Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

Units: umol/l. Method: spatial interpolation produced with DIVA (Data-Interpolating Variational Analysis). URL: http://modb.oce.ulg.ac.be/DIVA. Comment: Every year of the time dimension corresponds to a 10-year centred average for each season : - winter season (December-February), - spring (March-May), - summer (June-August), - autumn (September-November). Diva settings: Snr=1.0, CL=0.7.

EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). This regional aggregated dataset contains all unrestricted EMODnet Chemistry data on contaminants (49 parameters), and covers the Baltic Sea with 3818 CDI records divided per matrices: 1358 biota (396 Vertical profiles and 962 Time series),906 water profiles, 2510 sediment profiles. Vertical profiles temporal range is from 1985-04-16 to 2016-09-27. Time series temporal range is from 1972-05-02 to 2017-10-30. Data were aggregated and quality controlled by ‘Swedish Meteorological and Hydrological Institute (SMHI)’ from Sweden. Regional datasets concerning contaminants are automatically harvested. Parameter names in these datasets are based on P01, BODC Parameter Usage Vocabulary, which is available at: http://seadatanet.maris2.nl/bandit/browse_step.php . Each measurement value has a quality flag indicator. The resulting data collections for each Sea Basin are harmonised, and the collections are quality controlled by EMODnet Chemistry Regional Leaders using ODV Software and following a common methodology for all Sea Regions. Harmonisation means that: (1) unit conversion is carried out to express contaminant concentrations with a limited set of measurement units (according to EU directives 2013/39/UE; Comm. Dec. EU 2017/848) and (2) merging of variables described by different “local names” ,but corresponding exactly to the same concepts in BODC P01 vocabulary. The harmonised dataset can be downloaded as ODV spreadsheet (TXT file), which is composed of metadata header followed by tab separated values. This worksheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV ). The same dataset is offered also as XLSX file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms in subcomponents (measure, substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications users (e.g. LibreOffice Calc). The 49 parameter names in this metadata record are based on P02, SeaDataNet Parameter Discovery Vocabulary, which is available at: http://seadatanet.maris2.nl/v_bodc_vocab_v2/vocab_relations.asp?lib=P02 . Detailed documentation will be published soon. The original datasets can be searched and downloaded from EMODnet Chemistry Download Service: https://emodnet-chemistry.maris.nl/search

This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach per year from MSFD or non-MSFD surveys. It has two layers: - Official monitoring: where the Marine Strategy Framework Directive (MSFD) monitoring protocol has been applied; - Other sources: where non-MSFD monitoring surveys, research & cleaning operations have been applied; EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data, as these differ between the "official monitoring" and "other sources" layers, detailed information is available in the metadata of each layer. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This visualization product displays the number of MSFD or non-MSFD surveys and the associated temporal coverage per beach. It has two layers: - Official monitoring: where the Marine Strategy Framework Directive (MSFD) monitoring protocol has been applied; - Other sources: where non-MSFD monitoring surveys, research & cleaning operations have been applied; EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data, as these differ between the "official monitoring" and "other sources" layers, detailed information is available in the metadata of each layer. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

Based on DHI operational Hydrodynamic 3D Model DKBS2hd model run results, a point dataset describing for each year the probability of a certain seabed location in the Baltic Sea being located below the deep halocline was created. The spatial coverage was the Baltic Sea. The spatial resolution was 5km. The temporal coverage was 20 years (2001-2020). The 2 rasters made available here result from interpolation of the point dataset, and represent 20-year probability (respectively average and standard deviation) values. The development of the original point dataset was funded by the EMODnet lot ‘Seabed Habitats’ for use in the development of the EUSeaMap product, more specifically in modeling the spatial distribution of the biological zones ‘circalittoral’ and ‘offshore circalittoral’. According to the EUNIS habitat classification, in the Baltic Sea the circalittoral is above the halocline/pycnocline, while the offshore circalittoral is below.