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19 results for “emission rate”
Summertime methane and carbon dioxide emission rates and associated variables from a national-scale survey of 146 reservoirs in the United States, 2016-2023
Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the conterminous United States, plus data from one reservoir in Washington and another in Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physiochemistry were measured at 15-70 locations during one 22 to 64-hour period during the summers of 2016-2023, with four reservoirs revisited a second time. Concomitant water chemistry measurements were also made at an index site. The dataset is comprised of two geospatial files and seven .csv files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. These data comprise the largest multi-reservoir emissions dataset ever assembled using consistent measurement methods.
Dataset: High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes
<p>Dataset used in the article "High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes"</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Vocus- proton transfer reaction mass spectrometer (Vocus-PTR) at a subarctic fen during 2021. It also contains PAR (Photosynthetic Active Radiation), temperature and flux quality criteria.</p>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
Gridded products of global river methane concentrations, flux rates and emissions
<p><strong>Information on the products on this repository</strong></p> <p>These data is created using the R scripts with the random forest models and upscaling procedures found in: https://github.com/rocher-ros/RiverMethaneFlux.</p> <p>Raw files to reproduce this product can be found in https://doi.org/10.5281/zenodo.7733604</p> <p>The results of this analysis are published in the article "Global Methane emissions form rivers and streams" (in Nature) (https://doi.org/10.1038/s41586-023-06344-6).</p> <p>Main author is Gerard Rocher-Ros, for which correspondence can be sent to g.rocher.ros@gmail.com</p> <p>Units of the variables in the product are:<br> -River methane concentration: mmol CH4 m-3<br> -River methane diffusive flux rates: mmol CH4 m-2 d-1 (of river area)<br> -River methane diffusive emissions: Mega grams of C-CH4 (for each pixel).</p> <p>The spatial resolution of the product is 0.25 degrees (which corresponds to around 27 km). The files are in WGS84.</p> <p>There are four main products in this folder, packed as geotiff files, and described below.</p> <p>+ The file "river_methane_yearly.tiff" contains three layers:<br> - Yearly average river CH4 concentrations (ch4_conc_avg)<br> - Yearly average river CH4 diffusive flux rates (ch4_flux_avg)<br> - Yearly total river CH4 diffusive emissions (ch4_emissions_year)</p> <p>+ The file "river_methane_concs_monthly.tiff" contains twelve layers, with the modelled river methane concentrations for each month.</p> <p>+ The file "river_methane_flux_monthly.tiff" contains twelve layers, with the modelled river methane flux rates for each month.</p> <p>+ The file "river_methane_emissions_monthly.tiff" contains twelve layers, with the total river methane emissions for each month.</p>
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
<p>This repository contains the socioeconomic and emissions projections generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al., "<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates</a>" , forthcoming at the <em>Brookings Papers on Economic Activity</em>. The data take the form of a Monte Carlo simulation with n = 10,000 draws.</p> <p>File structure and column metadata are described here: </p> <p>--- <br> emissions/ <br> --- </p> <p>- rffsp_co2_emissions.csv <br> - rffsp_ch4_emissions.csv <br> - rffsp_n2o_emissions.csv </p> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row: </p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000). <br> - year contains the calendar year of the prediction. <br> - value contains the projected annual global emissions of the gas specified in the filename. IMPORTANT: Units are as follows: "rffsp_co2_emissions.csv" is in gigatons C (not CO2), "rffsp_ch4_emissions.csv " is in megatons CH4, and "rffsp_n2o_emissions.csv " is in megatons N2 (not N2O). </p> <p>--- <br> pop_income/ <br> --- </p> <p>- rffsp_pop_income_run_1.feather <br> - rffsp_pop_income_run_2.feather <br> - rffsp_pop_income_run_3.feather <br> . <br> . <br> . <br> - rffsp_pop_income_run_9999.feather <br> - rffsp_pop_income_run_10000.feather </p> <p>This folder contains 10,000 files in the .feather file format (https://arrow.apache.org/docs/python/feather.html), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (https://www.iso.org/iso-3166-country-codes.html) of the country whose GDP and population are projected. <br> - Year contains the calendar year of the predictions. <br> - Pop contains the projected population for a given country and year, in units of thousands of people. <br> - GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD. </p> <p>--- </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), based in part on those from Müller, Stock, and Watson (forthcoming). </p> <p>---</p> <p><strong>References </strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a> </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a> </p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity. Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/ </a></p>
Reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole-neutron star mergers"
<p>This is a basic reproduction package for the paper "Investigating the detection rates and inference of gravitational-wave and radio emission from black hole neutron star mergers".</p>
Data from: Rates and drivers of carbon emissions from hydropower reservoirs in the southeastern United States
<p>Reservoirs are a significant source of carbon (C) to the atmosphere, but their emission rates vary in space and time. We compared C emissions via diffusive and ebullitive pathways at several stations in six large hydropower reservoirs in the southeastern US that were previously sampled in summer 2012. We found that carbon dioxide (<span>CO<sub>2</sub></span>) diffusion was the dominant flux pathway during 2012 and 2022, with only three exceptions where methane (<span>CH<sub>4</sub></span>) diffusion or <span>CH<sub>4</sub></span> ebullition dominated. <span>CH<sub>4</sub></span> diffusion rates were positively associated with water temperature. However, we found no clear predictors of <span>CH<sub>4</sub></span> ebullition, which had extremely high variability, with rates ranging from 0 to 739 mg C m<sup>-2</sup> day<sup>-1</sup>. For <span>CO<sub>2</sub></span> diffusion, the direction of the flux shifted between 2012 and 2022, where all but three stations across all reservoirs emitted <span>CO<sub>2</sub></span> in summer 2012, but every station sequestered <span>CO<sub>2</sub></span> in summer 2022. Here, indicators of greater algal production were associated with <span>CO<sub>2</sub></span> sequestration, including surface chlorophyll-<em>a</em> concentration, surface dissolved oxygen saturation, and pH. Additional sampling campaigns outside the summer season highlighted the importance of seasonal phenology in primary production on the direction of <span>CO<sub>2</sub></span> diffusive fluxes, which shifted to positive <span>CO<sub>2</sub></span> fluxes by the end of August as productivity decreased. Our results demonstrate the importance of capturing <span>CO<sub>2</sub></span> sequestration in field and modelling measurements and understanding the seasonal drivers of these estimates. Measuring C emissions from multiple pathways in reservoirs and understanding their spatiotemporal responses and variability is vital to reducing uncertainties in global upscaling efforts.</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>
Emission library populated with emission rates and effectiveness rates from risk mitigation measures.
<p>This document contains an emission library populated with emission rates from at least 10-15 industrial activities and effectiveness rates from at least 30 risk mitigation measures (RMMs)”, which includes a literature review of emission factors from diverse sources and a review of effectiveness rates for different risk mitigation measures (RMMs), as well as a description of the methodology applied during the reviews.</p> <p>This document is part of the report of Action B5 of NANOHEALTH project (LIFE20 ENV/ES/000187).</p>
Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada
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Emission rates of species-specific volatiles vary across communities of Clarkia species: evidence for multi-modal character displacement
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Dataset for "Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment"
<p>Dataset for "Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment". Each directory contains a README.md including descriptions in Markdown format.</p>
The role of the discount rate for negative emissions under a carbon budget
<p>Data of the figures from the 'The role of the discount rate for negative emissions under a carbon budget'.</p>
Data set of Precipitation rates of electrons interacting with lower-band chorus emissions in the inner magnetosphere
<p>Simulation data for paper entitled "Precipitation rates of electrons interacting with lower-band chorus emissions in the inner magnetosphere"</p>
Comparative aquatic greenhouse gas emission rates across multiple land-use types and along impounded river systems
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Soil VOC emission rates and associated parameters from forest and oil palm in the SAFE landscape
<p><strong>Description: </strong></p> <p>Monoterpene fluxes measured by the static chamber method including associated environmental parameters</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><strong>Characterising soil microbial communities and measuring associated biogeochemical fluxes</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3698115">here</a></p> <p><strong>Files: </strong>This consists of 1 file: 4_VOC_jdrewer.xlsx</p> <p><strong>4_VOC_jdrewer.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>data one off field</strong> (described in worksheet Data_one_off)</p> <p>Description: Soil and litter parameters</p> <p>Number of fields: 14</p> <p>Number of data rows: 28</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: Location measurement was taken (Field type: location)</li> <li><strong>site</strong>: Location measurement was taken (Field type: id)</li> <li><strong>chamber_id</strong>: Chamber ID (Field type: id)</li> <li><strong>landuse</strong>: Land use of location (Field type: categorical)</li> <li><strong>pH</strong>: Soil pH (Field type: numeric)</li> <li><strong>bulk_density</strong>: dry weight of soil (Field type: numeric)</li> <li><strong>soil_N%</strong>: Percentage of soil N (Field type: numeric)</li> <li><strong>soil_C%</strong>: Percentage of soil C (Field type: numeric)</li> <li><strong>litter_N%</strong>: Percentage of leaf Nitrogen (Field type: numeric)</li> <li><strong>litter_C%</strong>: Percentage of leaf Carbon (Field type: numeric)</li> <li><strong>C/N_soil</strong>: Ratio of soil Carbon: Nitrogen (Field type: numeric)</li> <li><strong>Latitude</strong>: Latitude of sampling point (Field type: latitude)</li> <li><strong>Longitude</strong>: Longitude of sampling point (Field type: longitude)</li> <li><strong>Elevation</strong>: Elevation of sampling point (Field type: numeric)</li> </ul> </li> <li> <p><strong>data of repeated measures</strong> (described in worksheet Data_repeated_measures)</p> <p>Description: Soil VOCs and associated variables</p> <p>Number of fields: 15</p> <p>Number of data rows: 336</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: Location measurement was taken (Field type: location)</li> <li><strong>site</strong>: Location measurement was taken (Field type: id)</li> <li><strong>chamber_id</strong>: Chamber ID (Field type: id)</li> <li><strong>landuse</strong>: Land use of location (Field type: categorical)</li> <li><strong>date</strong>: Date the measurement was taken (Field type: date)</li> <li><strong>time</strong>: Time the measurement was taken (Field type: time)</li> <li><strong>alpha pinene-C flux</strong>: alpha pinene flux (Field type: numeric)</li> <li><strong>beta pinene-C flux</strong>: beta pinene flux (Field type: numeric)</li> <li><strong>limonene-C flux</strong>: limonene flux (Field type: numeric)</li> <li><strong>3-carene-C flux</strong>: 3-carene flux (Field type: numeric)</li> <li><strong>camphene-C flux</strong>: camphene flux (Field type: numeric)</li> <li><strong>eucalyptol-C flux</strong>: eucalyptol flux (Field type: numeric)</li> <li><strong>air_temp</strong>: Air temperature around the flux chamber (Field type: numeric)</li> <li><strong>soil_temp</strong>: Soil temperature around the flux chamber (Field type: numeric)</li> <li><strong>soil_moisture</strong>: Soil moisture around the flux chamber (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2015-01-01 to 2016-12-31</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p>
Dataset accompanying: "Applying and Validating Coulomb Rate-and-State Seismicity Models in Acoustic Emission Experiments")
<p>Dataset accompanying: "Applying and Validating Coulomb Rate-and-State Seismicity Models in Acoustic Emission Experiments" by Heimisson, Naderloon, Chandra and Barnhoorn.</p> <p>Please reference the following publication if the data is used:<br><br>Heimisson, E.R., Naderloo, M., Chandra, D. and Barnhoorn, A., 2024. Applying and validating Coulomb rate-and-state seismicity models in acoustic emission experiments. <em>Tectonophysics</em>, p.230574. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.tecto.2024.230574" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.tecto.2024.230574 </span></span></a></p>
Correlation Between Estimated Glomerular Filtration Rate(eGFR) and Vascular Inflammation Measured by Positron Emission Tomography (PET) With 18F-fluoro-deoxyglucose (FDG)
ClinicalTrials.gov study NCT02394743. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Source data: High respiration rates induce net CO2 emissions in an urban allotment garden in Finland
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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)
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DANDI Archive for NWB datasets
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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.