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Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
<p><strong> Introduction</strong></p> <p>A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO<sub>2</sub> emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO<sub>2</sub> emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).</p> <p><strong>Time series data </strong></p> <p>The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (<em>Pinus sylvestris</em> L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).</p> <p>The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018).</p> <p>Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.</p> <p>The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO<sub>2</sub> release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.</p> <p>The pre-prepared input data are given in files, see Table 1 for descriptions.</p> <p> </p> <p> </p> <p>Table 1. Description of input data files.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description of data</strong></p> </td> </tr> <tr> <td> <p>basal.areas.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual average basal area (m<sup>2</sup> ha<sup>-1</sup>) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> <p>Values of tree species or group correspond to:</p> <p>1 Scots pine</p> <p>2 Norway spruce</p> <p>3 Broadleaved species</p> </td> </tr> <tr> <td> <p>biomass.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual biomass (biomass, t ha<sup>-1</sup> of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>dead_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha<sup>-1</sup> of dry mass; lognat_litter).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>ghgi_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha<sup>-1</sup>) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of ground correspond to litter deposition environment:</p> <p>above Above-ground litter</p> <p>below Below-ground litter</p> </td> </tr> <tr> <td> <p>lognat_decomp.csv</p> </td> <td> <p>Time series of years 1990-2022 for C, t ha<sup>-1</sup> of dry mass, decomposed from logging residues and natural mortality by region.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>logyasso_weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> </td> </tr> <tr> <td> <p>total_area.csv</p> </td> <td> <p>Areas (ha) of drained peatland forests remaining forest land by region and peat_type.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>The R-scripts</strong></p> <p>The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO<sub>2</sub> emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.</p> <p>Guidance for running the R-scripts is given in the README.txt.</p> <p><strong>References</strong></p> <p>Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.</p> <p>LUKE Statistics</p> <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> <p>Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).</p> <p>Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014.</p> <p>R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.</p> <p>Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.</p>
Global High Resolution Dust Emission Inventory for Chemical Transport Models
<p><strong>Overview:</strong><br> ==================================================================================</p> <p>Offline dust emissions in 2016 are now available at 0.25° x 0.3125° resolution. This dataset is calculated using the native resolution <a href="http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-FP">GMAO meteorology (GEOS-FP) fields</a>. </p> <p>Codes and Instructions (README file in the GitHub repository) to generate these offline emissions can be found on <a href="https://github.com/Jun-Meng/geos-chem/tree/v11-01-Patches-UniCF-vegetation">GitHub</a>.</p> <p>The offline emissions in this database have no scale factor applied, so users should apply the required scale factor in their application. Suggested scale factor to make the global total annual dust emission to 2000 Tg is 5.7141e-4. </p> <p><br> <strong>Zip File Details:</strong><br> ===============================================================================</p> <p>2016.zip contains daily (366 in total) netCDF files (stored in monthly folders) of global gridded hourly mineral dust emission flux rate. </p> <p> </p> <p>Individual file: </p> <p>/YYYY/MM/dust_emissions_025x0.3125.YYYYMMDD.nc</p> <p> Resolution : 0.25 x 0.3125 grid (721 x 1152 boxes)<br> Units : kg m-2 s-1<br> Timestamps : Hourly, 2016<br> Compression : Level 1 (nccopy -d1)<br> Chunking : nccopy -c lon/1152,lat/721,time/24</p> <p> </p> <p>Variables in each file: </p> <p>EMIS_DST1, EMIS_DST2, EMIS_DST3 and EMIS_DST4 represent dust emission flux rate in four size bins (0.1-1.0, 1.0-1.8, 1.8-3.0, and 3.0-6.0 micro in radius). </p> <p> </p> <p>*<em>Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted</em></p>
Dataset for Gidden et.al. 2023 Updated AR6 Mitigation Benchmarks using National Emissions Inventories
<p>Scenario variables related to LULUCF emissions and removals in IPCC-assessed scenarios from AR6 calculated using OSCAR. Both direct fluxes, corresponding to model-reporting conventions, and indirect fluxes, which constitute an alignment factor to national inventories, are provided. See the original publication for more details (<a href="https://www.nature.com/articles/s41586-023-06724-y">https://www.nature.com/articles/s41586-023-06724-y</a>).</p><h4>Change log from version 1</h4><ul><li><i>AR6 Reanalysis|OSCARv3.2|Emissions|CO2|AFOLU</i> and <i>AR6 Reanalysis|OSCARv3.2|Carbon Removal|Land</i> are removed, users should explicitly calculate if needed but otherwise use direct and indirect fluxes.</li><li><i>AR6 Reanalysis|OSCARv3.2|Carbon Removal</i> is now calculated only using the direct flux component (i.e., <i>AR6 Reanalysis|OSCARv3.2|Carbon Removal|Land|Direct</i>)</li></ul>
2014 EPA National Emissions Inventory allocated to the grid cells of InMAP Source-Receptor Matrix
<p>This dataset is the 2014 EPA National Emissions Inventory (NEI) v1 allocated to the individual grid cells of InMAP Source-Receptor Matrix (<a href="https://zenodo.org/record/2589760#.Yds79GjMI2w">ISRM</a>). The source types is classified by EPA Source Classification Codes (SCCs). The dataset includes emissions of both primary and secondary PM<sub>2.5</sub>. Secondary PM<sub>2.5</sub> includes four precursors: NO<sub>x</sub>, SO<sub>x</sub>, NH3, and VOC. The detailed description of emission processing is in <a href="https://doi.org/10.1073/pnas.1818859116">Tessum et al. (2019</a>).</p> <p>Each shapefile in the dataset is in the format of input file of <a href="http://spatialmodel.com/inmap/">InMAP</a>/ISRM, which includes the emission amounts of five pollutants (Primary PM<sub>2.5</sub>, NO<sub>x</sub>, SO<sub>x</sub>, NH3, and VOC), stack information (height, diameter, temperature, and velocity), and SCCs. The unit of emissions is <span class="math-tex">\(\mu g/s\)</span>. (If these emissions are used directly with the ISRM, the resulting outputs will be concentrations, in units of <span class="math-tex">\(\mu g/m^3\)</span>.)</p>
County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China
<p>County-level activity data and emission factors</p>
VIIRS-based Fire Emission Inventory (data)
<p>The VIIRS-based Fire Emission Inventory provides daily open biomass burning emission fluxes for 46 species of aerosols and gases at ~500 m resolution (globally). The data starts on early 2012 because it uses the VIIRS I-band active fire product.</p>
VIIRS-based Fire Emission Inventory (data)
<p>The VIIRS-based Fire Emission Inventory provides daily open biomass burning emission fluxes for 46 species of aerosols and gases at ~500 m resolution (globally). The data starts on early 2012 because it uses the VIIRS I-band active fire product.</p>
EV hourly CO2 emission inventory
<p>This is the appendix supporting data for the manuscript entitled "Developing an hourly-resolution well-to-wheel carbon dioxide emission inventory of electric vehicles" submitted to Applied Energy. </p>
Canada_construction_emissions_inventory_[Public_Data]
<p><strong>Overview</strong>:</p> <p>This data was created using the code stored at the following <a href="https://github.com/Leopoldwdb/Canada_construction_emissions_inventory">GitHub</a>. The code applies OpenIO-Canada to Canadian Supply-Use Tables to create EEIO matrices. It then performs additional analyses on said matrices to obtain consumption-based accounts of GHG emissions driven by Canadian construction sectors, as well as data on the geographic flows and GDP intensity of embodied emissions in construction.</p> <p><strong>File descriptions:</strong></p> <p><strong>1. T2_Figure2_data.csv </strong>A file containing the data represented in Figure 2. </p> <p><strong>2. T3_Figure3_data.csv </strong>A file containing the data represented in Figure 3. </p> <p><strong>3. T4_Figure4_data.csv </strong>A file containing the data represented in Figure 4. </p> <p><strong>4. T5_Figure5_data.csv </strong>A file containing the data represented in Figure 5. </p> <p><strong>5. T6_Figure6_data.csv </strong>A file containing the data represented in Figure 6. </p> <p><strong>6. T7_Figure7_data.csv </strong>A file containing the data represented in Figure 7. </p> <p><strong>7. CanCons_workbook_V2.xlsx</strong></p> <p>A workbook summarizing all the analyses performed to create the figures in the paper "Developing a comprehensive account of embodied emissions within the Canadian construction sector". Contains all the data exported by the jupyter notebook stored at the GitHub link, as well as the additional analysis & formatting steps taken.</p> <p><strong>8. RAW_data-for-analysis</strong><em><strong>_</strong></em><strong>figure2-7.zip</strong></p> <p>A Zip file containing the raw data created by <a href="https://github.com/Leopoldwdb/Canada_construction_emissions_inventory/blob/main/CanCons_Analysis_notebook.ipynb">CanCons_Analysis_notebook.ipynb</a> and used by the CanCons_workbook (above) to produce the figures in question. This includes:</p> <ul> <li><strong>Figure2_RAW_data.csv</strong></li> </ul> <p>A direct export of the D matrix, along with totals.</p> <ul> <li><strong>Figure3_RAW_data.csv</strong></li> </ul> <p>Results of a contribution analysis of the final demand for construction-based Gross Fixed Capital Formation (GFCF) from all Canadian provinces and territories. Results show, for construction demand in each province, all the environmental impacts driven by each construction sub-sector.</p> <ul> <li><strong>Figure3_RAW_data_GDP.csv</strong></li> </ul> <p>Accompanies Figure3_RAW_data.csv. Contains data from the Y matrix on the final demand for construction-based GFCF in $. Used for calculating intensities per unit GDP used in Table 1.</p> <ul> <li><strong>Figure4_RAW_data_[construction sector].csv</strong></li> </ul> <p>3 files which contain the results of contribution analyses of 3 individual construction sectors: Roads & Highways, Communications Infrastructure, and Residential Buildings. Results show, for each province, the embodied environmental impacts associated with the inputs into these 3 sectors.</p> <ul> <li><strong>Figure5+6_RAW_data_Baseline.csv</strong></li> </ul> <p>Results of a standard contribution analysis of Construction GFCF to serve as a baseline for the following files.</p> <ul> <li><strong>Figure5+6_RAW_data_Zero[Region].csv</strong></li> </ul> <p>Each file represents the result of a contribution analysis for Construction GFCF where the S matrix values for the [Region] (representing the environmental impacts caused by supply-chain steps within a region) have been zeroed out. This means that the results in these files represent a world where the [Region]'s contribution to the final impacts of all other regions have been removed. Subtracting these values from the baseline results in values representing each [Region]'s contribution to consumption-based impacts driven by construction every other region. This allows the flows of embodied emissions in construction materials to be mapped.</p> <ul> <li><strong>Figure7_RAW_data.csv</strong></li> </ul> <p>Subset of results from Figure3_RAW_data on the distribution of energy (TJ) and emissions (kgCO2) across regions and construction sectors.</p> <p> </p> <p><strong>9. IO_system.zip</strong></p> <p>A Zip file containing CSVs of all the matrices which represent the EEIO system. These include the A, Y, S, FY matrices, as well as matrices which were used in the development of the IO system (Z, F, U, V, g, q), and the matrices representing the results of the analysis and the characterization of impacts (C, D, E). For more information on these matrices please see the methodology section of the paper "Developing a comprehensive account of embodied emissions within the Canadian construction sector".</p> <p>The .zip includes the following matrices:</p> <ul> <li>A.csv - Technology matrix (normalized Z matrix).</li> <li>C.csv - Characterization matrix.</li> <li>D.csv - Matrix of total characterized environmental impacts associated with each category of final demand.</li> <li>E.csv - Matrix of total environmental impacts associated with each category of final demand.</li> <li>F.csv - Matrix of direct impacts associated with the production of each product category.</li> <li>FY.csv - Matrix of direct impacts associated with final consumption.</li> <li>L.csv - Leontief inverse.</li> <li>S.csv - Matrix of direct impacts associated with the production of each product category normalized by dollar of input.</li> <li>U.csv - Use matrix for intermediates (value of products used by industries in 2018 Canadian Dollars).</li> <li>V.csv - Supply matrix for intermediates (products supplied by industries in Can$). </li> <li>Y.csv - Matrix of spending by final demand categories.</li> <li>Z.csv - Input-Output table for Canada.</li> <li>g.csv - Diagonalized vector representing total industry output in Can$.</li> <li>q.csv - Diagonalized vector of total product output (also known as x vector).</li> </ul> <p>Canadian regional abbreviations:</p> <ul> <li>'CA-AB' - Alberta</li> <li>'CA-BC' - British Columbia</li> <li>'CA-MB' - Manitoba</li> <li>'CA-NB' - New Brunswick</li> <li>'CA-NL' - Newfoundland and Labrador</li> <li>'CA-NS' - Nova Scotia</li> <li>'CA-NT' - Northwest Territories</li> <li>'CA-NU' - Nunavut</li> <li>'CA-ON' - Ontario</li> <li>'CA-PE' - Prince Edward Island</li> <li>'CA-QC' - Quebec</li> <li>'CA-SK' - Saskatchewan</li> <li>'CA-YT' - Yukon</li> </ul> <p>Country codes for international trade:</p> <ul> <li>AT Austria<br>BE Belgium<br>BG Bulgaria<br>CY Cyprus<br>CZ Czech Republic<br>DE Germany<br>DK Denmark<br>EE Estonia<br>ES Spain<br>FI Finland<br>FR France<br>GR Greece<br>HR Croatia<br>HU Hungary<br>IE Ireland<br>IT Italy<br>LT Lithuania<br>LU Luxembourg<br>LV Latvia<br>MT Malta<br>NL Netherlands<br>PL Poland<br>PT Portugal<br>RO Romania<br>SE Sweden<br>SI Slovenia<br>SK Slovak Republic<br>GB United Kingdom<br>US United States<br>JP Japan<br>CN China<br>CA Canada<br>KR South Korea<br>BR Brazil<br>IN India<br>MX Mexico<br>RU Russian Federation<br>AU Australia<br>CH Switzerland<br>TR Turkey<br>TW Taiwan<br>NO Norway<br>ID Indonesia<br>ZA South Africa<br>WA RoW Asia and Pacific<br>WL RoW America<br>WE RoW Europe<br>WF RoW Africa<br>WM RoW Middle East</li> </ul>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Kampala
<p>This paper is under review</p> <p>Abstract:</p> <p>Kampala, the political and economic capital of Uganda and one of the fastest urbanising cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality with emissions from multiple diffused local sources like transportation, domestic and outdoor cooking, and industries, and sources outside the city airshed like seasonal open fires in the region. PM2.5 (particulate matter under 2.5um size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60-100 ug/m3. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programmes including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents an integrated assessment of air quality in Kampala drawing from ground measurements (from a hybrid network of stations), satellite observations (from NASA’s MODIS and OMI), global reanalysis fields (from GEOS-chem and CAMS simulations), high resolution (~1km) multi-pollutant emissions inventory for the airshed, WRF-CAMx based PM2.5 pollution analysis, and a qualitative review of institutional and policy environment for air quality management in Kampala. The proposed clean air action plans aim for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.</p> <p>This data archive serves as a supplemenary to the journal article and with a short description of the files below:</p> <ul> <li>File: AQ-Kampala-Analysis-Summary.pptx (Caution: large 60MB) <br>A composite presentation including the following<br> <ul> <li>Grid summaries</li> <li>Snapshots of airshed GIS files, emission activities</li> <li>Summary of meteorology from WRF simulations and historical synoptics</li> <li>Summaries of ambient monitoring data</li> <li>CAMS reanalysis summary</li> <li>Summaries of Emission inventory and WRF-CAMx modelling (annual and monthly)</li> <li>Summaries of PM2.5 Source apportionment (annual and monthly)</li> </ul> </li> <li>File: grids_kampala.rar<br>Grid file (KML and ESRI shapefiles format) for the airshed spanning 0.0N to 0.6N and 32.3E to 32.9E with a spatial resolution of 0.01deg (~1km)</li> <li>File: gis_roads_from_opensteetmaps.rar<br>ESRI shapefiles of primary roads and all roads, extracted from the openstreetmaps<br>Raw data archive @ https://download.geofabrik.de/index.html</li> <li>File: gis-scanned2021image-quarries.kml<br>KML file of quarries scanned using the imagery on Google Earth platform</li> <li>File: population_kampala_2000-2022.csv<br>Gridded population data 2000 to 2022<br>Raw data archive is from LANDSCAN - https://landscan.ornl.gov</li> <li>File: Monitoring-Kampala_USEmbassy_2017-2024.xlsx<br>Summary of monitoring data collected at the US Embassy in Kampala<br>Raw data archive is @ https://www.airnow.gov/international/us-embassies-and-consulates/#Uganda$Kampala</li> <li>File: meteo_wrf_stats.xlsm<br>Summary of output of WRF simulations for the Kampala region. Have to activiate macros to summarize the results by month and update the charts. The tool can be used to other cities also by changing the input data.</li> <li>File: meteo_precip-era5-reanalysis.csv<br>Summary of monthly precipitation date (mm/day) from ERA5 reanalysis fields<br>Raw data archive is @ https://psl.noaa.gov/data/atmoswrit/timeseries</li> <li>File: TROPOMI_EastAfrica_NO2_Maps.zip<br>Images of monthly average TROPOMI NO2 extracts covering East Africa (Uganda and Ethiopia)<br>Extracted from Google Earth Engine, using 10% cloud fraction</li> <li>File: TROPOMI_EastAfrica_CSVs.zip<br>CSV files of gridded monthly average NO2, SO2, HCHO, and Ozone columnar densities<br>Extracted from Google Earth Engine, using 10% cloud fraction<br>Read the data descriptions and applicability of the data for analysis before using (for example, negative numbers in the SO2 file). <br>NO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 <br>SO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2<br>HCHO - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO<br>O3 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3</li> <li>File: composite_emisson_factors_gains.xlsx<br>A composite library of emission factors for reference</li> <li>File: kampala_gridded_emissions_2018.rar<br>Gridded emissions inventory for Kampala - PM25, PM10, SO2, NO, NO2, and CO<br>PM25 is speciated into FPRM, BC, and OC (sum all for PM25)<br>PM10 is speciated into FPRM, CPRM, BC, OC (sum all for PM10)<br>All emissions in tons/year/grid<br>Emissions are seggragted into sectors and fuels - included in the filenames</li> <li>File: kampala_gridded_modelled_monthavgp25.csv<br>Gridded PM2.5 concentrations for 2018, from WRF-CAMx modelling system<br>Monthly averages in ug/m3</li> </ul>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Addis Ababa
<p>This paper is under review. For additional information or queries, send email to sguttikunda@urbanemissions.info</p> <p>****</p> <p>ABSTRACT: Ground measurements and satellite observations over Addis Ababa airshed show a deteriorating trend of air pollution, especially for PM2.5 (all particulate matter under 2.5um). In this paper, we present a review of available monitoring data; a model-ready multi-pollutant (PM10, PM2.5, SO2, NOx, CO, non-methane VOCs, and CO2) emissions inventory at 0.01º resolution for the designated airshed; a heatmap of PM2.5 concentrations and an estimate of source contributions constructed using WRF-CAMx chemical transport modelling system; and a discussion on proposed actions towards establishing an air quality management plan for the city. Emissions from road transport; residential and commercial cooking; resuspended dust on roads and from construction activities; residential and industrial heating; lighting; open waste burning; and other industrial activities contributed the most to ambient PM2.5 pollution. Particularly, vehicle exhaust is estimated to contribute up to 29% of total PM2.5, followed by biomass combustion in the residential and industrial sectors.</p> <p>File included in this dataset:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Report-Addis-Data-Summary.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Ambient monitoring data<br>File: AddisAbaba_AllEmbassy_Data.xlsx (summary of data till May2024)<br>File: monitoring_addisair_cleaned.xlsx (summary of sensor data)</li> <li>Gridded emissions inventory<br>File: addis_gridded_emissions.rar<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_addis_grids.rar</li> <li>TROPOMI UVAI and MODIS AOD<br>Data files extracted from google earth engine as TIF and CSV, for the designated airshed<br>File: ADDISABABA_yearly_tifs.zip</li> <li>Meteorlogical data summary for the airshed, extracted from the WRF sumulations<br>File: meteo_addis.houravg_summary.csv</li> <li>Reference reports<br>File: Report-C40-2016-Addis-Ababa-GHG-Emssions.pdf<br>File: Report-CSE-Ethiopia-Urban-AQM-Guidance.pdf<br>File: Report-WB-Ethiopia-motorization-management.pdf<br>File: Report-UNEP-Addis-AQM-Plan-Draft.pdf</li> <li>GIS files<br>File: gis_addis_grids.rar (shapefile and KML file for the airshed grid)<br>File: gis_addis_osm_roads.rar (shapefiles extracted from openstreetmaps)<br>File: pop_extracts_4selection_domain.xlsm (gridded population data, with macros can be used to extract population totals around a monitoring station)<br>File: gis_addis_multiple_layers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> </ol>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Bishkek
<p>Full paper is published here<br><a href="https://doi.org/10.3390/air2040021">https://doi.org/10.3390/air2040021</a><br>Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan<br><br></p> <p>****</p> <p>Harsh winters, aging infrastructure and control technologies, and the increasing demand for urbanization and modernization of amenities are major factors contributing to the deteriorating air quality in Bishkek, the capital city of Kyrgyzstan and a burgeoning economic hub in Central Asia. The heating energy needs are met via combustion of coal at the central heating plant, heat only boilers, and in-situ heating equipment and the mobility needs via combustion of diesel and petrol. Other mapped sources contributing to daily air pollution levels in Bishkek’s airshed include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with average age more than 10 years, 7.5 km2 of quarries, and one landfill. Annual PM2.5 emission load for the airshed is approximately 5,500 tons, resulting in an annual average concentration of 48 ug/m3, which is 9-10 times higher than the World Health Organization (WHO) guideline of 5 ug/m3. Wintertime daily averages range from 200-300 ug/m3. Proposed emissions management policies for the city include shift to clean fuels like gas and electricity at the heating plants and households, restricting the secondhand vehicle imports and incentivizing newer standard vehicles, promotion of public transport system with newer buses, at least doubling of the waste collection efficiency and landfill management capacity and encouraging greening and maintaining road infrastructure to control dust emissions. PM2.5 levels from mid- to long-term implementation of these options is expected to drop by 50-70%. A long-term plan for Bishkek must include an expansion of the ambient monitoring network using a combination of reference-grade and low-cost sensors to track progress of air quality management efforts and to support information dissemination for public awareness.</p> <p>Files included here:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Bishkek_AQ_Analysis_Composite.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Meteorological data summary for the airshed, extracted from the WRF sumulations<br>File: Bishkek_Met_Summary.pptx<br>File: bishkek_meteorology_stats.xlsm (activate macros for stats and making images for final use)</li> <li>GIS files<br>File: gis_bishkek-grids-pop.rar (shapefile and KML file for the airshed grid, csv file for gridded population 20 years, and images)<br>File: gis_bishkek_roads.rar (shapefiles extracted from openstreetmaps)<br>File: gis_bishkek_adm0.rar (shapefiles of administrative boundaries)<br>File: gis_bishkek_multiplelayers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> <li>CAMx output (units is ug/m3)<br>File: bishkek_camx_pm25_monthlyavg.csv (see the grid file for mapping)</li> <li>Gridded model-ready emissions inventory<br>File: bishkek_gridded_emissions_2018.rar (see the grid file for mapping)<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_bishkek-grids-pop.rar</li> <li>Ambient monitoring data<br>File: bishkek_monitoring_claritysensor_2021.xlsx<br>File: bishkek_monitoring_us.embassy2019-2024.rar</li> </ol>
2014 US National Emissions Inventory data
<p>This is the emissions dataset used in the following publication:</p> <p>Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. Christopher W. Tessum, Joshua S. Apte, Andrew L. Goodkind, Nicholas Z.Muller, Kimberley A. Mullins, David A. Paolella, Stephen Polasky, Nathaniel P. Springer, Sumil K. Thakrar, Julian D. Marshall, Jason D. Hill. <em>Proceedings of the National Academy of Sciences.</em> 2019, 116 (13) 6001-6006; DOI:10.1073/pnas.1818859116</p> <p>All data is originally from the U.S. Environmental Protection Agency and is subject to any licenses or restrictions applicable to the original data. The dataset is reproduced here for archival purposes.</p>
PAPILA: High resolution inventory of atmospheric emissions in Latin America.
<p><strong>Brief description</strong></p> <p>PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NMVOC, SO2, NH3, NOx), Greenhouse gases (CO2, CH4), and particles (PM25, PM10, BC, OC) from anthropogenic sources in South America, for the period 2014–2020. Here is presented PAPILA version 2.0, The first version of this inventory is available <a href="https://data.mendeley.com/datasets/btf2mz4fhf/3">here.</a></p> <p>PAPILA is the first AEI from anthropogenic sources covering the continental SA region, which combines local available information with a global database in a proper and rigorous way. For this purpose, global datasets were used as a basis, enriching it with locally developed inventories available in the literature until 2023 for Argentina, Chile, Colombia, Mexico and Ecuador.</p> <p>The Dataset consider emissions from 13 sectors which are organized and denominated inspired mostly on the nomenclature given by CAMS: thermal power plants (ENE); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries, including fuel consumption in manufacturing industries and construction industrial processes, (IND); solvents (SLV); refineries (REF); agricultural soils (AGS); agricultural livestock (AGL); domestic and international navigation (SHP); solid waste disposal (including solid waste, wastewater, and incineration) (SWD); open biomass burning (OBB); residential , commercial and other sectors (RCO)</p> <p>To consult the main methodological considerations , review the methodological document available for download</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>The inventories are presented as netCDF4 files, one for each year and specie, gridded in WGS84 projection (lon-lat) with a spatial resolution of 0.1∘ × 0.1∘ covering the domain 32–120∘ W and 34∘ N–58∘ S.</p> <p>Each file contains 14 variables corresponding to the emissions in Tg yr−1 from the13 sectors estimated and the sum of all categories (SUM)</p>
Measurement-Based Spatially Explicit Methane Emission Inventory (EI-ME)
<p>Accurate and comprehensive assessment of methane emissions, a powerful climate warming pollutant, is a key first step in reducing these emissions, while supporting the ability to track progress toward such reductions over time. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, adn their reliance on generic emission factors produces underestimations when compared with measurement-based inventories.</p> <p>In this work, we compile and analyze previous peer-reviewed measurement-based data on facility-level methane emissions in the US oil and gas sector and use these data to develop statistically robust emissions models from which we estimate total methane emissions for the population of major US oil and gas facilities.</p> <p>This dataset (EI_ME_v1.0.gpkg) aggregates the results of this measurement-based methane emission inventory (EI-ME), which is focused on oil and gas methane emissions in the US onshore production regions. The emissions estimates are spatially resolved at 0.1 x 0.1 degree spatial scales.</p> <p>The data layers in the GeoPackage are:</p> <ul> <li><em>EI-ME_gridded_ch4_emissions:</em> estimated methane emissions, spatially resolved at 0.1x0.1 degree spatial grids</li> <li><em>EI-ME_US_oil_gas_basins:</em> major US oil and gas basin boundaries, based on <a href="https://www.eia.gov/maps/maps.php">EIA</a> basin boundary definitions.</li> <li><em>EI-ME_facility_ch4_measurements_data: </em>A compilation of previous peer-reviewed facility-level measurement-based data for oil and gas methane emissions in the US.</li> </ul> <p>We also provide a netcdf version ("EI_ME_2021_inventory_CONUS_point1_degrees_v1.nc") which includes estimated mean oil and gas methane emissions over the contiguous US (excludes Alaska) aggregated over a slightly offset spatial grid compared to the full domain in the above .gpkg.</p> <p>Complete details of the emissions model development and dataset creation can be found in the following manuscript:</p> <ul> <li><strong>How to cite: </strong>Omara, M., Himmelberger, A., MacKay, K., Williams, J. P., Benmergui, J., Sargent, M., Wofsy, S. C., and Gautam, R.: Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021), Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, 2024.</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>UPDATE (10/10/2025):</p> <p>The spatially explicit measurement-based oil and gas methane emissions inventory (EI-ME) is developed by MethaneSAT, a wholly owned subsidiary of Environmental Defense Fund, to support comprehensive oil and gas methane assessment, methane source attribution, and mitigation. The inventory combines ground-based measurement-based data with statistically robust methane emissions modeling to provide representative estimates of total methane emissions for key facility categories in the contiguous US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. It is spatially resolved at 0.1x.0.1 degree spatial scales.</p> <p> Version 1 of the EI-ME inventory for the contiguous United States was published in 2024 and provided an estimate of the 2021 oil and gas methane emissions and uncertainties that are spatially resolved at 0.1x0.1 degree spatial scales.</p> <p> Here, we provide an update to the EI-ME inventory for the years 2023 and 2024. In this update, we follow the same methodology and use the same input emissions datasets as described in detail in Omara et al. (2024), https://doi.org/10.5194/essd-16-3973-2024. We incorporate the latest available oil and gas activity data for the years 2023 and 2024 based on data from Enverus Prism (<a href="http://www.eneverus.com/">www.eneverus.com</a>), supplemented with additional information from the Oil and Gas Infrastructure Mapping database (OGIM v2.7, <a href="https://doi.org/10.5281/zenodo.15103476">https://doi.org/10.5281/zenodo.15103476</a>) and global annual gas flaring data from VIIRS (Visible Infraed Imagin Radiometer Suite), available from the Earth Observation Group (<a href="https://eogdata.mines.edu/products/vnf/global_gas_flare.html">https://eogdata.mines.edu/products/vnf/global_gas_flare.html</a>).</p> <p>---</p> <p>Contact at Environmental Defense Fund: Mark Omara (momara@edf.org), Anthony Himmelberger (ahimmelberger@methanesat.org)</p> <p>---</p>
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>
Data from: A local-to-global emissions inventory of macroplastic pollution
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Global nitrous oxide emissions from livestock manure during 1890−2020: An IPCC Tier 2 inventory
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Wildfire burn severity and emissions inventory: an example implementation over California
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Repository: A global emission inventory of particulate phosphorus from fertilizer production and handling
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.