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The figure shows estimated Market Demand in 2021 for the EU-27 plus United Kingdom, Switzerland, and Norwey in percentages. The above data are rounded estimations. Demand data are built on estimations of quantities bought by European converters, including imports. Demand for recycled plastics and bio-based/bio-attributed plastics is not included. Polymers that are not used in the conversion of plastic parts and products (i.e. for textiles, adhesives, sealants, coatings, etc.) are not included.
See report Marine Litter in Europe-An integrated assessment from source to sea (https://forum.eionet.europa.eu/etc-icm-consortium/library/subvention-2022/tasks-and-milestones-2022/1.1.8.1-marine-litter-assessment/milestone-2-addressing-comments-eea-and-finalization-assessment-report/final-version-november-2022-eea-approval/etc-icm_marine-litter-assessment_master_v5.2_06112022_for-eea-approval)
The figure shows the change in plastics demand for packaging in Europe.
Peak and 2100 concentrations of total greenhouse gases in the atmosphere consistent with a 67% probability of keeping the average global temperature increase below 1.5°C (left) and 2°C (right) are shown. The periods within which peak concentrations could be exceeded are shown by light blue arrows, based on the trend of the past 10 years in total greenhouse gas concentrations and without allowing for a temperature overshoot (based on IPCC, 2018)
The example present results from EEA's regular mapping for the indicators ‘Imperviousness and imperviousness change in Europe’, for the inventory year 2018.
Time series (1870-2021) of decadal average observed sea surface temperature anomalies (°C), with respect to the period 1991-2020, for each of the European basins, for the European seas as a whole, and for the global ocean. Data sources: HadSST4.0.1.0 (1850-2021), ERSSTTv5 (1880-2021), HadISST1 (1870-2021) and satellite-based ESA CCI/C3S SST Climate Data Record v2.1 (1991-2021).
Water exploitation index plus (WEI+) illustrates the percentage of water use versus water available in the respective subbasin.
The graph presents trend with the area of the European Union affected by water scarcity conditions between 2000-2019. Water scarcity conditions is adopted, i.e. when WEI+ values are above 20% for at least a quarter of the year in a given river sub basin; annual quarters are: Q1 (January-March), Q2 (April-June), Q3 (July-September), Q4 (October-December). No sufficient data available from Italy, hence Italian river basins have not been included in the analysis.
This figure gives an overview of the worst quarterly water scarcity conditions (maximum WEI+ in a consecutive 3-month period) of 2019 across countries in Europe. Seasonal WEI+ values are estimated as quarterly averages per country. The worst quarter of the year for water scarcity conditions is provided in brackets next to the name of the country. Annual quarters are: Q1 (January-March), Q2 (April-June), Q3 (July-September), Q4 (October-December). No data is available for Montenegro and Lichtenstein.
The figure shows EU underwater radiated noise (URN) emissions per sea basin per year.
The figure shows the potential number of oil spills each year as detected by the CleanSeaNet service.
This data shows median of marine litter items by beach and marine region, based on Marine Litter Watch (MLW) dataset in the period 2013-2020.
Datasets showing SO2 (2014 and 2019), NOx (2019), PM2.5 (2019) emissions in European shipping areas. These datasets have been prepared in relation to the development of the first European Maritime Transport Environmental Report (EMSA-EEA report, 2021: https://www.eea.europa.eu/publications/maritime-transport).
This figure combines two data sources. The left part of the figure (black lines) shows the observed change in Arctic sea ice over the period 1979 to 2022 for March (maximum ice cover) and September (minimum ice cover) based on the EUMETSAT OSI SAF Sea Ice Index v2.1 dataset. The right part shows projections of the Arctic sea ice area in March and September from CMIP6 simulations for three emissions scenarios. The thick lines denote the multi-model ensemble mean (24-27 models, depending on the scenario), and the shading shows the likely uncertainty interval (one standard deviation around the multi-model mean). The dashed black line indicates a threshold for near ice-free conditions.
Maximum extent of ice cover in the Baltic Sea in the winters 1719/20–2021/22 (blue bars) and 15 year moving average (black line). Source: Jouni Vainio, Finnish Meteorological Institute (updated from Seinä and Palosuo 1996; Seinä et al. 2001).
Trends: The arrows show the observed trend in sea level relative to land since 1970 for those tide gauges along the Europe coastline with sufficiently long time series. Projections: European sea level change for 2081–2100 for SSP5-8.5 in metres. Results use CMIP6 model projections for long term scenario (2081-2100), for SSP5-8.5, and with respect to a baseline of 1995-2014.
The left chart shows the global mean sea level (GMSL) rise based on tide gauge reconstructions (grey line) and satellite observations (black line). The figure depicts the rise in global mean sea level from 1900 to 2021 based on two data sources. All values are relative to the average level of the period 1993-2010, during which the two datasets overlap. The grey line (Palmer et al., 2021) shows the ensemble sea-level reconstruction (using five members) of sea level anomalies during 1900-2010 (Palmer et al., 2021; https://iopscience.iop.org/article/10.1088/1748-9326/abdaec#erlabdaecs2). The black line (CMEMS) shows the filtered sea level anomalies corrected for the TOPEX-A instrumental drift (Ablain et al., 2017; WCRP Sea Level Budget Group, 2018), corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004), for the time series from 1993 to 2021. The right chart shows global mean sea level projections under different SSP scenarios. Sea level projections considering only processes for which projections can be made with at least medium confidence are provided, relative to the period 1995-2014, for five Shared Socioeconomic Pathway (SSP) scenarios. The scenarios are described in sections TS1.3 and 1.6 and Cross-Chapter Box 1.4 of the Working Group 1 contribution. Sea level projections considering only processes for which projections can be made with at least medium confidence are provided, relative to the period 1995-2014, for five Shared Socioeconomic Pathway (SSP) scenarios. The scenarios are described in sections TS1.3 and 1.6 and Cross-Chapter Box 1.4 of the Working Group 1 contribution.
This viewer presents the combined health impact of air pollution and road traffic noise at 1km x 1km resolution in cities where data is available. The impact of air pollution is measured in terms of mortality and the impact of road noise pollution is measured in terms of long-term high annoyance. The viewer is based on data submitted under the Environmental Noise Directive and the Air Quality Directive and represents the situation in 2017.
For references, please go to https://www.eea.europa.eu/data-and-maps/find/global or scan the QR code.
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