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140 results for “Methane emissions”
Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables - Version 2</p>
Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems
<div> <p><span>Coastal </span><span>methane (CH<sub>4</sub>) emissions dominate the global ocean CH<sub>4</sub> budget and can offset the "blue carbon" storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets </span><span>sensitive to statistical assumptions and uncertainties</span><span>. Using continuous CH<sub>4</sub> concentrations, δ<sup>13</sup>C-CH<sub>4</sub> values, and CH<sub>4</sub> sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CH<sub>4 </sub>distribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH<sub>4 </sub>concentrations ranging two orders of magnitude (i.e., 6 – 460 nM CH<sub>4</sub>) with habitat-specific seasonal and diurnal patterns. We observed (1) δ<sup>13</sup>C-CH<sub>4</sub> signatures that revealed habitat-specific CH<sub>4</sub> production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH<sub>4 </sub>distribution signified by apparent activation energies of </span><span>∼</span><span>1 eV that drove seasonal changes. </span><span>Bootstrapping simulations show that scaling the CH<sub>4</sub> distribution from few samples involves large errors,</span><span> and that </span><span>∼</span><span>50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH<sub>4</sub> sources (i.e., releasing ≥100 μmol CH<sub>4</sub> m<sup>−2</sup> day<sup>−1</sup> in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH<sub>4</sub> estimates and confine the habitat-specific contribution to regional and global CH<sub>4</sub> budgets.</span></p> </div>
Data from: High methane emissions from an anoxic fjord driven by mixing and oxygenation
<p>Tracking sources, sinks and long-term trends of methane emissions is imperative under climate change. Marine methane fluxes remain elusive and uncertain even though the ocean plays a major role in global budgets. High-latitude fjord ecosystems are widespread, store large amounts of sediment carbon, and undergo cycles of water column mixing, making them potential but still overlooked sources of methane to the atmosphere. Here, state-of-the-art benthic lander robots and multi annual observations revealed that anoxic fjords emit substantially more methane during mixing events than under water stratification. Fjords only cover 0.1% of global sea surface area, but may contribute as much methane as that released by the open ocean, which covers 84% of the global sea surface.</p>
Methane emission and 13C data - Mycklemossen
<p>Picarro data from automatic chamber system</p> <p>Temperature and PAR data from automatic chamber system</p>
A 130-year global inventory of methane emissions from livestock: trends, patterns, and drivers
<p>Livestock contributes approximately one-third of global anthropogenic methane (CH4) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country-level information is reported regularly through national inventories and global databases, spatially-explicit quantification of century-long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~ 9 km at the equator) during the period 1890−2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5−37.1] (mean [minimum−maximum of 95% confidence interval) Tg CH4 yr-1 in 1890 to 131.7 [109.6−153.7] Tg CH4 yr-1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial-temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales.</p>
Increasing methane emissions from natural land ecosystems due to sea-level rise
<p>The file contains the datasets (data, scripts and GIS data) presented in "Increasing methane emissions from natural land ecosystems due to sea-level rise"</p>
Datafiles supporting the article: Impact of methane and other precursor emission reductions on surface ozone in Europe: Scenario analysis using the EMEP MSC-W model
<p>This repository contains the input and output files of the EMEP model related to the article "Impact of methane and other precursor emission reductions on surface ozone in Europe: Scenario analysis using the EMEP MSC-W model" submitted to ACP.</p> <p>The folder Python_scripts includes the .py files used to generate the MAGICC7 model input files and simulation results, as well as the files used to create the figures in both the manuscript and supplementary information. For users, the paths in these scripts will have to be adjusted to point to the data files included in the EMEP_output folder. The latter folder contains the 5-year average scenario simulations as discussed in the main body of the text, as well as the ozone sensitivity to methane concentrations.</p> <p>The EMEP_input folder contains the IIASA scenario emission files for the baseline, 2050 CLE, 2050 MFR, and 2050 LOW scenarios, including separate international shipping files. The regional 0.1 by 0.1 degree emission files are a combination of IIASA files for EECCA countries and EMEP reported emissions scaled by the included 'femis' files. The EMEP model code and reported emissions are available from the open-source release, available here: https://zenodo.org/record/8431553.</p>
Data for Methane Emission Metrics Assessment: Sum44
<h1>Synthetic Datasets</h1> <p>Synthetic datasets created for "Assessing alternative methane emission metrics conducive to quantifying global warming and setting near-term climate goals". All datasets were uploaded as .ZIP files and data is in .TXT format.</p> <p>1) Synthetic Dataset - contains 1000 methane emissions pathways and their AGTP-derived temperature outputs used in the main text. Data generated according to methods detailed in the article text. </p> <p>2) Balanced Synthetic Dataset - contains 1000 methane emissions pathways and their AGTP-derived temperature outputs used in the Supplemental Materials Section 1.1. Pathways balanced for increases and decreases.</p> <p>3) Idealized Pathways - contains the step and pulse methane emissions pathways and their AGTP-derived temperature outputs. </p>
Quantification of methane emissions from indoor-fed Fogera dairy cows using laser methane detector
<p>Using the laser methane detector (LMD) in a respiration chamber (Linze Grassland Agriculture Trial Station, Lanzhou University, Gansu Province, China).</p>
Quantification of methane emissions from indoor-fed Fogera dairy cows using laser methane detector
<p>Using the laser methane detector to measure the methane emissions from Fogera dairy cows (Andassa Livestock Research Center, Amhara Region Agricultural Research Institute, Ethiopia).</p>
High-resolution (10 km × 10 km) anthropogenic methane emissions in China in 2020
<p>This dataset is a comprehensive and bottom-up estimate of China's anthropogenic CH4 emissions in 2020 from 45 sub-sectors and ~130,000 point sources. Generally, we classify all sources into two major categories: fossil-fuel system and food system. Fossil-fuel system releases CH4 emissions throughout the fossil-fuel related activities, which contains coal/gas/oil supply chain and fossil-fuel related waste management in this study. Food system contains rice cultivation, livestock, biomass burning and food-related waste management in this study. We allocate the annual CH4 emissions in 2020 at a spatial resolution of 0.1° × 0.1° (nearly 10km ×10km), wherein emissions from point sources are precisely allocated at their location information. The remaining anthropogenic sources in provincial level are allocated by the corresponding surrogate indexes, such as gridded gross domestic product (GDP), population, cultivated area and the source-specific surrogate indexes.</p>
Methane emissions in seagrass meadows as a small offset to carbon sequestration
<p>Station P: Masterfile includes environmental data and CO2, CH4 and radon concentration at Station P </p> <p>Station S: Masterfile includes the environmental data and CO2, CH4 and radon concentration at Station S </p> <p>Sediment Core: Master file includes the porewater DIC and CH4 concentration</p> <p>Survey: Master file of spatial survey includes GPS location, CH4 concentration </p>
Methane emissions in a subtropical mangrove forest (Ishikaki Island, Japan)
<p>The data set provides CH<sub>4</sub> flux from buttress roots and stems of individuals of <em>Bruguiera gymnorrhiza</em> of different sizes in Ishigaki Island, Japan. It also provides CH<sub>4</sub> flux from trees and sediments over a 24-hour cycle.</p>
Methane emissions from contrasting production regions within Alberta, Canada: Implications under new federal methane regulations
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Data from: Methane emission offsets carbon dioxide uptake in a small productive lake
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2018 methane emission quantification controlled release test at the Merced Vernal Pools and Grassland Reserve with sUAS
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Data from: Impact of native and non-native aquatic plants on methane emission and phytoplankton growth
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Data from: Influence of oil and gas emissions on ambient atmospheric non-methane hydrocarbons in residential areas of northeastern Colorado
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Data from: High methane emissions from an anoxic fjord driven by mixing and oxygenation
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Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems
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