Xin Zhang

Understanding the spatial distribution and magnitude of methane emissions is critical for developing effective mitigation strategies, particularly in rapidly growing economies like India, the world’s most populous country and a top global methane emitter with diverse emission sources. We quantify India’s 2021 methane emissions at up to 0.25°×0.3125° resolution using TROPOspheric Monitoring Instrument (TROPOMI) observations in a Bayesian inversion with the Integrated Methane Inversion framework (IMI). Prior emissions come from state-of-the-art global gridded bottom-up inventories and incorporate GHGSat-based estimates for eighteen landfills. The high-resolution inversion and incorporation of GHGSat data enable us to evaluate and interpret results at multiple policy-relevant scales. The national posterior emission estimate is 34.4 (32.0 – 40.4) Tg/yr, of which 31.5 (29.6 – 36.7) Tg yr^-1 is anthropogenic, consistent with bottom-up prior emissions but 68 % higher than India’s UNFCCC inventory. National landfill and oil & gas emissions are 30 % higher, while coal emissions are 57 % lower than prior estimates. State-level analysis highlights seven states with higher emissions, with notably higher oil and gas emissions in Assam and Gujarat, and lower coal mining emissions in Rajasthan and Odisha. Urban-scale posterior estimates for fourteen cities reveal significant differences from the prior in ten cities. Posterior wastewater emissions are higher in nine cities, with the largest increases in Kolkata and Delhi, consistent 60–90 % of their populations lacking access to wastewater treatment facilities. GHGSat observations reveal landfills contribute 10–38 % of emissions in eleven cities, emphasizing the critical role of solid waste management. These results illustrate how satellite-based analyses can inform methane mitigation.

We present HyperGas , an open-source Python package for the retrieval and estimation of atmospheric greenhouse gas concentration enhancements and plume emission rates using data from hyperspectral imagers such as the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP), and the Earth Surface Mineral Dust Source Investigation (EMIT). The software is designed for compatibility with any three-dimensional hyperspectral radiance dataset. HyperGas supports multiple retrieval algorithms, including matched filter and lognormal matched filter, and offers two emission rate estimation methods: the integrated mass enhancement and cross-sectional flux approaches. The software provides a scalable batch-processing framework that supports data workflows from radiances to emission rates and an interactive graphical user interface that enables visualization of gas plumes. Built on high-level data structures such as xarray and CSV, HyperGas simplifies metadata handling and facilitates robust analysis and visualization. The package provides a robust foundation for community use and expansion. This toolkit aims to advance atmospheric monitoring capabilities and support both research and operational applications of greenhouse gas monitoring.

We present HyperGas, an open-source Python package for the retrieval and estimation of atmospheric greenhouse gas concentration enhancements and plume emission rates using data from hyperspectral imagers such as the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP), and the Earth Surface Mineral Dust Source Investigation (EMIT). The software is designed for compatibility with any three-dimensional hyperspectral radiance dataset. HyperGas supports multiple retrieval algorithms, including matched filter and lognormal matched filter, and offers two emission rate estimation methods: the integrated mass enhancement and cross-sectional flux approaches. The software provides a scalable batch-processing framework that supports data workflows from radiances to emission rates and an interactive graphical user interface that enables visualization of gas plumes. Built on high-level data structures such as xarray and CSV, HyperGas simplifies metadata handling and facilitates robust analysis and visualization. The package provides a robust foundation for community use and expansion. This toolkit aims to advance atmospheric monitoring capabilities and support both research and operational applications of greenhouse gas monitoring.

We present HyperGas, an open-source Python package for the retrieval and estimation of atmospheric greenhouse gas concentration enhancements and plume emission rates using data from hyperspectral imagers such as the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP), and the Earth Surface Mineral Dust Source Investigation (EMIT). The software is designed for compatibility with any three-dimensional hyperspectral radiance dataset. HyperGas supports multiple retrieval algorithms, including matched filter and lognormal matched filter, and offers two emission rate estimation methods: the integrated mass enhancement and cross-sectional flux approaches. The software provides a scalable batch-processing framework that supports data workflows from radiances to emission rates and an interactive graphical user interface that enables visualization of gas plumes. Built on high-level data structures such as xarray and CSV, HyperGas simplifies metadata handling and facilitates robust analysis and visualization. The package provides a robust foundation for community use and expansion. This toolkit aims to advance atmospheric monitoring capabilities and support both research and operational applications of greenhouse gas monitoring.

The mitigation of methane emissions is one of the prime targets of global climate policy due to methane’s large contribution to global warming. Satellite instruments have proven to be very effective in mapping and tracking methane super-emitters. There are several new Copernicus Contributing Missions (CCMs) that are able to provide high resolution (~ 25m) methane abundance data that enables detection of emissions from individual facilities. We introduce a new Copernicus Atmosphere Monitoring Service (CAMS) service where we will use these CCMs to pinpoint individual methane sources all around the world to provide insights on emissions in support of mitigation efforts. The service will start with GHGSat data and aims to incorporate GEISAT and GESat data later. We will obtain observations over hundreds of methane hot spots and sites of interest around the world in support of climate policy. We process the satellite data starting from the methane abundance data provided by the CCMs using the SRON-developed HyperGas package. The data are standardized and potential methane plumes in the abundance field are automatically masked. Multiple expert operators then determine and agree on which masked features are true methane plumes. The automatically generated masks are then used for emission rate estimation using the integrated mass enhancement (IME) method. This methodology is calibrated using instrument-specific synthetic observations and will be evaluated using observations of controlled releases. Our semi-supervised approach allows for a consistent quantification of plumes over the full range of observations. Our processing is done independently from the analysis done by the CCMs themselves and thus serves as an evaluation. We also compare our results with bottom-up emission estimates such as included in the “TNO Emission Atlas”. This way, our work can provide a crucial link between satellite methane observations and facility level bottom-up inventories. We present our approach in consistently handling this large volume of data as well as initial results and interesting cases.

Understanding the spatial distribution and magnitude of methane emissions is critical for developing effective mitigation strategies, particularly in rapidly growing economies like India, the world’s most populous country and a top global methane emitter with diverse emission sources. We quantify India’s 2021 methane emissions at up to 0.25°×0.3125° resolution using TROPOspheric Monitoring Instrument (TROPOMI) observations in a Bayesian inversion with the Integrated Methane Inversion framework (IMI). Prior emissions come from state-of-the-art global gridded bottom-up inventories and incorporate GHGSat-based estimates for eighteen landfills. The high-resolution inversion and incorporation of GHGSat data enable us to evaluate and interpret results at multiple policy-relevant scales. The national posterior emission estimate is 34.4 (32.0 – 40.4) Tg/yr, of which 31.5 (29.6 – 36.7) Tg yr^-1 is anthropogenic, consistent with bottom-up prior emissions but 68 % higher than India’s UNFCCC inventory. National landfill and oil & gas emissions are 30 % higher, while coal emissions are 57 % lower than prior estimates. State-level analysis highlights seven states with higher emissions, with notably higher oil and gas emissions in Assam and Gujarat, and lower coal mining emissions in Rajasthan and Odisha. Urban-scale posterior estimates for fourteen cities reveal significant differences from the prior in ten cities. Posterior wastewater emissions are higher in nine cities, with the largest increases in Kolkata and Delhi, consistent 60–90 % of their populations lacking access to wastewater treatment facilities. GHGSat observations reveal landfills contribute 10–38 % of emissions in eleven cities, emphasizing the critical role of solid waste management. These results illustrate how satellite-based analyses can inform methane mitigation.

Understanding the spatial distribution and magnitude of methane emissions is critical for developing effective mitigation strategies, particularly in rapidly growing economies like India, the world’s most populous country and a top global methane emitter with diverse emission sources. We quantify India’s 2021 methane emissions at up to 0.25°×0.3125° resolution using TROPOspheric Monitoring Instrument (TROPOMI) observations in a Bayesian inversion with the Integrated Methane Inversion framework (IMI). Prior emissions come from state-of-the-art global gridded bottom-up inventories and incorporate GHGSat-based estimates for eighteen landfills. The high-resolution inversion and incorporation of GHGSat data enable us to evaluate and interpret results at multiple policy-relevant scales. The national posterior emission estimate is 34.4 (32.0 – 40.4) Tg/yr, of which 31.5 (29.6 – 36.7) Tg yr^-1 is anthropogenic, consistent with bottom-up prior emissions but 68 % higher than India’s UNFCCC inventory. National landfill and oil & gas emissions are 30 % higher, while coal emissions are 57 % lower than prior estimates. State-level analysis highlights seven states with higher emissions, with notably higher oil and gas emissions in Assam and Gujarat, and lower coal mining emissions in Rajasthan and Odisha. Urban-scale posterior estimates for fourteen cities reveal significant differences from the prior in ten cities. Posterior wastewater emissions are higher in nine cities, with the largest increases in Kolkata and Delhi, consistent 60–90 % of their populations lacking access to wastewater treatment facilities. GHGSat observations reveal landfills contribute 10–38 % of emissions in eleven cities, emphasizing the critical role of solid waste management. These results illustrate how satellite-based analyses can inform methane mitigation.

We present HyperGas, an open-source Python package for the retrieval and estimation of atmospheric greenhouse gas concentration enhancements and plume emission rates using data from hyperspectral imagers such as the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP), and the Earth Surface Mineral Dust Source Investigation (EMIT). The software is designed for compatibility with any three-dimensional hyperspectral radiance dataset. HyperGas supports multiple retrieval algorithms, including matched filter and lognormal matched filter, and offers two emission rate estimation methods: the integrated mass enhancement and cross-sectional flux approaches. The software provides a scalable batch-processing framework that supports data workflows from radiances to emission rates and an interactive graphical user interface that enables visualization of gas plumes. Built on high-level data structures such as xarray and CSV, HyperGas simplifies metadata handling and facilitates robust analysis and visualization. The package provides a robust foundation for community use and expansion. This toolkit aims to advance atmospheric monitoring capabilities and support both research and operational applications of greenhouse gas monitoring.

Solid waste is the third largest source of anthropogenic methane, and mitigating emissions is crucial for addressing climate change. We combine three high-resolution (30-60 m) hyperspectral satellite imagers (EMIT, EnMAP, and PRISMA) to quantify emissions from 38 strongly emitting disposal sites across worldwide urban methane hotspots. The imagers give consistent emission estimates, with EMIT and EnMAP having better sensitivity than PRISMA. Total observed emissions add up to 230 ± 15 t h^-1, representing 5% of reported global solid waste emissions. Our estimates exceed the facility-level Climate TRACE inventory by a factor of 1.8, while we only detect emissions from 9 of the inventory's 20 highest-emitting sites, highlighting the importance of facility-level information. Furthermore, multimonth observations reveal emission patterns potentially linked to facility operations. We estimate that these instruments could detect 35-60% of global landfill emissions, critically expanding on satellite instruments designed for methane and supporting emission mitigation.