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140 results for “Methane emissions”
China's coal methane emissions over 2011-2019
<p>The gridded inventory of China's coal methane emissions over 2011-2019 (gridded raster files of abandoned mine methane (AMM) and underground coal mine methane (CMM)). This dataset also concludes the original codes for the emission calculation.</p>
Data from: Drivers of intra-individual spatial variability in methane emissions from tree trunks in upland forest
<p><span>CH</span><sub><span>4</span></sub><span> emissions from </span><span>tree </span><span>trunks in upland forests should be scaled accurately</span><span> in order</span><span> to assess the role of tree trunk</span><span>s</span><span> in </span><span>the </span><span>forest CH<sub>4</sub></span><span> budget. As </span><span>the </span><span>chambers used to measure emission</span><span>s</span><span> cover</span><span> only</span><span> a small part of the tree trunks, </span><span>it is necessary to </span><span>understand the intra-individual spatial variability </span><span>in</span><span> trunk CH<sub>4</sub></span><span> emission</span><span>s.</span> <span>W</span><span>e measured trunk CH<sub>4</sub></span><span> flux at nine locations per individual </span><span>on</span><span> four trees in a cool-temperate upland forest. To </span><span>appreciate</span> <span>the origin of th</span><span>is</span><span> variability and </span><span>the underlying</span><span> processes, we also measured</span><span> the</span><span> potential</span><span> rate of </span><span>CH</span><span>4</span><span> production and CH</span><sub><span>4</span></sub><span> concentration </span><span>at</span><span> sapwood and </span><span>characterized </span><span>wood and bark. Up to 15-fold spatial </span><span>variation in CH<sub>4</sub></span><span> fluxes were observed</span><span> at</span><span> the</span><span> individual</span><span> level</span><span>. This variability </span><span>can be highlighted </span><span>by the variation </span><span>in </span><span>the sapwood CH<sub>4</sub></span><span> concentration which </span><span>was</span><span> further </span><span>explained</span> <span>by </span><span>the variation in CH<sub>4</sub></span><span> production rate. The radial CH<sub>4</sub></span><span> diffusivity calculated from concentration gradient</span><span>s</span><span> and emission</span><span>s</span><span> was not related to the </span><span>measured </span><span>characteristics of </span><span>either </span><span>wood </span><span>or</span><span> bark, </span><span>raising the question of</span><span> the diffusion pathway. We emphasize</span><span>d</span><span> the importance </span><span>of</span><span> sampl</span><span>ing</span><span> trunk CH<sub>4</sub></span><span> flux at multiple locations on the surface of a tree trunk</span> <span>to capture spatial variability, a prerequisite for estimating tree-level CH<sub>4</sub></span><span> emissions.</span></p>
Methane concentration and emissions data for the northern China
Open the record for dataset details and reuse information.
Datasets for airborne in-situ quantification of methane emissions from oil and gas production in Romania
<p>This dataset includes airborne in-situ measurements taken around target clusters and regions of oil and gas production sites in Romania, as well as two models outputs interpolated to the flight tracks during the ROMEO (ROmanian Methane Emissions from Oil and gas) campaign that took place in Romania in 2019. The dataset is used for the evaluation presented in the manuscript titled: "Airborne in-situ quantification of methane emissions from oil and gas production in Romania."</p>
Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements
<p>Raw data for Andersen et al., 2021 (Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements)</p>
In-vitro gas production kinetics and methane emission potential of selected indigenous legume fodder tree and shrubs in the semi-humid condition of the southern Ethiopia
<p>The data is about the gas production production characteristics and methane emission potential of the selected eleven species of indigenous legume fodder tree and shrubs in the semi-humid condition of the southern Ethiopia. </p>
Stem methane emissions and wood methane production
<p>The dataset contains net stem <span class="jlqj4b">CH<sub>4</sub></span> emissions measured <em>in situ</em> <span class="q4iawc"><span>during the snow-free period and </span></span>potential CH<sub>4</sub> production in wood core segments incubated under anoxic conditions. The study was conducted in 2020 and 2021 on five tree species in the Ashiu Experimental Forest of Kyoto University. The study site is located in the upper Yura River watershed around Chojidani (35.34 N, 135.76 E) at an altitude of 630–660 m. Individuals<span class="q4iawc"><span> of different sizes were studied at different heights along their stems.</span></span></p>
Emission Scenarios used for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>CH<sub>4</sub> emission scenarios, based on bottom-up emission estimates (Chandra et al., CEE, 2024; Fig 2) for simulating the long-term trends and latitudinal gradients of CH<sub>4</sub> and <em>δ</em><sup>13</sup>C-CH<sub>4</sub>. The details can be found at </p> <p>Chandra, N., Patra, P.K., Fujita, R. <em>et al.</em> Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020. <em>Commun Earth Environ</em> <strong>5</strong>, 147 (2024). https://doi.org/10.1038/s43247-024-01286-x</p>
Ebullition drives high methane emissions from a eutrophic coastal basin
<p>Dataset used in the article: "Ebullition drives high methane emissions from a eutrophic coastal basin".</p>
Dataset for "Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations"
<p>This dataset provides wetland methane (CH<sub>4</sub>) emissions, their uncertainties and underlying CH<sub>4</sub> flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH<sub>4</sub> flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH<sub>4</sub> flux densities are provided only for grid cells with > 5 % wetland coverage.</p> <p>The three data products are provided in netCDF format files (.nc). Please see more details in the attributes saved in the netCDF files.</p> <p>RF-DYPTOP.nc<br> Upscaling based on DYPTOP dynamic wetland map. At 1 by 1 deg resolution.</p> <p>RF-GLWD.nc<br> Upscaling using GLWD static wetland map. At 0.5 by 0.5 deg resolution.</p> <p>RF-PEATMAP.nc<br> Upscaling using PEATMAP static wetland map. At 0.5 by 0.5 deg resolution.</p> <p> </p> <p>This dataset is related to Peltola et al. (2019) manuscript submitted to Earth System Science Data. Please cite this publication if you use this dataset in your work.</p> <p>Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-28, in review, 2019.</p>
ML scripts and data for Buzacott et al. "Drivers and annual totals of methane emissions from Dutch peatlands (2024)"
<div> <div>This repository includes the machine learning (ML) scripts, data, and output for the article "Drivers and annual totals of methane emissions from Dutch peatlands" submitted to Global Change Biology. The scripts utilise the ML FCH4 gapfilling framework described in Irvin et al. (2021) (https://doi.org/10.1016/j.agrformet.2021.108528) which is available at https://github.com/stanfordmlgroup/methane-gapfill-ml and needed to run the scripts.</div> </div>
Measurement-Based Spatially Explicit Methane Emission Inventory (EI-ME)
<p>Accurate and comprehensive assessment of methane emissions, a powerful climate warming pollutant, is a key first step in reducing these emissions, while supporting the ability to track progress toward such reductions over time. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, adn their reliance on generic emission factors produces underestimations when compared with measurement-based inventories.</p> <p>In this work, we compile and analyze previous peer-reviewed measurement-based data on facility-level methane emissions in the US oil and gas sector and use these data to develop statistically robust emissions models from which we estimate total methane emissions for the population of major US oil and gas facilities.</p> <p>This dataset (EI_ME_v1.0.gpkg) aggregates the results of this measurement-based methane emission inventory (EI-ME), which is focused on oil and gas methane emissions in the US onshore production regions. The emissions estimates are spatially resolved at 0.1 x 0.1 degree spatial scales.</p> <p>The data layers in the GeoPackage are:</p> <ul> <li><em>EI-ME_gridded_ch4_emissions:</em> estimated methane emissions, spatially resolved at 0.1x0.1 degree spatial grids</li> <li><em>EI-ME_US_oil_gas_basins:</em> major US oil and gas basin boundaries, based on <a href="https://www.eia.gov/maps/maps.php">EIA</a> basin boundary definitions.</li> <li><em>EI-ME_facility_ch4_measurements_data: </em>A compilation of previous peer-reviewed facility-level measurement-based data for oil and gas methane emissions in the US.</li> </ul> <p>We also provide a netcdf version ("EI_ME_2021_inventory_CONUS_point1_degrees_v1.nc") which includes estimated mean oil and gas methane emissions over the contiguous US (excludes Alaska) aggregated over a slightly offset spatial grid compared to the full domain in the above .gpkg.</p> <p>Complete details of the emissions model development and dataset creation can be found in the following manuscript:</p> <ul> <li><strong>How to cite: </strong>Omara, M., Himmelberger, A., MacKay, K., Williams, J. P., Benmergui, J., Sargent, M., Wofsy, S. C., and Gautam, R.: Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021), Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, 2024.</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>UPDATE (10/10/2025):</p> <p>The spatially explicit measurement-based oil and gas methane emissions inventory (EI-ME) is developed by MethaneSAT, a wholly owned subsidiary of Environmental Defense Fund, to support comprehensive oil and gas methane assessment, methane source attribution, and mitigation. The inventory combines ground-based measurement-based data with statistically robust methane emissions modeling to provide representative estimates of total methane emissions for key facility categories in the contiguous US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. It is spatially resolved at 0.1x.0.1 degree spatial scales.</p> <p> Version 1 of the EI-ME inventory for the contiguous United States was published in 2024 and provided an estimate of the 2021 oil and gas methane emissions and uncertainties that are spatially resolved at 0.1x0.1 degree spatial scales.</p> <p> Here, we provide an update to the EI-ME inventory for the years 2023 and 2024. In this update, we follow the same methodology and use the same input emissions datasets as described in detail in Omara et al. (2024), https://doi.org/10.5194/essd-16-3973-2024. We incorporate the latest available oil and gas activity data for the years 2023 and 2024 based on data from Enverus Prism (<a href="http://www.eneverus.com/">www.eneverus.com</a>), supplemented with additional information from the Oil and Gas Infrastructure Mapping database (OGIM v2.7, <a href="https://doi.org/10.5281/zenodo.15103476">https://doi.org/10.5281/zenodo.15103476</a>) and global annual gas flaring data from VIIRS (Visible Infraed Imagin Radiometer Suite), available from the Earth Observation Group (<a href="https://eogdata.mines.edu/products/vnf/global_gas_flare.html">https://eogdata.mines.edu/products/vnf/global_gas_flare.html</a>).</p> <p>---</p> <p>Contact at Environmental Defense Fund: Mark Omara (momara@edf.org), Anthony Himmelberger (ahimmelberger@methanesat.org)</p> <p>---</p>
Methane emissions of ditches of boreal forestry-drained peatlands
<p>Six datasets regarding our study on methane emissions of ditches of boreal forestry-drained peatlands of Finland:</p> <p>Dataset 1: Accepted methane fluxes analysed via manual chamber technique from ditches of 21 boreal forestry-drained peatland study areas located in Finland.</p> <p>Dataset 2: Temperature within ditches of 3 boreal forestry-drained peatlands (Ränskälänkorpi, Lettosuo and Paroninkorpi) studied in 2021. </p> <p>Dataset 3: Relative water table level (relative to the level in the beginning of June 2021) in ditches of 3 boreal forestry-drained peatlands (Ränskälänkorpi, Lettosuo and Paroninkorpi) studied in 2021. </p> <p>Dataset 4: Yearly methane emission factors calculated for different types of ditches in the study areas.</p> <p>Dataset 5: Tested random forest models' variables and their mean decrease in accuracy, and models' error rates in classifying boreal peatland forest ditches into moss-covered and moss-free categories.</p> <p>Dataset 6: Tested random forest models (as zipped .RData files) to classify boreal peatland forest ditches into moss-covered and moss-free categories.</p> <p> </p>
Enhanced methane emissions driven by glacial retreat in the high Arctic
<p>Methane concentration data of proglacial groundwater springs supporting the publication 'Enhanced methane emissions driven by glacial retreat in the high Arctic.' The data also includes icing depth and area measurements. In addition, we include fjord surface methane concentrations adjacent to marine-terminating glaciers.</p>
Quantifying the effects sizes of common controls on methane emissions from an ombrotrophic peat bog
<p>Northern wetlands and bogs are a large source of biogenic methane (CH<sub>4</sub>) to the atmosphere and shape climate. There is then substantial interest in quantifying the effect sizes of controls on bog CH<sub>4</sub> emissions. Yet quantification is complicated because wetland emissions are, like the controls that regulate them, spatially and temporarily heterogeneous within individual wetland systems. To account for this heterogeneity, we established 55 measurement plots in a sampling design that had extensive spatial, and repeated temporal, coverage over one ombrotrophic bog. This coverage allowed us to robustly estimate the effect sizes of known environmental controls on CH<sub>4</sub> emissions such as temperature, water table depth (WTD), and plant functional composition. As expected, 89% of the cumulative measured flux came from 16% of the plots, with consistently high CH<sub>4</sub> emissions over two consecutive growing seasons. Soil temperature exhibited the strongest control on emissions, while WTD and vegetation composition had much smaller relative effects compared to temperature. Interestingly, at high temperatures, WTD was a strong determinant of whether a plot had high vs. lower emissions. Despite temperature's large effect size, the controls we measured only explained ~29% of the variance in CH<sub>4</sub> fluxes. Overall, our data confirm observations from other wetlands that CH<sub>4</sub> emissions are highly variable at local scales, and yet our data also reveal that effect sizes can be quantified robustly within this heterogeneity. Highly-replicated, local designs may therefore be useful for generating data that can serve to help refine wetland CH<sub>4</sub> emissions uncertainties.</p>
Fencing farm dams to exclude livestock halves methane emissions and improves water quality
<p>Agricultural practices have created tens of millions of small artificial water bodies ("farm dams" or "agricultural ponds") to provide water for domestic livestock worldwide. Among freshwater ecosystems, farm dams have some of the highest greenhouse gas (GHG) emissions per m<sup>2</sup> due to fertilizer and manure run-off boosting methane production – an extremely potent GHG. However, management strategies to mitigate the substantial emissions from millions of farm dams remain unexplored. We tested the hypothesis that installing fences to exclude livestock could reduce nutrients, improve water quality, and lower aquatic GHG emissions. We established a large-scale experiment spanning 400 km across south-eastern Australia where we compared unfenced (N = 33) and fenced farm dams (N = 31) within 17 livestock farms. Fenced farm dams recorded 32% less dissolved nitrogen, 39% less dissolved phosphorus, 22% more dissolved oxygen, and produced 56% less diffusive methane emissions than unfenced dams. We found no effect of farm dam management on diffusive carbon dioxide emissions and on the organic carbon in the soil. Dissolved oxygen was the most important variable explaining changes in carbon fluxes across dams, whereby doubling dissolved oxygen from 5 to 10 mg L<sup>-1</sup> led to a 74% decrease in methane fluxes, a 124% decrease in carbon dioxide fluxes, and a 96% decrease in CO<sub>2</sub>-eq (CH<sub>4</sub> + CO<sub>2</sub>) fluxes. Dams with very high dissolved oxygen (>10 mg L<sup>-1</sup>) showed a switch from positive to negative CO<sub>2</sub>-eq. (CO<sub>2</sub> + CH<sub>4</sub>) fluxes (i.e., negative radiative balance), indicating a positive contribution to reducing atmospheric warming. Our results demonstrate that simple management actions can dramatically improve water quality and decrease methane emissions while contributing to more productive and sustainable farming.</p>
Global methane and nitrous oxide emissions from inland waters and estuaries
<p><span>Inland waters (rivers, reservoirs, lakes, ponds, streams) and estuaries are globally significant emitters of methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O) to the atmosphere, while global estimates of these emissions have been hampered due to the lack of a worldwide comprehensive dataset with the collection of complete CH<sub>4</sub>and N<sub>2</sub>O flux components. Here, we synthesize 2,997<em> in-situ</em> flux or concentration measurements of CH<sub>4</sub> and N<sub>2</sub>O from 277 peer-reviewed publications to explore the role of inland waters and estuaries in shaping climate change. We estimate that inland waters including rivers, reservoirs, lakes, and streams together release 95.18 Tg CH<sub>4</sub> yr<sup>-1</sup> (ebullition plus diffusion) and 1.48 Tg N<sub>2</sub>O yr<sup>-1</sup> (diffusion) to the atmosphere, yielding an overall CO<sub>2</sub>-equivalent emission total of 3.06 Pg CO<sub>2</sub> yr<sup>-1</sup>, representing roughly 60% of CO<sub>2</sub> emissions (5.13 Pg CO<sub>2</sub> yr<sup>-1</sup>) from these four inland aquatic systems,</span> <span>among which lakes act as the largest emitter for both CH<sub>4</sub>and N<sub>2</sub>O. Ebullition is noticed as a dominant flux component of CH<sub>4</sub>, contributing up to 62–84% of total CH<sub>4</sub>fluxes across all inland waters. Chamber-derived CH<sub>4 </sub>emission rates are significantly greater than those determined by diffusion model-based methods for commonly capturing both diffusive and ebullitive fluxes. Water dissolved oxygen (</span><span>DO) showed as a dominant factor among all variables to influence both CH<sub>4</sub>(diffusive and ebullitive) and N<sub>2</sub>O fluxes from inland waters</span><span>. Our study reveals a major oversight in regional and global CH<sub>4</sub>budgets from inland waters, caused by neglect of the dominant role of ebullition pathways in those emissions. The indirect N<sub>2</sub>O EF<sub>5</sub> values established in this study generally suggest a downward revision is required in current IPCC default EF<sub>5</sub> values for inland waters and estuaries.</span><span> Our findings further indicate that a comprehensive understanding of the </span><span>magnitude and patterns of</span><span> CH<sub>4 </sub>and </span><span>N<sub>2</sub>O emissions</span> <span>from </span><span>inland waters and estuaries </span><span>is essential in defining how these aquatic systems will shape our climate.</span></p>
Data for "biochar-composting substantially reduces methane and air pollutant emissions from dairy manure"
<p>Dataset showing:</p> <p>1) Weekly compost NH4 concentrations</p> <p>2) Weekly compost NO3 concentrations</p> <p>3) Weekly compost C, N, O, H</p> <p>4) Weekly compost pH and EC</p> <p>5) Daily compost temperature</p> <p>6) Weekly compost moisture</p> <p>7) Weekly compost porosity</p> <p>8) Final compost CEC</p> <p>9) Final compost germination indexes</p> <p>10) AP3 integrated assessment model results</p> <p>11) Daily compost gas fluxes</p>
Decreasing seasonal cycle amplitude of methane in the northern high latitudes being driven by lower latitude changes in emissions and transport
<p>This dataset contains the smoothed and detrended time series of methane from the TOMCAT regional tagged tracer simulations between 1995-2020 at 22 NOAA surface observation sites. Each file contains the Site Code and the latitude and longitude of of each observation site. The regional file contains the region code of each regional tagged tracer. More information on the TOMCAT simulation and the Site Codes and Region Codes can be found at: https://doi.org/10.5194/egusphere-2023-132, 2023</p>
Data for Radiation and temperature drive diurnal variation of aerobic methane emissions from Scots pine canopy
<p>Aerobic methane emissions from plant foliage may play an important role in the global methane cycle, but their size and the underlying source processes remain poorly understood. Here, we quantify methane fluxes from the shoots Scots pine trees, a dominant tree species in boreal forests, identify source processes and environmental drivers, and evaluate the potential of leaf emissions to constrain methane emissions at the ecosystem-level eddy covariance flux measurements. We show that shoot-level measurements conducted in forest, garden, or greenhouse settings; on mature trees and saplings; manually and with an automated CO<sub>2</sub> -, temperature-, and water-controlled chamber system; and with multiple methane analysers all resulted in comparable daytime flux rates (0.094±0.035 to 0.241±0.090 nmol CH<sub>4</sub> g<sup>-1</sup> foliar dw h<sup>-1</sup>). We find that methane emissions from Scots pine shoots exhibit a pronounced diurnal cycle that closely follows photosynthetically active radiation (PAR) and is further modulated by temperature. These diurnal patterns indicate that methane production is associated with diurnal cycle of sunlight, suggesting that methane is either a byproduct of photosynthesis-associated biochemical reactions (e.g. the methionine cycle) or produced through non-enzymatic photochemical reactions in plant biomass. Moreover, we identified a light-dependent component in stand-level methane fluxes, which showed an order-of-magnitude agreement with shoot-level measurements (0.968±0.031 nmol CH<sub>4</sub> g<sup>-1</sup> h<sup>-1</sup>), and which provides an upper limit to shoot methane emissions.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.