2 - Supporting information

Definition

This indicator shows concentrations of phosphate and nitrate in rivers, total phosphorus in lakes and nitrate in groundwater bodies. The indicator can be used to illustrate geographical variations in current nutrient concentrations and temporal trends. Large inputs of nitrogen and phosphorus to water bodies from waste water and agricultural areas can lead to eutrophication. This causes ecological changes that can result in a loss of aquatic biodiversity (reduction in ecological status) and can have negative impacts on the use of water for human consumption and recreation.

Methodology

Annual mean concentrations are used as a basis in the indicator analyses. The aggregation to annual mean concentrations is done by the EEA, unless the country has reported aggregated data only.

Automatic quality control procedures are applied both to the disaggregated and aggregated data, and data failing certain tests are excluded from further analysis. In addition, a semi-manual procedure is applied, focusing on suspicious values having a major impact on the country time series and on the most recently reported data. This comprises e.g.:

• outliers;
• consecutive values deviating strongly from the rest of the time series;
• whole time series deviating strongly in level compared to other time series for that country and determinand;
• where values for a specific year are consistently far higher or lower than the remaining values for that country and determinand.

Such values are removed from the analysis and checked with the country.

For time series analyses, only complete series after inter/extrapolation are used. This is to ensure that the aggregated time series are consistent, i.e. including the same sites throughout. Inter/extrapolation of gaps up to 3 years are allowed, to increase the number of available time series. At the beginning or end of the data series 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 linearly interpolated. The selected time series are aggregated to country and European level by averaging across all sites for each year.

Trends are analysed with the Mann-Kendall method  in the free software R , using the wql package. This is a non-parametric test suggested by Mann (1945) and has been extensively used for environmental time series . Mann-Kendall is a test for a monotonic trend in a time series y(x), which in this analysis is nutrient concentration (y) as a function of year (x). The size of the change is estimated by calculating the Sen slope . Absolute and relative Sen slopes are summarized across Europe and countries by averaging. For the trend analysis the same time series as for the time series analysis are used, but without gap filling.

For analysis of the present state, average concentrations are calculated across the last 3 years with data. In this way data from far more groundwater bodies and lake and river sites can be used than in the time series analysis. The 3-year average is used to remove some inter-annual variability. Also, since data are not available for all sites each year, selecting data from 3 years gives more sites. The sites are assigned to different concentration classes and summarised per country (percentage of sites per concentration class). The purpose of the analysis is to compare the distribution of concentrations among countries. The class boundaries are thus mainly selected to represent the range of concentrations and are neither linked to targets or goals of specific policies nor to national based thresholds. The only exception is the threshold of 50 mg NO₃/l (11.3 mg NO₃-N/l), which is related to the maximum allowable concentration for nitrate in the Drinking Water Directive (2020/2184) the Groundwater Directive (2006/118).

Policy/environmental relevance

Freshwater quality with respect to eutrophication and nutrient concentration is an objective of several directives and other policies: the Nitrates Directive (91/676/EEC); the Urban Waste Water Treatment Directive (91/271/EEC); the Industrial Emissions Directive (2010/75/EU); the Convention on Long-range Transboundary Air Pollution and the National Emission Ceilings Directive (2016/2284/EU); and the Water Framework Directive (2000/60/EC). The Drinking Water Directive (2020/2184) and the Groundwater Directive (2006/118) sets the maximum allowable concentration for nitrate of 50 mg NO₃/l.

Reducing nutrient pollution from agriculture is an aspect of the European Green Deal, the ‘Farm to Fork’ Strategy, the Biodiversity Strategy and the Common Agricultural Policy.

Accuracy and uncertainties

Methodology uncertainty

Nutrient conditions vary throughout the year depending on, for example, season and flow conditions. Hence, the annual average concentrations should ideally be based on samples collected throughout the year. Using annual averages representing only part of the year introduces some uncertainty, but it also makes it possible to include more sites, which reduces the uncertainty in spatial coverage. Moreover, the majority of the annual averages represent the whole year.

Nitrate concentrations in groundwater originate mainly from anthropogenic activities as a result of agricultural land use. Concentrations in water are the effect of a multidimensional and time-related process, which varies from groundwater body to groundwater body and is less quantified. To properly evaluate the nitrate concentration in groundwater and its development, closely-related parameters such as ammonium and dissolved oxygen should be taken into account.

Data sets uncertainty

The indicator is meant to give a representative overview of nutrient conditions in European rivers, lakes and groundwater. This means it should reflect the variability in nutrient conditions over space and time. Countries are asked to provide data on rivers, lakes and important groundwater bodies according to specified criteria.

The datasets for groundwater and rivers include almost all countries within the EEA, but the time coverage varies from country to country. The coverage of lakes is less good. It is assumed that the data from each country represents the variability in space in their country. Likewise, it is assumed that the sampling frequency is sufficiently high to reflect variability in time. In practice, the representativeness will vary between countries.

Each annual update of the indicator is based on the updated set of monitoring sites. This also means that due to changes in the database, including changes in the QC procedure that excludes or re-includes individual sites or samples and retroactive reporting of data for the past periods, which may re-introduce lost time series that were not used in the recent indicator assessments, the derived results of the assessment vary in comparison to previous assessments.

Waterbase contains a large amount of data collected over many years. Ensuring the quality of the data has always been a high priority. Still, suspicious values or time series are sometimes detected and the automatic QC routines exclude some of the data. Through the communication with the reporting countries, the quality of the database can be further improved.

Rationale uncertainty

Using annual average values provides an overview of general trends and geographical patterns in line with the aim of the indicator. However, the severity of shorter-term, high-nutrient periods are not reflected.

Data sources and providers:

Waterbase – Water quality ICM, available at the EEA Datahub. The processed data used for the indicator can be queried using the EEA Discodata platform under [WISE_Indicators].[v4r1].

Institutional mandate:

EEA AWP

DPSIR:

State

Topics:

Water and marine environment

Tags:

Nitrates; 8^th EAP; Freshwater quality; Lakes; Rivers; Groundwater; Phosphates; Phosphorous; WAT003

Temporal coverage

1992-2021

Geographic coverage

Albania

Austria

Belgium

Bosnia and Herzegovina

Bulgaria

Croatia

Cyprus

Czechia

Denmark

Estonia

Finland

France

Germany

Greece

Hungary

Iceland

Ireland

Italy

Latvia

Liechtenstein

Lithuania

Luxembourg

Malta

Montenegro

Netherlands

North Macedonia

Norway

Poland

Portugal

Romania

Serbia

Slovakia

Slovenia

Spain

Sweden

Switzerland

Turkey

Typology:

Descriptive indicator (Type A - What is happening to the environment and to humans?)

UN SDGs

6 – Clean water and sanitation

Unit of measure:

The concentration of nitrate is expressed as milligrams of nitrate per litre (mg NO₃/l) for groundwater and milligrams of nitrate-nitrogen per litre (mg NO₃-N/l) for rivers.

The concentration of phosphate in rivers is expressed as milligrams of phosphate-phosphorus per litre (mg PO₄-P/l) and total phosphorus in lakes is expressed as milligrams of phosphorus per litre (mg P/l)

The river sites are assigned to different concentration classes to visualise the distribution (percentage) of data in the dataset.

Frequency of dissemination:

Once a year

Contact:

info@eea.europa.eu

References

1. Jassby, A. D., Cloern, J. E. and Stachelek, J., 2017, 'Exploring water quality monitoring data', (https://cran.rproject.org/web/packages/wql/vignettes/wql-package.html) accessed March 25, 2022.
2. R Core Team, 2020, 'R: The R Project for Statistical Computing', (https://www.r-project.org/index.html) accessed March 25, 2022.
3. Mann, H. B., 1945, 'Nonparametric Tests Against Trend', Econometrica 13(3), pp. 245–259 (https://www.jstor.org/stable/1907187) accessed March 25, 2022.
4. Hipel, K. and McLeod, A., 2005, 'Time Series Modelling of Water Resources and Environmental Systems, Volume 45 - 1st Edition', (https://www.elsevier.com/books/time-series-modelling-of-water-resources-and-environmentalsystems/hipel/978-0-444-89270-6) accessed March 25, 2022.
5. Theil, H., 1992, 'A Rank-Invariant Method of Linear and Polynomial Regression Analysis', in: Raj, B. and Koerts, J. (eds), Henri Theil’s Contributions to Economics and Econometrics: Econometric Theory and Methodology, Springer Netherlands, Dordrecht, pp. 345–381.
6. Sen, P. K., 1968, 'Estimates of the Regression Coefficient Based on Kendall’s Tau', Journal of the American Statistical Association 63(324), pp. 1379–1389 (https://www.tandfonline.com/doi/abs/10.1080/01621459.1968.10480934) accessed March 25, 2022