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29 results for “Transport emissions”
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Hypersonic Transport: 3D Emission Inventory of STRATOFLY-MR3 Fleet Operated on Brussels to Sydney Route in 2075
<p>High-resolution 3D inventories of future hypersonic transport (HST) are compiled for the year 2075, integrating the gaseous engine emissions of a fleet of 200 hydrogen-powered Mach 8 passenger aircraft*. These aircraft are operated once a day for 360 days on a reference route from Brussels (BRU) to Sydney (MYA) with either NO<sub>x</sub>-optimized (ICA**: 114 000 ft; 34.75 km) or H<sub>2</sub>O-optimized (ICA**: 107 500 ft; 32.77 km) flight profiles, derived to minimize environmental impacts in terms of total emissions. The emissions are spatially gridded at a horizontal resolution of 1° in longitude and latitude, with a vertical resolution of 1000 ft, and are temporally accumulated on an annual basis. Note that the 3D emission inventories encompass detailed data on species-specific HST emissions***, fuel burn, and total distance traveled: </p> <ul> <li>Species: NO, H<sub>2</sub>O; H<sub>2</sub></li> <li>Temporal information: 2075; annually</li> <li>Spatial information: 1° x 1° x 1000 ft</li> <li>Data Format: NetCDF</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------<br>* The hypersonic aircraft concept under consideration is the <a href="https://arc.aiaa.org/doi/abs/10.2514/6.2021-1877">STRATOFLY-MR3</a> vehicle, which was conceptually developed in <br> the framework of the <a href="https://cordis.europa.eu/project/id/769246">H2020 STRATOFLY project</a>.<br>** Initial Cruise Altitude<br>*** with a unit of kg/km<sup>3 </sup>(corrected in v0.2)</p> <p> </p>
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>
Analysis of public transport in Vienna with Co2 emissions in Austria's households
<p>This repository serves as a backup and longterm storage for the datasets and plots resulting from the analysis on means of transport in Vienna in the time period 2010 - 2021 together with the Co2 emissions of Austria's households.</p>
Data and code for 'Worldwide greenhouse gas emissions of green hydrogen production and transport'
<p>This data and code accompanies a Nature Energy article with the title 'Worldwide greenhouse gas emissions of green hydrogen production and transport'. In the article ‘Worldwide greenhouse gas emissions of green hydrogen’, we quantify project-specific greenhouse gas emissions for 1,025 green hydrogen projects in 2030, as well as green hydrogen transport emissions for three transport modes: pipeline, liquid hydrogen shipping and ammonia shipping. This repository entry contains the data and code used to produce the outputs presented in the article.</p>
Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches
<p>The primary goal of this dataset is to enhance the spatial and temporal distribution of emissions from civil aviation (NFR1.A.3.a), road transport (NFR1.A.3.b), railways (NFR1.A.3.c), and military aviation (NFR1.A.5), using Portugal as case study. For more information, please refer to the published article “Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches” (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.102097" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.uclim.2024.102097</span></span></a>). This dataset contains the following folders and files:</p> <p><strong>1. Spatial_Location</strong></p> <p> 1.1. NFR1_A_3_a.gdb: Geodatabase containing the locations of Portuguese airports and aerodromes.</p> <p> 1.2. NFR1_A_3_b.gdb: Geodatabase containing the locations of Portuguese roads.</p> <p> 1.3 NFR1_A_3_c.gdb: Geodatabase containing non-electrified Portuguese railways and train station locations.</p> <p> 1.4 NFR1_A_5.gdb: Geodatabase containing the locations of Portuguese military airport facilities.</p> <p><strong>2. Temporal_Profiles</strong></p> <p><em> 2.1. Daily</em></p> <p> 2.1.1. Daily_NFR1_A_3_a.csv: This csv file contains the daily movements profiles of civil aviation sites in Portugal.</p> <p><em> 2.2. Hourly</em></p> <p> 2.2.1. Hourly_NFR1_A_3_b.txt: This txt file contains the hourly road traffic volume profiles for the road transport activities in Portugal at different locations (BigAir column).</p> <p> 2.2.2. Hourly_NFR1_A_3_c.txt: This text file contains the hourly railway profile in Portugal, categorized by line and train station.</p> <p><strong>3. Emission_Factors</strong></p> <p> 3.1. EF_NFR1_A_3_a.xlsx: This Excel file contains emission factors for civil aviation activities, categorized by technology, flight phase, fuel, and pollutant. Additionally, it includes information about engines and aircraft.</p> <p> 3.2. EF_NFR1_A_3_b.xlsx: This Excel file contains emission factors for road transport activities, categorized by vehicle type, technology, fuel, abatement, and pollutant. Emission factors for road resuspension are not provided because the papers using this dataset are still under review.</p> <p> 3.3. EF_NFR1_A_3_c.xlsx: This Excel file contains emission factors for railways activities, categorized by technology, fuel, and pollutant.</p> <p> 3.4. EF_NFR1_A_5.xlsx: This Excel file contains emission factors for military aviation activities, categorized by fuel, and pollutant.</p> <p><strong>4. Other_Info</strong></p> <p> 4.1 NFR1_A_3_b: This folder contains information organized by road segments, including fuel consumption (in the “FuelConsumption” folder), hourly meteorology (in the “Meteorology” folder), population data (in the “Population” folder), daily traffic volume (in the “TrafficVolume” folder), vehicle categories (in the “VehicleCategory” folder), and vehicle classes (in the “VehicleClasses” folder). Additionally, it includes the link between road traffic volume measurement points and the Portuguese road network (in the “sensorsVSroads” folder)</p>
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></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>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
Carbon emission and NPV data of hydrogen system in transport and building sector
<p>The dataset contains the simulation environment data, including meteorological data, renewable energy system configuration, grid energy structure data, and energy balance data of the hydrogen systems; the comparison on the lifecycle carbon emissions between different vehicles; and results of the building-transportation energy sharing model, including the calculation process and results of net present value and carbon intensity of the hydrogen system.</p>
H-alpha Emission as a Diagnostic of Plasma Transport Mechanics in Centrifugal Magnetospheres
<p>Approximately one quarter of magnetic early (B5-B0) B-type type stars display Balmer line emission originating in centrifugal magnetospheres (CMs). Indeed, the CMs of these stars are uniquely detectable in all available magnetospheric diagnostics (radio synchrotron, NIR, Balmer emission, UV emission, and X-rays), and therefore represent an excellent opportunity for detailed study of magnetospheric plasmas. All CM host-stars are young, possess strong magnetic fields, and are rapid rotators, properties consistent with expectations from the Rigidly Rotating Magnetosphere model and evolutionary models incorporating magnetic braking. The nature of mass transport within CMs has been debated, with two competing scenarios: centrifugally driven magnetic reconnection due to overloading of the magnetic field by the stellar wind, and leakage arising from diffusion and drift of ions across magnetic field lines. We have conducted the first detailed study of the Halpha emission properties of the population of CM host stars. The results demonstrate that mass balancing must be accomplished by breakout. We find that emission strength is independent of mass-loss rate and can, to first order, be predicted simply by the strength of the equatorial magnetic field at the Kepler corotation radius. However, an apparent cutoff in the presence of emission lines at low luminosities may be indicative of diffusion/drift mechanisms becoming dominant in the weak-wind regime. We briefly point to potential implications of these results for X-ray and radio synchrotron emission.</p>
Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions
<p>The files provide additional information to the paper “Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions". The supporting data file contains excel sheets of data used in the analysis, and the supporting information file provides some assumptions, background information and other results not included in the paper</p>
Replication Data for: CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
<p>This dataset includes three files necessary for understanding CHEEREIO model output in the demo section of my initial submission to GMD for the paper: <em>CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.</em> Detailed guides for how to handle these datasets are provided in the <a href="https://cheereio.readthedocs.io/en/latest/Postprocess-workflow.html">CHEEREIO documentation postprocessing page</a>.</p> <ul> <li>control_hemco_diagnostics.nc contains the source-separated prior methane emissions.</li> <li>combined_hemco_diagnostics.nc contains the source-separated and ensemble member separated posterior methane emissions.</li> <li>bigY.pkl contains a Python dictionary which aligns TROPOMI XCH4 with simulated prior and posterior GEOS-Chem XCH4.</li> </ul>
Decreasing seasonal cycle amplitude of methane in the northern high latitudes being driven by lower latitude changes in emissions and transport
<p>This dataset contains the smoothed and detrended time series of methane from the TOMCAT regional tagged tracer simulations between 1995-2020 at 22 NOAA surface observation sites. Each file contains the Site Code and the latitude and longitude of of each observation site. The regional file contains the region code of each regional tagged tracer. More information on the TOMCAT simulation and the Site Codes and Region Codes can be found at: https://doi.org/10.5194/egusphere-2023-132, 2023</p>
Generation, Emission, Transportation, and Deposition of Aeolian Dust from Loess Deposits in the Southern China
<p>Datasets support the paper "Generation, Emission, Transportation, and Deposition of Aeolian Dust from Loess Deposits in the Southern China".</p>
PM2.5 data for "Contribution of fire emissions to PM2.5 and its transport mechanism over the Yungui Plateau, China during 2015–2019"
<p>PM2.5 data for "Contribution of fire emissions to PM2.5 and its transport mechanism over the Yungui Plateau, China during 2015–2019"</p>
Observation of Reactive Transport in Soil Columns with Positron Emission Tomography (GeoPET)
<p>Here we investigate reactive transport in soils with GeoPET as quantitative spatiotemporal molecular imaging method. As PET directly yields tracer concentrations, the data can be utilised for parameterization and validation of reactive transport computer models.</p> <p>Artificial soil columns (sand/silt/clay/Goethit, length 90 mm, diameter 40 mm) have been prepared under CO<sub>2</sub>-atmosphere. Four different experiments on one and the same soil column are shown as motion pictures from the GeoPET-observations:</p> <p>1) Injection of water, labelled with [<sup>18</sup>F]KF, into the CO<sub>2</sub>-saturated column</p> <p>2) Conservative transport of [<sup>18</sup>F]KF solution through the water-saturated column</p> <p>3) Injection of the reactive tracer [<sup>64</sup>Cu]Cu(MCPA)<sub>2</sub> into the unconditioned water-saturated column</p> <p>4) Injection of the reactive tracer [<sup>64</sup>Cu]Cu(MCPA)<sub>2</sub> into the preconditioned column.</p> <p> </p> <p>The study was supported within the framework of the priority program “Biogeochemical Interfaces in Soil” by the German Science Foundation (DFG SPP 1315: KE508/19 and LI872/5).</p> <p> </p> <p>References:</p> <p>Kulenkampff, J., Zakhnini, A., Gründig, M., and Lippmann-Pipke, J.: Quantitative experimental monitoring of molecular diffusion in clay with positron emission tomography, Solid Earth, <strong>7</strong>, 1207-1215, 2016.</p> <p>Kulenkampff, J., Gründig, M., Zakhnini, A., Gerasch, R., and Lippmann-Pipke, J.: Process tomography of diffusion, using PET, to evaluate anisotropy and heterogeneity, Clay Miner., <strong>50</strong>, 369–375, 2015.</p> <p>Kulenkampff, J., Gründig, M., Zakhnini, A., and Lippmann-Pipke, J.: Geoscientific process monitoring with positron emission tomography (GeoPET), Solid Earth, <strong>7</strong>, 1217–1231, 2016.</p> <p>Zakhnini, A., Kulenkampff, J., Sauerzapf, S., Pietrzyk, U., and Lippmann-Pipke, J.: Monte Carlo simulations of GeoPET experiments: 3D images of tracer distributions (<sup>18</sup>F, <sup>124</sup>I and <sup>58</sup>Co) in Opalinus Clay, anhydrite and quartz, Comput. Geosci., 57, 183–196, 2013.</p> <p>Lippmann-Pipke, J., Gerasch, R., Schikora, J., and Kulenkampff, J.: Benchmarking PET for geoscientific applications: 3D quantitative diffusion coefficient estimation in clay rock, Comput. Geosci. <strong>101</strong>, 21-27, 2017.</p> <p>Stoll, M., Kulenkampff, J., Gründig, M., Lippmann-Pipke, J., and Kersten, M.: Molecular positron emission tomography imaging of Cu mobility enhanced by the herbicide 4-chloro-2-methylphenoxy-acetic acid in a soil column, <em>submitted</em></p> <p>Lippold, H., Karimzadeh L., Kulenkampff, J. Wissmeier, L., Stuhlfauth, C., Stoll, M., Lippmann-Pipke, L.: Effect of pH on the mobility of the herbicide MCPA in a goethite-sand matrix: 1D and 2D reactive transport modelling, <em>to be submitted</em></p> <p> </p>
Data for manuscript "Wildfire Emissions Transport Pathways in the Global Atmosphere"
<p>Model results and emission dataset that was used to produce the data presented in the manuscript by Daskalakis et al., submitted in GRL in May 2023.</p>
Positron Emission Tomography Assessment of Ketamine Binding of the Serotonin Transporter
ClinicalTrials.gov study NCT02717052. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Model simulation data used in "Attributing ozone and its precursors to land transport emissions in Europe and Germany" (Mertens et al., ACP, 2020)
<p>This dataset contains the output of the MECO(n) regional model simulations analysed and discussed in Mertens et al. (ACP., 2020). An overview of the numerical experiments is given in the article. The README files in the dataset give additional information about the metadata.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.