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<p>The second most important greenhouse gas, methane (CH<sub>4</sub>), has a significantly greater potential to cause global warming. This study aimed to evaluate the effects of hydro-meteorological factors on CH<sub>4</sub> concentration in eastern Indian ( Bihar, Chhattisgarh, Jharkhand, Odisha, and West Bengal) during 2019–2024 using Sentinel-5P products. The highest concentration was found at an elevation less than 100&#xa0;m and concentration decreased with increase in elevation level. Higher CH<sub>4</sub> concentration was associated with higher range of Topographical Wetness Index. Crop land constitutes the maximum concentration followed by grassland and forest. Methane concentration followed distinct seasonal pattern with maxima in post-monsoon and minima in summer. Multi-linear regression predicted CH<sub>4</sub> with less explanation of variation while Random Forest Regression, Support Vector Regression and Multi-Layer Perceptron (MLP) predicted CH<sub>4</sub> with higher accuracy. The MLP model was evaluated as the best. Surface soil moisture, precipitation and actual evapotranspiration were found to exhibit greater weightage on CH<sub>4</sub> prediction. The results highlight the association of hydro-meteorological drivers with CH<sub>4</sub> concentration and the critical role of the drivers. The differential impact of the drivers on CH<sub>4</sub> across a large geographical region indicated the importance of detailed regional studies for accurate prediction.</p>

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Assessing the impact of hydro-meteorological factors on tropospheric methane concentration using satellite observations from 2019 to 2024

  • 期刊论文

  • 发表日期：

    2025年12月13日

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  • Argha Ghosh,
  • Sudipta Manna,
  • Arnab Mandal,
  • Akhilesh Kumar Gupta

摘要

The second most important greenhouse gas, methane (CH4), has a significantly greater potential to cause global warming. This study aimed to evaluate the effects of hydro-meteorological factors on CH4 concentration in eastern Indian ( Bihar, Chhattisgarh, Jharkhand, Odisha, and West Bengal) during 2019–2024 using Sentinel-5P products. The highest concentration was found at an elevation less than 100 m and concentration decreased with increase in elevation level. Higher CH4 concentration was associated with higher range of Topographical Wetness Index. Crop land constitutes the maximum concentration followed by grassland and forest. Methane concentration followed distinct seasonal pattern with maxima in post-monsoon and minima in summer. Multi-linear regression predicted CH4 with less explanation of variation while Random Forest Regression, Support Vector Regression and Multi-Layer Perceptron (MLP) predicted CH4 with higher accuracy. The MLP model was evaluated as the best. Surface soil moisture, precipitation and actual evapotranspiration were found to exhibit greater weightage on CH4 prediction. The results highlight the association of hydro-meteorological drivers with CH4 concentration and the critical role of the drivers. The differential impact of the drivers on CH4 across a large geographical region indicated the importance of detailed regional studies for accurate prediction.