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32 results for “Anthropogenic Emissions”
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>
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
MacFarlane Australian Anthropogenic Mercury Emissions
<p><strong>Australian anthropogenic mercury emissions inventory.</strong></p> <p>A detailed description of the emissions is provided in MacFarlane et al., currently (as of March 2022) in review for <em>Environmental Science: Processes and Impacts</em> and available as a pre-print on EarthArXiv (<a href="https://doi.org/10.31223/X5RK84">https://doi.org/10.31223/X5RK84</a>).</p> <p>The dataset posted here includes:</p> <ul> <li>Total annual emissions for each sector (kg), summed over Australia as a whole, as a .csv file</li> <li>Gridded emissions for each sector (kg/m<sup>2</sup>/s), as netcdf (.nc) files</li> </ul> <p>The netcdf files are provided in GEOS-Chem compliant format, with metadata included within the files. Note that the gridded files do not all have the same horizontal resolution, with distributed emissions at 0.25° resolution and point-source emissions at 0.1° resolution.</p>
European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data
<p>The files uploaded under this doi number represent the updated data sets used in the manuscript submitted to ESSDD in its revised version entitled: "European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data" Excel files including the data behind all the manuscript figures are available for download only for review purposes. We added, as sugggested by the referees, metadata belonging to UNFCCC 2018, FAOSTAT, EDGAR v4.3.2, CAPRI and CBM.</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>
Deciphering anthropogenic and biogenic contributions to selected NMVOC emissions in an urban area
<p>This netcdf data file is related to a study published in ACP by Peron et al., 2024. Selected concentration, fluxes of NMVOC as well as meteorological data are reported for the urban area of Innsbruck, Austria. For a complete site description we also refer to Ward et al., 2022: https://doi.org/10.5194/acp-22-6559-2022 and Karl et al., 2020: https://doi.org/10.1175/BAMS-D-19-0270.1</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>
Data and code for the publication: "Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines"
<p>A. Feinberg, Aug 2024</p> <p>arifeinberg@gmail.com</p> <p> </p> <p>Essential data and code for the publication: Feinberg et al. : Unexpected anthropogenic emission decreases are required to explain recent atmospheric mercury concentration declines</p> <p> </p> <p>The directories include:</p> <p>1) analysis<strong>_</strong>plotting<strong>_</strong>scripts/ - all analysis scripts used to analyze observations, produce input data, and plot figures for paper</p> <p>2) GC<strong>_</strong>code/ - Archived GEOS-Chem code used to simulate the runs in this paper</p> <p>3) GC<strong>_</strong>data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:</p> <p>BASE - run2021</p> <p>BASE+LEG - run2022</p> <p>DEC<strong>_</strong>LEG<strong>_</strong>ONLY - run2024</p> <p>ZHANG23 - run2025</p> <p>DEC<strong>_</strong>ANT<strong>_</strong>NH - run2026</p> <p> </p> <p>4) input<strong>_</strong>data/ - input data used to run GEOS-Chem</p> <p> </p> <p>Please refer to other README.md files within sub-directories and contact me for any questions.</p>
Herbarium specimens reveal century-long trait shifts in poison ivy due to anthropogenic CO2 emissions
<p>Dataset for manuscript entitled "Herbarium specimens reveal century-long trait shifts in poison ivy due to anthropogenic CO<sub>2</sub> emissions." Contains one spreadsheet file ("Ng et al 2023 Poison Ivy trait data.xlsx"). Note that metadata can be found in first tab.</p>
Dataset from 'Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison'
<p>This dataset supplements the following manuscript:</p> <p>Okuljar, M., Garmash, O., Olin, M., Kalliokoski, J., Timonen, H., Niemi, J. V., Paasonen, P., Kontkanen, J., Zhang, Y., Hellén, H., Kuuluvainen, H., Aurela, M., Manninen, H. E., Sipilä, M., Rönkkö, T., Petäjä, T., Kulmala, M., Dal Maso, M., and Ehn, M.: Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-524, 2023.</p>
Anthropogenic Climate Forcers Historical Emissions Analysis
<p>[DRAFT - final version May 2024] Input data necessary to run the jupyter notebook code script located at the <a href="https://github.com/bwalkowiak/Anthropogenic-Climate-Forcer">Anthropogenic Climate Forcer GitHub</a></p> <p>Files include:</p> <ul> <li>Final Emissions Data for major GHGs and air pollutants from <a href="../records/10904361">Community Emissions Data System (CEDS)</a> version v_2024_04_01. Emissions pre-1970 are from v_2021_04_21 fuel data with sector partitioning from latest version 1970 allocation. <ul> <li>Emissions data files by emission species (SO2, NOx, BC, OC, NH3, NMVOC, CO, CO2, CH4, N2O), country, and sector produced by the April-01-2024 release of CEDS.</li> <li>See the <a href="https://github.com/JGCRI/CEDS/">CEDS GitHub</a> site for details including journal paper reference information and any known issues with this data.</li> </ul> </li> <li> HC (CFC, HCFC) emissions data from <a href="https://csl.noaa.gov/assessments/ozone/2022/">WMO Ozone 2022 Report</a>, HFC and HCFC emission data from <a href="https://edgar.jrc.ec.europa.eu/emissions_data_and_maps">EDGAR - Emissions Database for Global Atmospheric Research</a>, specifically EDGAR v8.0, with other species from <a href="https://ozone.unep.org/countries/data-table">UNEP Ozone Depleting Substance databases</a>]. HC emissions sectoral fractions calculated using UNFCCC AFEAS database and UNEP ODS. See HC Section for specific details.</li> <li><a href="https://github.com/IPCC-WG1/Chapter-6_Fig12_22_24">IPCC AR6 Ch6</a> ouput data for effective radiative forcing (ERF) and temperture response (del_T) over the calculation time period (1750-2022)</li> <li><a href="https://www.iea.org/data-and-statistics/data-product/world-energy-balances">IEA World Energy Balances 2023</a> data used to allocate sectoral emissions data</li> <li>Species lifetime from IPCC AR6</li> <li>CMIP model thermal reponse parameters from <a href="https://gmd.copernicus.org/articles/14/3007/2021/">Leach et al., (2021)</a></li> </ul>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
Data used in manuscript Spatial modelling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki
<p>This data set includes data used to develop and evaluate carbon dioxide emission modelling component in the Surface Urban Energy and Water balance Scheme (SUEWS). The data files are:</p> <ol> <li>CO2_Model_Parameter_Fitting.zip contains m-files (Matlab) used to calculate parameters for photosynthesis modelling <ul> <li>F_pho_data.mat includes meteorological and EC data used to fit photosynthesis model parameters in Kumpula</li> <li>FitKumpulaData.m calculates the model parameters in Kumpula</li> <li>FitViikkiData.m calculates the model parameters in Viikki</li> <li>Other m-files needed by the above two codes</li> </ul> </li> <li>Data.zip contains measured data used to develop and evaluate SUEWS <ul> <li>KumpulaData2012.txt and TorniData2012.txt include eddy covariance data measured at the two sites in Helsinki</li> <li>SMEARIII_meteorology_2016MM_30.m meteorological data used to fit model parameters in Viikki street trees (see 00 ReadMe_SMEARIII_Meteorology.TXT for details)</li> <li>Viikki_SWC_2016.txt measured soil moisture from Viikki in 2016</li> <li>Kumpula_2016_HH_RLAI6_Output.out is SPP output used to fit model parameters in Viikki street trees</li> </ul> </li> <li>SUEWS_EC_Site_Model_runs: SUEWS input and output files for Kumpula and Torni model runs</li> <li>SpatialRun_input.zip: SUEWS input files for the spatial model run</li> <li>spatmatHel_final.mat: SUEWS output files for spatial model run in mat-format</li> </ol>
Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: a case study of London terraced houses
<p>This archive includes the data used (e.g. Time use survey (UK-TUS) data), model files (idf files for running EnergyPlus) and codes for analysis in the paper (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.enbuild.2024.114668" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.enbuild.2024.114668</a>).</p> <p>Files in this archive should include:</p> <ul> <li>Time use survey data analysis</li> </ul> <p>o Main dataset: TUS_activity.zip</p> <p>o Code: TUS_clustering_code.zip</p> <p>o Output: InternalHeatProfile.zip</p> <ul> <li>Building energy modeling </li> </ul> <p>o Main dataset (run in EnergPlus 9.4): IDFfiles.zip</p> <p>o Output: Eplus_output.zip</p> <ul> <li>PostProcess analysis</li> </ul> <p>o Code: QF_analysis_code.zip</p> <p>o Output: QF_output.zip</p> <p> </p> <p>Note: this version currently only includes the outputs of all processes, the main dataset and code will be updated later.</p>
Anthropogenic carbon monoxide emissions during 2014-2020 in China constrained by in-situ observations
<p><strong>The description of the NetCDF files (12×200×350):</strong></p> <ol> <li> <p>The first dimension represents the months, the second represents latitude, and the third represents longitude.</p> </li> <li> <p>The latitude ranges from 15.1°N to 54.9°N, and the longitude ranges from 66.1°E to 135.9°E, with a uniform grid spacing of 0.2° for both.</p> </li> </ol> <p><strong>The units for all files are as follows:</strong></p> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Format</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>All emission data.zip</p> </td> <td> <p>netcdf</p> </td> <td> <p>kg·m<sup>-2</sup>·s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Emissions in seven regions.csv</p> </td> <td> <p>csv</p> </td> <td> <p>10<sup>3</sup> kt</p> </td> </tr> <tr> <td> <p>Simulated CO concentrations.zip</p> </td> <td> <p>txt</p> </td> <td> <p>μg·m<sup>-3</sup> </p> </td> </tr> </tbody> </table>
Dataset for Manuscript: Comparing Urban Anthropogenic NMVOC Measurements with Representation in Emission Inventories - A Global Perspective
<p>Urban observations of individual NMVOCs and the calculated or reported emission ratios used for comparison to emission inventories.</p>
Response of the ozone-related health burden in Europe to changes in local anthropogenic emissions of ozone precursors (version 2.0)
<p>This data set includes the model output of the peer-reviewed paper: Gu, Y., Henze, D. K., Nawaz, M. O., and Wagner, U. J., 2023, Response of the ozone-related health burden in Europe to changes in local anthropogenic emissions of ozone precursors, Environ. Res. Lett., <strong>doi:</strong>10.1088/1748-9326/ad0167.</p> <p>The data set includes:</p> <p>-The receptor functions, expressed as the total ozone-related premature deaths from respiratory diseases in 2005 and 2015.</p> <p>-The marginal benefit, expressed as the ozone-related premature deaths avoided by a 20% reduction in anthropogenic emissions of NOx, NMVOCs, and CO in 2005 and 2015, respectively</p> <p>-The marginal contribution (per unit), expressed as the ozone-related premature deaths caused by per unit (kg/m2/yr) anthropogenic emissions of NOx in 2005 and 2015</p>
Ocean alkalinity destruction by anthropogenic seafloor disturbances generates a hidden CO2 emission
Open the record for dataset details and reuse information.
Impacts of anthropogenic emission change scenarios on U.S. water and carbon balances at national and state scales in a changing climate
Open the record for dataset details and reuse information.
KORUS-AQ posterior anthropogenic emissions
Top-down emissions of CO from anthropogenic sources resulting from the assimilation of multispectral CO retrieval profiles (V8J) from the Measurements of Pollution in the Troposphere (MOPITT). The assimilation is performed using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). The initial conditions of CO and some non-methane volatile organic compounds (NMVOCs), as well as CO emission inventories from anthropogenic and biomass burning sources, are optimized during the analysis step.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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