The dataset contains sand and gravel extraction activity during 2011–2015. The dataset is based on data submission by HELCOM Contacting Parties in response to data request. The dataset is quality assured and contains data from all the Baltic Sea countries. Common extraction sites with information on volume of extraction was included in the dataset. The other extraction sites (building sites, exclusive sites) had only summed data for all the sites, as volumes of extraction in specific sites was classified as confidential information. For these sites the volumes was calculated from reported sum of "other extraction areas". The amount was equally divided between corresponding extraction areas. For some common extraction sites a shared amount was reported.

This dataset is built from following Human activities datasets: • http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/81c917ea-492d-48e2-9f00-e1bb7fe3e4fc • http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/4fcd51dd-b8be-4e83-8cad-37c566782e8f The game hunting of seabirds data (see separate metadata): The total number of hunted seabirds were averaged over 2011-2015 (number of hunted seabirds / year). The area of the reporting unit was used to calculate the number of hunted seabirds / km2 and the data was converted to 1km x 1km grid. The predator control of seabirds data (see separate metadata): The total number of hunted cormorants were averaged over 2011-2015 (number of hunted cormorants / year). The area of the reporting unit was used to calculate the number of hunted cormorants / km2 and the data was converted to 1km x 1km grid. The two datasets were first separately log transformed and then summed, to get the total value for each grid cell. Zero values were given to all grid cells with no reported seabird hunting activity. The layer was normalized.

The map of herring relative abundance is mainly based on Baltic International acoustic surveys (BIAS), years 2011-2016 (ICES WGBIFS reports), reported as millions of herring / ICES rectangle. Also herring landings data were used to complement the data. For ICES rectangles surveyed by BIAS, values shown are the mean values per ICES rectangle based on BIAS data, average for 2011-2016. For ICES rectangles not surveyed by BIAS, values are calculated as: MAX-value x Weighting factor. The weighting factor is specific to each ICES rectangle, calculated as the ratio between the commercial landings in that rectangle and the commercial landings in the ICES rectangle with highest landings (based on averages for 2011-2016). MAX-value = millions of herring according to BIAS in the ICES rectangle with highest landings. ICES rectangles outside the BIAS survey area with no reported herring landings were given the value 0. The relative abundance values in each ICES rectangle were divided by the area of the rectangle to obtain values per 1km2. If the values in small coastal ICES rectangles (outside BIAS area) became unrealistically large due to high herring landings, the value of the neighboring rectangle was given. The final layer was converted to 1 km x 1km grid cells. Values were first log transformed and normalized.

Distribution of eelgrass based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of eelgrass were submitted, originally gathered in national mapping and monitoring campaigns, or for scientific research. Polygon data from Puck Bay (Poland) was digitized based on Polish Marine Atlas and Orlowo cliff area was added based on expert knowledge. From Estonian waters, a predictive model was used (200m resolution), that was converted to presence/absence using minimized difference threshold (MDT) criteria. All data (points, polygon and the raster presenting predicted presence of eelgrass in the Estonian waters) were generalized to 5km x 5km grid cells.

Amount of hunted birds (number of birds/area) per year per area (county) is given separately for each target species: common scooter (Melanitta nigra), velvet scoter (Melanitta fusca), eider (Somateri molissima) and long tailed duck (Clangula hymalis). The data was made available by HELCOM Contracting Parties in response to data request. The data was received from Denmark, Estonia, Finland and Sweden. The activity was declared as not relevant in Germany, Latvia, Lithuania and Poland. For each species, a total number of hunted birds during the time period and a calculated average (hunted birds/year), is given. Data includes a total number (sum) of all hunted birds during the time period per county (total number of hunted birds/ county) and an average for hunted birds annually (hunted individuals/year). Velvet scoter is protected species in Sweden and Finland, and not listed as a game in Estonia. Common scoter is also protected species in Finland, thus hunting data is not available. Attribute specification and units: Country: Country AreaCode: County’s national code Area: County, unit area TOTAL: Total number of hunted birds in 2011-2015 Average: An average of hunted birds in a year (hunted birds/year) 2011_Sco – 2015_Sco: Number of hunted common scoters in 2011-2015 SUM_Sco: Total number of hunted common scoters in 2011-2015 Mean_Sco: An average number of hunted common scoters in a year (hunted individuals/year) 2011_VSco – 2015_VSco: Number of hunted velvet scoters in 2011 - 2015 SUM_Vsco: Total number of hunted velvet scoters in 2011-2015 Mean_Vsco: An average number of hunted velvet scoters in a year (hunted individuals/year) 2011_Eider – 2015_Eider: Number of hunted eiders in 2011 - 2015 SUM_Eider: Total number of hunted eiders in 2011-2015 Mean_Eider: An average number of hunted eiders in a year (hunted individuals/year) 2011_LTDuc – 2015_LTDuc: Number of hunted long tailed ducks in 2011 – 2015 SUM_LTDuck: Total number of hunted long tailed ducks in 2011-2015 Mean_LTDuc: An average number of hunted long tailed ducks in a year (hunted individuals/year) Notes: Notes regarding the data

Concentration of phosphorus pressure layer is interpolated from annual seasonal average of total phosphorus measurements from surface waters (0-10 m) extracted from ICES’s oceanographic database, database of Swedish Meteorological and Hydrological Institute, EEA’s Eionet database and Data from Gulf of Finland year 2014. The points were interpolated to cover the entire Baltic Sea with Spline with barriers interpolation method. Values were log-transformed and normalised (more detailed description below).

This layer is based on data from the BIAS project representing ambient underwater noise, modelled into a 0.5 km x 0.5 km grid, and representing sound pressure levels at 1/3 octave bands of 125 Hz exceeded at least 5% of the time. Measured and modelled acoustic data is provided as Sound Pressure Level (SPL). The time period for the data is annual values for year 2014. The selected depth interval is 0 m – bottom to represent the ambient underwater noise in the whole water column. The data were normalized setting level 0 at 92 db re 1µPa and level 1 at 127 db re 1µPa.

Distribution of Furcellaria lumbricalis based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of Furcellaria were submitted, originally gathered in national mapping and monitoring campaigns, or for scientific research purposes. From Estonian waters, a predictive model was used (200m resolution), that was converted to presence/absence using minimized difference threshold (MDT) criteria. For Poland, only confirmed occurrence of Furcellaria were included (Slupsk bansk, Rowy reef and reef at Orlowo cliff). All data (Furcellaria points and the raster presenting predicted presence of Furcellaria) were generalized to 5km x 5km grid cells.

The extraction of herring data set is based on: 1. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/a3b67a55-7c1e-469e-b692-58c4e7b79279 for years 2011-2016 reported per ICES statistical rectangles (tonnes / ICES rectangle). Landing values were redistributed within each ICES rectangle by the c-square fishing effort data provided by ICES (all gears, 2011-2013). Tonnes / km² was calculated and the results were converted to 1 km x 1 km grid cells. The layer was log-transformed and normalised to produce the final pressure layer on extraction of herring. Please see "lineage" section below for further details on attributes, data source, data processing, etc.

Input of heat pressure dataset contains delta heat values from warm water discharge of - Discharge of warm water from nuclear power plants - Fossil fuel energy production. The Discharge of warm water from nuclear power plants was requested from HELCOM Contracting Parties. Average temperature change of the cooling water (°C) and amount of cooling water (m3) was used to calculate the heat load in TWh. No data on heat load was available for the Leningrad nuclear power plant; therefore the average heat load (TWh) of discharge of warm water from nuclear power plants was given. No heat load information was available for fossil fuel energy production sites. Heat load of 1 (TWh) was given to all production sites, based on average heat load of individual production site in Helsinki from recent years. 1 km buffer was used for both datasets with steep decline around the outlet. Overlapping areas were summed. Heat load from both layers were summed and the layer was normalized.