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16 results for “shipping emission”
EPITOME ship emissions: Projections of shipping emissions towards 2050.
<p>As part of the EPITOME project, we have setup global shipping emission scenarios. They are based on a combination of the global CO<sub>2</sub> ship emission inventory for 2015 produced with the Ship Traffic Emissions Assessment Model (STEAM) (Johansson et al., 2017) and Arctic fuel consumption and emission scenarios calculated with the DCE ship emission model (Winther et al., 2017).</p> <p>The scenarios include a Baseline scenario, a SO<sub>x</sub> Emission Control Area (SECA) and a heavy fuel oil (HFO) ban scenario. The Baseline scenario is calculated in two variants involving Business As Usual (BAU) and High Growth (HiG) traffic growths. The SECA and HFO ban scenarios are given with the BAU traffic development.</p> <p>Additionally a Polar route scenario is included, with new (diversion) ship traffic routes in the future Arctic with less sea ice. The applied traffic growths and the polar routes are Corbett et al. (2010).</p> <p>The emissions are monthly on a spatial resolution of 0.1º×0.1º. </p> <p>Base year is 2015 and the scenarios are for 2050.</p> <p>A scientific paper providing details on the methodology behind these data will be submitted to ACPD (Geels et al, submitted). In this paper we apply the data to assess the contribution from shipping emissions to air pollution in the Nordic and Arctic area and the potential benefits of the mitigation options included in the shipping emission scenarios. This paper should be referenced if the data is used. </p> <p>The data are given as netcdf files for a number of components. The emission related to the diversion routes is given as a separate field and can be added the other field. <br> </p>
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</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>
Dataset for 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'
<p>Dataset for ACP manuscript 'Inland ship emissions and their contribution to NOx and ultrafine particle concentrations at the Rhine'. Further explanations on the datasets can be found in the 'README.txt' file. In case of any questions please contact: Philipp Eger (eger@bafg.de), Federal Institute of Hydrology.</p> <p>1_Timeseries_Worms</p> <p>Description: Time series of measured gaseous and particulate species for station "BRI" in Worms on 10 December 2021. </p> <p><br> 2_All_ship_peaks_Worms</p> <p>Description: All analyzed ship peaks fulfilling the quality check criteria. </p> <p><br> 3_Ship_contribution_Worms</p> <p>Description: Mean monthly contribution from shipping based on analyzed peaks. </p> <p><br> 4_Examples_Worms</p> <p>Description: Particle size distribution of five exemplary peaks (A-E).</p>
Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea
<p>This data submission is connected to a scientific paper submitted to<br> Atmospheric Chemistry and Physics ("Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea" by Walden et al.). It consists of measurement results conducted beside the ship routs at the Baltic Sea near Helsinki, Finland. The gaseous and particle concentrations were measured along with the meteorological parameters, and the fluxes were calculated by the micrometeorological methods. The content of sulfur in the marine fuel, FSC, used by the passing ships was also calculated. We paid attention to calculate the uncertainties of the measurement results, both for the fluxes and for the FSC.</p> <p>The released data of:<br> 1. Gases, particles and met data (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles) as minute values. </p> <p>Data_ACP_Fig4_acbd.xlsx.</p> <p> <br> 2. Size distribution of nanoparticles (number concentration of nanoparticles at size class). Data_ACP_Fig6.xlsx</p> <p> <br> 3. Profiles of 30 min averages of gases, nanoparticles and meteorological parameters (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles), wind direction and wind speed, friction velocity, stability parameter and Monin-Obukhov length. Calculated values of atmospheric turbulence parameters and calculated fluxes of CO2 and nanoparticles by gradient and/or eddy covariance method.</p> <p>Data_ACP_Fig8_abcd_Fig9_abcd.xlsx<br> <br> 4. CO2 fluxes by Eddy covariance method from land based and sea based measurements. Concentration of CO2 in seawater and in air.</p> <p>Data_ACP_Fig10_ab.xlsxEngl</p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Equivalent Black Carbon Emission Factors from Ships Within a Sulfur Emission Control Area
<p>Equivalent black carbon (eBC) emission factors, for ship plumes sampled in the Port of Gothenburg during two measurement campaigns in autumn 2014 and autumn 2015 respectively. </p> <p>Measurement site coordinates: N57.6849, E11.838</p> <p>Equivalent black carbon measured with a Multi Angle Absorption Photometer (MAAP, Thermo Fisher Scientific), wavelength 637 nm. CO2 sampled with a non-dispersive infrared gas analyzer (LI840, LI-COR).</p> <p>Unit (EF_BC): ug (kg fuel)^-1</p> <p>File creator: Stina Ausmeel<br> Contact e-mail: stina.ausmeel@nuclear.lu.se</p>
Impact of Marine Shipping Emissions on Ozone Pollution during the Warm Seasons in China
<ul> <li><strong>all.city.2017-01.nc: </strong>The hourly observations of six major air pollutants at 336 cities in China in 2017, including latitude and longitude information for these cities. These observations are provided by CNEMC.</li> <li><strong>china_CMAQ_appmap_no_gat.nc: </strong>The mask file for mainland China.</li> <li><strong>CMAQ_HCHO_VCDs.zip</strong>: Daily HCHO VCDs data calculated from CMAQ output and its calculation script.</li> <li><strong>conc_evaluation.zip</strong>: The code lists of the cities located in all coastal regions, BTH, YRD, and PRD region, respectively (<strong>XXXXXX_citycode.csv</strong>), the statistical results of MDA8 O3 and NO2 in these regions and corrsponding calulation scripts.</li> <li><strong>Data_era5.zip: </strong>All the ERA5 data used in the article (which have been extracted into nc files), including hourly meteorological parameters for Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta regions, respectively.</li> <li><strong>GRIDCRO2D_2017100100_d01:</strong></li> <li><strong>met_evaluation.zip: </strong>The information of the meteorological stations located in all coastal regions (<strong>MET_STATION_COASTAL.csv</strong>), The statistical results of T2, RH2, WS10 in these stations and corrsponding calulation scripts.</li> <li><strong>nest_vs_base_evaluation.zip: </strong>Statistical results of the simulation performance of the nested and basic experiments for April, containing mesh data for the nested inner domains, results for the models of the nested experiments, the scripts used to calculate the statistical results, and the results of the calculations.</li> <li><strong>OMIHCHOd_interpolate_data_control.nc: </strong>Daily HCHO VCDs data from OMHCHOd dataset spatially interpolated to have the same horizontal grid as the CMAQ results.</li> <li><strong>pattern_sa_polluted_clean.zip: type_stat_sa_newdef.ncl </strong>is to calculate ozone impacts of MSEs on polluted days and clean days under different weather patterns and the csv files are the results.</li> <li><strong>region_port_city.zip: </strong> The mask files for the three main regions, latitude/longitude for the 13 coastal port cities (<strong>port_city_lat_lon.csv</strong>), the results of seasonally averaged impacts of MSEs on ozone for the 3 regions and 13 cities (<strong>region_port_city_mda8o3_sa_rsa.csv</strong>), the ncl file with the same name is the script for the calculations.</li> <li><strong>sim_hourly_o3_no2_base.nc: </strong>The extracted data from the WRF-CMAQ results file, which includes modelled hourly surface ozone and NO2 concentrations for the simulation period.</li> <li><strong>sim_hourly_sa_o3_ocean_base.nc: </strong>The extracted data from the CMAQ-ISAM model simulation results and includes the hourly impact of marine shipping emissions of ozone obtained from the simulation.</li> <li><strong>T-PCA_BTH_YRD_PRD.zip: </strong>The daily weather pattern of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions obtained by the TPC-A method of classification, with 2 to 10 categories.</li> <li><strong>CJK_domain.ncl</strong> is used to produce the manuscript’s <strong>Figure 1</strong>.</li> <li><strong>sim_obs_dis_MDA8_uv_coastal.ncl </strong>is used to produce the manuscript’s <strong>Figure 2</strong>.</li> <li><strong>absolute_dis_MDA8h.ncl </strong>is used to produce the manuscript’s <strong>Figure 3</strong>.</li> <li><strong>region_city_o3_sa.ncl </strong>is used to produce the manuscript’s <strong>Figure 4</strong>.</li> <li><strong>type_mean_slp_o3_sa.ncl </strong>is used for the distribution maps in<strong> Figures 5, 6 and 7.</strong></li> <li><strong>bar_type_sa.ncl </strong>is used to produce of the bar charts in <strong>Figure 5</strong>, <strong>Figure 6</strong> and <strong>Figure 7</strong>, which were combined with the distribution maps obtained from <strong>type_mean_slp_o3_sa.ncl.</strong></li> <li><strong>cal_FDR_PCT.ncl </strong>is used to calculate the number of pairs of indistinguishable patterns obtained based on the FDR method for different number of classifications in the three regions (<strong>Figure S1</strong>).</li> <li><strong>OMI_xhcho.ncl </strong>is used to produce <strong>Figure S2</strong> in the Supplementary Materials.</li> <li><strong>MSEs_nox_voc_dis.ncl </strong>is used to produce <strong>Figure S3 </strong>in the Supplementary Materials.</li> <li><strong>relative_dis_DA8h.ncl </strong>is used to produce <strong>Figure S4</strong> in the Supplementary Materials.</li> <li><strong>region_city_o3_sa_rc.ncl </strong>is used to produce <strong>Figure S5</strong> in the Supplementary Materials.</li> <li><strong>type_mean_slp.ncl </strong>is used to produce <strong>Figure S6</strong> in the Supplementary Materials.</li> <li><strong>type_mean_met.ncl </strong>is used to produce <strong>Figures S7-S9</strong> in the Supplementary Materials, just replace the region name in this script.</li> <li><strong>stat_season_pattern.zip </strong>includes frequency of occurrence of the four synoptic patterns during three seasons, the ncl file is the script for the calculations, and the csv file is the output (<strong>Table S1</strong>).</li> </ul>
Global shipping emissions for the years 2013 and 2016-2021
<p>The shipping emission datasets of the globe with 9<strong> </strong>species (NO<em><sub>x</sub></em>, CO, HC, SO<sub>2</sub>, PM<sub>2.5</sub>, CO<sub>2</sub>, N<sub>2</sub>O, CH4, and BC) with a spatial resolution of 0.1<sup>o</sup> × 0.1<sup>o</sup>, the temporal resolution of days for the years 2013 and 2016-2021 were established based on SEIM (Sipping Emission Inventory Model). Based on well-cleaned AIS (Automatic Identification System) data and STSD (Ship Technical Specifications Database), SEIM model can simulate ship emissions for every ship by every two subsequent AIS signals, which are typically only a few seconds or tens of seconds apart. For a detailed introduction to the SEIM, please refer to our previous articles: https://www.nature.com/articles/nclimate3091, https://acp.copernicus.org/articles/21/13835/2021/</p>
Analysis and emissions data for "What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds?"
<p>Analysis scripts and SOx emissions data produced from STEAM emissions model (courtesy of Jukka-Pekka Jalkanen, FMI). Used in "What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds?" submitted to GRL.</p>
Data from: Modeling 2020 regulatory changes in international shipping emissions helps explain 2023 anomalous warming
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Support videos for the article "Damage categorization in full-scale, full-composite ship hull under high-energy impacts by unsupervised-learning-enabled acoustic emission monitoring and laser shearography inspection"
<p>Video 1: Video showing one of the impact from a general perspective</p> <p>Video 2: Slow-motion video of the second impact</p>
Historical Emission Inventory Database for Shipping
<p><span>The global historical shipping emission inventory dataset covers CO<sub>2</sub> and five key </span><span>atmospheric pollutants (</span><span>NO<em><sub>x</sub></em>, SO<sub>2</sub>, CO, NMVOCs and PM) over the time period from 1970 – 2021 and has been allocated to 0.1</span><span>°×</span><span>0.1</span><span>°</span><span> grids on a bilateral basis.</span><span> </span></p>
Acoustic emission signals from consecutive 20 kJ impacts on full-composite ship hull section
<p>Acoustic emission signals from consecutive 20 kJ impacts on full-composite ship hull section</p>
High accuracy calculation and data quality evaluation of ship emissions based on the sniffer method
<p>The uploaded file includes the matlab code used in the study and the actual measurement data set collected.</p>
Data from: Modeling 2020 regulatory changes in international shipping emissions helps explain 2023 anomalous warming
Open the record for dataset details and reuse information.
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