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2,208 results for “emissions”
Influence of biogenic emissions from boreal forests on aerosol-cloud interactions
<p>Datasets that support the major results of the study "Influence of biogenic emissions from boreal forests on aerosol-cloud interactions".</p> <p>Acknowledgements: </p> <p>The work was supported by Academy of Finland via Center of Excellence in Atmospheric Sciences (project no. 272041), Flagship program for Atmospheric and Climate Competence Center (ACCC, 337549, 337552, 337550) and grants 317380, 320094 and 334792, 328290, 302958, 1325656, 316114, 325647, 1325681 and 341271, European Research Council Advanced Grants (227463-ATMNUCLE, 742206-ATM-GTP,) and Starting Grants (638703-COALA, 714621-GASPARCON), the Arena for the gap<br> analysis of the existing Arctic Science Co-Operations (AASCO) funded by Prince Albert Foundation Contract No 2859, and “Quantifying carbon sink, CarbonSink+ and their interaction with air quality” INAR project funded by Jane and Aatos Erkko Foundation. This work was partly supported by the Office of Science (BER), U.S. Department of Energy via BAECC<br> (Petäjä, DE-SC0010711), BAECC-SNEX (Moisseev), European Commission via projects This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 821205 (Understanding and reducing the long-standing uncertainty in anthropogenic aerosol radiative forcing, FORCeS) and ACTRIS, ACTRIS-TNA,<br> ACTRIS2, ACTRIS-IMP, BACCHUS, eLTER, ICOS, PEGASOS and Nordforsk via Cryosphere-Atmosphere Interactions in a Changing Arctic Climate, CRAICC, The BAECC SNEX was also supported by NASA Global Precipitation Measurement (GPM) Mission ground validation program. The deployment of AMF2 to Hyytiälä was enabled and supported by ARM. Argonne National<br> Laboratory's work was supported by the U.S. Department of Energy, Assistant Secretary for Environmental Management, Office of Science and Technology, under contract DE-AC02-06CH11357. The authors gratefully acknowledge the support of AMF2, SMEAR2 and the BAECC community for their support in initiating the BAECC campaign, its implementation,<br> operation, data analysis and interpretation. </p>
Food systems Emissions shares, 1990-2019
<p><strong>Greenhouse gas emissions from agri-food systems </strong></p> <p><strong>(1990-2019)</strong></p> <p> </p> <p><strong>Overview</strong></p> <p>We present results from the <a href="https://www.fao.org/faostat/en/#data/EM">FAOSTAT emissions shares</a> database which disseminates emissions from all economic sectors and from agri-food systems by gases (CO<sub>2</sub>, CH4, N<sub>2</sub>O, F-gases and their total in CO<sub>2</sub>eq) relative to 236 countries and territories over the period 1990 – 2019.</p> <p>In 2019, global greenhouse gas emissions from all economic sectors totaled about 54 billion tonnes CO<sub>2</sub>eq (54 Gt CO<sub>2</sub>eq), emissions from agri-food systems totaled 16.5 billion tonnes (Gt CO<sub>2</sub>eq) representing 31 percent of the total anthropogenic emissions from all economic sectors.</p> <p>This dataset focuses on emissions from agri-food systems which includes data on emissions from the farm-gate, land use change and pre- and post-production. The sum of these three sectors comprises the agri-food system emissions (16.5 Gt CO<sub>2</sub>eq).</p> <p>Pre- and post-production include emissions from: fertilizers manufacturing, on-farm electricity use, food processing, food transport, food retail, food waste disposal, food household consumption and food packaging.</p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p><strong>Files included in the dataset</strong></p> <p>We have included two files available for downloaded. The csv file contains the data in .csv format and a excel file contains data in .xlsx format.</p> <p><strong>Global warming potential (GWP)</strong></p> <p>In this data, the emissions total (CO<sub>2</sub>eq) is computed by applying the GWP values from the IPCC Fifth Assessment Report (AR5) as given below:</p> <p> </p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>Greenhouse gas</strong></p> </td> <td> <p><strong>GWPAR5 (IPCC, 2014)</strong></p> </td> </tr> <tr> <td> <p><em>Single</em></p> <p><em>gases</em></p> </td> <td> <p>N<sub>2</sub>O</p> </td> <td> <p>265</p> </td> </tr> <tr> <td> <p>CO<sub>2</sub></p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>CH<sub>4</sub></p> </td> <td> <p>28</p> </td> </tr> <tr> <td> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><em>F-gases </em></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> </td> <td> <p>HFC-23</p> </td> <td> <p>12,400</p> </td> </tr> <tr> <td> <p>HFC-32</p> </td> <td> <p>677</p> </td> </tr> <tr> <td> <p>HFC-41</p> </td> <td> <p>116</p> </td> </tr> <tr> <td> <p>HFC-125</p> </td> <td> <p>3,170</p> </td> </tr> <tr> <td> <p>HFC-134</p> </td> <td> <p>1,120</p> </td> </tr> <tr> <td> <p>HFC-134a</p> </td> <td> <p>1,300</p> </td> </tr> <tr> <td> <p>HFC-143</p> </td> <td> <p>328</p> </td> </tr> <tr> <td> <p>HFC-143a</p> </td> <td> <p>4,800</p> </td> </tr> <tr> <td> <p>HFC-152</p> </td> <td> <p>16</p> </td> </tr> <tr> <td> <p>HFC-152a</p> </td> <td> <p>138</p> </td> </tr> <tr> <td> <p>HFC-161</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>HFC-227ea</p> </td> <td> <p>3,350</p> </td> </tr> <tr> <td> <p>HFC-236cb</p> </td> <td> <p>1,210</p> </td> </tr> <tr> <td> <p>HFC-236ea</p> </td> <td> <p>1,330</p> </td> </tr> <tr> <td> <p>HFC-236fa</p> </td> <td> <p>8,060</p> </td> </tr> <tr> <td> <p>HFC-245ca</p> </td> <td> <p>716</p> </td> </tr> <tr> <td> <p>HFC-245fa</p> </td> <td> <p>858</p> </td> </tr> <tr> <td> <p>HFC-365mfc</p> </td> <td> <p>804</p> </td> </tr> <tr> <td> <p>HFC-43-10mee</p> </td> <td> <p>1,650</p> </td> </tr> <tr> <td> <p>Sulfur hexafluoride (SF<sub>6</sub>)</p> </td> <td> <p>23500</p> </td> </tr> <tr> <td> <p>Nitrogen trifluoride (NF<sub>3)</sub></p> </td> <td> <p>16,100</p> </td> </tr> <tr> <td> <p>PFC-14</p> </td> <td> <p>6,630</p> </td> </tr> <tr> <td> <p>PFC-116</p> </td> <td> <p>11,100</p> </td> </tr> <tr> <td> <p>PFC-218</p> </td> <td> <p>8,900</p> </td> </tr> <tr> <td> <p>PFC-318</p> </td> <td> <p>9,540</p> </td> </tr> <tr> <td> <p>PFC-31-10</p> </td> <td> <p>9,200</p> </td> </tr> <tr> <td> <p>PFC-41-12</p> </td> <td> <p>8,550</p> </td> </tr> <tr> <td> <p>PFC-51-14</p> </td> <td> <p>7,910</p> </td> </tr> <tr> <td> <p>PCF-91-18</p> </td> <td> <p>7,190</p> </td> </tr> <tr> <td> <p>µGWP</p> </td> <td> <p>5,195</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Data Sources</strong></p> <p>FAOSTAT climate change, emissions shares <a href="https://www.fao.org/faostat/en/#data/EM">data</a>, <a href="https://fenixservices.fao.org/faostat/static/documents/EM/cb7514en.pdf">analytical brief</a>.</p> <p>The primap-hist national historical emissions timeseries <a href="https://zenodo.org/record/5494497#.Yf18Cy8w2ic">data</a>, <a href="https://essd.copernicus.org/articles/8/571/2016/essd-8-571-2016.html">paper</a>.</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>
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>
PM2.5 emissions from Siberian forest fires 2004-2021
<p>The dataset contains Supplementary Materials for the article <em>''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis''</em> in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis. <br> <br> <strong>Supplementary Figures</strong>:<br> - Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line); </p> <p>- Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha. </p> <p>- Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map);</p> <p>- Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region;</p> <p>- Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic;</p> <p>- Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region; </p> <p>- Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory; </p> <p>- Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory. <br> </p> <p>We share Copernicus Atmosphere Monytoring Service <strong>PM2.5 emissions maps</strong> (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594. </p> <p><br> To determine emissions from the territory of Russia, we provide <strong>shapefiles</strong> with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334). </p> <p>Also, there are<strong> initial dataset</strong> for analysis (Initital data_SARIMAX archive) and <strong>SARIMAX model settings</strong> (doc.). </p>
Photon-emission statistics induced by electron tunnelling in plasmonic nanojunctions
<p>OPEN DATA related to the research publication:</p> <p>R. Avriller, Q. Schaeverbeke, T. Frederiksen, and F. Pistolesi<br> <em>Photon-emission statistics induced by electron tunnelling in plasmonic nanojunctions</em><br> Phys. Rev. B <strong>104</strong>, L241403 (2021) [arXiv:2107.07860]</p>
On-road traffic emission over megacity Delhi
<p>This dataset presents an estimate of hourly gridded on-road traffic exhaust emission of PME, BC, OM, CO, NOx, VOC, NH3, N2O and CH4, for the megacity Delhi (National Capital Territory of Delhi) for 2018 at a spatial resolution of 100m×100m. This dataset is presented as a netDCF covering the rectangular domain around National Capital Territory (NCT) of Delhi. </p>
BRAVES database Version 1.1 (REVISED): multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil.
<p>The BRAzilian Vehicular Emissions inventory Software (BRAVES) database is a multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil. We provide this database using a spatial disaggregation based on road density, temporal disaggregation using vehicular flow profiles, and chemical speciation based on SPECIATE database from the United States Environmental Protection Agency. We provide netCDF files with spatial resolution of 0.05x0.05 and annual emissions from 2013 to 2019. Files are divided by vehicle type (light, commercial-light, motorcycles, and heavy). We also provide the total vehicular emissions (sum of emissions from all vehicle types). The database contains emissions of 41 chemical species, such as ACET, ACROLEIN, ALD2, BENZ, BUTADIENE13, CH4, CO, CO2, ETH, ETHA, ETHY, ETOH, FORM, ISO, N2O, NAPH, NO, NO2, PAL, PCA, PCL, PEC, PFE, PK, coarse mode primary PM (PMC), PMG, PMN, unspeciated PM2.5 (PMOTHR), PNA, PNH4, PNO3, POC, PRPA, PSI, PSO4, PTI, SO2, TERP, TOL, VOC, and XYLMN. Codes from BRAVES are available by registering at <a href="https://hoinaski.prof.ufsc.br/BRAVES/">https://hoinaski.prof.ufsc.br/BRAVES/</a> and <a href="https://github.com/leohoinaski/BRAVES">https://github.com/leohoinaski/BRAVES</a>, where users can access instructions to run the database and download the input files.</p> <p>In this updated version from the first BRAVES database version, we have preserved estimates of ETOH and RCHO from CETESB. Brazil has a unique chemical signature of the chemical composition due to the biofuels (27% of gasoline is ethanol and 7% of diesel is bio-diesel). The emissions of C2H4O (ALD2), CH2O (FORM), and C3H6O (ACET) have been derived from RCHO emissions. We have used US-EPA Speciate to speciate compounds only when local emission factors of RCHO and ETOH are not available, such as in the case of motorcycles and heavy vehicles.</p> <p>We have also included emissions of PM2.5 (PMFINE), speciating coarse PM emissions from brake and tires (40%), road wear (53%), road dust resuspension (17%), and exhaust emissions (100%). Pixel center coordinates (longitude, latitude), pixel area (AREA), and pixel local time zone (LTZ) shift from UTC has been added to the netCDF files.</p> <p>This database has been currently part of the preprint currently under review for the journal ESSD (https://doi.org/10.5194/essd-2022-74).</p> <p> </p> <p>Files description:</p> <p>BRAVESdatabaseAnnual_BR_(typeEmiss)_(Vehicle Type)_(resolution)_(year).nc - Annual emissions in Brazil by vehicle type and 0.05x0.05 degree of resolution.</p> <p>Domain:</p> <p>lati = -36 #(Brazil) #lati = int(round(bound.miny)) # Initial latitude</p> <p>latf = 8 #(Brazil) #latf = int(round(bound.maxy)) # Final latitude</p> <p>loni = -76 #(Brazil) #loni = int(round(bound.minx)) # Initial longitude</p> <p>lonf = -32 #(Brazil) #lonf = int(round(bound.maxx)) # Final longitude</p> <p>deltaX = 0.05 # Grid resolution/spacing in x direction</p> <p>deltaY = 0.05 # Grig resolution/spacing in y direction</p> <p> </p> <p>typeEmiss:</p> <p>'TOTAL' = Total emissions/sum of emissions types<br> 'Exhaust' = Only exhaust emissions<br> 'non-exaust' = Only non-exhaust emissions<br> 'non-exaustMP' = Only Particulate Matter non-exhaust emissions<br> 'non-exaustMP_no_resusp'= Only Particulate Matter non-exhaust emission excluding road resuspension </p>
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
Estimating global ammonia (NH3) emissions based on IASI observations from 2008 to 2018
<p>The dataset ia produced based on the Infrared Atmospheric Sounding Interferometer (IASI) observations in Luo et al.,(2022), by updating the prior ammonia (NH3) emission fluxes with the ratio between biases in simulated NH3 concentrations and effective NH3 lifetimes against the loss of the NHx family (NHx ≡ NH3 + NH4+). We then include sulfur dioxide (SO2) column to correct the NH3 emission trends over India and China, where SO2 emissions have changed rapidly in recent years. Finally, we quantify the uncertainty of NH3 emission by a series of perturbation and sensitivity experiments. The GEOS-Chem simulation driven by top-down estimates has lower bias with the IASI observations than prior emissions, demonstrating the consistency of our estimates with observations.</p>
Dataset: High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes
<p>Dataset used in the article "High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes"</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Vocus- proton transfer reaction mass spectrometer (Vocus-PTR) at a subarctic fen during 2021. It also contains PAR (Photosynthetic Active Radiation), temperature and flux quality criteria.</p>
Dataset Cumulative CO2 Emissions of International Transport
<p>This dataset considers the year 1783, when the first steamship was built, as the first year of the international transport CO2 emissions.</p> <p>The global cumulative CO2 emissions including international transport are converted to the 1875 baseline, similar to the Global Warming baseline (1850-1900). </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>
TNO-CAMS European CO2 emissions 2000-2014 v1
<p><strong>Introduction</strong></p> <p>This TNO_CAMS_CO2 emission dataset was prepared by TNO as a contribution to the H2020 project MACC-III and the subsequent Copernicus Atmospheric Monitoring Service. This model-ready historic emission inventory at high spatial resolution (~7x7 km) for UNECE-Europe for 15 consecutive years (2000–2014) providing CO<sub>2</sub> from fossil fuels and CO<sub>2</sub> from biofuels is intended to support modelling and sub-national scale identification of emissions. Where available and considered fit for purpose, we have used CO<sub>2</sub> estimates as reported by the Parties to UNFCCC. The data have been supplemented by other estimates, most notable from the IIASA GAINS model and the JRC EDGAR database to create a complete coverage. The approach to the spatial distribution of the dataset is similar to the TNO-MACC emission dataset for air pollutants ( see Kuenen et al., ACP, 2014).</p> <p>The emission grid consists of UNECE-Europe in WGS84 projection (lon-lat) with a spatial resolution of 1/8 x 1/16 degrees (lon x lat). The lower left of the grid is at lon = -60, lat = 30 and the upper right is at lon = 60, lat = 72.</p> <p>The grid files TXT (.csv) & netcdf (.nc) both contain annual total emissions per grid cell for the year 2000-2014. A separate file has been prepared for each year. </p> <p>The unit in the .csv files is Mg/gridcell/yr</p> <p>The unit in the .nc files is kg/gridcell/yr</p> <p>Sectoral breakdown uses the SNAP classification. Compared to the default SNAP1 sectors (1 to 10), a couple of refinements have been made to the sectors:</p> <ul> <li> <p>SNAP 3 and SNAP 4 are grouped as SNAP 34</p> </li> <li> <p>SNAP 7 is split in SNAP 71 to 75</p> </li> </ul> <p>The dataset is described in </p> <p>Denier van der Gon, H.A.C., J.J.P. Kuenen, G. Janssens-Maenhout, U. Döring, S. Jonkers, A.J.H. Visschedijk., TNO_CAMS high resolution European emission inventory for anthropogenic CO<sub>2</sub> for 2000-2014 and future years following two different pathways, ESSD, in preparation, 2017.</p> <p> </p>
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Effective realization of abatement measures can reduce HFC-23 emissions
<p>Atmospheric observations (mole fractions) of halogenated greenhouse gases (HFC-23 (CHF<sub>3</sub>), PFC-318 (c-C<sub>4</sub>F<sub>8</sub>), HCFC-22 (CHClF<sub>2</sub>), HCFC-21 (CHCl<sub>2</sub>F), HFC-4310mee (C<sub>5</sub>H<sub>2</sub>F<sub>10</sub>), HFC-161 (C<sub>2</sub>H<sub>5</sub>F)) at the tall tower site at Cabauw, the Netherlands (51.972 °N, 4.927 °E, altitude -0.7 m a.s.l., 207 m a.g.l.), for the duration of a tracer (HFC-161) release experiment (17.06.2022 – 07.08.2022) within an extended (19.11.2021 – 7.8.2022) measurement campaign of halogenated greenhouse gases (>60 substances) at the Cabauw tall tower site. The measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography and mass spectrometry (GC-MS), as is used within the global AGAGE network (<a href="https://agage.mit.edu/">https://agage.mit.edu/</a>). The HFC-161 tracer was released at 22 km distance from the Cabauw tall tower site, at 4 m a.s.l., 10 m a.g.l, at various flow rates. HFC-161 mole fractions are provided as the measured mole fractions and as the measured mole fractions normalised to the set tracer release flow rates.</p> <p>In addition, a subset of the above-described data is provided. This was used to assess the emissions of the above listed halogenated greenhouse gases from an industrial factory, by reference to the released HFC-161 tracer.</p> <p>The data are related to an article in Nature (https://doi.org/10.1038/s41586-024-07833-y).</p>
Scenario emissions and temperature data for PROVIDE project
<p>Data for tier 1 and tier 2 PROVIDE scenarios. </p> <p>Tier 1 scenarios are mostly from integrated assessment models. Tier 2 scenarios are much more numerous and are kept in a separately zipped folder for temperatures and csv file for emissions data. The temperature folders contains the full set of FaIR runs for scenarios entirely defined by emissions. Summaries are much smaller files containing quantile info for each scenario, including the scenarios defined by combinations of emissions and temperature trends. </p> <ul> <li>10 Tier 1 scenarios until 2100</li> <li>15 Tier 1 scenarios defined until 2300, all of which are variations of the original 10</li> <li>Many Tier 2 scenarios, aiming to completely tile reasonable emissions space parameterised with 4 variables</li> </ul> <p><em>Several objectives of the PROVIDE project depend on a set of scenarios that can be modelled through either a ‘classical’ forward-looking approach or by a novel approach that ‘reverses the impact chain’. These scenarios are also key elements for the integration of PROVIDE findings in the outward-looking stakeholder Dashboard of the project. Here we describe the set of scenarios that has been developed and will be used within PROVIDE. In total, PROVIDE explores <strong>three complementary approaches</strong>:</em></p> <ol> <li><em>10 distinct tier 1 scenarios extending until 2100, mostly based on the existing literature, used for short-term assessments of impacts</em></li> <li><em>15 distinct tier 1 scenarios extending until 2300, based on different extensions of the 10 literature scenarios, used for assessing longer-run impacts and the geophysical impact of significant temperature overshoot</em></li> <li><em>~1350 distinct tier 2 scenarios, exploring several dimensions of emissions space systematically, such as CO<sub>2</sub> net zero date and relative methane intensity. This is used to explore which scenarios are compatible with given climate outcomes. These scenarios can be used to reverse the traditional impact chain, going from acceptable climate risks to descriptions of acceptable emissions. </em></li> </ol>
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