One key approach of the Sixth Environment Action Programme of the European Community 2001-2010 was to 'integrate environmental concerns into all relevant policy areas', which could result in a more intense application of agri-environmental measures to reduce nutrient pollution of the aquatic environment (e.g. in the Common Agricultural Policy).

]]>The environmental quality of surface waters with respect to eutrophication and nutrient concentrations is an objective of several directives: the Water Framework Directive, the Nitrate Directive, the Urban Waste Water Treatment Directive, the Surface Water Directive and the Freshwater Fish Directive.

Data on groundwater bodies, rivers and lakes is collected annually through the WISE-SoE data collection process. WISE SoE was previously known as EUROWATERNET (EWN) and EIONET-Water.

The data requested on ground water includes the physical characteristics of the groundwater bodies, proxy pressures on the groundwater area, as well as chemical quality data on nutrients and organic matter and hazardous substances in groundwater.

The data requested on rivers and lakes includes the physical characteristics of the river/lake monitoring stations, proxy pressures on the upstream catchment areas, as well as chemical quality data on nutrients and organic matter, and hazardous substances in rivers and lakes. It also includes the biological data (primarily calculated as national Ecological Quality Ratios), as well as information on the national classification systems for each Biological Quality Element and water body type.

These reporting obligations are EIONET Priority Data flows and are used for EEA core set of indicators.

**Station selection:** No criteria are used for station selection (except for time series and trend analysis - see below). For groundwater, time series are based on data for groundwater bodies, while the WISE maps of the most recent data are based on data for groundwater monitoring stations. For EU countries, there are two sets of groundwater body delineations available, the Eionet and the WFD delineation. Sometimes these do not overlap completely. As a general rule, the WFD delineation is chosen, but there are some exceptions, where the Eionet delineation is used: Croatia (a new EU Member State), Lithuania, Slovakia, Netherlands and Finland (all or many long time series are lost if using WFD delineation) and a composite Italian groundwater body not overlapping with the WFD groundwater bodies. For non-EU countries, only the Eionet delineation is available.

**Determinants:** The determinants selected for the indicator and extracted from Waterbase are:

- for groundwater: nitrate,
- for rivers: nitrate, total oxidised nitrogen and orthophosphate,
- for lakes: total phosphorus.

**Mean:** Annual mean concentrations are used in the present concentration and time series presentations. In a few cases, where annual data seem to have been reported incorrectly as annual and/or where the inclusion of seasonal data gives a higher number of complete time series, gaps in annual data series have been filled with data from seasonal time series. Countries are asked to substitute any sample results below the limit of detection (LOD) or limit of quantification (LOQ) by a value equivalent to half of the LOD or LOQ before calculating the station annual mean values. Average mean concentration values of zero or null are discarded from the calculations or replaced by the median value, as long as this value is not zero or null.

An automatic QA/QC procedure excludes data (stations*year) from further analysis. This is based on flagging in Waterbase, deriving from QA/QC tests. In addition a semi-manual QA procedure is applied, to identify outliers which are not identified in the QA/QC tests. This comprises e.g. values deviating strongly from the whole time series, values not so different from values in other parts of the time series, but deviating strongly from the values closest in time, consecutive values deviating strongly from the rest of the time series or whole data series deviating strongly in level compared to other data series in the country. Such values are eventually flagged in Waterbase (if not confirmed valid), but not until the year after, due to timing issues. More details on the QA/QC procedure are found here:

- groundwater QA/QC description
- rivers QA/QC description
- lakes QA/QC description

For rivers where nitrate and total oxidised nitrogen (TON) are monitored at the same station and at the same time, nitrate values are given precedence. For stations where only TON is reported, this data is used instead of nitrate. Also, in cases where more years of data are available for TON than nitrate for a single station, the TON data is used. All values are labelled as nitrate in the graphs, but it is indicated in the graph notes for which countries TON data are used.

**Inter/extrapolation and consistent time series**

For time series and trend analyses, only series that are complete after inter/extrapolation (i.e. no missing values in the station data series) are used. This is to ensure that the aggregated data series are consistent, i.e. including the same stations throughout the time series. In this way assessments are based on actual changes in concentration, and not changes in the number of stations. For rivers and lakes, “stations” in this context means individual monitoring stations. For groundwater it means groundwater bodies, i.e. the basis for inter/extrapolation and selection of complete data series is groundwater body data series. Each groundwater body may have several monitoring stations, and in some cases the number of monitoring stations has changed over the years. This means that some of the complete data series for groundwater (after inter/extrapolation) are not truly consistent, and must hence be regarded as more uncertain than the complete series for lakes and rivers. The purpose of choosing this approach is to increase the number of consistent groundwater time series.

*Changes in methodology: Station selection and inter/extrapolation.*

Until 2006, only complete time series (values for all years from 1992 to 2004) were included in the assessment. However, a large proportion of the stations was excluded by this criterion. To allow the use of a considerably larger part of the available data, it was in 2007 (i.e. when analysing data up until 2005) decided to include all time series with at least seven years of data. This was a trade-off between the need for statistical rigidity and the need to include as much data as possible in the assessment. However, the shorter series included might represent different parts of the whole time interval, and the overall picture may therefore not be reliable. In 2009, it was decided rather to inter/extrapolate all gaps of missing values of 1-2 year for each station. At the beginning or end of the data series 1 missing value was replaced by the first or last value of the original data series, respectively. In the middle of the data series, missing values were replaced by the values next to them for gaps of 2 years and by the average of the two neighbouring values for gaps of 1 year.

In 2010 this approach was modified, allowing for gaps of up to 3 years, both at the ends and in the middle of the data series. At the beginning or end of the data series up to 3 years of missing values are replaced by the first or last value of the original data series, respectively. In the middle of the data series, missing values are replaced by the values next to them, except for gaps of 1 year and for the middle year in gaps of 3 years, where missing values are replaced by the average of the two neighbouring values. Only time series with no missing years for the whole period from 1992 after such inter/extrapolation are included in the assessment. This procedure increases the number of stations that can be included in the time series/trend analysis. Still, the number of stations is markedly reduced compared to the analysis of the present situation, where all available data can be used.

**Aggregation of time series **

The selected time series (see above) must be aggregated into a smaller number of groups and averaged before the aggregated series can be displayed in a time series plot. Data for all determinands are grouped into five geographic regions of Europe, containing the following countries:

Eastern: CZ, EE, HU, LT, LV, PL, SI, SK.

Northern: FI, IS, NO, SE.

Southern: CY, ES, GR, IT, PT.

South-Eastern: AL, BA, BG, HR, ME, MK, RO, RS, TR, XK.

Western: AT, BE, CH, DE, DK, FR, IE, LI, LU, NL, UK.

Some of the listed countries are not included in the figures because there were no stations with complete time series after inter/extrapolation.

Data for river determinants are in addition grouped into six sea region catchments, which are defined not by countries but by river basin districts. The data thus represents rivers or river basins draining into that particular sea. The sea regions are defined as Arctic Ocean, Greater North Sea, Celtic Seas, Bay of Biscay and the Iberian Coast, Baltic Sea, Black Sea and Mediterranean Sea. The sea region delineation is according to the Marine Strategy Framework Directive (MSFD) Article 4, with the Arctic Ocean added as a separate region. As the catchment area draining into what is defined as the North-East Atlantic region of the MSFD is very big, it was decided rather to use the sub-region level here, but merging the Celtic Seas and the Bay of Biscay and the Iberian Coast.

Determinants are also aggregated for the whole of Europe.

**Trend analyses**

Trends are analysed by the Mann-Kendall method (Jassby and Cloern 2013) in the free software R (R Core Team 2013). This is a non-parametric test suggested by Mann (1945) and has been extensively used for environmental time series (Hipel and McLeod, 2005). Mann-Kendall is a test for monotonic trend in a time series y(x), which in this analysis is nutrient concentration (y) as a function of year (x). The test is based on Kendall's rank correlation, which measures the strength of monotonic association between the vectors x and y. In the case of no ties in the x and y variables, Kendall's rank correlation coefficient, tau, may be expressed as tau=S/D where S = sum_{i<j} (sign(x[j]-x[i])*sign(y[j]-y[i])) and D = n(n-1)/2. S is called the score and D, the denominator, is the maximum possible value of S. The p-value of tau is computed by an algorithm given by Best and Gipps (1974). The tests reported here are two-sided (testing for both increasing and decreasing trends). Data series with p-value < 0.05 are reported as significantly increasing or decreasing ("strong trends"), while data series with p-value >= 0.05 and <0.10 are reported as marginally significant ("weak trends"). The results are summarised by calculating the percentage of units (groundwater body/river station/lake station) within each category relative to all units within the specific aggregation (Europe or region). The test analyses only the direction and significance of the change, not the size of the change.

The size of the change is estimated by calculating the Sen slope (or the Theil or Theil-Sen slope)(Theil 1950; Sen 1968) using the R software. The Sen slope is a non-parametric method where the slope mis determined as the median of all slopes (y_{j} − y_{i})/(x_{j} − x_{i}) when joining all pairs of observations(x_{i},y_{i}). Here the slope is calculated as the change per year for each unit (groundwater body/river station/lake station). This is summarisedby calculating the average slope (regardless of the significance of the trend) for all units in Europe or a selected region. Multiplying this by the number of years of the time series gives an estimate of the absolute change over time. This can be related to the mean value of the aggregated time series to give a measure of relative change. The Sen slope was introduced for this indicator in 2013.

The Mann-Kendall method or the Sen slope will only reveal monotonic trends, and will not identify changes in the direction of the time series over time. Hence a combination of approaches is used to describe the time series: A visual inspection of the time series, describing whether the general impression is a monotonic trend, no apparent trend, clear shifts in direction of the trend or high variability with no clear direction; an evaluation of significant versus non-significant and decreasing versus increasing monotonic trends using the Mann-Kendall results; an evaluation of the average size of the monotonic trends using the Sen slope results.

**Present concentration distributions:**

The latest year for which there are concentration data for the river, lake and groundwater stations is selected for each country separately. The number of stations with annual mean concentrations occurring in the selected concentration classes are then calculated and presented. The allocation of a station to a particular class is based only on the face value concentration and not on the likely statistical distribution around the mean values.

- The class defining values for nitrate are based on typical background concentrations in the different water categories and the legislative standards (50 mg NO
_{3}/l) and guide values (25 mg NO_{3}/l). - The class defining values for orthophosphate (rivers) and total phosphorus (lakes) concentrations are based on typical background concentrations in the different water categories and on the range of concentrations found in Waterbase and only give an indication of the relative concentrations of phosphorus in each country.

More information is given in the WISE maps.

]]>- Drinking Water Directive (98/ 83/EC) - maximum allowable concentration for nitrate of 50 mg/l.

- Surface Water for Drinking Directive (75/440/EEC) - guideline concentration for nitrate of 25 mg/l

- Nitrates Directive (91/676/EEC) - requires the identification of groundwater sites/bodies where annual average nitrate concentrations exceed or could exceed 50 mg NO_{3}/l.

- Urban Waste Water Treatment Directive (91/71/EEC) - aims to decrease organic pollution

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