This dataset provides an overview of the European Union CO2 and GHG emissions - national total: represents the sum of total emissions, without LULUCF, with indirect CO2 and with international aviation.For more explanations on terminology please visit Eurostat metadata and the glossary available here: http://ec.europa.eu/eurostat/statistics-explained/index.php/Category:Energy_glossary For complementary notes please see the DG ENER energy statistical pocketbook 2020 at: https://ec.europa.eu/energy/en/data-analysis/energy-statistical-pocketbookFor more detailed info and updates please consult Eurostat online database at: http://ec.europa.eu/eurostat/data/database For updates or more detailed CO2/GHG emissions data please consult EEA/UNFCCC at: https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives.

This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO2 and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO2) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport.

The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions.

The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr.

A detailed description of the Monte Carlo simulation is presented in:

Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019.

N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care.

NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.

The Emissions Database for Global Atmospheric Research (EDGAR) provides global past and present day anthropogenic emissions of greenhouse gases and air pollutants by country and on spatial grid. The current development of EDGAR is a joint project of the European Commission JRC Joint Research Centre and the Netherlands Environmental Assessment Agency (PBL).

Reference values from [18] are provided in the top row, mean and standard deviation (St. Dev.) across all selected cities are provided on top of the individual city listings.

In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map This is the 3rd Version

These data come from the European Union Transaction Log, EUTL which keeps the accounts of allowances issued under the EU Emissions Trading System, as well as verified emissions. The data were extracted from: https://ec.europa.eu/clima/ets/oha.do For France, they were geolocated via their postal code. Https://www.euets.info offers a different, geolocated extraction for all of Europe via the Google Maps API. Extraction scripts are available on https://github.com/cedricr/eutl under license [CC BY 4.0] All emissions data are in tonnes of CO₂ equivalent

In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 998 cc 74 kW Euro 6 petrol engine that has been applied in the The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.

The ‘Emission Database for Global Atmospheric Research version 4.2’, is a product of the Joint Research Centre and the PBL Netherlands Assessment Agency, referred to as EDGAR 4.2 and contains global emission inventories for greenhouse gases and air pollutants. These emissions are calculated as total by country and sector for the 1970-2008 period, and distributed on the grid using proxy data. The global emissions for all countries are spatially allocated on 0.1x0.1, 0.5x0.5 and 1x1 degree resolution grids over the globe. The standard sources are defined using the IPCC source categories and codes developed by the IPCC National Greenhouse Gas Inventories Programme (Reporting guidelines in the revised 1996 IPCC guidelines). The first number identifies the main source sector: 1.Energy (including biofuel combustion and gas leakage, venting and flaring); 2.Industrial processes (nonfuel combustion sources, incl. F-gas use); 3.Solvents and other product use; 4.Agriculture (including savannah fires); 5.Land-Use Change and Forestry (including post-burn decay and drained peatlands); 6.Waste; 7.Other. The EDGAR v4.2 provides independent estimates of the global anthropogenic emissions and emission trends, based on publicly available statistics, for the use in atmospheric models and policy evaluation. This scientific independent emissions inventory is characterized by a coherent world historical trend.

This metadata refers to the map showing the difference in SO2 emissions in European shipping areas between the years 2014 and 2019. The map indicates the changes in ship SO2 emissions, 2019 minus 2014 situation. The numerical values reported in the map are tonnes of SO2 per each grid cell. It can be seen that in 2019 SO2 emissions from shipping in the English Channel and the North and Baltic Seas are much lower compared to 2014 than those in areas outside the sulphur emission control areas (SECAs), including the Mediterranean Sea, where SO2 emissions have remained largely unaltered or increased.

The dataset has been prepared in the context of the development of the first European Maritime Transport Environmental Report (EMSA-EEA report, 2021: https://www.eea.europa.eu/publications/maritime-transport).

Available training areas for European cities.

The HTAP_V2 dataset consists of 0.1degx0.1deg gridmaps (left bottom corner centered) of CH4, CO, SO2, NOx, NMVOC, NH3, PM10, PM2.5, BC and OC for the years 2008 and 2010. HTAP_V2 uses nationally reported emissions combined with regional scientific inventories in the format of sector-specific gridmaps. The gridmaps are complemented with EDGARv4.3 data for those regions where data are absent. The global gridmaps are a joint effort from US-EPA, the MICS-Asia group, EMEP/TNO, the REAS and the EDGAR group to serve in the first place the scientific community for hemispheric transport of air pollution.

In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. This specific data set refers to the 1968 cc 100 kW Euro 5b diesel engine that has been applied in the Volkswagen Crafter, Passat, Sharan, and Tiguan, Audi A3, Q3, A4, A5, and A6, Seat Alhambra. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI http://doi.org/10.5281/zenodo.4268034 refers to a updated meta-data document that provides the full description of the standardized emission map.

This VLOPS map shows the modeled concentration of ammonia (NH3) in the air (in µg/m³) for the whole of Flanders on 1x1 km². This map was calculated using VLOPS20 (based on OPS4.5.2), the NH3 emission figures in 2017 and the 2017 meteorological data. For this map, the raw VLOPS model outputs were multiplied by the calibration factor x0.87.

Reference values from [18] are provided in the top row, mean and standard deviation (St. Dev.) across all Urban Atlas cities are provided on top of the individual city listings.

In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code. The uploaded maps are the fallback maps for petrol for emissions class Euro 3-5 and diesel with emission class Euro 4-5. Incomplete AEM fallback maps are not uploaded. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: the engine taxonomy code, total driven kilometers over which the data was gathered, total time in hours over which the data was gathered, the number of vehicles which were tested to create the emission map, the DOI (Digital Object Identifier) reference, Which emission maps are available in the file. The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.

153 views (1 recent) Dataset extent Map data © OpenStreetMap contributors. The location of emission points from Waste facilities (EPA Licensed, applied, closed etc.) In terms of usage of this dataset please note that there is a period of time between when an emission points is licensed and when it appears in this dataset.

Greenhouse gas emissions (CO₂ equivalents) by pollutor group. Map type: Charts. Spatial extent: Europe. Time: 1990 – 2019

This dataset provides UK maps of baseline prior uncertainty (UQ) in fluxes of Greenhouse Gases (GHGs) carbon dioxide, CO2 (2014-15) and methane, CH4 (2015). Spatial maps of these GHG emissions are produced annually in the National Atmospheric Emissions Inventory (NAEI) but it is important to quantify uncertainty in these maps. These uncertainty estimates come from sectoral uncertainty data provided by the NAEI. Here, we propagate the uncertainty in the maps for each of the sectors contributing to the emissions using a Monte Carlo method, in order to quantify the uncertainty in the total emissions spatially. The Monte Carlo method employed here uses a novel approach (Nearest Neighbour Gaussian Process) to make calculations computationally affordable. These estimate the influence on the overall uncertainty of unknown errors in the model structure. Further details of the methodology used here can be found in the supporting documentation included with this data. In the near term, this methodology will be used and developed further in the NERC-funded project, DARE-UK (NE/S003614/1), to update UQ in maps of CO2 and CH4 for the UK. For that work and in general, it is useful to have a baseline prior uncertainty quantification against which future UK maps of uncertainty in CO2 and CH4 fluxes can be compared. Full details about this dataset can be found at https://doi.org/10.5285/739c65a5-12c0-439b-bbcd-1252a4086e87

This VLOPS map shows the modelled dry deposition rate (VD) of NHx (in cm/s) for the whole of Flanders at 1x1 km2. This map was calculated using VLOPS20 (based on OPS4.5.2), the emission figures for NH3 in 2017 and the meteorological data from 2017.