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626 results for “Methanation”
Data from: Carbon dioxide and methane fluxes from different surface types in a created urban wetland
<p><span>Many wetlands have been drained due to urbanization, agriculture, forestry or other purposes, which has resulted in losing their ecosystem services. To protect receiving waters and to achieve services such as flood control and stormwater quality mitigation, new wetlands are created in urbanized areas. However, our knowledge of greenhouse gas exchange in newly created wetlands in urban areas is currently limited. In this paper we present measurements carried out at a created urban wetland in boreal climate.</span></p> <p><span>We conducted measurements of ecosystem CO<sub>2 </sub>flux (NEE) and CH<sub>4</sub> flux (F<sub>CH4</sub>) at the constructed stormwater wetland Gateway in Nummela, Vihti, Southern Finland using eddy covariance (EC) technique. The measurements were commenced the fourth year after construction and lasted for one full year and two subsequent growing seasons. Besides ecosystem scale fluxes measured by EC tower, the diffusive CO<sub>2 </sub>and<sub> </sub>CH<sub>4</sub> fluxes from the open-water area (F<sub>w</sub>_CO<sub>2</sub> and F<sub>w</sub>_CH<sub>4, </sub>respectively) were modelled based on measurements of CO<sub>2 </sub>and<sub> </sub>CH<sub>4 </sub>concentration in the water. Fluxes from vegetated area were estimated by applying a simple mixing model using above-mentioned fluxes and footprint-weighted fractional area. The half-hourly footprint-weighted contribution of diffusive fluxes from open water ranged from 0 to 25.5 % in year 2013.</span></p> <p><span>The annual NEE of the studied wetland was 8.0 g C-CO<sub>2 </sub>m<sup>-2</sup> yr<sup>-1 </sup>with the 95 % confidence interval between<sup> </sup>-18.9 and 34.9 g C-CO<sub>2 </sub>m<sup>-2</sup> yr<sup>-1 </sup>and F<sub>CH4 </sub>was 3.9 g C-CH<sub>4</sub> m<sup>-2</sup> yr<sup>-1</sup> with the 95 % confidence interval between 3.75 and 4.07 g C-CH<sub>4</sub> m<sup>-2</sup> yr<sup>-1</sup>. The ecosystem sequestered CO<sub>2 </sub>during summer months (June-August), while the rest of the year it was a CO<sub>2</sub> source. CH<sub>4</sub> displayed strong seasonal dynamics, higher in summer and lower in winter, with a sporadic emission episode in the end of May 2013. Both CH<sub>4 </sub>and CO<sub>2 </sub>fluxes<sub>, </sub>especially those obtained from vegetated area, exhibited strong diurnal<sub> </sub>cycle during summer with synchronized peaks around noon. The annual F<sub>w</sub>_CO<sub>2 </sub>was 297.5 g C-CO<sub>2 </sub>m<sup>-2</sup> yr<sup>-1 </sup>and F<sub>w</sub>_CH<sub>4 </sub>was 1.73 g C-CH<sub>4 </sub>m<sup>-2</sup> yr<sup>-1</sup>. The peak diffusive CH<sub>4</sub> flux was 137.6 nmol C-CH<sub>4</sub> m<sup>-2</sup> s<sup>-1</sup>, which was<sup> </sup>synchronized with the F<sub>CH4</sub>.</span></p> <p><span>Overall, during the monitored time period, the established stormwater wetland had a climate warming effect with 0.263 kg CO<sub>2</sub>-eq m<sup>-2</sup> yr<sup>-1 </sup>of<sup> </sup>which 89 % was contributed by CH<sub>4</sub>. The radiative forcing of the open-water exceeded the vegetation area (1.194 kg CO<sub>2</sub>-eq m<sup>-2</sup> yr<sup>-1</sup> and<sup> </sup>0.111 kg CO<sub>2</sub>-eq m<sup>-2</sup> yr<sup>-1</sup>, respectively), which implies that, when considering solely the climate impact of a created wetland over a 100-year horizon, it would be more beneficial to design and establish wetlands with large patches of emergent vegetation, and to limit the areas of open-water to the minimum necessitated by other desired ecosystem services.</span></p>
Methane and nitrous oxide data from the North American Arctic Ocean during summer, 2015
<p>Methane, nitrous oxide, carbon isotope, and ancillary data used in "Methane and nitrous oxide distributions across the North American Arctic Ocean during summer, 2015"</p>
Raw data for "Lithium carbonate-promoted mixed rare earth oxides as a generalized strategy for oxidative coupling of methane with exceptional yields"
<p>Raw data for "Lithium carbonate-promoted mixed rare earth oxides as a generalized strategy for oxidative coupling of methane with exceptional yields"</p>
Phytoplanktonic polysaccharide-mediated sedimentation of particulate organic carbon triggers lagged methane emissions
<p>Reservoirs act as carbon sinks when sedimentation of particulate organic carbon (POC) exceeds CO<sub>2</sub> and CH<sub>4 </sub>emissions. Here, we study the poorly explored process where phytoplankton-derived acidic polysaccharides (APs) aggregate into particulate organic matter, promoting carbon export to sediments. This source of particulate organic carbon (POC) in sediments can mineralize to CO<sub>2</sub> and CH<sub>4</sub> over various timescales. Our research, centered on a Mediterranean reservoir, elucidates phenological trends of APs and POC sedimentation and identifies their predominant drivers. Our findings present synchronic sedimentation patterns of POC and APs but identify a two-week delay between POC sedimentation and CH<sub>4 </sub>emissions. Despite its eutrophic status, our data demonstrate that this reservoir acts as a carbon sink by sequestering 4.33 g C m<sup>-2 </sup>y<sup>-1</sup>, which accentuates the importance of temporal integration at multiple scales when analyzing carbon budget within reservoirs.</p>
CESM2/CAM6 Idealized Methane Simulations
<p>Netcdf files contain monthly near-surface air temperature (TREFHT) and total precipitation (PRECT) for years 50-90 of CESM2/CAM6 coupled ocean-atmosphere idealized methane simulations. Files with "MOD" indicate no shortwave absorption by methane (i.e., only longwave absorption by methane). Files without "MOD" indicate both shortwave and longwave absorption by methane. Methane perturbations include 2x , 5x and 10x preindustrial atmospheric methane concentrations. UPDATE: New 2.5x methane perturbation experiments have been added. The control simulations (using preindustrial methane concentrations) lack "2x, 5x and 10x" identifiers in their names (i.e., B1850_TEST and B1850_MOD_TEST).</p>
Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems
<p>Files (input and output) for the publication <em>Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems</em></p> <p> </p>
Law_methane dispersal
<p>This repository contains the temperature, salinity, dissolved oxygen, dissolved methane and bottom current measurements presented in the publication ‘Dispersion and fate of methane emissions from cold seeps on Hikurangi Margin, New Zealand” submitted to Frontiers in Marine Science. The data were obtained from CTD casts collected at three methane seeps along the Hikurangi margin as well as a mooring outfitted with an ADCP and a MicroCat depolyed at ~15m above the seafloor at one of the seeps. <br>Additionally, the Regional Ocean Modelling System (ROMS) was used to simulate the advection of passive tracers (a proxy for methane) released at five seeps located along the southern Hikurangi margin over a period of one year. The animations from the ROMS model output shows the lateral transport and dispersal of dissolved methane plumes along the Hikurangi Margin, east of New Zealand. The model domain spans the continental shelf of the eastern North Island of New Zealand at a resolution of 2 km. Passive tracers were released 150-200 m above the seafloor (bottom three grid cells) at each of the five seep sites. </p>
Replication Data for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>Model and observation data, used to prepare the figures in the main text, are submitted at this repository.</p>
Turkmenistan methane point source detections and retrievals from Landsat 5 (1986-2011)
<p>Global atmospheric methane concentrations rose by 10-15 ppb/yr in the 1980s before abruptly slowing to 2-8 ppb/yr in the early 1990s. This period in the 1990s is known as the "methane slowdown" and has been attributed to the collapse of the former Soviet Union (USSR) in December 1991, which may have decreased the methane emissions from oil and gas operations. Here we develop a methane plume detection system based on probabilistic deep learning and human-labelled training data. We use this method to detect methane plumes from Landsat 5 satellite observations over Turkmenistan from 1986 to 2011. We focus on Turkmenistan because economic data suggest it could account for half of the decline in oil and gas emissions from the former USSR. We find an increase in both the frequency of methane plume detections and the magnitude of methane emissions following the collapse of the USSR. We estimate a national loss rate from oil and gas infrastructure in Turkmenistan of more than 10% at times, which suggests the socioeconomic turmoil led to a lack of oversight and widespread infrastructure failure in the oil and gas sector. Our finding of increased oil and gas methane emissions from Turkmenistan following the USSR's collapse casts doubt on the long-standing hypothesis regarding the methane slowdown, begging the question: "what drove the 1992 methane slowdown?"</p>
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>
Dataset for Hydrogen Atom Abstraction from Methane by Hydroxyl Radical
<p>Data for the reaction OH + CH4 −−→ CH3 + H2O. Contains 167196 geometries and corresponding {omega}B97X/6-31G(D) energies, 12416 geometries and corresponding CCSD(T)/aug-cc-pvtz energies.</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>
Figure 10 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 10. Stratigraphic distribution of macrofaunal elements and associations.
Table 1 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
<p><b>Table 1.</b> Isotope standards used in rock analyses</p><table><tbody><tr><th><b>Standard</b></th><th><b>d</b> <b>13</b> <b>C</b> <b>(VPDB)</b><b>, ‰</b></th><th><b>d</b> <b>18</b> <b>O</b> <b>(VPDB)</b><b>, ‰</b></th><th><b>Mineral</b></th></tr></tbody><tbody><tr><th>NBS-18</th><td>-5.01 ± 0.03</td><td>-23.01 ± 0.22</td><td>Calcium carbonate</td></tr><tr><th>NBS-19</th><td>1.95 ± 0</td><td>-2.20 ± 0</td><td>Calcium carbonate</td></tr><tr><th>IRU-Marble</th><td>2.10 ± 0.06</td><td>-2.64 ± 0.09</td><td>Calcium carbonate</td></tr><tr><th>VPDBBB</th><td>1.37 ± 0.03</td><td>0.30 ± 0.06</td><td>Calcite</td></tr></tbody></table>
Table 2 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
<p><b>Table 2</b>. Isotope data from sediment samples and foraminifera. Foraminiferal samples are <i>Globobulimina auriculata, G. pacifica</i>, or <i>Uvigerina cocoaensis.</i></p><table><tbody><tr><th><b>Carbonate sediment sample</b></th><th><b>Foraminifera taxon</b></th><th><b>δ</b> <b>13</b> <b>C % PDB</b></th><th><b>δ18O % PDB</b></th></tr></tbody><tbody><tr><th>RC9</th><td></td><td>5.44</td><td>3.81</td></tr><tr><th>RC10A</th><td></td><td>-45.18</td><td>12.85</td></tr><tr><th>RC10B</th><td></td><td>-30.37</td><td>-8.83</td></tr><tr><th>RC11</th><td></td><td>-40.25</td><td>-4.57</td></tr><tr><th>RC20</th><td></td><td>-13.08</td><td>-10.48</td></tr><tr><th>RC21</th><td></td><td>-54.66</td><td>-0.92</td></tr><tr><th>RC22</th><td></td><td>-43.41</td><td>-8.31</td></tr><tr><th>RC23</th><td></td><td>-48.47</td><td>-8.41</td></tr><tr><th>RC24</th><td></td><td>-45.38</td><td>-9.55</td></tr><tr><th>RC25B</th><td></td><td>-42.33</td><td>-8.94</td></tr><tr><th>RC26</th><td></td><td>-41.76</td><td>-8.56</td></tr><tr><th>RC27</th><td></td><td>-43.28</td><td>-9.04</td></tr><tr><th>WS1</th><td></td><td>-43.33</td><td>-7.18</td></tr><tr><th>WS2</th><td></td><td>-44.80</td><td>-6.66</td></tr><tr><th>WS3A</th><td></td><td>-46.96</td><td>-9.05</td></tr><tr><th>WS3B</th><td></td><td>-41.37</td><td>-6.51</td></tr><tr><th>W4</th><td></td><td>-52.48</td><td>-4.99</td></tr><tr><th></th><td><i>G. auriculata</i></td><td>-2.6</td><td>-2.8</td></tr><tr><th></th><td><i>G. auriculata</i></td><td>-17.7</td><td>0.6</td></tr><tr><th></th><td><i>G. auriculata</i></td><td>-5.8</td><td>-0.1</td></tr><tr><th></th><td><i>G. auriculata</i></td><td>-40.6</td><td>0.3</td></tr><tr><th></th><td><i>G. auriculata</i></td><td>-9.7</td><td>0.8</td></tr><tr><th></th><td><i>G. pacifica</i></td><td>-34.9</td><td>-1.5</td></tr><tr><th></th><td><i>G. pacifica</i></td><td>-46.0</td><td>0.7</td></tr><tr><th></th><td><i>G. pacifica</i></td><td>-45.9</td><td>0.3</td></tr><tr><th></th><td><i>G. pacifica</i></td><td>-4.4</td><td>-2.7</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.2</td><td>0.4</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.6</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.3</td><td>0.4</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.3</td><td>0.4</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-0.3</td><td>0.5</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-30.9</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-4.1</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.4</td><td>0.5</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-1.2</td><td>0.5</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.6</td><td>0.4</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-0.7</td><td>0.2</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.1</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-0.7</td><td>0.0</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.0</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-12.6</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-1.4</td><td>-1.6</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.0</td><td>-1.1</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-0.1</td><td>0.2</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>0.0</td><td>0.3</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-5.1</td><td>0.2</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-5.1</td><td>0.0</td></tr><tr><th></th><td><i>U. cocoaensis</i></td><td>-0.4</td><td>0.7</td></tr></tbody></table>
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>
Dataset for 'How do the products in methane dehydroaromatization impact the distinct stages of the reaction?'
<p>Dataset for the publication 'How do the products in methane dehydroaromatization impact the distinct stages of the reaction?': https://zenodo.org/record/6341774#.YnPySOhBxhF</p>
Northern hemispheric atmospheric ethane trends in the upper troposphere and lower stratosphere (2006-2016) with reference to methane and propane
<p>Datasets for northern hemispheric atmospheric ethane, propane and methane (2006-2016) from airborne measurements by IAGOS-CARIBIC project. </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.