Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region

D.Venkata, Mahalakshmi; Pathakoti, Mahesh; Kanchana, A. Lakshmi; Peethani, Sujatha; Shaik, Ibrahim; Rajan, Krishnan Sundara; Sagar, Vijay Kumar; Raja, Pushpanathan; Tiwari, Yogesh Kumar; Prakash, Chauhan

doi:https://doi.org/10.5194/egusphere-2024-405

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https://doi.org/10.5194/egusphere-2024-405

Preprints

15 Mar 2024

| 15 Mar 2024

Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region

Mahalakshmi D.Venkata, Mahesh Pathakoti, A. Lakshmi Kanchana, Sujatha Peethani, Ibrahim Shaik, Krishnan Sundara Rajan, Vijay Kumar Sagar, Pushpanathan Raja, Yogesh Kumar Tiwari, and Chauhan Prakash

Abstract. Atmospheric methane (CH₄) is a potent climate change agent responsible for a fraction of global warming. The present study investigated the spatial and temporal variability of atmospheric column-averaged (X) CH₄ (XCH₄) concentrations using Greenhouse gases Observing SATellite (GOSAT) and TROPOspheric Monitoring Instrument onboard the Sentienl-5 Precursor (S5P/TROPOMI) data from 2009 to 2022 over the South Asia region. During the study period, the long-term trends in XCH₄ increased from 1700 ppb to 1950 ppb with an annual growth rate of 8.76 ppb year^-1. Among all natural and anthropogenic sources of CH₄, the rate of increase in XCH₄ was higher over the Mundra thermal power station and Mundra ultra mega power plant at about 9.62 ppb year^-1, followed by the coal site at about 8.76 ppb year^-1 (Korba). With a growth rate of 8.61 ppb year^-1, the Sundarbans natural wetland competes with coal sites, producing over 30 MT, indicating an equivalent anthropogenic source. For the 15 Indian Agroclimatic zones, significant high emissions of CH₄ were observed over the Middle Gangetic Plains (MGP), Trans Gangetic Plains (TGP), Upper Gangetic Plains (UGP), East Coast Plains & Hills (ECPH), Lower Gangetic Plains (LGP) and East Gangetic Plains (EGP). Further, the bottom-up anthropogenic CH₄ emissions data are mapped against the XCH₄ concentrations and found high correlation in the Indo Gangetic Plains (IGP) region, indicating the hotspots of anthropogenic CH₄. The present study highlighted the impact of natural and anthropogenic sources of XCH₄ and quantified the spatio-temporal changes in XCH₄ at each study site over the Indian region.

Received: 12 Feb 2024 – Discussion started: 15 Mar 2024

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Journal article(s) based on this preprint

20 Nov 2024

Emissions of methane from coal fields, thermal power plants, and wetlands and their implications for atmospheric methane across the south Asian region

Mahalakshmi Venkata Dangeti, Mahesh Pathakoti, Kanchana Lakshmi Asuri, Sujatha Peethani, Ibrahim Shaik, Rajan Krishnan Sundara, Vijay Kumar Sagar, Raja Pushpanathan, Yogesh Kumar Tiwari, and Prakash Chauhan

Atmos. Chem. Phys., 24, 12843–12859, https://doi.org/10.5194/acp-24-12843-2024,https://doi.org/10.5194/acp-24-12843-2024, 2024

Short summary

Mahalakshmi D.Venkata, Mahesh Pathakoti, A. Lakshmi Kanchana, Sujatha Peethani, Ibrahim Shaik, Krishnan Sundara Rajan, Vijay Kumar Sagar, Pushpanathan Raja, Yogesh Kumar Tiwari, and Chauhan Prakash

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-405', Anonymous Referee #1, 27 Mar 2024

The manuscript “Emissions of Methane from Coal, Thermal power plants and Wetlands and its Implications on Atmospheric Methane across the South Asian Region” investigates the spatial and temporal dynamics of atmospheric methane (CH4) concentrations over the South Asia region from 2009 to 2022, utilizing data from the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor (S5P/TROPOMI). The analysis identifies specific sources contributing to this increase, notably the Mundra thermal power station and Mundra ultra mega power plant, exhibiting higher rates of increase in XCH4 compared to other natural and anthropogenic sources. The study also highlights the significant methane emissions from the Sundarbans natural wetland, competing with coal sites in terms of emission rates, thus emphasizing its importance as an equivalent anthropogenic source. Furthermore, the investigation delves into the distribution of CH4 emissions across 15 Indian Agroclimatic zones; and employs bottom-up anthropogenic CH4 emissions data to map against XCH4 concentrations, revealing a high correlation in the Indo Gangetic Plains (IGP) region, thereby identifying key anthropogenic CH4 hotspots. Overall, the manuscript provides crucial insights into the impact of both natural and anthropogenic sources on XCH4 concentrations over the Indian region, quantifying spatio-temporal changes at each study site. The findings hold significance for understanding and addressing the complex dynamics of atmospheric methane, a potent climate change agent.
Atmospheric methane (CH4) is one of the high-potential greenhouse gases (GHG) that regulates the chemical reactions in the free troposphere and stratosphere.
Comment: Here, ‘regulates’ does not seem appropriate.
Comment: Could figures 1a, 1b, and 1c be merged into a single figure where all the sources are indicated with different shapes/colors?
Three heterogenic CH4 source regions
Comment: Are the entities depicted in the figure referred to as heterogenic source regions or individual sources? Coal fields and thermal power plants are primarily situated within the same region.
The Ministry of Environment, Forests and Climate Change (MoEF&CC), Government of India, has identified 75 Ramsar Wetland sites in India as of November 2022. These sites span a total area of 115 13,35,530 ha. Based on the high total geographical area coverage (Table 1), the top 10 places were determined for the current investigation. The size varies from 423000 ha (Sundarbans Wetland, West Bengal) to 18900 ha (Wular Lake, Jammu and Kashmir).
Comment: Reference for the aforementioned statement.
In the present study, the atmospheric CH4 was obtained from 2009 to 2020….
Comment: As previously stated and indicated in Figure 2, data up to 2022 was utilized for the current study.
Comment: There is no mention of gridding of level 2 data, if any has been applied. Have all the datasets been gridded to the same resolution?
Comment: In Figure 3, regarding the TROPOMI data from 2019 to 2022, is it averaged over this period or does it represent all the observations?
There are data gaps in Tropomi, which are assumed to occur during the monsoon season. However, these gaps are absent in GOSAT. Are you employing any data-filling method for the GOSAT data?
Figures 5a-c shows the monthly time series of XCH4 over the specific sources of CH4 dotted in the Indian region during 2009 to 2020.
Comment: dotted?
The seasonal cycle (peak and trough) of XCH4 is strongly associated with the vegetation during the active phase of cultivation and reduced photochemical reaction by the hydroxyl radicals, respectively.
Comment: could you provide a reference for the statement? Considering wetlands and coal as the largest emitter of CH4, how is the seasonal cycle associated with this?
A seasonal maximum of XCH4 was observed over Coal and Thermal power plants from September to October and a minimum in pre-monsoon (March-May).
Comment: Are you associating this cycle with rice cultivation?
Comment: There is a typo in Figure 7, indi. Does the spatial distribution represent the mean of all the years?
Comment: The descriptions of Figures 7 and 8 do not match their respective figures. Additionally, the description of Figure 8 appears before that of Figure 7.
Significant high emissions of CH4, as shown in Figure 7c
Comment: Figure 7c is missing.
Additional Suggestions
Emissions from EDGAR only verify the anthropogenic emissions, but there is a significant wetland source situated over India that requires verification with the appropriate inventory.
Additionally, previous studies have demonstrated the limitations of satellite data during the monsoon season and the biases associated with global inventories such as EDGAR, over the Indian region. Therefore, it would be appropriate to study the uncertainty associated with the emissions and XCH4 from their respective datasets.
There are extensive studies of XCH4 over India from satellites have been conducted by Mottungan et al. However, the authors have not reffered them in their study.

Citation: https://doi.org/10.5194/egusphere-2024-405-RC1
- AC3:
  'Reply on RC1', Mahesh Pathakoti, 27 May 2024
  Response to Reviewer -1
  The manuscript “Emissions of Methane from Coal, Thermal power plants and Wetlands and its Implications on Atmospheric Methane across the South Asian Region” investigates the spatial and temporal dynamics of atmospheric methane (CH₄) concentrations over the South Asia region from 2009 to 2022, utilizing data from the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor (S5P/TROPOMI). The analysis identifies specific sources contributing to this increase, notably the Mundra thermal power station and Mundra ultra mega power plant, exhibiting higher rates of increase in XCH₄ compared to other natural and anthropogenic sources. The study also highlights the significant methane emissions from the Sundarbans natural wetland, competing with coal sites in terms of emission rates, thus emphasizing its importance as an equivalent anthropogenic source. Furthermore, the investigation delves into the distribution of CH₄ emissions across 15 Indian Agroclimatic zones; and employs bottom-up anthropogenic CH₄ emissions data to map against XCH₄ concentrations, revealing a high correlation in the Indo Gangetic Plains (IGP) region, thereby identifying key anthropogenic CH₄ hotspots. Overall, the manuscript provides crucial insights into the impact of both natural and anthropogenic sources on XCH₄ concentrations over the Indian region, quantifying spatio-temporal changes at each study site. The findings hold significance for understanding and addressing the complex dynamics of atmospheric methane, a potent climate change agent.
  Reply: Thank you for the overall feedback on the present work. We thank you for the constructive comments. In the revised work, we have addressed all the suggested comments point-by-point carefully.
  1. Atmospheric methane (CH₄) is one of the high-potential greenhouse gases (GHG) that regulates the chemical reactions in the free troposphere and stratosphere. Comment: Here, ‘regulates’ does not seem appropriate.
  Reply: Thank you for the suggestion. We have modified the sentence as below and the same has been updated in the revised manuscript.
  Atmospheric methane (CH₄) is one of the high-potential greenhouse gases (GHG) and plays a vital role in the chemistry of the atmosphere. In the troposphere CH₄ oxidation is due to hydroxyl radical (OH) and results in the production of carbon monoxide, CO₂ and ozone in the presence of increased amounts of oxides of nitrogen where as in the stratosphere, oxidation of CH₄ is by OH radical, atomic oxygen and chlorine (Nair & Kavitha, 2020).
  2. Comment: Could figures 1a, 1b, and 1c be merged into a single figure where all the sources are indicated with different shapes/colors? Three heterogenic CH₄ source regions. Comment: Are the entities depicted in the figure referred to as heterogenic source regions or individual sources? Coal fields and thermal power plants are primarily situated within the same region.
  Reply: As suggested, Figure 1a, 1b and 1c are merged into single figure as shown below. The entities depicted in the figure are individual sources. The figure 1 below shows the top 10 wetlands (circle) which is natural sources of CH₄. The top 10 coal mine locations (star symbol), top 10 thermal power plants (triangle) are anthropogenic sources of CH_4. The same has been updated in the revised manuscript.
  3. The Ministry of Environment, Forests and Climate Change (MoEF&CC), Government of India, has identified 75 Ramsar Wetland sites in India as of November 2022. These sites span a total area of 13,35,530 ha. Based on the high total geographical area coverage (Table 1), the top 10 places were determined for the current investigation. The size varies from 423000 ha (Sundarbans Wetland, West Bengal) to 18900 ha (Wular Lake, Jammu and Kashmir). Comment: Reference for the aforementioned statement.
  Reply: The reference for the aforementioned statements are given below:
  https://indianwetlands.in/wetlands-overview/indias-wetlands-of-international-importance/
  
  PIB Press Release on World Wetlands Day dated 26^th August, 2022.
  
  The same has been updated in the revised manuscript.
  4. In the present study, the atmospheric CH₄ was obtained from 2009 to 2020…. Comment: As previously stated and indicated in Figure 2, data up to 2022 was utilized for the current study.
  Reply: Thank you for the suggestion. For studying the natural sources (wetlands) and anthropogenic sources (coal and thermal power plants) of CH₄, the XCH₄ concentration was taken for the period 2009 to 2022 is shown in figure 5. To maintain the uniformity in the datasets, we have updated the figure 5 with 2021 and 2022 years as shown below.
  Figure 5. Monthly time series of XCH₄ over the a) wetlands, b) thermal power stations, and c) coal fields: sources of emissions, along with the overall growth rate at the respective site.
  5. Comment: There is no mention of gridding of level 2 data, if any has been applied. Have all the datasets been gridded to the same resolution?
  Reply: We have re-gridded the data to the same resolution
  6. Comment: In Figure 3, regarding the TROPOMI data from 2019 to 2022, is it averaged over this period or does it represent all the observations? There are data gaps in Tropomi, which are assumed to occur during the monsoon season. However, these gaps are absent in GOSAT. Are you employing any data-filling method for the GOSAT data?
  Reply: Figure 3c shows the averaged TROPOMI data from 2019 to 2022. As you pointed there are data gaps observed both in TROPOMI and GOSAT. The spatiotemporal variability of XCH₄ as discussed in figure 5 has few data gaps in the daily data during monsoon season which is due to the influence of cloud cover and sensor observation mode. However over the study locations a good number of representative XCH₄ data was observed in each month. Therefore we could analyse space time variability of XCH₄ over the study sites.
  7. Figures 5a-c shows the monthly time series of XCH₄ over the specific sources of CH₄ dotted in the Indian region during 2009 to 2020. Comment: dotted?
  Reply: Thank you for the comment. Our intended meaning was different sources situated/pointed in the figure. However sentence has been modified now. The figures 5a-c shows the monthly time series of XCH₄ over the specific sources of CH₄ plotted for the Indian Region during 2009 to 2022. This figure is now updated with 2021 and 2022 data and same is reflected in the revised manuscript.
  8. The seasonal cycle (peak and trough) of XCH₄ is strongly associated with the vegetation during the active phase of cultivation and reduced photochemical reaction by the hydroxyl radicals, respectively. Comment: could you provide a reference for the statement? Considering wetlands and coal as the largest emitter of CH₄, how is the seasonal cycle associated with this? A seasonal maximum of XCH₄ was observed over Coal and Thermal power plants from September to October and a minimum in pre-monsoon (March-May). Comment: Are you associating this cycle with rice cultivation?
  Reply: Sreenivas et al. (2016) in his studies observed high CH₄ concentrations during post-monsoon which may be associated with Kharif season. Low concentrations of CH₄ are observed during monsoon which is due to reduction in atmospheric hydrocarbons because of the reduced photochemical reactions and significant drop in solar intensity. As you rightly pointed seasonal maximum is also strongly associated with rice cultivation. Kavitha et al. (2016) also reported that different seasonal behaviour with seasonal peak in post-monsoon and low during monsoon in the Southern peninsular regions. These regional variations are to distribution of sources like livestock population, rice cultivation, wetland, biomass burning and oil and gas mining.
  9. Comment: There is a typo in Figure 7, indi. Does the spatial distribution represent the mean of all the years?
  Reply: The typo has been corrected in figure 7 (indi to Indian Region).
  10. Comment: The descriptions of Figures 7 and 8 do not match their respective figures. Additionally, the description of Figure 8 appears before that of Figure 7.
  Reply: Thank you. The figures numbers were wrongly written in the text of the manuscript. Figure 7 shows the continuous XCH₄ data from S5P/ TROPOMI complementing the GOSAT efforts instead of figure 8 which was written in the manuscript. In section 4.4, significant high emissions of CH₄ as shown in figure 9c, instead of figure 7c which was written in the manuscript. The figures numbers were corrected in the revised manuscript.
  11. Significant high emissions of CH₄, as shown in Figure 7c. Comment: Figure 7c is missing.
  Reply: The figure in section 4.3 is figure 9c instead of figure 7c and the same has been corrected in the revised manuscript.
  Additional Suggestions
  1. Emissions from EDGAR only verify the anthropogenic emissions, but there is a significant wetland source situated over India that requires verification with the appropriate inventory.
  Reply: We thank you for the important suggestion. We have implemented the emissions from the natural sources (wetlands) using the inventory “WetCHARTs v1.3.1”. This has really helped us to apportion the contribution from the natural and anthropogenic emissions of CH₄. The below figure 8 shows the monthly time series of methane emissions (mg m^-2 month^-1) over the wetland sites, inset figure shows the seasonal methane emissions over the wetland sites from 2001 to 2019. As suggested, introduced new figure 8 in the revised manuscript.
  2. Additionally, previous studies have demonstrated the limitations of satellite data during the monsoon season and the biases associated with global inventories such as EDGAR, over the Indian region. Therefore, it would be appropriate to study the uncertainty associated with the emissions and XCH₄ from their respective datasets.
  Reply: We agree with you suggestion. Present study included the relevant supporting information attributed to the uncertainties associated with the EDGAR based bottom-up CH₄ emission inventory and biases associated with the XCH₄ retrievals from the Remote Sensing sensors. However, this inventory helps to map the high emission (hotspots) zones of CH₄ and associated activities.
  Uncertainties in the information on source intensity, activity and other statistical data are the key parameters for the uncertainties in the EDGAR emission inventory (Janardanan et al., 2017).
  
  Bottom up inventory uncertainties range between 20 and 35% for agriculture, waste and fossil fuel sectors; 50% for biomass burning and natural wetland emissions and 100% or higher for natural sources such as geological seeps and inland waters for global methane emissions (Saunois et al.,. 2020).
  
  Ground based FTIR measurements of XCH₄ by the Total Carbon Column Observing Network (TCCON) are used extensively to validate the GOSAT retrievals. Retrieval bias and precision of column abundance from GOSAT SWIR observations have been estimated as approximately 15-20 ppb and 1% respectively (Morino et al., 2011; Yoshida et al., 2013).
  
  Methane retrieval from TROPOMI is in overall agreement with correlative ground based from TCCON and Network for Detection of Atmospheric Composition Change (NDACC). The systematic differences of the bias corrected XCH₄ data with respect to TCCON data and NDACC data are on average, -0.26±0.56% and 0.57±0.83% respectively (Song et al., 2023).
  
  3. There are extensive studies of XCH₄ over India from satellites have been conducted by Mottungan et al. However, the authors have not reffered them in their study.
  Reply: We sincerely appreciate the previous works carried out by Mottungan et al., which were missed in our work inadvertently. However, we could read those papers and utilised in the revised work to strengthen the present work.
  
  Citation: https://doi.org/10.5194/egusphere-2024-405-AC3
RC2:
'Comment on egusphere-2024-405', Anonymous Referee #2, 23 Apr 2024
The current manuscript titled “Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region” by Mahalakshmi et al., carried out a detailed study on atmospheric column CH4 using the satellite data and bottom-up emission inventory data. This work is well executed and importantly carried out an extensive analysis over different source type of methane which is an important approach. The content is well-written and structured. The study looked into the effects of changes Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region. Author (s) could use potentially the S5P/TROPOMI observations to map the point level sources. The present study has many important points in which it highlights the emission source versus concentrations in the IGP region. Also they carried out methane emissions variability in the agroclimatic zones.
Therefore, I believe this paper may be accepted with the following minor corrections.

In the title capitalisation of every letter of the word may not be required.

205: Is there any references supporting this statement “higher concentrations of CH4 were observed in the Indo-Gangetic Plain (IGP) and northwest (NW) areas of India, southeast of China, and NW of China. Southern China and north China are marked with wetlands and rice paddy fields, which are the primary sources of CH4”

Figures 5c adjust the x axis scale accordingly with the Fig. 5(a) and (b)

There is a typo in the caption of Figure 7, indi. “Figure 7. S5P/TROPOMI XCH4 gridded to 0.05° × 0.05° over Indi and XCH4 over wetland, coal, and thermal power plant sites with a radius of 100 km”

Significant high emissions of CH4, as shown in Figure 7c, but there is no Figure 7c it is missing.

Figures 7 and 8 are described differently than their respective figures. Furthermore, the description of Figure 8 comes before that of Figure 7.
Citation: https://doi.org/10.5194/egusphere-2024-405-RC2
- AC1: 'Reply on RC2', Mahesh Pathakoti, 27 May 2024
  
  Publisher’s note: this comment is a copy of AC2 and its content was therefore removed.
  
  Citation: https://doi.org/10.5194/egusphere-2024-405-AC1
- AC2:
  'Reply on RC2', Mahesh Pathakoti, 27 May 2024
  The current manuscript titled “Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region” by Mahalakshmi et al., carried out a detailed study on atmospheric column CH₄ using the satellite data and bottom-up emission inventory data. This work is well executed and importantly carried out an extensive analysis over different source type of methane which is an important approach. The content is well-written and structured. The study looked into the effects of changes Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region. Author (s) could use potentially the S5P/TROPOMI observations to map the point level sources. The present study has many important points in which it highlights the emission source versus concentrations in the IGP region. Also they carried out methane emissions variability in the agroclimatic zones. Therefore, I believe this paper may be accepted with the following minor corrections.
  Reply: Thank you for the overall comments and positive feedback provided from the present study. All the comments provided by you are addressed in the revised manuscript line-by-line.
  In the title capitalisation of every letter of the word may not be required.
  
  Reply: Thank you for the suggestion. The title is modified as “Emissions of methane from coal, thermal power plants and wetlands and its implications on atmospheric methane across the South Asian region” and same has been updated in the revised manuscript.
  205: Is there any references supporting this statement “higher concentrations of CH₄ were observed in the Indo-Gangetic Plain (IGP) and northwest (NW) areas of India, southeast of China, and NW of China. Southern China and north China are marked with wetlands and rice paddy fields, which are the primary sources of CH₄”
  
  Reply: Thank you for the suggestion. The references supporting the above statement are (Kavitha et al., 2018; Chandra et al., 2019; Guo et al., 2023).The same has been updated in the revised manuscript.
  Figures 5c adjust the x axis scale accordingly with the Fig. 5(a) and (b).
  
  Reply: Figures 5a-c shows the monthly time series of XCH₄ over the specific sources of CH₄ plotted in the Indian region during 2009 to 2022 along with the overall growth rate at the respective site from 2009 to 2022. The revised figure is updated with 2021 and 2022 data and x axis scale is made uniform for the figures 5a,b,c.
  There is a typo in the caption of Figure 7, indi. “Figure 7. S5P/TROPOMI XCH4 gridded to 0.05° × 0.05° over Indi and XCH4 over wetland, coal, and thermal power plant sites with a radius of 100 km”
  
  Reply: The typo in the caption of Figure 7 is corrected as below
  Figure 7.S5P/TROPOMI XCH₄ gridded to 0.05° × 0.05° over Indian region and XCH₄ over wetland, coal, and thermal power plant sites with a radius of 100 km.
  Significant high emissions of CH₄, as shown in Figure 7c, but there is no Figure 7c it is missing.
  
  Reply: The figure in section 4.3 is figure 9c instead of figure 7c and the same has been corrected in the revised manuscript.
  Figures 7 and 8 are described differently than their respective figures. Furthermore, the description of Figure 8 comes before that of Figure 7.
  
  Reply: Thank you for the comment. The figures numbers were wrongly written in the text of the manuscript. Figure 7 shows the continuous XCH₄ data from S5P/ TROPOMI complementing the GOSAT efforts instead of figure 8 which was written in the manuscript. In section 4.3, significant high emissions of CH₄ as shown in figure 9c, instead of figure 7c which was written in the manuscript. The figures numbers were corrected in the revised manuscript.
  
  Citation: https://doi.org/10.5194/egusphere-2024-405-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-405', Anonymous Referee #1, 27 Mar 2024

The manuscript “Emissions of Methane from Coal, Thermal power plants and Wetlands and its Implications on Atmospheric Methane across the South Asian Region” investigates the spatial and temporal dynamics of atmospheric methane (CH4) concentrations over the South Asia region from 2009 to 2022, utilizing data from the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor (S5P/TROPOMI). The analysis identifies specific sources contributing to this increase, notably the Mundra thermal power station and Mundra ultra mega power plant, exhibiting higher rates of increase in XCH4 compared to other natural and anthropogenic sources. The study also highlights the significant methane emissions from the Sundarbans natural wetland, competing with coal sites in terms of emission rates, thus emphasizing its importance as an equivalent anthropogenic source. Furthermore, the investigation delves into the distribution of CH4 emissions across 15 Indian Agroclimatic zones; and employs bottom-up anthropogenic CH4 emissions data to map against XCH4 concentrations, revealing a high correlation in the Indo Gangetic Plains (IGP) region, thereby identifying key anthropogenic CH4 hotspots. Overall, the manuscript provides crucial insights into the impact of both natural and anthropogenic sources on XCH4 concentrations over the Indian region, quantifying spatio-temporal changes at each study site. The findings hold significance for understanding and addressing the complex dynamics of atmospheric methane, a potent climate change agent.
Atmospheric methane (CH4) is one of the high-potential greenhouse gases (GHG) that regulates the chemical reactions in the free troposphere and stratosphere.
Comment: Here, ‘regulates’ does not seem appropriate.
Comment: Could figures 1a, 1b, and 1c be merged into a single figure where all the sources are indicated with different shapes/colors?
Three heterogenic CH4 source regions
Comment: Are the entities depicted in the figure referred to as heterogenic source regions or individual sources? Coal fields and thermal power plants are primarily situated within the same region.
The Ministry of Environment, Forests and Climate Change (MoEF&CC), Government of India, has identified 75 Ramsar Wetland sites in India as of November 2022. These sites span a total area of 115 13,35,530 ha. Based on the high total geographical area coverage (Table 1), the top 10 places were determined for the current investigation. The size varies from 423000 ha (Sundarbans Wetland, West Bengal) to 18900 ha (Wular Lake, Jammu and Kashmir).
Comment: Reference for the aforementioned statement.
In the present study, the atmospheric CH4 was obtained from 2009 to 2020….
Comment: As previously stated and indicated in Figure 2, data up to 2022 was utilized for the current study.
Comment: There is no mention of gridding of level 2 data, if any has been applied. Have all the datasets been gridded to the same resolution?
Comment: In Figure 3, regarding the TROPOMI data from 2019 to 2022, is it averaged over this period or does it represent all the observations?
There are data gaps in Tropomi, which are assumed to occur during the monsoon season. However, these gaps are absent in GOSAT. Are you employing any data-filling method for the GOSAT data?
Figures 5a-c shows the monthly time series of XCH4 over the specific sources of CH4 dotted in the Indian region during 2009 to 2020.
Comment: dotted?
The seasonal cycle (peak and trough) of XCH4 is strongly associated with the vegetation during the active phase of cultivation and reduced photochemical reaction by the hydroxyl radicals, respectively.
Comment: could you provide a reference for the statement? Considering wetlands and coal as the largest emitter of CH4, how is the seasonal cycle associated with this?
A seasonal maximum of XCH4 was observed over Coal and Thermal power plants from September to October and a minimum in pre-monsoon (March-May).
Comment: Are you associating this cycle with rice cultivation?
Comment: There is a typo in Figure 7, indi. Does the spatial distribution represent the mean of all the years?
Comment: The descriptions of Figures 7 and 8 do not match their respective figures. Additionally, the description of Figure 8 appears before that of Figure 7.
Significant high emissions of CH4, as shown in Figure 7c
Comment: Figure 7c is missing.
Additional Suggestions
Emissions from EDGAR only verify the anthropogenic emissions, but there is a significant wetland source situated over India that requires verification with the appropriate inventory.
Additionally, previous studies have demonstrated the limitations of satellite data during the monsoon season and the biases associated with global inventories such as EDGAR, over the Indian region. Therefore, it would be appropriate to study the uncertainty associated with the emissions and XCH4 from their respective datasets.
There are extensive studies of XCH4 over India from satellites have been conducted by Mottungan et al. However, the authors have not reffered them in their study.

Citation: https://doi.org/10.5194/egusphere-2024-405-RC1
- AC3:
  'Reply on RC1', Mahesh Pathakoti, 27 May 2024
  Response to Reviewer -1
  The manuscript “Emissions of Methane from Coal, Thermal power plants and Wetlands and its Implications on Atmospheric Methane across the South Asian Region” investigates the spatial and temporal dynamics of atmospheric methane (CH₄) concentrations over the South Asia region from 2009 to 2022, utilizing data from the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor (S5P/TROPOMI). The analysis identifies specific sources contributing to this increase, notably the Mundra thermal power station and Mundra ultra mega power plant, exhibiting higher rates of increase in XCH₄ compared to other natural and anthropogenic sources. The study also highlights the significant methane emissions from the Sundarbans natural wetland, competing with coal sites in terms of emission rates, thus emphasizing its importance as an equivalent anthropogenic source. Furthermore, the investigation delves into the distribution of CH₄ emissions across 15 Indian Agroclimatic zones; and employs bottom-up anthropogenic CH₄ emissions data to map against XCH₄ concentrations, revealing a high correlation in the Indo Gangetic Plains (IGP) region, thereby identifying key anthropogenic CH₄ hotspots. Overall, the manuscript provides crucial insights into the impact of both natural and anthropogenic sources on XCH₄ concentrations over the Indian region, quantifying spatio-temporal changes at each study site. The findings hold significance for understanding and addressing the complex dynamics of atmospheric methane, a potent climate change agent.
  Reply: Thank you for the overall feedback on the present work. We thank you for the constructive comments. In the revised work, we have addressed all the suggested comments point-by-point carefully.
  1. Atmospheric methane (CH₄) is one of the high-potential greenhouse gases (GHG) that regulates the chemical reactions in the free troposphere and stratosphere. Comment: Here, ‘regulates’ does not seem appropriate.
  Reply: Thank you for the suggestion. We have modified the sentence as below and the same has been updated in the revised manuscript.
  Atmospheric methane (CH₄) is one of the high-potential greenhouse gases (GHG) and plays a vital role in the chemistry of the atmosphere. In the troposphere CH₄ oxidation is due to hydroxyl radical (OH) and results in the production of carbon monoxide, CO₂ and ozone in the presence of increased amounts of oxides of nitrogen where as in the stratosphere, oxidation of CH₄ is by OH radical, atomic oxygen and chlorine (Nair & Kavitha, 2020).
  2. Comment: Could figures 1a, 1b, and 1c be merged into a single figure where all the sources are indicated with different shapes/colors? Three heterogenic CH₄ source regions. Comment: Are the entities depicted in the figure referred to as heterogenic source regions or individual sources? Coal fields and thermal power plants are primarily situated within the same region.
  Reply: As suggested, Figure 1a, 1b and 1c are merged into single figure as shown below. The entities depicted in the figure are individual sources. The figure 1 below shows the top 10 wetlands (circle) which is natural sources of CH₄. The top 10 coal mine locations (star symbol), top 10 thermal power plants (triangle) are anthropogenic sources of CH_4. The same has been updated in the revised manuscript.
  3. The Ministry of Environment, Forests and Climate Change (MoEF&CC), Government of India, has identified 75 Ramsar Wetland sites in India as of November 2022. These sites span a total area of 13,35,530 ha. Based on the high total geographical area coverage (Table 1), the top 10 places were determined for the current investigation. The size varies from 423000 ha (Sundarbans Wetland, West Bengal) to 18900 ha (Wular Lake, Jammu and Kashmir). Comment: Reference for the aforementioned statement.
  Reply: The reference for the aforementioned statements are given below:
  https://indianwetlands.in/wetlands-overview/indias-wetlands-of-international-importance/
  
  PIB Press Release on World Wetlands Day dated 26^th August, 2022.
  
  The same has been updated in the revised manuscript.
  4. In the present study, the atmospheric CH₄ was obtained from 2009 to 2020…. Comment: As previously stated and indicated in Figure 2, data up to 2022 was utilized for the current study.
  Reply: Thank you for the suggestion. For studying the natural sources (wetlands) and anthropogenic sources (coal and thermal power plants) of CH₄, the XCH₄ concentration was taken for the period 2009 to 2022 is shown in figure 5. To maintain the uniformity in the datasets, we have updated the figure 5 with 2021 and 2022 years as shown below.
  Figure 5. Monthly time series of XCH₄ over the a) wetlands, b) thermal power stations, and c) coal fields: sources of emissions, along with the overall growth rate at the respective site.
  5. Comment: There is no mention of gridding of level 2 data, if any has been applied. Have all the datasets been gridded to the same resolution?
  Reply: We have re-gridded the data to the same resolution
  6. Comment: In Figure 3, regarding the TROPOMI data from 2019 to 2022, is it averaged over this period or does it represent all the observations? There are data gaps in Tropomi, which are assumed to occur during the monsoon season. However, these gaps are absent in GOSAT. Are you employing any data-filling method for the GOSAT data?
  Reply: Figure 3c shows the averaged TROPOMI data from 2019 to 2022. As you pointed there are data gaps observed both in TROPOMI and GOSAT. The spatiotemporal variability of XCH₄ as discussed in figure 5 has few data gaps in the daily data during monsoon season which is due to the influence of cloud cover and sensor observation mode. However over the study locations a good number of representative XCH₄ data was observed in each month. Therefore we could analyse space time variability of XCH₄ over the study sites.
  7. Figures 5a-c shows the monthly time series of XCH₄ over the specific sources of CH₄ dotted in the Indian region during 2009 to 2020. Comment: dotted?
  Reply: Thank you for the comment. Our intended meaning was different sources situated/pointed in the figure. However sentence has been modified now. The figures 5a-c shows the monthly time series of XCH₄ over the specific sources of CH₄ plotted for the Indian Region during 2009 to 2022. This figure is now updated with 2021 and 2022 data and same is reflected in the revised manuscript.
  8. The seasonal cycle (peak and trough) of XCH₄ is strongly associated with the vegetation during the active phase of cultivation and reduced photochemical reaction by the hydroxyl radicals, respectively. Comment: could you provide a reference for the statement? Considering wetlands and coal as the largest emitter of CH₄, how is the seasonal cycle associated with this? A seasonal maximum of XCH₄ was observed over Coal and Thermal power plants from September to October and a minimum in pre-monsoon (March-May). Comment: Are you associating this cycle with rice cultivation?
  Reply: Sreenivas et al. (2016) in his studies observed high CH₄ concentrations during post-monsoon which may be associated with Kharif season. Low concentrations of CH₄ are observed during monsoon which is due to reduction in atmospheric hydrocarbons because of the reduced photochemical reactions and significant drop in solar intensity. As you rightly pointed seasonal maximum is also strongly associated with rice cultivation. Kavitha et al. (2016) also reported that different seasonal behaviour with seasonal peak in post-monsoon and low during monsoon in the Southern peninsular regions. These regional variations are to distribution of sources like livestock population, rice cultivation, wetland, biomass burning and oil and gas mining.
  9. Comment: There is a typo in Figure 7, indi. Does the spatial distribution represent the mean of all the years?
  Reply: The typo has been corrected in figure 7 (indi to Indian Region).
  10. Comment: The descriptions of Figures 7 and 8 do not match their respective figures. Additionally, the description of Figure 8 appears before that of Figure 7.
  Reply: Thank you. The figures numbers were wrongly written in the text of the manuscript. Figure 7 shows the continuous XCH₄ data from S5P/ TROPOMI complementing the GOSAT efforts instead of figure 8 which was written in the manuscript. In section 4.4, significant high emissions of CH₄ as shown in figure 9c, instead of figure 7c which was written in the manuscript. The figures numbers were corrected in the revised manuscript.
  11. Significant high emissions of CH₄, as shown in Figure 7c. Comment: Figure 7c is missing.
  Reply: The figure in section 4.3 is figure 9c instead of figure 7c and the same has been corrected in the revised manuscript.
  Additional Suggestions
  1. Emissions from EDGAR only verify the anthropogenic emissions, but there is a significant wetland source situated over India that requires verification with the appropriate inventory.
  Reply: We thank you for the important suggestion. We have implemented the emissions from the natural sources (wetlands) using the inventory “WetCHARTs v1.3.1”. This has really helped us to apportion the contribution from the natural and anthropogenic emissions of CH₄. The below figure 8 shows the monthly time series of methane emissions (mg m^-2 month^-1) over the wetland sites, inset figure shows the seasonal methane emissions over the wetland sites from 2001 to 2019. As suggested, introduced new figure 8 in the revised manuscript.
  2. Additionally, previous studies have demonstrated the limitations of satellite data during the monsoon season and the biases associated with global inventories such as EDGAR, over the Indian region. Therefore, it would be appropriate to study the uncertainty associated with the emissions and XCH₄ from their respective datasets.
  Reply: We agree with you suggestion. Present study included the relevant supporting information attributed to the uncertainties associated with the EDGAR based bottom-up CH₄ emission inventory and biases associated with the XCH₄ retrievals from the Remote Sensing sensors. However, this inventory helps to map the high emission (hotspots) zones of CH₄ and associated activities.
  Uncertainties in the information on source intensity, activity and other statistical data are the key parameters for the uncertainties in the EDGAR emission inventory (Janardanan et al., 2017).
  
  Bottom up inventory uncertainties range between 20 and 35% for agriculture, waste and fossil fuel sectors; 50% for biomass burning and natural wetland emissions and 100% or higher for natural sources such as geological seeps and inland waters for global methane emissions (Saunois et al.,. 2020).
  
  Ground based FTIR measurements of XCH₄ by the Total Carbon Column Observing Network (TCCON) are used extensively to validate the GOSAT retrievals. Retrieval bias and precision of column abundance from GOSAT SWIR observations have been estimated as approximately 15-20 ppb and 1% respectively (Morino et al., 2011; Yoshida et al., 2013).
  
  Methane retrieval from TROPOMI is in overall agreement with correlative ground based from TCCON and Network for Detection of Atmospheric Composition Change (NDACC). The systematic differences of the bias corrected XCH₄ data with respect to TCCON data and NDACC data are on average, -0.26±0.56% and 0.57±0.83% respectively (Song et al., 2023).
  
  3. There are extensive studies of XCH₄ over India from satellites have been conducted by Mottungan et al. However, the authors have not reffered them in their study.
  Reply: We sincerely appreciate the previous works carried out by Mottungan et al., which were missed in our work inadvertently. However, we could read those papers and utilised in the revised work to strengthen the present work.
  
  Citation: https://doi.org/10.5194/egusphere-2024-405-AC3
RC2:
'Comment on egusphere-2024-405', Anonymous Referee #2, 23 Apr 2024
The current manuscript titled “Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region” by Mahalakshmi et al., carried out a detailed study on atmospheric column CH4 using the satellite data and bottom-up emission inventory data. This work is well executed and importantly carried out an extensive analysis over different source type of methane which is an important approach. The content is well-written and structured. The study looked into the effects of changes Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region. Author (s) could use potentially the S5P/TROPOMI observations to map the point level sources. The present study has many important points in which it highlights the emission source versus concentrations in the IGP region. Also they carried out methane emissions variability in the agroclimatic zones.
Therefore, I believe this paper may be accepted with the following minor corrections.

In the title capitalisation of every letter of the word may not be required.

205: Is there any references supporting this statement “higher concentrations of CH4 were observed in the Indo-Gangetic Plain (IGP) and northwest (NW) areas of India, southeast of China, and NW of China. Southern China and north China are marked with wetlands and rice paddy fields, which are the primary sources of CH4”

Figures 5c adjust the x axis scale accordingly with the Fig. 5(a) and (b)

There is a typo in the caption of Figure 7, indi. “Figure 7. S5P/TROPOMI XCH4 gridded to 0.05° × 0.05° over Indi and XCH4 over wetland, coal, and thermal power plant sites with a radius of 100 km”

Significant high emissions of CH4, as shown in Figure 7c, but there is no Figure 7c it is missing.

Figures 7 and 8 are described differently than their respective figures. Furthermore, the description of Figure 8 comes before that of Figure 7.
Citation: https://doi.org/10.5194/egusphere-2024-405-RC2
- AC1: 'Reply on RC2', Mahesh Pathakoti, 27 May 2024
  
  Publisher’s note: this comment is a copy of AC2 and its content was therefore removed.
  
  Citation: https://doi.org/10.5194/egusphere-2024-405-AC1
- AC2:
  'Reply on RC2', Mahesh Pathakoti, 27 May 2024
  The current manuscript titled “Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region” by Mahalakshmi et al., carried out a detailed study on atmospheric column CH₄ using the satellite data and bottom-up emission inventory data. This work is well executed and importantly carried out an extensive analysis over different source type of methane which is an important approach. The content is well-written and structured. The study looked into the effects of changes Emissions of Methane from Coal, Thermal power plants and Wetlands and its implications on Atmospheric Methane across the South Asian Region. Author (s) could use potentially the S5P/TROPOMI observations to map the point level sources. The present study has many important points in which it highlights the emission source versus concentrations in the IGP region. Also they carried out methane emissions variability in the agroclimatic zones. Therefore, I believe this paper may be accepted with the following minor corrections.
  Reply: Thank you for the overall comments and positive feedback provided from the present study. All the comments provided by you are addressed in the revised manuscript line-by-line.
  In the title capitalisation of every letter of the word may not be required.
  
  Reply: Thank you for the suggestion. The title is modified as “Emissions of methane from coal, thermal power plants and wetlands and its implications on atmospheric methane across the South Asian region” and same has been updated in the revised manuscript.
  205: Is there any references supporting this statement “higher concentrations of CH₄ were observed in the Indo-Gangetic Plain (IGP) and northwest (NW) areas of India, southeast of China, and NW of China. Southern China and north China are marked with wetlands and rice paddy fields, which are the primary sources of CH₄”
  
  Reply: Thank you for the suggestion. The references supporting the above statement are (Kavitha et al., 2018; Chandra et al., 2019; Guo et al., 2023).The same has been updated in the revised manuscript.
  Figures 5c adjust the x axis scale accordingly with the Fig. 5(a) and (b).
  
  Reply: Figures 5a-c shows the monthly time series of XCH₄ over the specific sources of CH₄ plotted in the Indian region during 2009 to 2022 along with the overall growth rate at the respective site from 2009 to 2022. The revised figure is updated with 2021 and 2022 data and x axis scale is made uniform for the figures 5a,b,c.
  There is a typo in the caption of Figure 7, indi. “Figure 7. S5P/TROPOMI XCH4 gridded to 0.05° × 0.05° over Indi and XCH4 over wetland, coal, and thermal power plant sites with a radius of 100 km”
  
  Reply: The typo in the caption of Figure 7 is corrected as below
  Figure 7.S5P/TROPOMI XCH₄ gridded to 0.05° × 0.05° over Indian region and XCH₄ over wetland, coal, and thermal power plant sites with a radius of 100 km.
  Significant high emissions of CH₄, as shown in Figure 7c, but there is no Figure 7c it is missing.
  
  Reply: The figure in section 4.3 is figure 9c instead of figure 7c and the same has been corrected in the revised manuscript.
  Figures 7 and 8 are described differently than their respective figures. Furthermore, the description of Figure 8 comes before that of Figure 7.
  
  Reply: Thank you for the comment. The figures numbers were wrongly written in the text of the manuscript. Figure 7 shows the continuous XCH₄ data from S5P/ TROPOMI complementing the GOSAT efforts instead of figure 8 which was written in the manuscript. In section 4.3, significant high emissions of CH₄ as shown in figure 9c, instead of figure 7c which was written in the manuscript. The figures numbers were corrected in the revised manuscript.
  
  Citation: https://doi.org/10.5194/egusphere-2024-405-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Mahesh Pathakoti on behalf of the Authors (01 Jun 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (03 Jun 2024) by Bryan N. Duncan

RR by Anonymous Referee #3 (31 Jul 2024)

Suggestions for revision or reasons for rejection

Venkata et al. present an analysis of satellite observations of methane column mixing ratios over the Indian subcontinent. They compare spatiotemporal trends in observed mixing ratio over different emission hotspots (coal mines, power plants, wetlands) and compare with trends in bottom-up emission inventories. They find that methane concentrations increased more rapidly from 2009-2022 over the hotspot locations than on average for the subcontinent.

General comments

- The study conflates emissions and concentrations by assuming a neat relationship between methane column concentration and emissions without consideration of other controlling factors: wind and the methane chemical sink. I recommend the authors discuss this question in a revised manuscript. OH trends are difficult to quantify, but observed concentrations can vary year to year based only on annual variations in the local wind speed, which is readily quantifiable. The authors could examine a reanalysis product (ERA5, GEOS-FP/MERRA-2) to assess trends in wind speed at their different source locations. This could support the argument that the observed trends in methane column mixing ratio are the result of emission trends.
- The study should better address the very large uncertainties in the BU inventories it cites. The inventory estimates are not observations (despite several passages describing them as such, e.g., L. 29, 455), and EDGAR in particular has previously been shown to contain major spatial errors (e.g., see Maasakkers et al., 2023; https://pubs.acs.org/doi/10.1021/acs.est.3c05138).

Specific comments

- L. 29: No CH4 flux is observed by WetCHARTS. Inventory values are reported, not observed.
- L. 57-60: What is the citation for these figures / the decadal budget?
- L. 146: The meaning of Ramsar is not immediately clear. I suggest writing “(see below)” or similar so readers know the term will be explained shortly.
- L. 147: How can a number of coal mines vary, and why is the range so large? Are these different estimates of coal mine count? If so, the large range is really surprising.
- L. 185-188: These resolutions are not correct for the methane product. The methane product started at 7x7 km2 resolution and was upgraded to 5.5x7 km2 resolution at nadir (in contrast with the NO2 product).
- L. 210: This be cited as Pai et al., not just a dataverse link.
- Figure 3 and related text: I assume the study region is shown as the blue box in Fig. 3b. Please clarify and include lat/lon ranges in text to facilitate reproduction of results and comparison with other work.
- L. 273-275: Please make these statements more quantitative. With respect to which year are the residuals defined? And how is the acceleration post-2015 diagnosed? The growth looks steady over the period to my eye.
- Figure 3c and related text: One would expect the tropics to show the highest columnar methane concentrations, because those columns are more sensitive to the troposphere than at higher latitude. I do not think these high concentrations can be said to reflect high emissions. Please clarify if I am misunderstanding what is being shown here.
- Figure 4: This looks interesting, but the figure is qualitative and should be accompanied by concrete figures; otherwise, it’s difficult to articulate the finding. Can you support the claim of “accelerated diffusion of CH4 to SH” with numerical values? A clearer figure would plot the trend in XCH4 by latitude (bin) over time.
- L. 311-313: Earlier in the manuscript, Parker is cited for the claim that seasonal CH4 variation is dominated by wetland emission variability, but here the effect of seasonal OH trends is highlighted. Suggest discussing the roles of wetlands + OH in both passages.
- L. 374-379: These numbers all look very similar. What are the std’s / error bars? Are they statistically different? At present I cannot tell if the results are strong enough for a meaningful trend comparison.
- Figure 8: I cannot read the text inset, and it is not clear to me what is learned from this figure. Is it saying that WetCHARTS doesn’t show a trend in wetland emissions, whereas the satellites do? Again, it is essential to also examine potential trends in wind speed.
- L. 455: Again, no emissions are being “observed” here. The figure just shows how EDGAR values vary in time.
- Figure 10: What correlation is being plotted? Is this the correlation between annual TROPOMI vs. annual EDGAR per grid cell? Please clarify.

Corrections

- L. 67: “contribute to 20-40%” -> ”contribute 20-40%”
- L. 104: “Sentienl” -> “Sentinel”

Hide

ED: Reconsider after major revisions (31 Jul 2024) by Bryan N. Duncan

AR by Mahesh Pathakoti on behalf of the Authors (05 Sep 2024) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (05 Sep 2024) by Bryan N. Duncan

ED: Publish subject to minor revisions (review by editor) (19 Sep 2024) by Bryan N. Duncan

AR by Mahesh Pathakoti on behalf of the Authors (22 Sep 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (23 Sep 2024) by Bryan N. Duncan

AR by Mahesh Pathakoti on behalf of the Authors (25 Sep 2024) Manuscript

Journal article(s) based on this preprint

20 Nov 2024

Emissions of methane from coal fields, thermal power plants, and wetlands and their implications for atmospheric methane across the south Asian region

Atmos. Chem. Phys., 24, 12843–12859, https://doi.org/10.5194/acp-24-12843-2024,https://doi.org/10.5194/acp-24-12843-2024, 2024

Short summary

Mahalakshmi D.Venkata, Mahesh Pathakoti, A. Lakshmi Kanchana, Sujatha Peethani, Ibrahim Shaik, Krishnan Sundara Rajan, Vijay Kumar Sagar, Pushpanathan Raja, Yogesh Kumar Tiwari, and Chauhan Prakash

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The present study investigated the variability of CH₄ over coal fields, power plants, and wetlands using the long-term GOSAT and TROPOMI data. Interestingly noticed a slow growth rate of CH₄ over the second-largest wetland areas of India. The Sundarbans wetland growth rate competes with coal sites with the production of over 30 MT. Further mapped CH₄ concentrations against the emissions in the Agro-climatic zones and found a statistically high correlation in the Indo-Gangetic Plain regions.


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