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 urban areas, industry and agricultural areas can lead to eutrophication. This causes ecological changes that can result in a loss of plant and animal species (reduction in ecological status) and can have negative impacts on the use of water for human consumption and other purposes.

Methodology

Annual mean concentrations are used as a basis in the indicator analyses. Unless the country reports aggregated data, the aggregation to annual mean concentrations is done by the EEA.

Automatic quality control procedures are applied both to the disaggregated and aggregated data, excluding data failing the tests 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 method¹ in the free software R², using the wql package. This is a non-parametric test suggested by Mann (1945)³ and has been extensively used for environmental time series⁴. 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^5,6. The same time series as for the time series analysis, but without gap filling.

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) sets the maximum allowable concentration for nitrate of 50mg NO3/l.

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

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. A revision of Waterbase reporting and the database-composition process took place in the period 2015-2017. This included restructuring of the data model and corresponding reporting templates; transformation of the legacy data (i.e. data reported in the past, for the period up to and including 2012); re-definition of specific data fields, such as the aggregation period defining the length of aggregation in a year; update of the datasets according to correspondence with national reporters; re-codification of monitoring site codes across Eionet dataflows; and connection of the legacy data time series with the newly-reported data in restructured reporting templates. 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.

Metadata

Data sources and providers: Waterbase

Institutional mandate: WISE SoE - Water quality (WISE-6)

DPSIR: State

Topics: Nitrates; Phosphorus; Freshwater quality; Lakes; Rivers; Groundwater; Phosphorous; WAT003

Temporal coverage: 1992-2020

 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?)

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).

Frequency of dissemination

Once a year

 References and footnotes

1. Jassby, A. D., Cloern, J. E. and Stachelek, J., 2017, 'Exploring water quality monitoring data', (https://cran.r-project.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-environmental-systems/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.