, as well as for integrated Measurement, Reporting, and Verification (MRV) systems within the Copernicus CO2 Monitoring and Verification Support (CO2MVS) initiative.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Jithin Sukumaran, Dhanyalekshmi Pillai, Gerrit Kuhlmann, Vishnu Thilakan, and Abhinav Dhiman

Status: open (until 12 Oct 2026)

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Jithin Sukumaran, Dhanyalekshmi Pillai, Gerrit Kuhlmann, Vishnu Thilakan, and Abhinav Dhiman
Jithin Sukumaran, Dhanyalekshmi Pillai, Gerrit Kuhlmann, Vishnu Thilakan, and Abhinav Dhiman
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Short summary
This study addresses the challenge of quantifying CO2 emissions from coal-fired thermal power plants in India. A novel plume detection and quantification methodology, using TROPOMI NO2 and CO2M XCO2 measurements, is evaluated across 14 plants. The method reliably quantifies emissions from power plants exceeding 20 megatons annually. By decoupling anthropogenic signals, the study's outcome aids future satellite missions and contributes to the development of reliable MRV systems for the nation.
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