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138 results for “CH4”
Greenhouse gas partial pressure (CO2, CH4, N2O) and environmental variables (physical, chemical, and biological) measured in urban ponds of Barcelona during summer and winter (2023-2024)
This dataset provides information on the partial pressure of greenhouse gases (CO₂, CH₄, and N₂O) measured in 41 artificial urban ponds—28 naturalized and 13 non-naturalized—using the headspace technique. Additionally, GPS coordinates, as well as physical, chemical, and biological variables for each pond, are included. Data were collected during the summer and winter seasons, during daytime. Furthermore, a subset of 16 ponds (8 naturalized and 8 non-naturalized) was also sampled at night in both seasons. All samples were taken from the water surface.
CO2 and CH4 fluxes from living and standing dead trees in Howland Research Forest, Maine USA, 2024
Methane (CH4) is the second-largest contributor to human-induced climate change, with significant uncertainties in its terrestrial sources and sinks. Tree stems, both living and dead, play crucial roles in forest ecosystem CH4 and carbon dioxide (CO2) flux dynamics, yet much remains unknown regarding the environmental drivers of fluxes. We measured CH4 and CO2 fluxes from 51 living trees (Picea rubens, Tsuga canadensis, Acer rubrum) along an upland-to-wetland gradient at Howland Research Forest, a net annual sink of CH4, in Maine USA. We also measured CH4 and CO2 fluxes from six standing dead red spruce stems (snags). We measured fluxes every two weeks throughout the growing season (April to November 2024) and at three heights (for a subset of red spruce stems) to capture a range of environmental conditions.
Dissolved CO2, CH4, and ions in groundwater from five sites in the NEON network (CARI, COMO, KING, MART, WALK), U.S., 2021-2024.
This package contains groundwater chemistry measurements collected from groundwater wells between June 2021 – May 2024 at five sites in the U.S. National Ecological Observatory Network (NEON): CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN. The dataset includes concentrations of dissolved gases (CO2 and CH4), ions (F, Cl, NO2, Br, NO3, PO4, SO4, Na, NH4, K, Mg, Ca), silica (Si), and nutrients measured by colorimetric methods (NO3, NH4, PO4). When available, we also report measurements of water temperature, specific conductivity (SpC), pH, dissolved O2, and barometric pressure. Sample collection and analysis was conducted across three labs with additional assistance from the NEON Research Support Services program. Whenever possible, we matched our field sampling methods to NEON’s protocols for groundwater sampling to ensure samples would be comparable to preexisting data from these sites. Any deviations from these protocols are described in the methods.
data-base of CO2, CH4, N2O and ancillary data in the Congo River
<p>data-base of CO2, CH4, N2O and ancillary data in the Congo River relative to paper "Variations of dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity" by Borges et al. (https://doi.org/10.5194/bg-2019-68)</p>
Autochamber CH4 Fluxes and δ13C Values at Stordalen Mire
<p>Autochamber-based CH<sub>4</sub> fluxes and δ<sup>13</sup>C values measured with a Tunable Infrared Laser Direct Absorption Spectrometer (TILDAS, Aerodyne Research Inc.); and ancillary data, including CO<sub>2</sub> fluxes (measured with a LGR Greenhouse Gas Analyzer), temperatures, atmospheric pressure, and photosynthetically active radiation (PAR).</p> <p>In addition to the data published here, data from 2011 is also available in the supplementary files to <a href="https://doi.org/10.1038/nature13798"><strong>McCalley et al. (2014)</strong></a> under the <strong><a href="https://static-content.springer.com/esm/art%3A10.1038%2Fnature13798/MediaObjects/41586_2014_BFnature13798_MOESM61_ESM.xlsx">Source data to Fig. 1</a></strong> link.</p> <p> </p> <p>METHODS:</p> <p>Methane fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m<sup>2</sup> (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation. The chambers are instrumented with thermocouples measuring air and surface ground temperature, and water table depth and thaw depth are measured manually 3–5 times per week. The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min<sup>-1</sup>. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure.</p> <p>We measured methane concentration using a Tunable Infrared Laser Direct Absorption Spectrometers (TILDAS, Aerodyne Research Inc.) connected to the main chamber circulation using ¼” Dekoron tubing (McCalley et al 2014). Calibrations were done every 90 min using 3 calibration gases spanning the observed concentration range (1.8–10 ppm). For each autochamber closure we calculated flux using a method consistent with that detailed by Bäckstrand et al 2008 for CO<sub>2</sub> and total hydrocarbons, using a linear regression of changing headspace CH<sub>4</sub> concentration over a period of 2.5 min. Eight 2.5 min regressions were calculated, staggered by 15 sec, and the most linear fit (highest r<sup>2</sup>) was then used to calculate flux. Daily average flux for each chamber was used to calculate daily flux and standard error for each cover type.</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <p>McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FILES:</p> <p>Files are named with the year or date range, followed by a suffix indicating data resolution:</p> <ul> <li>*<strong>_CH4output_clean_ckm.txt</strong> - Individual measurements of CH<sub>4</sub> fluxes (CH4Flux), CO<sub>2</sub> fluxes (CO2flux; for select years), and δ<sup>13</sup>C signature of emitted CH<sub>4</sub> (Flux13CH4) for each chamber closure. CH4FluxRsq is the R<sup>2</sup> value of the linear fit used to calculate CH<sub>4</sub> flux, CO2Rsq is the R<sup>2</sup> value of the linear fit used to calculate CO<sub>2</sub> flux, and Flux13CH4_stdev is the standard deviation of the δ<sup>13</sup>C signature (standard deviation of the intercept of the Keeling plot).</li> <li>*<strong>_DailyCH4output_ckm.txt</strong> - Daily average CH<sub>4</sub> fluxes (CH4Flux) and δ<sup>13</sup>C values (13CH4), grouped by site: Palsa, Bog, Fen, and Chamber 9 (bog/fen transition); along with standard deviations (stdev) and standard errors (se) of the flux or δ<sup>13</sup>C for each site type. For the Palsa, Bog, and Fen sites, these averages are calculated by chamber (n=3 for Palsa and Bog, n=2 for Fen), so each chamber's daily average is calculated, and then a daily average for that site is calculated as the average of the chambers. For Chamber 9 (bog/fen intermediate; n=1 chamber), averages are calculated by day as there are no chamber replicates.</li> </ul> <p>MEASUREMENT UNITS (same for both file types):</p> <ul> <li>CH<sub>4</sub> flux: mg CH<sub>4</sub> m<sup>−2</sup> hr<sup>−1</sup></li> <li>CO<sub>2</sub> flux: mg C m<sup>−2</sup> h<sup>−1</sup></li> <li>δ<sup>13</sup>C: ‰</li> <li>Temperature: °C</li> <li>Air pressure: mbar</li> <li>PAR: µmol photons m<sup>−2</sup> s<sup>−1</sup></li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.<br>Autochamber measurements between 2013 and 2017 were supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
Autochamber CH4 Fluxes at Stordalen Mire, 2014 (from LGR)
<p>METHODS:</p> <p>Fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m<sup>2</sup> (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation.</p> <p>The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min<sup>-1</sup>. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure. Gas concentration in the chamber headspace was measured with a Los Gatos Research (LGR) Fast Greenhouse Gas Analyzer, with timing control and data acquisition using a Campbell CR10x (Holmes et al., 2022).</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <div> <div>Holmes, M. E., Crill, P. M., Burnett, W. C., McCalley, C. K., Wilson, R. M., Frolking, S., Chang, K. ‐Y., Riley, W. J., Varner, R. K., Hodgkins, S. B., IsoGenie Project Coordinators, IsoGenie Field Team, McNichol, A. P., Saleska, S. R., Rich, V. I., Chanton, J. P. (2022). Carbon accumulation, flux, and fate in Stordalen Mire, a permafrost peatland in transition. <em>Global Biogeochemical Cycles</em>, 36, e2021GB007113, doi:10.1029/2021GB007113.</div> </div> <p>McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.<br>These autochamber measurements were also supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
Dataset for 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination'
<p>Associated data for the manuscript 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination' (doi://10.26434/chemrxiv-2023-djhp2)</p> <p> </p> <p> </p>
Chamber measurements of carbon dioxide (CO2) and methane (CH4) in Everglades following Hurricane Irma: 2017 - 2019
On September 9, 2017, highwinds from Hurricane Irma impacted the Florida Everglades. With 24 hours of heavy rain along with strong winds and storm surge, the storm caused higher than normal water levels, wind-induced defoliation, uprooting of plants and soil disturbance in Everglades short-stature freshwater wetlands. The hurricane redistributed short-stature vegetation into dead mats in Everglades National Park. The high post-storm water levels saturated many of these dead mats, slowing decomposition and increasing their persistence on the landscape. In this study, we measure carbon dioxide (CO2) and methane (CH4) fluxes at dead mats and compared them to the ridge and slough of freshwater marsh and to the marl prairie.
Contribution of CO2 and CH4 emissions at ice-melt to annual emissions from 450 and 270 lakes, respectively, 1986 to 2014
The ice-covered period on lakes in the northern hemisphere can be extensive, lasting up to 7 months of the year. During this time, C cycling in lakes is altered affecting CO2 and CH4 dynamics below ice. Lake ice impedes atmospheric exchange, trapping CO2 and CH4 in the lake over winter. As lake ice-melts, CO2 and CH4 that has accumulated over winter is emitted from the into the atmosphere. To investigate the importance of CO2 and CH4 emissions during the ice-melt period, we conducted a literature search for studies that had CO2 and CH4 emission estimates for both the ice-melt and open water period. From these literature values, we could calculate the percent contribution of the ice-melt period to annual CO2 and CH4 emissions. We obtained data for 271 (n= 258) and 447 (n= 689) individual lakes, for CH4 and CO2, respectively.
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
Data and model for 'JWST transmission spectroscopy of HD 209458b: a super-solar metallicity, a very low C/O, and no evidence of CH4, HCN, or C2H2'
<p>Supplementary materials for https://arxiv.org/abs/2310.03245 </p> <p>include:</p> <p>1. <strong>spectra_final.csv: </strong>transmission spectrum reduced by Eureka! and SPARTA (Figure 6), the best-fit model presented in Figure 1(a).</p> <p>2. <strong>Opacities </strong>used in the retrieval that are compatible with PLATON described in section 3.</p> <p>All opacity numpy pickle files are generated by <code>Python 3.9.7</code> and <code>Numpy 1.24.2</code>.</p> <p> </p> <p>**Bestfit in spectra_final.csv and all opacities are updated on Jan 23, 2024</p> <p>For any additional data requests or questions, please contact: qiaox@uchicago.edu</p>
Monthly methane emissions estimated with the atmospheric inversion model CarbonTracker Europe - CH4
<p>Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.</p>
Output of global termite CH4 emission estimation (unit corrected: g CH4/m2/yr)
<p>NetCDF file: grid map of annual emissions from 1901 to 2021</p>
CO2 and CH4 gas fluxes in disturbed and intact northern peatlands
<p>The data were collected in seven Estonian peatlands (5 disturbed and 2 intact) during three to four (2017–2020) years with closed chamber technique. Table 1 (see CO2_CH4_fluxes_README.docx) shows the variables presented in this dataset.</p>
Experimental erosion of microbial diversity decreases soil CH4 consumption rates
<p>Biodiversity-ecosystem functioning (BEF) experiments have predominantly focused on communities of higher organisms, in particular plants, with comparably little known to date about the relevance of biodiversity for microbially-driven biogeochemical processes. Methanotrophic bacteria play a key role in Earth’s methane (CH<sub>4</sub>) cycle by removing atmospheric CH<sub>4</sub> and reducing emissions from methanogenesis in wetlands and landfills. Here, we used a dilution-to-extinction approach to simulate diversity loss in a methanotrophic landfill cover soil community. Replicate samples were diluted 10<sup>1</sup> to 10<sup>7</sup>-fold, and pre-incubated under a high CH<sub>4</sub> atmosphere for the microbial communities to recover to approximately equal size. Then, the samples were incubated for 86 days at constant or diurnally-cycling temperature. Our hypotheses were that (1) CH<sub>4</sub> consumption would decrease as methanotrophic diversity was lost, and that (2) this effect would be more pronounced under variable environmental conditions (here: variable temperature). We followed net CH<sub>4</sub> consumption by gas chromatography. Microbial community composition was determined four times by DNA extraction and sequencing of amplicons specific to methanotrophs and bacteria (pmoA and 16S gene fragments). We found that the richness of operational taxonomic units (OTU) of methanotrophic and non-methanotrophic bacteria decreased approximately linearly with <em>log</em>-dilution. CH<sub>4</sub> consumption decreased with the number of taxonomic units lost. This effect was independent of community size, which we determined by quantitative PCR, and consistent over the study period. The temperature treatment (constant vs. cycling temperature) did not affect any of these results. The diversity effects we found occurred in relatively diverse communities, challenging the notion of high functional redundancy mediating high resistance to diversity erosion in natural microbial systems. The effects we report resemble the ones for higher organisms, suggesting that BEF-relationships are universal across taxa and spatial scales.</p>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
Two years of CO2 CO and CH4 from The Cyprus Institute at Nicosia, Cyprus
<p>Two years of carbon dioxide (CO2), carbon monoxide (CO) and methane (CH4) concentration measurements, were performed for the first time in the city of Nicosia, Cyprus from 11/02/2020 to 07/09/2022.</p> <p>The dataset is generated from three Picarro G2401s. Property of LSCE (187) and CYI (1172 &1173). They were consecutively installed at the Cyprus Institute, on top of the NTL building, in Nicosia residential area. The dataset was processed by LSCE at Gif-sur-Yvette in France and calibrated against a World Meteorological Organization (WMO) reference scale. </p> <p>The Eastern Mediterranean and Middle East (EMME) region, with its population of more than 400 million, is identified as one of the primary climate “hot spots” worldwide, experiencing adverse impacts ranging from extreme weather events to poor air quality. Projections show that these phenomena are expected to further exacerbate in the coming decades. At central position, lies Cyprus, an island country that receives long-range transported pollution from various anthropogenic and natural sources.</p> <p> </p>
Dissolved CO2 and CH4 dynamics in Delmarva headwater wetlands, 2020-2022
This data product includes measurements of dissolved CO2 and CH4 in surface water and groundwater, along with hydrological and biogeochemical variables, across 20 headwater wetlands in the Delmarva Peninsula in the Mid-Atlantic region of the United States. Greenhouse gas samples and water chemistry samples were collected every 1-3 months over a period of 2 years. We also monitored water level at each of the wetlands with a pressure transducer.
CO2, CH4, and H2O flux data and associated environmental variables for the BBC collapse scar for 2004
This data set contains flux measurements for the transect from the center of the BBC collapse scar (0m) into the surrounding fire scar (30m) of the Survey Line Fire (burned in June-July 2001). We measured CO2, H2O and CH4 fluxes every one to two weeks throughout the growing season of 2004. We measured fluxes at permanent plots established from the center of the bog into the surrounding burn at 0, 6, 12, 18, 24, and 30 m on the east and west side of the transect. Flux measurements on either side of the transect were treated as replicates. CO2 and H2O fluxes were measured using a Li-840 infrared gas analyzer (Licor Inc., Lincoln, NB, USA). The IRGA was calibrated before each trip to the field using a span of 400 ppm and a zero of N2 gas. We logged data every 0.5 seconds for 2 min. To account for measurement variability, we conducted two measurements in succession at each location, after flushing the chamber for accumulated CO2 and H2O. For the flux measurements, we built plexiglass chambers with pipe insulation bases with dimensions of 61 x 61 x 30.5 cm, 61 x 61 x 61 cm, or 61 x 61 x 122 cm. The shorter chambers were used in the collapse portion of the transect. Chambers included fans for air circulation, inlet and outlet ports for CO2 measurements, or just outlet ports for CH4 measurements (Carroll and Crill 1997). We placed chambers directly on the soil surface and used pipe insulation and plastic sheeting to make a solid seal during the measurement. To estimate the volume for each chamber measurement, we measured the distance to the soil surface from a 6 cm grid suspended 30 cm above each plot, the surface area was then used to calculate the chamber volume for each measurement. Dark measurements were used to determine CO2 derived from soil and root respiration (ecosystem respiration) using a two-layer cloth shroud with a reflective surface to exclude solar radiation. To estimate net ecosystem exchange (NEE) of CO2, we conducted chamber measurements of plant and soi
Modeling CH4 and CO2 cycling using porewater stable isotopes in a thermokarst bog in Interior Alaska: Results from three conceptual reaction networks
Quantifying rates of microbial carbon transformation in peatlands is essential for gaining mechanistic understanding of the factors that influence methane emissions from these systems, and for predicting how emissions will respond to climate change and other disturbances. In this study, we used porewater stable isotopes collected from both the edge and center of a thermokarst bog in Interior Alaska to estimate in situ microbial reaction rates. We expected that near the edge of the thaw feature, actively thawing permafrost and greater abundance of sedges would increase carbon, oxygen and nutrient availability, enabling faster microbial rates relative to the center of the thaw feature. (full abstract available in supplemental file 610_NeumannPorewaterExtendedMetadataText.pdf)
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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
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