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139 results for “carbon emissions”
Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities, Toolik Field Station, North Slope Alaska, Arctic LTER 1991.
Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities.
Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities Arctic LTER experimental plots, Toolik Field Station, 1992.
Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities. This is the second year of collection data.
Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities, Toolik Field Station, North Slope Alaska, Arctic LTER 1993.
Methane and carbon dioxide emissions were monitored in control, greenhouse, and nitrogen and phosphorus fertilized plots of three different plant communities. This is the third year of collection data.
Equivalent Black Carbon Emission Factors from Ships Within a Sulfur Emission Control Area
<p>Equivalent black carbon (eBC) emission factors, for ship plumes sampled in the Port of Gothenburg during two measurement campaigns in autumn 2014 and autumn 2015 respectively. </p> <p>Measurement site coordinates: N57.6849, E11.838</p> <p>Equivalent black carbon measured with a Multi Angle Absorption Photometer (MAAP, Thermo Fisher Scientific), wavelength 637 nm. CO2 sampled with a non-dispersive infrared gas analyzer (LI840, LI-COR).</p> <p>Unit (EF_BC): ug (kg fuel)^-1</p> <p>File creator: Stina Ausmeel<br> Contact e-mail: stina.ausmeel@nuclear.lu.se</p>
Carbon emission from Western Siberian inland waters
<p>Datasets supporting the manuscript entitled "Carbon emission from Western Siberian inland waters", in press. More detailed information will be added prior publication</p> <p> </p>
Data from: Avoided emissions and conservation of scrub mangroves: a potential Blue Carbon project in the Gulf of California, Mexico
Mangroves are considered ideal ecosystems for Blue Carbon projects. However, because of their short stature, some mangroves ("scrub" mangroves, < 2m) do not fulfil the current definition of "forests" which makes them ineligible for emission reduction programs such as REDD+. Short stature mangroves can be the dominant form of mangroves in arid and poor nutrient landscapes, and emissions from their deforestation and degradation could be substantial. Here, we describe a potential Blue Carbon project in the Gulf of California, Mexico, to illustrate that projects which avoid emissions from deforestation and degradation could provide financial resources to protect mangroves that cannot be included in other emission reduction programs. The goal of the project is to protect 16,058 ha of mangroves through conservation concessions from the Mexican Federal Government. The cumulative avoided emissions of the project are 2.84 million Mg CO2 over 100 years, valued at $US 426,000 per year (US$15 per Mg CO2 in the California market). The funds could be used for community-based projects that will improve mangrove management, such as surveillance, eradication of invasive species, rehabilitation after tropical storms, and environmental education. The strong institutional support, secure financial status, community engagement, and clear project boundaries provide favourable conditions to implement this Blue Carbon project. Financial resources from Blue Carbon projects, even in mangroves of short stature, can provide substantial resources to enhance community resilience and mangrove protection.
Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor
<p>Summary of published carbon dioxide field emission data and their influence factors used for generating Tier 1 emission factor of peat extractions in IPCC 2013 Wetland Supplementary and extra data published after IPCC (2014). </p><p>The dataset is supplementary to the published paper "Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor" By Hongxing He and Nigel Roulet: He, H., Roulet, N.T. Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor. <i>Commun Earth Environ</i> <strong>4</strong>, 436 (2023). https://doi.org/10.1038/s43247-023-01091-y. </p><p> </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>
Dataset of 'Unequal Impacts of Urban Industrial Land Expansion on Economic Growth and Carbon Dioxide Emissions'
<p>This dataset is an integral component of the research presented in 'Unequal Impacts of Urban Industrial Land Expansion on Economic Growth and Carbon Dioxide Emissions' by Yoo et al., 2024. It encompasses:</p> <p><strong>1. Links to two publicly accessible datasets:</strong></p> <ul> <li>Industrial land mapping data derived from Google Earth Engine.</li> <li>Data employed in a longitudinal analysis to evaluate the effects of urban industrial land expansion on CO2 emissions and economic growth.</li> </ul> <p><strong>2. Comprehensive input data </strong>utilized in Mixed Effects Random Forest (MERF) longitudinal modeling to assess the separate impacts on CO2 emissions and economic growth in developing and developed regions.</p> <p><strong>3. Python scripts provided for:</strong></p> <ul> <li>Executing MERF longitudinal modeling.</li> <li>Computing SHAP (SHapley Additive exPlanations) values to interpret the contributions of each predictor variable.</li> <li>Generating figures that visually summarize the findings for both developing and developed regions.</li> </ul> <p> </p> <p><strong>The manuscript is available</strong> at: <a href="https://doi.org/10.1038/s43247-024-01375-x"><span>https://doi.org/10.1038/s43247-024-01375-x</span></a></p> <div> <div> </div> </div>
Carbon emission and NPV data of hydrogen system in transport and building sector
<p>The dataset contains the simulation environment data, including meteorological data, renewable energy system configuration, grid energy structure data, and energy balance data of the hydrogen systems; the comparison on the lifecycle carbon emissions between different vehicles; and results of the building-transportation energy sharing model, including the calculation process and results of net present value and carbon intensity of the hydrogen system.</p>
Wastewater alkalinity addition enhancement for carbon emission reduction and marine CO2 removal
<p>The ROMS_RCA model settings of 2010 runs. The reference date of the 'TIME' variable is 1983-01-01.</p> <p>The files with 'Y2010_' in their names contain the boundary data and initial fileds for the model run. The file "ROMSeutro_1strun.inp" lists all the model parameters, while the files with 'CPB_WWTP_ps' in the names are the settings of the discharges from each WWTP outlet. </p> <p>The data used to generate the figures are provided in the MAT file.</p>
Dataset: Experimental carbon emissions from degraded Mediterranean seagrass (Posidonia oceanica) meadows under current and future summer temperatures.
<p> Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows.</p> <p> </p> <p>Guillem Roca, Javier Palacios, Sergio Ruíz-Halpern, Núria Marbà</p> <p>Contact details: Guillem Roca, guillemrocac@gmail.com</p> <p>Issue date:</p> <p>Identifier:</p> <p> </p> <p>Citation: Roca, Guillem; Palacios, Javier; Ruíz-Halpern, Marbà, Núria;</p> <p>Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows. [Dataset]</p> <p> </p> <p>Abstract: The dataset provides data on sediment C0<sub>2 </sub>efflux rates (μmol CO<sub>2 </sub>m<sup>-2 </sup>s<sup>-1</sup>), carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content (g m<sup>-2</sup>) of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions.</p> <p> </p> <p>Keywords: C0<sub>2 </sub>efflux rates, C0<sub>2</sub> emissions, Sediment, Seagrass, <em>Posidonia Oceanica</em>, experiment, temperature treatment, Sediment suspension Blue carbon, Organic Carbon.</p> <p> </p> <p>Description: The dataset contains data on sediment C0<sub>2 </sub>efflux rates, carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the <em>P. Oceanic</em>a meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO<sub>3</sub> content. CO<sub>2</sub> incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC.</p> <p> </p> <p><strong>Organic Carbon analysis</strong></p> <p>In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b)</p> <p> </p> <p>y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60)</p> <p> </p> <p>OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC.</p> <p> </p> <p><strong>Sediment CO<sub>2</sub> production</strong></p> <p>Container headspace CO<sub>2</sub> gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO<sub>2</sub> air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO<sub>2</sub> in seawater (in μmol CO<sub>2</sub> L<sup>−1</sup>) was calculated from the concentration of CO<sub>2</sub> (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO<sub>2</sub> remaining in seawater after equilibration with the air phase ([CO<sub>2</sub>]SW−eq) by,</p> <p> </p> <p>[CO<sub>2</sub>]SW−eq = 10−6 β [C CO<sub>2</sub>]Air P</p> <p> </p> <p>where β is the Bunsen solubility coefficient of CO<sub>2</sub>, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO<sub>2</sub>]Air is the CO<sub>2</sub> concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO<sub>2</sub> concentration in seawater before the equilibrium ([CO<sub>2]SW</sub>−before eq) was calculated (in ml CO<sub>2</sub> /ml H<sub>2</sub>O) by,</p> <p> </p> <p>[CO<sub>2</sub>]<sub>SW−before eq</sub> = ([CH<sub>4</sub>]<sub>SW−eq</sub> V<sub>Sw</sub> + 10−6 ([CO<sub>2</sub>]Air −[CO<sub>2</sub>]<sub>Air background</sub>) V<sub>Air</sub>)/V<sub>SW</sub></p> <p> </p> <p>Where V<sub>Sw</sub> is the volume of seawater in the core or in the seawater closed circuit, [CO<sub>2</sub>]<sub>Air background</sub> is the atmospheric CO<sub>2</sub> background level and V<sub>Air</sub> is the volume of the headspace or the closed air circuit. Finally, the initial CO<sub>2</sub> concentration was transformed to µmol CH<sub>4</sub> L<sup>−1</sup> by applying the ideal gas law.</p> <p>CO<sub>2</sub> efflux values were calculated from CO<sub>2</sub> variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m<sup>-2 </sup>s<sup>-1</sup>).</p> <p> </p> <p> </p> <p> </p> <p> </p>
MAgPIE model - Land use change and carbon emissions of a transformation to timber cities
<p>MAgPIE source code for reproducibility</p> <p>Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)</p> <p>DOI: 10.1038/s41467-022-32244-w</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022</p> <p>See README.md for further details.</p>
Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada
<p>Agroforestry systems (AFS) contribute to carbon (C) sequestration and reduction in greenhouse gas emissions from agricultural lands. However, previously understudied differences among AFS may underestimate their climate change mitigation potential. In this 3-year field study, we assessed various C stocks and greenhouse gas emissions across two common AFS (hedgerows and shelterbelts) and their component land uses: perennial vegetated areas with and without trees (woodland and grassland, respectively), newly planted saplings in grassland, and adjacent annual cropland in central Alberta, Canada. Between 2018 and 2020 (~April–October), nitrous oxide emissions were 89% lower under perennial vegetation relative to the cropland (0.02 and 0.18 g N m−2 year−1, respectively). In 2020, heterotrophic respiration in the woodland was 53% lower in shelterbelts relative to hedgerows (279 and 600 g C m−2 year−1, respectively). Within the woodland, deadwood C stock was particularly important in hedgerows (35 Mg C ha−1 or 7% of ecosystem C) relative to shelterbelts (2 Mg C ha−1 or < 1% of ecosystem C), and likely affected C cycling differences between the woodland types by enhancing soil labile C and microbial biomass in hedgerows. Deadwood C stock was positively correlated with annual heterotrophic respiration and total (to ~100 cm depth) soil organic C, water-soluble organic C, and microbial biomass C. Total ecosystem C was 1.90–2.55 times greater within the woodland than all other land uses, with 176, 234, 237, and 449 Mg C ha−1 found in the cropland, grassland, planted saplings treatment, and woodland, respectively. Shelterbelt and hedgerow woodlands contained 2.09 and 3.03 times more C, respectively, than adjacent cropland. Our findings emphasize the importance of AFS for fostering C sequestration and reducing greenhouse gas emissions and, in particular, retaining hedgerows (legacy woodland) and their associated deadwood across temperate agroecosystems to help mitigate climate change.</p>
Dataset for use with "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization"
<p>This sqlite file contains the input data and results used in "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization." The file is a version of the nine region Open Energy Outlook database for use with the Temoa model. The database is continually updated. Updated versions can be found in the Temoa GitHub repository, https://github.com/TemoaProject/oeo.</p>
Dataset used in publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"
<p>Essential model data use to produce figures and tables for the publication titled " Dependence of climate and carbon cycle response in net zero emission pathways on the magnitude and duration of positive and negative emission pulses"</p>
Substantial differences in source contributions to carbon emissions and health damage necessitate balanced synergistic control plans in China
<p>This dataset presents the ratio of gridded contributions to PM2.5 exposure-related health damage and CO2 emissions, alongside the gridded contributions to social costs from health damage, CO2-related climate change, and integrated costs. Additionally, the key code for calculating semi-normalized emission sensitivities based on CMAQ Adjoint outputs and source attribution is provided.</p>
Fig. 5 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 5. Net ecosystem production (NEP) in organic agroecosystems (mean±SE).
Fig. 4 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 4. Atmospheric CO2 sequestration in crops GPP (mean±SE).
Fig. 3 in Seasonal Carbon Emissions And Sequestration In Agroecosystems Of Organic Crops In Central Lithuania
Fig. 3. LAI and SLA in crops agroecosystems (mean±SE).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.