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2,208 results for “emission”
National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide
<p>A complete description of the dataset is given by <a href="http://doi.org/10.1038/s41597-023-02041-1">Jones et al. (2023)</a>. Key information is provided below.</p> <p><strong>Background</strong></p> <p>A dataset describing the global warming response to national emissions CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from fossil and land use sources during 1851-2021.</p> <p>National CO<sub>2 </sub>emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024). </p> <p>National CH<sub>4</sub> and N<sub>2</sub>O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).</p> <p>We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH<sub>4</sub> and N<sub>2</sub>O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).</p> <p>Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).</p> <p>The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or 3-GHG total) and source (fossil emissions, land use emissions or the total).</p> <p><strong>Data records: overview</strong></p> <p>The data records include three comma separated values (.csv) files as described below.</p> <p>All files are in ‘long’ format with one value provided in the <em>Data</em> column for each combination of the categorical variables <em>Year, Country Name, Country ISO3 code, Gas, and Component</em> columns.</p> <p><em>Component</em> specifies fossil emissions, LULUCF emissions or total emissions of the gas.</p> <p><em>Gas</em> specifies CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or the three-gas total (labelled 3-GHG).</p> <p><em>Country ISO3 codes</em> are specifically the unique ISO 3166-1 alpha-3 codes of each country.</p> <p><strong>Data records: specifics</strong></p> <p>Data are provided relative to 2 reference years (denoted <em>ref_year </em>below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.</p> <p><em>EMISSIONS_ANNUAL_{ref_year-20}-2023.csv:</em> <em>Data </em>includes annual emissions of CO<sub>2</sub> (Pg CO<sub>2</sub> year<sup>-1</sup>), CH<sub>4</sub> (Tg CH<sub>4</sub> year<sup>-1</sup>) and N<sub>2</sub>O (Tg N<sub>2</sub>O year<sup>-1</sup>) during the period <em>ref_year-20 </em>to 2023. The <em>Data</em> column provides values for every combination of the categorical variables. Data are provided from <em>ref_year-20</em> because these data are required to calculate GWP* for CH<sub>4</sub>.</p> <p><em>EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data </em>includes the cumulative CO<sub>2</sub> equivalent emissions in units Pg CO<sub>2</sub>-e<sub>100</sub> during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><em>GMST_response_{ref_year+1}-2023.csv:</em> <em>Data</em> includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><strong>Accompanying Code</strong></p> <p>Code is available at: <a href="https://github.com/jonesmattw/National_Warming_Contributions">https://github.com/jonesmattw/National_Warming_Contributions</a> .</p> <p>The code requires Input.zip to run (see README at the GitHub link).</p> <p><strong>Further info: Country Groupings</strong></p> <p>We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:</p> <ul> <li>Annex I countries (number of countries, n = 42)</li> <li>Annex II countries (n = 23)</li> <li>economies in transition (EITs; n = 15)</li> <li>the least developed countries (LDCs; n = 47)</li> <li>the like-minded developing countries (LMDC; n = 24).</li> </ul> <p>And other country groupings:</p> <ul> <li>the organisation for economic co-operation and development (OECD; n = 38)</li> <li>the European Union (EU27 post-Brexit)</li> <li>the Brazil, South Africa, India and China (BASIC) group.</li> </ul> <p>See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.</p>
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.
Greenhouse gas emissions from streams at North Temperate Lakes LTER 2012
Aquatic ecosystems can be important components of landscape carbon budgets. In lake-rich landscapes, streams may be important sources of greenhouse gases (CO2 and CH4) to the atmosphere in addition to lakes, but their source strength is poorly documented. The processes which control gas concentrations and emissions in these interconnected landscapes of lakes, streams and groundwater have not been adequately addressed. In this paper we use multiple datasets that vary in their spatial and temporal extent to investigate the carbon gas source strength of streams in a lake-rich landscape and to determine the roles of lakes and groundwater. We show that streams emit roughly the same mass of CO2 as regional lakes, and that stream CH4 emissions are an important component of the regional greenhouse gas balance.
Ebullitive methane emissions from oxygenated wetland streams at North Temperate Lakes LTER 2013
Stream and river carbon dioxide emissions are an important component of the global carbon cycle. Methane emissions from streams could also contribute to regional or global greenhouse gas cycling, but there are relatively few data regarding stream and river methane emissions. Furthermore, the available data do not typically include the ebullitive (bubble-mediated) pathway, instead focusing on emission of dissolved methane by diffusion or convection. Here, we show the importance of ebullitive methane emissions from small streams in the regional greenhouse gas balance of a lake and wetland-dominated landscape in temperate North America and identify the origin of the methane emitted from these well-oxygenated streams. Stream methane flux densities from this landscape tended to exceed those of nearby wetland diffusive fluxes as well as average global wetland ebullitive fluxes. Total stream ebullitive methane flux at the regional scale (103 Mg C yr-1; over 6400 km2) was of the same magnitude as diffusive methane flux previously documented at the same scale. Organic-rich stream sediments had the highest rates of bubble release and higher enrichment of methane in bubbles, but glacial sand sediments also exhibited high bubble emissions relative to other studied environments. Our results from a database of groundwater chemistry support the hypothesis that methane in bubbles is produced in anoxic near-stream sediment porewaters, and not in deeper, oxygenated groundwaters. Methane interacts with other key elemental cycles such as nitrogen, oxygen, and sulfur, which has implications for ecosystem changes such as drought and increased nutrient loading. Our results support the contention that streams, particularly those draining wetland landscapes of the northern hemisphere, are an important component of the global methane cycle.
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 years) compared to the baseline.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years — Datasets
<p><strong>Title</strong>: Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years — Datasets</p> <p><strong>Version</strong>: 1.0</p> <p><strong>Date of Release</strong>: December 06, 2021</p> <p><strong>Last Update</strong>: December 06, 2021</p> <p><strong>Identifier</strong>: 10.5281/zenodo.5652189</p> <p><strong>Permalink</strong>: <a href="https://doi.org/10.5281/zenodo.5652188">https://doi.org/10.5281/zenodo.5652188</a></p> <p><strong>Associated publication</strong>: Kinsley, C.W.; Bradtmiller, L.I.; McGee, D.; Galgay, M.; Stuut, J.-B.; Tjallingii, R.; Winckler, G.; deMenocal, P.B. 2021. Orbital- and Millennial-Scale Variability in Northwest African Dust Emissions Over the Past 67,000 years. Paleoceanography and Paleoclimatology. doi: <a href="https://doi.org/10.1002/essoar.10506290.1">10.1002/essoar.10506290.1</a></p> <p><strong>Link to publication preprint</strong>: <a href="https://doi.org/10.1002/essoar.10506290.1">https://doi.org/10.1002/essoar.10506290.1</a></p> <p><strong>Suggested citation</strong>: Please reference the associated publication above when using any datasets or materials in this repository.</p> <p><strong>Contact information</strong>: Christopher W. Kinsley, ckinsley@mit.edu OR cwkinsley@gmail.com</p> <p><strong>Dates of data collection and generation</strong>: August 2013 to February 2016</p> <p>---------------</p> <p><strong>DESCRIPTION OF DATA</strong></p> <p>This data repository contains the following datasets. We refer the user to the original manuscript (see above) and the text of the Supporting Information published alongside this manuscript for additional general information regarding the collection and generation of these data.</p> <p>DATA TABLES FOR ALL CORE SITES</p> <ul> <li><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-37 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-37 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-49 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-49 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for OC437-7-GC-68 core - v1</strong>: This Excel workbook contains all data used in the study for the OC437-7-GC-68 core site, taken by the R/V Oceanus during the 2007 Changing Holocene Environments of the Eastern Tropical Atlantic (CHEETA) cruise. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> <li> <p><strong>Kinsley et al. (2021) P&P - Data Tables for ODP 108-658C</strong><strong> core - v1</strong>: This Excel workbook contains all data used in the study for the ODP 108-658C core site, taken by the R/V JOIDES Resolution off Cap Blanc, Mauritania during Ocean Drilling Program Leg 108. This includes the age control and age model, biogenic %s, U-Th isotopic measurements, grain size distributions and endmember modeling, and <sup>230</sup>Th-normalized flux data. All previously published data is noted as such and referenced.</p> </li> </ul>
GHG Dataset for the frontiers publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region"
<p>GHG Dataset used in the Frontiers Publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region". Additionally including CO2 besides N2O and CH4. Includes 3 cropping seasons.</p> <p>The data is also available online on the GHG flux visualisation and calculation tool "gasflxvis": https://sae-interactive-data.ethz.ch/gasflxvis/</p> <p>Further details on the calulation are provided both on gasflxvis and the Frontiers publication. Calculation procedure according the following PLOS ONE publication: http://dx.doi.org/10.1371/journal.pone.0200876</p>
Dataset: Strong isoprene emission response to temperature in tundra vegetation
<p>Dataset used in the article "<em>Strong isoprene emission response to temperature in tundra vegetation</em>" published in the journal <em><strong>Proceedings of the National Academy of Sciences of the USA </strong></em><strong>119: e2118014119</strong> <a href="https://doi.org/10.1073/pnas.2118014119">https://doi.org/10.1073/pnas.2118014119</a></p> <p>The tab-delimited file contains direct surface-atmosphere isoprene fluxes, measured every 30-minutes by Eddy Covariance with a Proton Transfer Reaction -Time of Flight- Mass Spectrometer (PTR-ToF-MS) during the whole growing season at two different tundra sites in Scandinavia (near Abisko, Sweden in 2018, and near Finse, Norway during 2019). It also contains the MEGANv2.1 biogenic model predicted isoprene emissions for the same periods and sites. In addition, air temperature, vegetation surface temperature, and photosynthetic photon flux density (PPFD) measured at the sites are also reported, together with their past 24h and 240h averages (needed to run the MEGAN simulation accounting for the recent past environmental conditions).</p>
CoMobility project data: Warsaw road traffic, road traffic emissions, and air concentrations for greater Warsaw area
<p><strong>Introduction</strong></p> <p>Data here are for the Greater Warsaw area, Poland originating in the CoMobility project. It contains data relevant to traffic activity, emissions, air quality and related health studies in the area. Files contain road properties along with traffic volume and rushhour delays as well as emissions of NOx, NO2 and PM from road traffic on individual road segment level. Also 500m gridded surface air concentrations are included for PM2.5 and PM10, and for NOx, NO2.</p> <p><strong>Data production</strong></p> <p>Roads are from the macroscopic traffic model MTAW (Warsaw Municipality, 2016) (<em>Model Transportowy Aglomeracji Warszawskiej </em>in Polish). It was developed based on the 2015 comprehensive travel survey in Warsaw and it is the main strategic transport model for the Greater Warsaw area, revised most recently in 2019. </p> <p>The NERVE model (Grythe et al, 2022), developed by NILU, provides detailed estimates of greenhouse gas and air pollutant emissions specifically from road traffic. Using a bottom-up approach, it combines data from regional traffic model (RTM), vehicle fleet composition, and emission factors from the Handbook Emission Factors for Road Transport (HBEFA). NERVE can be set up to calculate emissions at various levels, including road link, municipality, or national levels. It is a tool researchers and policymakers use this model for environmental assessments, policy decisions, and constructing different emission scenarios. Its high level of detail makes it valuable not only for practical emissions estimation but also as a research tool. Emissions for other sources came from the Central Emission Database by the Environmental Protection - National Research Institute (IEP-NRI) in Poland (Gawuc et al., 2021). The background concentrations were taken from the Copernicus Atmospheric Monitoring Services (CAMS) ensemble forecast for 2019 (Marécal et al., 2015)</p> <p>The EPISODE model (Hamer et al. 2020), developed by NILU, is an Eulerian urban dispersion model designed to address the need for an accurate urban air quality model in support of policy, planning, and air quality management. EPISODE operates as a 3D grid model coupled with numerical weather prediction (NWP) data. It simulates dispersion from point and line sources to receptor points, with a focus on the photochemical production of ozone in urban areas. The model’s CityChem extension enhances its capabilities for complex pollution sources, incorporating numerical chemistry solvers, sub-grid photochemistry, and a simplified street canyon model. EPISODE serves as a valuable tool for understanding and managing air quality in urban environments.</p> <p><strong>Data files</strong></p> <p>The data on road traffic contains 60 084 road links that cover the Greater Warsaw area. The file input is a traffic file from the MTAW model and is processed and formatted with NREVE. The format is an ESRI shapefile with the following road parameters:</p> <p>“<em>DISTANCE</em>” -length of road segment in kilometers.</p> <p>“<em>CAPACITY</em>” -Hourly capacity of the road.</p> <p>“<em>SLOPE</em>” -Vertical gradientor slope of the road (in %)</p> <p>“<em>SPEEDLIM</em>” -Signed speed on the road (kilometers per hour)</p> <p>In addition there are traffic volume parameters;</p> <p>“<em>ADT_LIGHT</em>” – Annual Daily Traffic, light vehicles (personal cars + light duty vans) average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_HEAVY</em>” – Annual Daily Traffic, heavy duty vehicles average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_BUSES</em>” – Annual Daily Traffic, public transport buses average 2019.</p> <p>“<em>MRN_delay</em>” – delay during morning rush hour peak (%)</p> <p>“<em>EVE_delay</em>” – delay during evening rush hour peak (%)</p> <p>The files also contain the annual emissions:</p> <p>“<em>EM_NOx</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_ NO2</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_PM</em>” – 2019 annual emissions of NOx (gram).</p> <p>EPISODE output files for atmospheric concentration files are given on NetCDF file format. Concentrations are given as annual average grid concentration for each of the components. In addition, 42 000 spatially spread out receptor points gives the 2 meter concentrations to allow for surface air concentration levels at individual point locations. Furthermore, these allows for downgridding concentrations to higher resolution.</p> <p>The source contribution files are from EPISODE and gives atmospheric concentration fields for NOx, PM10 and PM2.5 from individual sources. The individual sources are</p> <p><em>“RDU” </em>-Road dust (PM only)</p> <p><em>“EXT”</em> – Exhaust (PM only)</p> <p><em>“TRA”</em> - Exhaust (NOx only)</p> <p><em>“IND” </em>– Industry</p> <p><em>“RES”</em> – Residential</p> <p><em>“OTH”</em> – Other (all other sources within the domain combined )</p> <p><em>“BGC”</em> – Background (all sources outside the domain combined )</p> <p> </p>
UNFCCC country-submitted greenhouse gas emissions data until 2024-07-05
<p>Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05.</p>
EU MarcoPolo project | SO2 emission inventory over China
<p>The aposteriori SO<sub>2</sub> emissions for year 2014, in the domain from 102°E to 132°E and from 15°N to 55°N, in a 0.25°x0.25° spatial resolution and monthly temporal resolution, have been provided to the MarcoPolo project and can be found at <a href="http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/">http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/</a>. For details on the creation of the inventory refer to <a href="http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf">http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf</a> and for the inclusion of the SO2 emission inventory to the MarcoPolo Emission Database refer to: <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf</a> as well as <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf</a> .</p> <p>The main reference to this dataset is found here:</p> <p>Koukouli, M. E., Theys, N., Ding, J., Zyrichidou, I., Mijling, B., Balis, D., and van der A, R. J.: Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817–1832, https://doi.org/10.5194/amt-11-1817-2018, 2018.</p> <p>The netcdf data files contain the following structure:</p> <ul> <li>Dimensions <ul> <li>lat = 129</li> <li>lon = 121</li> </ul> </li> <li>Attributes <ul> <li>author = "MariLiza Koukouli"</li> <li>contact information = "mariliza@auth.gr"</li> <li>institution = "Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki"</li> <li>time frame = "2014"</li> <li>sector classification = "total emissions"</li> <li>emis_cat_name = "sulphur dioxide emissions"</li> <li>source_type_name = "sulphur dioxide emissions"</li> <li>pollutant_description = "updated sulphur dioxide emissions based on the CHIMERE model running the MEIC emissions and the OMI/Aura observations"</li> <li>unit_emissions = "Mg/month"</li> <li>nodata_value = "-9999.0"</li> </ul> </li> <li>Variables <ul> <li>float emissions(lon, lat)</li> </ul> </li> </ul>
Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
Low-voltage Secondary Electron Emission Spectromicroscopy using a Scanning Auger Microscope
<p>Secondary electron emission is considered a well-established nano-scale probe for mapping the surface morphology of materials. It has also been demonstrated that secondary electrons (SE) emitted from materials can provide additional information on the local work function, bulk density of state (DOS), surface potential, charging/discharging characteristics, and elemental/chemical properties of bulk materials. The nano-scale lateral resolution and surface sensitivity of low-voltage scanning microscopes give them a unique advantage for the investigation of surfaces. However, the surface contamination caused by exposure to electron beams has always been a limiting factor for this purpose. Since the yield of SE emission is higher than that of Auger emission, the secondary electron emission spectromicroscopy (SEES) performed in an ultra-high vacuum chamber using a scanning Auger microscope (SAM) can be a very powerful tool for surface characterization, especially in the case of ultra-thin materials.</p> <p>We adapt our scanning auger microscope (SAM), equipped with a cylindrical mirror analyzer (CMA) and operated in an ultra-high vacuum, to SEES by tilting the sample holder and applying a negative bias to the sample. We also presented SEES signals of Chromium thin film at low voltages of 500 and 1000 V.</p>
Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission by Roman et al.
<p>Images of Neptune as used in <a href="https://arxiv.org/abs/2112.00033"><strong>Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission </strong></a>by Michael T. Roman, Leigh N. Fletcher, Glenn S. Orton, Thomas K. Greathouse, Julianne I. Moses, Naomi Rowe-Gurney, Patrick G. J. Irwin, Arrate Antunano, James Sinclair, Yasumasa Kasaba, Takuya Fujiyoshi, Imke de Pater, Heidi B. Hammel.</p> <p>Data are from various observatories/telescope instruments, including:</p> <ul> <li>The European Southern Observatory's Very Large Telescope, VISIR</li> <li>Keck Observatory, LWS</li> <li>Subaru Observatory, COMICS</li> <li>Gemini North, Michelle and TEXES</li> <li>Gemini South, T-ReCS</li> </ul> <p>Data were acquired from observatory archives and flux calibrated, when possible, by comparison to standard stars, with spectral radiances expressed in units of W/m<sup>2</sup>/sr/micron. North is up in the images. The first extension (ext=0) is the image in native spatial resolution. The second extension (ext=1) features the disk normalized in size to that of the finest data (i.e., to a disk with an equatorial width of 51.8 pixels, as imaged by VLT-VISIR on August 13, 2018).</p> <p>Image file names and times correspond to approximate mid-time of combined image sequences, and will differ from original file headers.</p> <p>Questions concerning these data should be directed towards Michael Roman, m.t.roman@le.ac.uk or michael.thomas.roman@gmail.com</p> <p> </p> <p> </p>
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories
<p>CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (<a href="https://coco2-project.eu/">https://coco2-project.eu/</a>). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available. </p> <p>Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories. </p> <p>For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.</p>
A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023
The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.
Data for: Climate warming and drought effects on volatile organic compound emissions from Solidago altissima
Volatile organic compounds (VOCs) were collected from Solidago altissima in drought and warming treatments in the KBS-LTER Rain Exclusion Experiment (REX). This sampling took place in July 2022 when the plants had been experiencing warming (via open-top chambers) for 20 months, and drought (via rainout shelters) for 3 weeks. The data presented here are the final data files used for analysis, and contain VOC abundance values per plant across the four climate treatments: ambient, warmed, drought, and warmed + drought. Code is available at: https://github.com/dobsonk2/REX_VOCs (https://doi.org/10.5281/zenodo.15169943)
Emissions from Residential Wood Combustion in Norway _ 2016
<p>Emissions at 250 m resolution from Residential Wood consumption (RWC) in Norway estimated by the MetVed model (Grythe et al., 2019). The MetVed model relies on several data-sets including dwelling number and type, available residential heating technology, location of RWC pipes/installations and wood consumption (for more detail see Grythe et al., 2019; Atmos. Chem. Phys., 19, 10217–10237, https://doi.org/10.5194/acp-19-10217-2019). </p> <ul> <li>Emissions from Residential Wood Combustion in Norway _ 2016</li> <li>Format: shapefile</li> <li>Pollutants: PM10, PM2.5, PAH, CO, CH4, BC</li> <li>Emission (units): g</li> <li>WC_WE: Wood consumption (dry wood) at the grid (units): kg</li> <li>SSBID: Grid identification number, https://www.ssb.no/natur-og-miljo/geodata (In Norwegian)</li> <li>KOMMUNERNR: Municipality number</li> <li>FYLKE: County number</li> <li>Projection: UTM33</li> </ul> <p> </p>
Augmented emission maps: the 1461 cc 81 kW Euro 6 diesel engine: update 1
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1461 cc 81 kW Euro 6 diesel engine that has been applied in the several Renault, Nissan and Mercedes-Benz models (Kadjar, Megane, Scenic, Talisman, Captur, Clio, Kangoo; Qashqai, Juke, Pulsar, NV200; Citan).</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
ScienceDex guides
Understand access before you commit
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.