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60 results for “Emission Inventories”
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>
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>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories
<p><strong>Data Access Notice</strong></p> <p>Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2023 can be accessed via the following link:</p> <p><a href="http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html">http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html</a></p> <p><strong>Product Description</strong></p> <p>See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below.</p> <p>GCP-GridFED (version 2024.0) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP; <a href="https://www.globalcarbonproject.org/">https://www.globalcarbonproject.org/</a>) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023).</p> <p>GCP-GridFEDv2024.0 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2023 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO<sub>2</sub>. Note that positive values in GridFED signify a surface-to-atmosphere CO<sub>2 </sub>flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink.</p> <p>GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021).</p> <p><strong>Core Methodology in Brief</strong></p> <p>GCP-GridFEDv2024.0 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2024 global carbon budget (GCP-NAE) for the years 1959-2023 (Friedlingstein et al., 2024). </p> <p>GCP-GridFEDv2024.0 uses a preliminary release of GCP-NAE covering the years 1959-2023 (timestamp 1st August 2024; an update from Andrew and Peters [2023]). The GCP-NAE estimates for year 2023 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2024), which will adopt updates to GCP-NAE since the timestamp.</p> <p>For full details of the core methodology, see Jones et al. (2021).</p> <p><strong>Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards</strong></p> <p>The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): </p> <ul> <li>Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States.</li> <li>State or province-level data is used for Brazil, China, Russia, and the United States.</li> <li>This also applies for the Bunker Aviation and Bunker Shipping sectors.</li> </ul> <p>Seasonality is determined in the following ways for those countries/sources:</p> <ul> <li>The seasonality of emissions in 2019-2023 is taken from Carbon Monitor.</li> <li>The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020).</li> </ul> <p>For all countries not listed above and all years 1959-2023, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021).</p> <p><strong>Other New Features of GCP-GridFEDv2024.0</strong></p> <ul> <li>There have been no changes to the functionality of the GridFED code in this update versus the previous update (v2023.1).</li> </ul> <p> </p>
Datasets for greenhouse gasses emissions and removals from inventories and global models over Africa
<p>This file includes the data from Mostefaoui et al. (ESSD, under submission), for 54 countries African countries</p> <p> The data includes: </p> <p>(1) CO2 fluxes from global models - satellite inversions and Dynamic Global Vegetation Models (DGVM) -, and from a collection of national inventories for LULUCF, GFEDv4 and FAO data.</p> <p> DGVM values are the median of 14 models, consistent with the Global Carbon Budget 2020 (https://essd.copernicus.org/articles/12/3269/2020/) LULUCF UNFCCC corrected values are from Grassi <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>(2) CH4 fluxes from global models consistent with the Global Methane Budget 2020 (https://essd.copernicus.org/articles/12/1561/2020/)</p> <p>(3 N2O fluxes from global models (three inversions)</p> <p>For further methodological details, see Mostefaoui et al. (ESSD, under submission):</p> <p>Mounia Mostefaoui, Philippe Ciais, Matthew J. McGrath, Philippe Peylin, Prabir Patra. Greenhouse gasses emissions and their trends over the last three decades across Africa, ESSD (under submission)</p>
High-resolution air pollution emission inventory for the Nordic countries
<p>This common Nordic (Denmark, Finland, Iceland, Norway, and Sweden) air pollution emission inventory was compiled using country total emissions from national emission inventories that the countries submit to the CLRTAP. Our inventory was based on the 2016-2018 submissions. The inventory contains annual emissions for 1990, 1995, 2000, 2005, 2010, 2012 and 2014. Components included in the inventory are: particulate matter (PM10 and PM2.5), black carbon (BC), organic carbon (OC), sulphur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOC) and ammonia (NH3). The gridding was done separately for each country, using national data and gridding methods. The emissions were harmonized to the same sector nomenclature, i.e. SNAP, and to the EEA reference grid. Spatial resolution for the inventory is 1 km × 1 km in the European grid ETRS89-LAEA (EPSG: 3035). Large point source emissions are provided with locations and stack heights included. Two modifications to the CLRTAP submissions were made: (1) road transport non-exhaust PM emissions were adjusted to better conform with Nordic traffic dust assessments; and (2) for OC emission, that are not included in the inventories, rough estimates were calculated based on expert estimates on OC/PM2.5-ratios on main SNAP level. The inventory was originally created for the NordicWelfAir-project (<a href="https://projects.au.dk/nordicwelfair">https://projects.au.dk/nordicwelfair</a>). The main aim of developing this new inventory was to provide air pollution modelers and health scientists a harmonized dataset to be used for studies on the link between air pollution exposure and negative impacts on the human health.<br>Description of the data can be found in this data article, which can be referenced when using the data: <a href="https://doi.org/10.5194/essd-16-1453-2024">https://doi.org/10.5194/essd-16-1453-2024</a>.</p>
Hypersonic Transport: 3D Emission Inventory of STRATOFLY-MR3 Fleet Operated on Brussels to Sydney Route in 2075
<p>High-resolution 3D inventories of future hypersonic transport (HST) are compiled for the year 2075, integrating the gaseous engine emissions of a fleet of 200 hydrogen-powered Mach 8 passenger aircraft*. These aircraft are operated once a day for 360 days on a reference route from Brussels (BRU) to Sydney (MYA) with either NO<sub>x</sub>-optimized (ICA**: 114 000 ft; 34.75 km) or H<sub>2</sub>O-optimized (ICA**: 107 500 ft; 32.77 km) flight profiles, derived to minimize environmental impacts in terms of total emissions. The emissions are spatially gridded at a horizontal resolution of 1° in longitude and latitude, with a vertical resolution of 1000 ft, and are temporally accumulated on an annual basis. Note that the 3D emission inventories encompass detailed data on species-specific HST emissions***, fuel burn, and total distance traveled: </p> <ul> <li>Species: NO, H<sub>2</sub>O; H<sub>2</sub></li> <li>Temporal information: 2075; annually</li> <li>Spatial information: 1° x 1° x 1000 ft</li> <li>Data Format: NetCDF</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------<br>* The hypersonic aircraft concept under consideration is the <a href="https://arc.aiaa.org/doi/abs/10.2514/6.2021-1877">STRATOFLY-MR3</a> vehicle, which was conceptually developed in <br> the framework of the <a href="https://cordis.europa.eu/project/id/769246">H2020 STRATOFLY project</a>.<br>** Initial Cruise Altitude<br>*** with a unit of kg/km<sup>3 </sup>(corrected in v0.2)</p> <p> </p>
EDGAR v5.0 emissions inventory speciated for the MOZART chemical mechanism
<p>Emission inventories need to be adapted to be used in chemical transport models (CTMs). They usually need ad-hoc preprocessing based on the chemical mechanism used in the CTM, including speciation of non-methane volatile organic compounds (NMVOCs). </p> <p><strong>Here we provide monthly <a href="https://edgar.jrc.ec.europa.eu/index.php/dataset_ap50">EDGAR v5.0 </a> global air pollutant emissions for the year 2015, speciated for the <a href="https://gmd.copernicus.org/articles/3/43/2010/">MOZART</a> chemical mechanism.</strong></p> <p><strong>The dataset is also ready to use in <a href="https://ruc.noaa.gov/wrf/wrf-chem/">WRF-Chem </a>atmospheric model with MOZART-MOSAIC options.</strong></p> <p>Emission files are provided as individual NetCDF files for each pollutant containing anthropogenic sector emissions as individual variables.</p> <p>In the folder you will find:</p> <ul> <li><strong>edgarv5_MOZART_data.tar.gz</strong>: EDGAR v5.0 monthly emissions for the year 2015 (NetCDFformat), speciated for MOZART chemical mechanism. Both total and individual sector emissions are included in each file. </li> <li><strong>edgarv5_MOZART_MOSAIC.inp</strong>: Input file for anthroemiss preprocessing tool for MOZART-MOSAIC options in WRF-Chem.</li> <li><strong>technical_note_EDGARv5_MOZART.pdf </strong>: documentation.</li> </ul> <p>These files are also ready-to be used in <a href="https://www2.acom.ucar.edu/wrf-chem/wrf-chem-tools-community">WRF-Chem anthro-emiss preprocessing tool</a> with the MOZART-MOSAIC options.</p> <p>Accompanying code for preparing the dataset can be found at repository: <a href="https://doi.org/10.5281/zenodo.6145846">https://doi.org/10.5281/zenodo.6145846</a></p> <p>For more detail, please refer to the technical documentation (technical_note_EDGARv5_MOZART.pdf).</p> <p> </p> <p> </p>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
Gridded ammonia emission inventory in mainland China
<p>We produce and provide an improved ammonia emission inventory in mainland China in 2016. The emission inventory have been developed with 1/12 by 1/12 degree spatial resolution. The unit of the emission inventory is t/grid/year. </p>
High-resolution oil and gas methane emission inventory for the Permian Basin
<p>This dataset consists of a high-resolution (0.01<sup>o</sup> × 0.01<sup>o</sup>) oil and gas methane emission inventory for the Permian Basin, developed at Environmental Defense Fund (<a href="http://www.edf.org">www.edf.org</a>). The Permian Basin in western Texas and southern New Mexico is the largest oil producing basin in the U.S., accounting for more than 40% of national oil production in 2021. It is also the nation's largest methane emitting basin, with recent measurement-based estimates of more than three million metric tons per year. Here, we develop an improved inventory of oil and gas methane emissions for the Permian Basin, based on recent facility-scale measurements and updated oil and gas activity data for the year 2021.</p> <p>Full details for the oil and gas methane emission inventory development and key results can be found in the following journal paper, which is under review at Earth System Science Data journal.</p> <p>Please cite the paper when using the methane inventory dataset:</p> <p>Omara, M., Gautam, R., O'Brien, M.A., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D.R., Chulakadaba, A., Miller, C.C., Franklin, J., Wofsy, S., and Hamburg, S.P. Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution. <em>In review</em>, Earth System Science Data journal (2023).</p> <p>Points of Contact at Environmental Defense Fund: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).</p>
CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NOx, SO2, CO, NH3, NMVOCs, BC, and OC from 1970-2017
<p><strong>CEDS_GBD-MAPS: Global Anthropogenic Emission Inventory of NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, BC, and OC from 1970-2017</strong></p> <p><strong>version tag: 2020_v1.0 (April 2020)</strong></p> <p>Annual anthropogenic emissions of 7 key atmospheric pollutants from 1970 - 2017, produced using the <a href="http://www.globalchange.umd.edu/ceds/">Community Emissions Data System</a>, updated for the Global Burden of Disease - Major Air Pollution Sources project (<a href="https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS">CEDS_GBD-MAPS</a>).</p> <p>Emissions are provided for NO<sub>x</sub>, SO<sub>2</sub>, CO, NH<sub>3</sub>, NMVOCs, Black Carbon (BC), and Organic Carbon (OC) from 11 anthropogenic sectors and four fuel categories as both annual country totals and global gridded emission fluxes (0.5 x 0.5 degree resolution).<br> Note: The CEDS_GBD-MAPS inventory does not include emissions from open fires or aircraft.<br> <strong>Sectors: </strong><br> 1. Agriculture (non-combustion sources only, excludes open fires)<br> 2. Energy (transformation and extraction)<br> 3. Industry (combustion and non-combustion processes)<br> 4. On-Road Transportation<br> 5. Off-Road/Non-Road Transportation (rail, domestic navigation, other)<br> 6. Residential Combustion<br> 7. Commercial Combustion<br> 8. Other Combustion<br> 9. Solvents<br> 10. Waste (disposal and handling)<br> 11. International Shipping<br> <strong>Fuel Categories:</strong><br> 1. Total Coal Combustion (hard coal + brown coal + coal coke)<br> 2. Solid Biofuel Combustion<br> 3. Liquid Fuel (light oil + heavy oil + diesel oil) plus Natural Gas Combustion<br> 4. CEDS Process Source Categories (see McDuffie, et al., (ESSD) 2020) for further details.<br> Note: Total anthropogenic emissions = the sum of fuel categories 1-4</p> <p><strong>Zip File Details:</strong><br> The CEDS_GBD-MAPS inventory is available in three different formats:<br> <br> 1. <em>CEDS_GBD-MAPS_annual_country_total_emissions_by_sector_fuel_1970-2017.zip</em></p> <ul> <li>Zip file contains 7 .csv files that each contain a complete times series (1970-2017) of total annual anthropogenic emissions of each compound from each country, as a function of 11 anthropogenic sectors and 4 fuel categories.</li> <li>Emissions are in units of kt yr<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, total NMVOCs, BC, and OC</li> </ul> <p>2. <em>CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_[year].zip</em></p> <ul> <li>Each .zip file contains 145 netCDF files of annual anthropogenic global gridded emission fluxes, reported as a function of 11 anthropogenic sectors and 5 fuel categories (1 file per compound per fuel category, plus 1 file for the sum of all fuel categories)</li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provided as monthly averages and have been formatted for use in the GEOS-Chem model (<a href="http://acmg.seas.harvard.edu/geos/">http://acmg.seas.harvard.edu/geos/</a>).</li> <li>Example: ALD2-em-liquid-fuel-plus-natural-gas_CEDS_1970.nc inside the CEDS_GBD-MAPS_gridded_emissions_by_sector_fuel_1970.zip file provides monthly emission fluxes in 1970 for the subVOC ALD2 that result from the combustion of liquid fuel and natural gas in each of the 11 source sectors.</li> </ul> <p>3. <em>CEDS_GBD-MAPS_[compound]_gridded_total_anthro_emissions_by_sector_input4CMIP_1970-2017.zip</em></p> <ul> <li><em>compound = [BC_OC], [CO_NOx_SO2_NH3], [speciated_NMVOCs_01-04], [speciated_NMVOCs_05-08], [speciated_NMVOCs_09-14], [speciated_NMVOCs_15-18], [speciated_NMVOCs_19-22], or [speciated_NMVOCs_23-25]</em></li> <li>Each .zip file contains between 2 - 4 netCDF files (1 per compound) of anthropogenic global gridded emission fluxes from 1970-2017, as a function of 11 anthropogenic sectors only (no disaggregation of fuel categories)</li> <li>netCDF files follow the CEDS CMIP6 gridded emissions format. More information available at: <br> <a href="http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/">http://www.globalchange.umd.edu/ceds/ceds-cmip6-data/</a></li> <li>Emission fluxes are in units of kg m<sup>-2</sup> s<sup>-1</sup> and include NO<sub>x</sub> (as NO<sub>2</sub>), CO, SO<sub>2</sub>, NH<sub>3</sub>, 25 speciated VOCs, BC, and OC</li> <li>Emission fluxes are provides as monthly averages</li> <li>Note: Zip files are group by compound only as a means to reduce the zipped file sizes. The file format for each compound is the same. </li> </ul> <p> </p> <p><strong>*Additional data details are provided in the README.txt file*</strong></p> <p> </p> <p>*Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> McDuffie, E. E., S. J. Smith, P. O'Rourke, K. Tibrewal, C. Venkataraman, E. A. Marais, B. Zheng, M. Crippa, M. Brauer, R. V. Martin, <strong>A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel- specific sources (1970- 2017): An application of the Community Emissions Data System (CEDS)</strong>, <em>Earth System Science Data, Submitted</em></p>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959-2018
<p>GCP-GridFED (version 2019.1) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2018 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, with mixed international bunker fuels considered separately, as well as for the calcination of limestone during cement production. GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas.</p> <p>GCP-GridFED was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR; version 4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2019 global carbon budget (GCB-NAE) for the years 1959-2018 (Friedlingstein et al., 2019).</p> <p>The data description article is under review.</p>
EPA Emissions Inventory 2014
<p>This dataset contains data from the <a href="https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-data">EPA National Emissions Inventory from 2014</a>, separated by sector, in shapefile format. Each line within each file contains the amount of each pollutant (VOC, NOx, SOx, NH3, or PM2.5) in micrograms per second and the coordinates for which that emission is located (X,Y).</p>
A high-resolution gridded inventory of coal mine methane emissions for India and Australia
<p>The dataset contains the high-resolution gridded coal mine methane emissions file (.csv) for India and Australia. The emissions are estimated for the year 2018 at a resolution of 0.1° × 0.1°. The emission unit is ton/grid/year.</p>
Summary of anthropogenic mercury emission inventories
<p>Streets: Streets2019_Hg.nc<br>Annual (2000-2010) emissions of Hg0, Hg2, Hgp, from all sectors. Years 2001-2009 are a linear interpolation of years 2000 and 2010. See Streets et al. (2019) and http://geoschemdata.wustl.edu/ExtData/HEMCO/MERCURY/v2020-07/Streets/ for more details.<br>References: D.G. Streets, H.M. Horowitz, Z. Lu, L. Levin, C.P. Thackray, E.M. Sunderland. 2019. Global and regional trends in mercury emissions and concentrations, 2010-2015. Atmospheric Environment. 201: 417-427.</p> <p>EDGAR: EDGAR_totals_$YYYY_Hg.nc<br>Annual (1970-2012) EDGARv4tox2 emissions of Hg0, Hg2, Hgp, from all sectors. See Muntean et al. (2018), https://edgar.jrc.ec.europa.eu/dataset_4tox2 and http://geoschemdata.wustl.edu/ExtData/HEMCO/MERCURY/v2020-07/EDGAR/ for more details.<br>References: Muntean M, Janssens-Maenhout G, Song S, Giang A, Selin NE, Zhong H, Zhao Y, Olivier JG, Guizzardi D, Crippa M, Schaaf E. Evaluating EDGARv4.tox2 speciated mercury emissions ex-post scenarios and their impacts on modelled global and regional wet deposition patterns. Atmospheric Environment. 2018; 184:56-68.</p> <p>AMAP: AMAP_comb.0.5x0.5.2010.nc, AMAP_inds.0.5x0.5.2010.nc and AMAP_intw.0.5x0.5.2010.nc<br>Annual (2010) AMAP/UNEP emissions of Hg0, Hg2, Hgp, from stationary combustion sources. See AMAP documentation, https://www.amap.no/mercury-emissions and https://doi.org/10.34894/SZ2KOI for more details.<br>References: Technical Background Report to the Global Mercury Assessment 2013;<br>AMAP/UNEP: Oslo, Norway and Geneva, Switzerland, 2013.<br>http://www.amap.no/mercury-emissions/datasets</p> <p>WHET: WHET_Hg0.geos.1x1.2010_final.nc, WHET_Hg2.geos.1x1.2010_final.nc, WHET_HgP.geos.1x1.2010_final.nc<br>Annual (2010) WHET emissions of Hg0, Hg2, Hgp, from all sectors. See Zhang et al. (2016) and http://geoschemdata.wustl.edu/ExtData/HEMCO/MERCURY/v2018-04/ for more details.<br>References: Zhang, Y.; Jacob, D. J.; Horowitz, H. M.; Chen, L.; Amos, H. M.; Krabbenhoft, D. P.; Slemr, F.; St. Louis, V. L.; Sunderland, E. M., Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions. Proceedings of the National Academy of Sciences 2016, 113 (3), 526-531.</p>
DEPA 2050 aviation emission inventories
<p>The dataset are global aviation emission inventories for the years 2020, 2025, 2030, 2035, 2040, 2045 and 2050.</p> <p>The original data was created for the project "Development Pathways for Aviation up to 2050" (DEPA 2050) which was carried out at the German Aerospace Center (DLR) in the years 2019-2020.</p> <p>The original datasets were converted into the Network Common Data Format (netCDF) and are suitable as input files for the OpenAirClim framework. OpenAirClim models the major responses of the atmosphere by evaluation of the approximate chemistry-climate impact of air traffic emissions. The framework will be released as Open Source software.</p>
Comparison of observation- and inventory- based CH4 emissions for eight large global emitters
<p>CoCO2 (https://coco2-project.eu/) is a scientific collaborative effort funded by the H2020 European Commissions, grant number 958927.</p> <p>This synthesis has been originally based on data and country specific plots from previous VERIFY project, for the EU27: https://webportals.ipsl.fr/VERIFY/FactSheets, v1.28 and on the WP8 deliverable Reports D8.1 (https://coco2-project.eu/node/333), D8.2 (https://coco2-project.eu/node/360) and D8.3 (https://coco2-project.eu/index.php/node/406) from the CoCO2 project website.</p> <p>This dataset is updated after we received two review comments. Each spreadsheet contains the CH4 data behind the manuscript figures, both as time series, mean values and uncertainties (min, max ranges). Units are mentioned for each figure. For the gridded figures data should be asked directly from the data providers.</p>
Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches
<p>The primary goal of this dataset is to enhance the spatial and temporal distribution of emissions from civil aviation (NFR1.A.3.a), road transport (NFR1.A.3.b), railways (NFR1.A.3.c), and military aviation (NFR1.A.5), using Portugal as case study. For more information, please refer to the published article “Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches” (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.102097" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.uclim.2024.102097</span></span></a>). This dataset contains the following folders and files:</p> <p><strong>1. Spatial_Location</strong></p> <p> 1.1. NFR1_A_3_a.gdb: Geodatabase containing the locations of Portuguese airports and aerodromes.</p> <p> 1.2. NFR1_A_3_b.gdb: Geodatabase containing the locations of Portuguese roads.</p> <p> 1.3 NFR1_A_3_c.gdb: Geodatabase containing non-electrified Portuguese railways and train station locations.</p> <p> 1.4 NFR1_A_5.gdb: Geodatabase containing the locations of Portuguese military airport facilities.</p> <p><strong>2. Temporal_Profiles</strong></p> <p><em> 2.1. Daily</em></p> <p> 2.1.1. Daily_NFR1_A_3_a.csv: This csv file contains the daily movements profiles of civil aviation sites in Portugal.</p> <p><em> 2.2. Hourly</em></p> <p> 2.2.1. Hourly_NFR1_A_3_b.txt: This txt file contains the hourly road traffic volume profiles for the road transport activities in Portugal at different locations (BigAir column).</p> <p> 2.2.2. Hourly_NFR1_A_3_c.txt: This text file contains the hourly railway profile in Portugal, categorized by line and train station.</p> <p><strong>3. Emission_Factors</strong></p> <p> 3.1. EF_NFR1_A_3_a.xlsx: This Excel file contains emission factors for civil aviation activities, categorized by technology, flight phase, fuel, and pollutant. Additionally, it includes information about engines and aircraft.</p> <p> 3.2. EF_NFR1_A_3_b.xlsx: This Excel file contains emission factors for road transport activities, categorized by vehicle type, technology, fuel, abatement, and pollutant. Emission factors for road resuspension are not provided because the papers using this dataset are still under review.</p> <p> 3.3. EF_NFR1_A_3_c.xlsx: This Excel file contains emission factors for railways activities, categorized by technology, fuel, and pollutant.</p> <p> 3.4. EF_NFR1_A_5.xlsx: This Excel file contains emission factors for military aviation activities, categorized by fuel, and pollutant.</p> <p><strong>4. Other_Info</strong></p> <p> 4.1 NFR1_A_3_b: This folder contains information organized by road segments, including fuel consumption (in the “FuelConsumption” folder), hourly meteorology (in the “Meteorology” folder), population data (in the “Population” folder), daily traffic volume (in the “TrafficVolume” folder), vehicle categories (in the “VehicleCategory” folder), and vehicle classes (in the “VehicleClasses” folder). Additionally, it includes the link between road traffic volume measurement points and the Portuguese road network (in the “sensorsVSroads” folder)</p>
Supporting data for the publication "Emission ensemble approach to improve the development of multi-scale emission inventories"
<p>This dataset includes the source IDL code as well as the three emission inventory aggregated emission datasets necessary to perform the analysis presented in the publication: "Emission ensemble approach to improve the development of multi-scale emission inventories (GMD)"</p>
INEMA: High resolution inventory of atmospheric emissions of Chile
<p><strong>Brief description</strong></p> <p>This study presents the first high-resolution national inventory of anthropogenic emission for Chile (INEMA from spanish Inventario Nacional de EMisiones Antropogénicas). INEMA emission dataset considers emissions for Vehicular, point sources (industrial, energy, and other sectors), residential, forest fires, and agricultural waste burning sectors estimated for 2015–2020 and spatially distributed onto a 0.01°x0.01° high-resolution grid. For all sectors, the pollutants included are CO2, NOx, SO2, CO, VOCs, NH3, PM10, and PM2.5. Also, CH4, N2O, and black carbon (BC) are included for transport, forest fires, agricultural waste burning, and residential sources.</p> <p>Emissions are classified on IPCC categories:</p> <table> <tbody> <tr> <td>Sector</td> <td>IPCC codes</td> </tr> <tr> <td>Energy production</td> <td>1A1</td> </tr> <tr> <td>Industrial Energy consumption</td> <td>1A2</td> </tr> <tr> <td>On road transport energy consumption</td> <td>1A3b</td> </tr> <tr> <td>Comercial energy consumption</td> <td>1A4a</td> </tr> <tr> <td>residential firewood consumption</td> <td>1A4b</td> </tr> <tr> <td>Agriculture energy consumption</td> <td>1a4c</td> </tr> <tr> <td>Industrial processes</td> <td>2</td> </tr> <tr> <td>Agriculture waste burning</td> <td>3F</td> </tr> <tr> <td>Forest fires</td> <td>4A1b.iii</td> </tr> </tbody> </table> <p>This work compiles new activity data and emissions factors and distributes them geographically based on census, Chile´s road network and CONAF information. To consult the main methodological considerations and results of the previous version of INEMA, review the article by Alamos et al.(2022).</p> <p>This inventory should contribute to the design of policies that seek to mitigate climate change and improve air quality by providing policy makers, stakeholders and scientists with qualified scientific spatial explicit emission information.</p> <p><strong>Metadata</strong></p> <p>Each .tar file contain netcdf (.nc) files for each pollutant of the sector and year of the .tar file. Netcdf contains annual total emissions for the pollutant and year indicated per grid cell </p> <p>The emission grid consists of Chilean territory in WGS84 projection (lon-lat) with a spatial resolution of 0.01 * 0.01 degrees (lon x lat). The extension boundaries of the grid are: [(-76-56.3), (-66,-17)]</p> <p>The unit in the .nc files is Gigagrames per year [Gg/year]</p> <p><strong>The dataset is described in </strong></p> <p>Álamos, N., Hunneus, N., Opazo, M., Osses, M., Puja, S., Pantoja, N., Calvo, R., Denier Van Der Gon, H.A.C., Schueftan, A., Reyes, R., High-resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential activities in Chile. <em>Earth System Science Data</em>, <em>14</em>(1), 361-379. 2022</p> <p> </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.