Oxygen consuming substances in European rivers

Indicator Specification
Indicator codes: CSI 019 , WAT 002
Created 07 Oct 2004 Published 25 Feb 2005 Last modified 05 Feb 2019
11 min read
This indicator illustrates the current situation regarding  biochemical oxygen demand ( BOD) and concentrations of total ammonium (NH 4 ) in rivers and examines trends in both. The key indicator for the oxygenation status of water bodies is BOD, which is the demand for oxygen resulting from organisms in water that consume oxidisable organic matter.

Assessment versions

Published (reviewed and quality assured)


Justification for indicator selection

Large quantities of organic matter (microbes and decaying organic waste) can result in reduced chemical and biological river water quality, impaired biodiversity of aquatic communities, and microbiological contamination that can affect the quality of drinking and bathing water. Sources of organic matter are discharges from waste water treatment plants, industrial effluents and agricultural runoff. Organic pollution leads to higher rates of oxygen demanding metabolic processes. This could result in the development of water zones without oxygen (anaerobic conditions). The transformation of nitrogen to reduced forms under anaerobic conditions, in turn, leads to increased concentrations of ammonium, which is toxic to aquatic life above certain concentrations, depending on water temperature, salinity and pH.

Scientific references

Indicator definition

This indicator illustrates the current situation regarding biochemical oxygen demand (BOD) and concentrations of total ammonium (NH4) in rivers and examines trends in both. The key indicator for the oxygenation status of water bodies is BOD, which is the demand for oxygen resulting from organisms in water that consume oxidisable organic matter.


The units used in this indicator are annual average BOD after 5 or 7 days incubation (BOD5/BOD5), expressed in mg O2/l, and annual average total ammonium concentrations expressed in µg N/l.

Policy context and targets

Context description

There are a number of EU directives that aim to improve water quality and reduce the loads and impacts of organic matter. First, the Water Framework Directive requires the achievement of good ecological status or good ecological potential of rivers across the EU by 2015 and repeals, step-by-step, several older water related directives. Alongside this, the following directives stay in place: the Nitrates Directive (91/676/EEC), aimed at reducing nitrate and organic matter pollution from agricultural land, the Urban Waste Water Treatment Directive (91/271/EEC), aimed at reducing pollution from sewage treatment works and certain industries (see also CSI24 Urban waste water treatment) and the Integrated Pollution Prevention and Control Directive (96/61/EEC), aimed at controlling and preventing the pollution of water by industry.


The indicator is not directly related to a specific policy target but shows the efficiency of waste water treatment (see CSI024). The environmental quality of surface waters with respect to organic pollution and ammonium, and the reduction of the loads and impacts of these pollutants are, however, objectives of several directives, including the Surface Water for Drinking Directive (75/440/EEC), which sets standards for the BOD and ammonium content of drinking water, as well as other directives mentioned in the previous chapter.

Related policy documents

Key policy question

Is organic matter and ammonium pollution in European rivers decreasing?


Methodology for indicator calculation

Data source: Data on rivers are collected annually through the WISE-SoE data collection process. WISE SoE was previously known as EUROWATERNET (EWN) and EIONET-Water. Biological quality elements in rivers have been integrated into the reporting of river water quality, starting from the 2012 reporting period. A formal request is sent to NFPs and NRCs every year with reference to templates to use and guidelines.

The requested data on rivers include the physical characteristics of the river monitoring stations, proxy pressures on the upstream catchment areas and chemical quality data on nutrients and organic matter, as well as hazardous substances in rivers. They also include 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. This reporting obligation is an EIONET Priority Data flow.

Station selection: No criteria are used for station selection (except for time series and trend analysis; see below)  

Determinants: The determinants selected for the indicator and extracted from Waterbase are BOD5, BOD7, total ammonium and ammonium.

Most countries monitor BOD5. Finland monitors BOD7. Lithuania monitored BOD5 up to 1995 and started monitoring BOD7 in 1996. Latvia monitored BOD7 from 1996 to 2001. Estonia monitored BOD5 in 2010, while it monitored BOD7 up to 2009. BOD is commonly used for BOD5. For countries reporting BOD7, these values have been converted to BOD5 (BOD7 = 1.16 BOD5) for reasons of comparability.

All countries reported total ammonium until 2006. In 2007, Greece and Liechtenstein started reporting ammonium instead of total ammonium. Instead of total ammonium, Cyprus, Lichtenstein and Slovenia began reporting ammonium in 2008, as did Austria and the Netherlands in 2009, Bulgaria and Latvia in 2010, and Estonia, Norway and Poland in 2011.  Besides total ammonium, Slovakia also started to report ammonium for some stations in 2008. Belgium, Germany, Italy, Luxembourg, Slovakia and the United Kingdom report either ammonium or total ammonium for an individual station in a selected year from 2008. Data for either of the two determinants were included in the assessment. For those stations in Slovakia where both were reported, total ammonium data were included in the assessment.

All values are labeled as BOD5/total ammonium in the graphs, but it is indicated in the graph notes for which countries BOD7/ammonium data are used.

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 that 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 with other data series in the country. If not explicitly validated by reporting countries, such values are flagged in Waterbase, but only excluded from the following year’s assessment due to timing issues. More details on the QA/QC procedure can be found here:

    • groundwater QA/QC description
    • rivers QA/QC description
    • lakes QA/QC description

Quality checked data: In the table on nutrients ('Waterbase_rivers_v12_Nutrients'), QA-fields are treated as follows:

      • Field 'QA_MVissues': all flagged values are excluded from the indicator calculation, except for zero values (flag 103).
      • Field 'QA_LRviolation': all flagged values are allowed, except for flagged values that break the rule 'Mean >= Minimum' (flag 201) and 'Mean <= Maximum' (flag 202). 
      • Field 'QA_outlier': all flagged values are excluded from the indicator calculation, except for outliers confirmed by country (flags 491, 493).  
      • Field 'QA_station_issues: all flagged values are allowed (including wrong coordinates or missing coordinates), except for "Water Category value is incompatible with this particular dataset' (flag 511) and 'station is not defined in the station table' (flag 599).
      • Field 'QA_CR violation': all flagged values are allowed.

Mean: Annual mean concentrations are used in the time series and present concentration graphics. Countries are asked to substitute any sample results below the limit of detection/determination by a value equivalent to half the limit of detection/determination before calculating the station annual mean values. Mean concentration values of zero are included in the indicator calculation as zero (0).

Inter/extrapolation and consistent time series

For time series (Fig. 1-5) 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.

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, in 2007 (i.e. when analysing data up until 2005), it was 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 to inter/extrapolate all missing value gaps of 1-2 years for each station. At the beginning or end of the data series one 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 1992-2011 after such inter/extrapolation are included in the assessment. The number of gaps is unlimited, only a gap length (size) of 3 years is defined. 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 with the analysis of the present situation, where all available data can be used. In Figure 1, two time series are used: 1992–2012 and 2000–2012.

Aggregation of time series

The selected time series (see above) must be aggregated to a smaller number of groups and averaged, before the aggregated series can be displayed in a time series plot. Determinants are grouped into five geographical regions of Europe, which contain the following countries: 

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

Northern: FI, IS, NO, SE. 

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

(List of country codes can be found here )

Not all countries listed per region are included in the figures due to no data being reported or no stations with complete time series after inter/extrapolation. Due to changes in the monitoring network (adapting to monitoring networks under Water Directives) the time series are broken and a limited number of time series is available for some countries. 

Determinants are, in addition, grouped into six sea region catchments, which are defined not by countries but by river basin districts or river basin district sub-units if consistent with sea catchment areas. 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 Ocean 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 (McLeod 2005) in the free software R (R Development Core Team 2006). The test was suggested by Mann (1945) and has been extensively used with 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 under the null hypothesis of no association is computed in the case of no ties using an exact 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'). Data series with p-value >0.10 have no significant trend. The test is non-parametric, which means that the amount of change from year to year is not considered, only the direction 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 m is determined as the median of all slopes (yj − yi)/(xj − xi) when joining all pairs of observations (xi,yi). Here the slope is calculated as the change per year for each unit (groundwater body/river station/lake station). This is summarised by 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 selected river stations are extracted from Waterbase. The number of stations with annual mean concentrations occurring in the selected concentration bands or 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 new/revised class defining values for BOD5 concentrations (mg O2/l): <1.4, 1.4 to 1.99, 2 to 2.99, 3 to 3.99, 4 to 4.99, >5. The two highest classes are merged to >4.
      • The new/revised class defining values for total ammonium concentrations (mg N/l): <0.04, 0.04 to 0.09, 0.1 to 0.19, 0.2 to 0.39, 0.4 to 0.99, >1. The two highest classes are merged to >0.4.

More information is given in the WISE maps on water quality in rivers and lakes under section 'Help': http://www.eea.europa.eu/themes/water/interactive/soe-rl (BOD in rivers, Total ammonium in rivers).

Methodology for gap filling

The methodology for gap filling is described under inter/extrapolation and consistent time series.

Methodology references

  • Hipel, K.W. and McLeod, A.I., (2005). Time Series Modelling of Water Resources and Environmental Systems. Electronic reprint of our book orginally published in 1994. 
  • Mann, H.B. (1945). Nonparametric tests against trend, Econometrica, 13, 245-259.
  • McLeod, A.I. (2005). Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.0.
  • R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  • Sen, Pranab Kumar (1968). "Estimates of the regression coefficient based on Kendall's tau", Journal of the American Statistical Association 63: 1379–1389
  • Theil, H. (1950). "A rank-invariant method of linear and polynomial regression analysis. I, II, III", Nederl. Akad. Wetensch., Proc. 53: 386–392, 521–525, 1397–1412

Data specifications

EEA data references

Data sources in latest figures


Methodology uncertainty

The methodologies used for aggregating and testing trends in concentration are relatively robust and illustrate the overall European and regional trends. However, uncertainty may be included in evaluating single countries or river basins.

Data sets uncertainty

The data sets for rivers include almost all countries in the EEA area, but the time coverage varies from country to country. The data set provides a general overview of concentration levels and trends in organic matter and ammonia in European rivers. Most countries measure organic matter as BOD over 5 days but a few countries measure BOD over 7 days, which may introduce a small uncertainty in comparisons between countries.

The river monitoring stations included in the assessment vary yearly due to availability of time series for the whole period starting from 1992. In the 2013 assessment, data for a significant number of stations were not reported. Conversely, some new stations were added, if the QA/QC procedure showed that stations reported under different names or codes could be treated as identical. This optimisation needs further quality checking. In the end, 702 stations were assessed in 2013 (compared with 849 stations in 2012) for BOD5 and 921 stations were assessed in 2013 (compared with 952 stations in 2012) for total ammonium.

Rationale uncertainty

Biochemical oxygen demand and total ammonium show oxygen consumption and are thus well suited to illustrating water pollution. However, using annual average values may not fully illustrate the severity of low oxygen conditions.

Further work

Short term work

Work specified here requires to be completed within 1 year from now.

Work description

This indicator could be improved as more countries implement the EIONET-water data flow on the basis of the monitoring networks to be established under the WFD. There are gaps in river characteristic information from some countries. This does not enable the stratification of all stations by river size, and thus limits to some extent the current dataset for size-stratification. Also many countries did not report all the requested summary statistics such as the median. A bigger gap in the information is in terms of catchment pressures. Some countries have used Corine land cover data to provide proxy indicators of pressures. It is expected that this aspect will improve significantly during the next year as new updated Corine data will be available, and as work is undertaken by the ETC/WTR and ETC/TE to fill in the gaps in the pressure indicators. More times series data would improve the dataset particular from Southern countries.

Resource needs

More data in terms of time series (esp. from southern countries)


Not started


2014/01/01 00:00:00 GMT+1

Long term work

Work specified here will require more than 1 year (from now) to be completed.

General metadata

Responsibility and ownership

EEA Contact Info

Peter Kristensen


European Environment Agency (EEA)


Indicator code
CSI 019
WAT 002
Version id: 1

Frequency of updates

Updates are scheduled once per year


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

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