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6 results for “traffic emissions”
CoMobility project data: Warsaw road traffic, road traffic emissions, and air concentrations for greater Warsaw area
<p><strong>Introduction</strong></p> <p>Data here are for the Greater Warsaw area, Poland originating in the CoMobility project. It contains data relevant to traffic activity, emissions, air quality and related health studies in the area. Files contain road properties along with traffic volume and rushhour delays as well as emissions of NOx, NO2 and PM from road traffic on individual road segment level. Also 500m gridded surface air concentrations are included for PM2.5 and PM10, and for NOx, NO2.</p> <p><strong>Data production</strong></p> <p>Roads are from the macroscopic traffic model MTAW (Warsaw Municipality, 2016) (<em>Model Transportowy Aglomeracji Warszawskiej </em>in Polish). It was developed based on the 2015 comprehensive travel survey in Warsaw and it is the main strategic transport model for the Greater Warsaw area, revised most recently in 2019. </p> <p>The NERVE model (Grythe et al, 2022), developed by NILU, provides detailed estimates of greenhouse gas and air pollutant emissions specifically from road traffic. Using a bottom-up approach, it combines data from regional traffic model (RTM), vehicle fleet composition, and emission factors from the Handbook Emission Factors for Road Transport (HBEFA). NERVE can be set up to calculate emissions at various levels, including road link, municipality, or national levels. It is a tool researchers and policymakers use this model for environmental assessments, policy decisions, and constructing different emission scenarios. Its high level of detail makes it valuable not only for practical emissions estimation but also as a research tool. Emissions for other sources came from the Central Emission Database by the Environmental Protection - National Research Institute (IEP-NRI) in Poland (Gawuc et al., 2021). The background concentrations were taken from the Copernicus Atmospheric Monitoring Services (CAMS) ensemble forecast for 2019 (Marécal et al., 2015)</p> <p>The EPISODE model (Hamer et al. 2020), developed by NILU, is an Eulerian urban dispersion model designed to address the need for an accurate urban air quality model in support of policy, planning, and air quality management. EPISODE operates as a 3D grid model coupled with numerical weather prediction (NWP) data. It simulates dispersion from point and line sources to receptor points, with a focus on the photochemical production of ozone in urban areas. The model’s CityChem extension enhances its capabilities for complex pollution sources, incorporating numerical chemistry solvers, sub-grid photochemistry, and a simplified street canyon model. EPISODE serves as a valuable tool for understanding and managing air quality in urban environments.</p> <p><strong>Data files</strong></p> <p>The data on road traffic contains 60 084 road links that cover the Greater Warsaw area. The file input is a traffic file from the MTAW model and is processed and formatted with NREVE. The format is an ESRI shapefile with the following road parameters:</p> <p>“<em>DISTANCE</em>” -length of road segment in kilometers.</p> <p>“<em>CAPACITY</em>” -Hourly capacity of the road.</p> <p>“<em>SLOPE</em>” -Vertical gradientor slope of the road (in %)</p> <p>“<em>SPEEDLIM</em>” -Signed speed on the road (kilometers per hour)</p> <p>In addition there are traffic volume parameters;</p> <p>“<em>ADT_LIGHT</em>” – Annual Daily Traffic, light vehicles (personal cars + light duty vans) average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_HEAVY</em>” – Annual Daily Traffic, heavy duty vehicles average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_BUSES</em>” – Annual Daily Traffic, public transport buses average 2019.</p> <p>“<em>MRN_delay</em>” – delay during morning rush hour peak (%)</p> <p>“<em>EVE_delay</em>” – delay during evening rush hour peak (%)</p> <p>The files also contain the annual emissions:</p> <p>“<em>EM_NOx</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_ NO2</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_PM</em>” – 2019 annual emissions of NOx (gram).</p> <p>EPISODE output files for atmospheric concentration files are given on NetCDF file format. Concentrations are given as annual average grid concentration for each of the components. In addition, 42 000 spatially spread out receptor points gives the 2 meter concentrations to allow for surface air concentration levels at individual point locations. Furthermore, these allows for downgridding concentrations to higher resolution.</p> <p>The source contribution files are from EPISODE and gives atmospheric concentration fields for NOx, PM10 and PM2.5 from individual sources. The individual sources are</p> <p><em>“RDU” </em>-Road dust (PM only)</p> <p><em>“EXT”</em> – Exhaust (PM only)</p> <p><em>“TRA”</em> - Exhaust (NOx only)</p> <p><em>“IND” </em>– Industry</p> <p><em>“RES”</em> – Residential</p> <p><em>“OTH”</em> – Other (all other sources within the domain combined )</p> <p><em>“BGC”</em> – Background (all sources outside the domain combined )</p> <p> </p>
Open-source traffic and CO2 emission dataset for commercial aviation
<p>This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. <br>It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. </p> <p>Context on the original work is given in the related articles (<a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2024.7365,</a> <a href="https://doi.org/10.59490/joas.2023.7201">https://doi.org/10.59490/joas.2023.7201)</a> and on the associated GitHub page (<a href="https://github.com/AeroMAPS/AeroSCOPE/">https://github.com/AeroMAPS/AeroSCOPE/</a>).<br>A simple data exploration interface will be available at <a href="www.aeromaps.eu/aeroscope">www.aeromaps.eu/aeroscope.</a><br>The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.<br> </p> <h4><br><strong>1- DISCLAIMER</strong></h4> <p><br>The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. </p> <p>Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use.</p> <p>As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance.</p> <h4><br><strong>2- DESCRIPTION</strong></h4> <p>Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.</p> <p><em>Please </em>refer<em> to </em>the<em> support article for more details (see above).</em></p> <p>The dataset contains the following columns:</p> <ul> <li>"First column" : index</li> <li><strong>airline_iata : </strong>IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.</li> <li><strong>acft_icao : </strong>ICAO code of the aircraft type</li> <li><strong>acft_class : </strong>Aircraft class identifier, own classification. <ul> <li>WB: Wide Body</li> <li>NB: Narrow Body</li> <li>RJ: Regional Jet</li> <li>PJ: Private Jet</li> <li>TP: Turbo Propeller</li> <li>PP: Piston Propeller</li> <li>HE: Helicopter</li> <li>OTHER</li> </ul> </li> <li><strong>seymour_proxy: </strong>Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.</li> <li><strong>source: </strong>Original data source for the record, before compilation and enrichment. <ul> <li>ANAC: Brasilian Civil Aviation Authorities</li> <li>AUS Stats: Australian Civil Aviation Authorities</li> <li>BTS: US Bureau of Transportation Statistics T100</li> <li>Estimation: Own model, estimation on Wikipedia-parsed route database</li> <li>Eurocontrol: Aggregation and enrichment of R&D database</li> <li>OpenSky</li> <li>World Bank</li> </ul> </li> <li><strong>seats: </strong>Number of seats available for the data entry, AFTER airport residual scaling</li> <li><strong>n_flights: </strong>Number of flights of the data entry, when available</li> <li><strong>iata_departure</strong>, <strong>iata_arrival : </strong>IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.</li> <li><strong>departure_lon</strong><em>, </em><strong>departure_lat</strong><em>, </em><strong>arrival_lon</strong><em>, </em><strong>arrival_lat : </strong>Origin and destination coordinates, could be NaN if the IATA identifier is erroneous</li> <li><strong>departure_country, arrival_country</strong>: Origin and destination country ISO2 code. <strong>WARNING: </strong>disable NA (Namibia) as default NaN at import</li> <li><strong>departure_continent, arrival_continent: </strong>Origin and destination continent code. <strong>WARNING: </strong>disable NA (North America) as default NaN at import</li> <li><strong>seats_no_est_scaling: </strong>Number of seats available for the data entry, BEFORE airport residual scaling</li> <li><strong>distance_km: </strong>Flight distance (km)</li> <li><strong>ask: </strong>Available Seat Kilometres</li> <li><strong>rpk: </strong>Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)</li> <li><strong>fuel_burn_seymour: </strong>Fuel burn <em>per flight</em> (kg) when seymour proxy available</li> <li><strong>fuel_burn: </strong>Total fuel burn of the data entry (kg)</li> <li><strong>co2: </strong>Total CO2 emissions of the data entry (kg)</li> <li><strong>domestic: </strong>Domestic/international boolean (Domestic=1, International=0)</li> </ul> <p> </p> <h4><strong>3- Citation</strong></h4> <p>Please cite the support paper instead of the dataset itself. </p> <blockquote> <p>Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2024). Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions. <em>Journal of Open Aviation Science</em>. <a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2023.7201</a></p> </blockquote>
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>
Data of "High concentration of atmospheric sub-3 nm particles in polluted environment of eastern China: new particle formation and traffic emission"
<p>The attached data is the measurement data at SORPES station in Yangtze Rive Delta of China. The data is for analysis and figures in the study of "High concentration of atmospheric sub-3 nm particles in polluted environment of eastern China: new particle formation and traffic emission". Currently the manuscript is submitted to JGR-A.</p>
Automated vehicles and central business district parking: the effects of drop-off-travel on traffic flow and vehicle emissions
Open the record for dataset details and reuse information.
Assessing the Impact of Non-tailpipe Emissions From Traffic on the Asthmatic Airway
ClinicalTrials.gov study NCT05848791. IPD Sharing: NO. Countries: 1. Publications: 0.
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