Physico-chemical supporting elements in inland waters under the water framework directive : a review of national standards to support good ecological status.
Date: 2022-04-06
Institutions: Joint Research Centre
Subjects: EU Member State, aquatic ecosystem, chemical process, comparative analysis, directive (EU), ecological footprint, environmental impact, environmental law, environmental research, inland waters, physical process, research report, territorial waters

Physico-chemical supporting elements in inland 
waters under the Water Framework Directive: A 
review of national standards to support good 
ecological status   

Martyn Kelly, Geoff Phillips, Heliana Teixeira, Fuensanta Salas Herrero, 
Gábor Várbíró, Agnieszka Kolada, Anne Lyche Solheim, Sandra Poikane 

2022 

Joint 
Research  
Centre 

EUR 31040 EN 

 
 
 
 
 
 
  
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s 
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JRC127875 

EUR 31040 EN 

PDF 

ISBN 978-92-76-51133-5 

ISSN 1831-9424 

doi:10.2760/470539 

Luxembourg: Publications Office of the European Union, 2022  

© European Union, 2022 

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How to cite this report:  Kelly, M., Phillips, G., Teixeira, H., Salas Herrero, F., Várbíró, G., Kolada A., 
Lyche  Solheim,  A.,  Poikane,  S.  Physico-chemical  supporting  elements  in  inland  waters  under  the 
Water Framework Directive: a review of national standards to support good ecological  status. EUR 
31040 EN, Publications Office of the European Union, Luxembourg, 2022. ISBN 978-92-76-51133-5, 
doi:10.2760/470539, JRC127875. 

 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ............................................................................................................................................................................................................................................ 1 

Acknowledgements ................................................................................................................................................................................................................ 1 

1. Introduction ............................................................................................................................................................................................................................. 2 

1.1  Objectives .......................................................................................................................................................................................................................... 2 

1.2  Approach ........................................................................................................................................................................................................................... 3 

1.2.1 

Harmonisation of data ........................................................................................................................................................................... 3 

1.2.2 

Linking freshwater data to broad types ................................................................................................................................... 3 

1.2.3 

Values applied to “All” water types .............................................................................................................................................. 7 

1.2.4 

Summarising data for presentation ............................................................................................................................................. 7 

1.2.5 

Linking the standards to sensitive biological quality elements ............................................................................. 7 

2  Overview of reported physico-chemical quality elements and metrics ................................................................................... 12 

2.1  Which physico-chemical quality elements are used? ................................................................................................................. 12 

2.2  Metrics used for most commonly used quality elements ....................................................................................................... 12 

2.3  Challenges encountered whilst collating and comparing data .......................................................................................... 16 

2.3.1 

Different parameters, depth strata and statistical summary metrics ............................................................. 16 

2.3.2 

Ambiguity with data reporting as ranges of values: ................................................................................................... 16 

3  Boundary values for supporting elements within and between common types .............................................................. 18 

3.1  Oxygen ............................................................................................................................................................................................................................. 18 

3.1.1 

Oxygen (lakes) ........................................................................................................................................................................................... 18 

3.1.2  Oxygen (rivers) .......................................................................................................................................................................................... 27 

3.1.3 

Dissolved oxygen: synthesis............................................................................................................................................................ 35 

3.2  Transparency ............................................................................................................................................................................................................... 36 

3.2.1 

Secchi depth (lakes) ............................................................................................................................................................................... 36 

3.2.2 

Secchi depth: synthesis ....................................................................................................................................................................... 40 

3.3  pH ......................................................................................................................................................................................................................................... 41 

3.3.1 

pH (lakes) ....................................................................................................................................................................................................... 41 

3.3.2 

pH (rivers) ...................................................................................................................................................................................................... 45 

3.3.3 

pH: synthesis ............................................................................................................................................................................................... 50 

3.4  BOD ..................................................................................................................................................................................................................................... 50 

3.4.1 

BOD (rivers) .................................................................................................................................................................................................. 50 

3.4.2 

BOD: synthesis ........................................................................................................................................................................................... 54 

3.5  Ammonium-N............................................................................................................................................................................................................. 55 

3.5.1 

Ammonium-N (rivers).......................................................................................................................................................................... 55 

3.5.2 

Ammonium-N: synthesis ................................................................................................................................................................... 60 

i 

 
 
3.6  Nitrogen........................................................................................................................................................................................................................... 60 

3.6.1 

Nitrate ............................................................................................................................................................................................................... 60 

3.6.2 

Total N.............................................................................................................................................................................................................. 65 

3.6.3 

Synthesis: nitrogen ................................................................................................................................................................................. 74 

3.7  Phosphorus ................................................................................................................................................................................................................... 75 

3.7.1  Orthophosphate (rivers) .................................................................................................................................................................... 75 

3.7.2 

Total P............................................................................................................................................................................................................... 80 

3.7.3 

Phosphorus: synthesis ......................................................................................................................................................................... 89 

3.8  Salinity ............................................................................................................................................................................................................................... 90 

3.8.1 

Salinity (rivers) ............................................................................................................................................................................................ 90 

3.8.2 

Salinity: synthesis...................................................................................................................................................................................... 98 

4  Discussion.................................................................................................................................................................................................................................... 99 

4.1  General comments.................................................................................................................................................................................................. 99 

4.2  Changes in nutrient standards ...................................................................................................................................................................... 99 

4.3  Challenges involved in setting threshold values .......................................................................................................................... 100 

4.4  Synthesis ....................................................................................................................................................................................................................... 105 

4.5  The way forward .................................................................................................................................................................................................... 107 

5  References ............................................................................................................................................................................................................................... 109 

List of abbreviations ................................................................................................................................................................................................................. 111 

Appendices 
……………………………………………………………………………………………………………………………………………………………103 

ii 

 
 
 
 
 
Abstract  

The task group on supporting physico-chemical elements have reviewed information reported by 
Member  States  to  WISE  (Water  Information  System  for  Europe)  on  the  standards  for  general 
physico‐chemical quality elements including nutrients used under the European Water Framework 
Directive  (WFD).    A  wide  range  of  supporting  physico-chemical  elements  are  used  by  Member 
States. This report focusses on those that are ecologically most relevant for inland waters and which 
are used by enough Member States to make realistic comparisons.  These are:  

Rivers: BOD (Biochemical Oxygen Demand), dissolved oxygen, pH, total phosphorus (TP), soluble 
reactive phosphorus (SRP), total nitrogen (TN), nitrate-N, ammonium-N and salinity (conductivity 
and chloride); 

Lakes: Transparency (Secchi depth), dissolved oxygen, pH, TP, TN. 

A separate report will document the situation for transitional and coastal waters. 

Systematic variation between both types and countries is apparent for several of these, although 
comparisons are complicated by differences in the ways that data have been reported. 

Some  of  the  standards  appear  unlikely  to  support  good  status  for  sensitive  biological  quality 
elements,  based  on  type-specific  comparisons  with  biological  data  in  the  EEA  database 
(https://www.eea.europa.eu/data-and-maps/data/waterbase-biology).  Further  considerations  and 
dialogue are needed with the countries using those standards. 

Acknowledgements 

This  work  was  carried  out  under  the  WFD  Common  Implementation  Strategy  working  group 
ECOSTAT  work  program.  We  greatly  acknowledge  the  contributions  of  the  ECOSTAT 
representatives  and  national  experts  checking  and  correcting  the  national  standards  and 
participating in the discussions.  

We would also like to thank Jeanne Boughaba (DG ENV) and members of the ECOSTAT working 
group who contributed to the improvement of this report and to the organization of the workshop 
dedicated to the discussion of this analysis. 

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1. Introduction 

1.1  Objectives 

If countries are to achieve good ecological status for all water bodies, then it is necessary to set 
thresholds for pressures that are consistent with this ambition. ECOSTAT  (the Water Framework 
Directive Common Implementation Strategy working group on ecological status)  recognized that 
nutrients  were  a  key  pressure  across  all  water  body  types,  noting  that  there  was  considerable 
variation in threshold concentrations between countries for comparable types.  The outcome of this 
work  was  the  JRC  Science  for  Policy  report  “Best  practice  in  setting  nutrient  concentrations  to 
support good ecological status” (Phillips et al., 2019) and a statistical toolkit to help countries review 
and revise their nutrient thresholds.    

However,  nutrients  are  not  the  only  supporting  physico-chemical  quality  elements  (hereafter 
“supporting elements”) likely to impede achievement of good ecological status and ECOSTAT’s work 
programme was therefore extended to other water quality variables.   The key questions addressed 
in this report is to what extent standards reported by countries are likely to support good ecological 
status and whether or not there is potential for ECOSTAT members to work together to produce 
ecologically-meaningful standards in cases where existing standards are inadequate.   This, in turn, 
will generate further discussion on the use and relevance of supporting elements in the classification 
of ecological status, as well as on the use of these standards as a basis for deriving management 
targets to improve water quality and ecological status.   As for the work on nutrients, the purpose 
here is not to impose a uniform approach on countries but, rather, to support them in the difficult 
task of achieving good ecological status across Europe’s many and diverse water bodies.   If nutrients 
or  other  physico-chemical  quality  elements  are  inhibiting  the  achievement  of  good  status,  then 
setting  appropriate  thresholds  will  contribute  to  the  long-term  sustainability  of  Europe’s  water 
resources. On the other hand, if a supporting element standard is not compatible with good status, 
then mitigation measures will be insufficient to restore the ecosystem back to good ecological status.    

Standards  for  many  supporting  elements  are  reported  to  WISE  (Water  Information  System  for 
Europe); however, this report focusses only on those that are widely used and where there is good 
evidence of direct or indirect relationships with BQEs (Biological Quality Element) and ecosystem 
services.  We have assumed that all national standards reported to WISE are used as part of formal 
assessments of ecological status but the manner in which each is used is beyond the scope of this 
report.  Where there is a direct relationship with a BQE, then it may be possible to adapt approaches 
advocated in the statistical toolkit (Phillips et al. (2018) to review or revise existing standards.   In 
some cases (e.g. oxygen conditions), there may be a combination of direct and indirect relationships, 
depending  on  the  BQE  and  may  also  increase  in  significance  as  global  warming  raises  water 
temperatures (Jane et al., 2021).   These and other supporting elements (e.g. transparency) may 
complement information on nutrients and thereby contribute to the decision-making process.   It is 
also important to note that Member States apply different combination rules to supporting elements 
(e.g. average, worst case or more complex scoring systems). The combination rule is very important, 
and  will  need  further  attention  when  making  detailed  comparisons  of  overall  ecological  status 
between  Member  States,  as  similar  threshold  values  may  give  different  classification  results 
depending on the combination rule applied. 

2 

 
 
 
 
It is also possible that standards currently in force around Europe are not tailored to the needs of 
the WFD but, rather, are historic standards set to fulfil requirements of earlier directives.  In particular, 
we note that many countries use a standard of 6 mg L-1 dissolved oxygen, which is a value specified 
in the Freshwater Fish Directive (78/659/EEC) for salmonid waters.   This was designed to “support 
fish life” and, as such, may be appropriate for WFD purposes.  However, it is not clear whether this 
assumption has been widely tested during the WFD era. A similar situation occurs for nitrates in 
freshwaters,  where  many  countries  use  values  derived  from  the  Nitrates  Directive  (91/676/EEC):  
surface water and groundwater are polluted with concentration more than 50 mg/l of NO3.  In such 
cases there is a need to revisit these standards with the particular requirements of the WFD in mind 
to ensure that they are also compatible with good ecological status.   

1.2  Approach 

The source data for this work were provided by European Environment agency (EEA) based on data 
reported by EU Member States and Norway to WISE (WISE-WFD-database) and were those used 
to produce a summary report (ETC ICM, 2019), which was presented to ECOSTAT in spring 2019.  
Following this, ECOSTAT representatives were invited to check the entries for their country and the 
revised  data,  incorporating  these  revisions,  is the  basis  for the  present  report.   As  this  work  was 
started before the UK left the EU, their data has also been included.    

The data consisted of lists of physico-chemical supporting elements with the values representing 
the  good/moderate  class  boundary  used  for  different  national  types  for  each  water  category, 
together with information on the units and summary metrics used. The data provided had been 
edited  to  harmonise  the  different  formats  used  during  reporting.  However,  further  editing  was 
necessary to convert text to numbers and enable a consistent approach to standardization of units, 
quality element names (see ETC/ICM, 2019) and national type codes.  

1.2.1  Harmonisation of data 

Standards are reported using a variety of units and to enable meaningful comparisons of standards 
we have harmonized to standard units wherever possible. This was carried out in two stages: initially 
a lookup table was used to make an initial conversion. For example, where a Member State reported 
units of mg P / L we converted this to µg P / L using a factor of 1000. The results of these conversions 
were  checked  for  each  different  physico-chemical  quality  element  using  box  plots  arranged  by 
country  and  this  revealed further  clear  errors  in the  original  reporting.  Where these  errors  were 
obvious further corrections were made.  However, it was not always possible to make appropriate 
corrections and where this is the case, data were initially omitted but noted and the Member State 
concerned  was  given  an  opportunity  to  amend  its  data.  Some  results  were  reported  as  ranges; 
interpretation of these is discussed below.    

This editing was carried out using R to provide an audit trail and avoid modifying the original source 
data. Full details of the initial generic editing are available in an R notebook (JoinDataV4.Rmd) and 
further corrections in specific R scripts dealing with each supporting element/Water Category.   

1.2.2 

Linking freshwater data to broad types 

The data had already been matched to intercalibration type codes, but for freshwaters records were 
additionally matched to broad types of rivers and lakes (Lyche Solheim et al., 2019) using a lookup 
table.  This was done because the broad types could be linked to a larger number of national types 
(>75%) than the intercalibration types. Although the broad typology generalises and simplifies many 
characteristics of lakes and rivers, and details of national classifications, it is the best means available 

3 

 
 
for obtaining an EU-wide overview of the consistency in standards between Member states.  As not 
all national type codes are unique to water categories it was necessary to select records that applied 
to lakes or rivers separately prior to linking with the broad type, using fields that identified whether 
the supporting element value applied to each of these water categories. 

In some cases, national type codes could not be matched to those in the broad type lookup tables 
as the code was missing. In most cases these were due to the record being marked as applying to 
both river and lake categories which we assume was an error. In addition, many national type codes 
that could not be allocated to a broad type are shown in the plots in chapter 3 as “LW-00” or “RW-
00” depending on  whether they represent lakes or rivers.   The original data are summarised in 
Table 1.1, with the new data after Member State checking summarised in Table 1.2. Most of these 
unassigned types probably relate to national types that hold very few water bodies and altogether 
constitute < 25% of all water bodies (Lyche Solheim et.al., 2019).  

4 

 
 
Table 1.1. Number of national freshwater types that could not be matched to a broad type (e.g. a 
standard  marked  as  applying  to  lakes,  but  having  a  river  type  code)  and  number  of  national 
freshwater  types  not  assigned to  a  broad type  (i.e.  national  type  code  did  not  link  to  any  existing 
broad type) at the start of the project. * mainly very small lakes which are not recognized as separate 
water bodies, and which are treated as part of a larger river water body. 

Lakes 

Rivers 

Country 

Not 
matched 

Not 
assigned 

Not 
matched 

Not 
assigned 

Austria 

9 

Belgium 

Bulgaria 

35 

Croatia 

Czechia 

Estonia 

Finland 

France 

Germany 

Hungary 

Latvia 

The 
Netherlands 

Malta 

Norway* 

Portugal 

7 

1 

33 

Romania 

66 

Slovenia 

Sweden 

Slovakia 

Spain 

1 

6 

6 

4 

4 

1 

7 

2 

8 

3 

1 

1 

13 

3 

1 

4 

16 

5 

5 

1 

1 

18 

28 

14 

4 

11 

1 

2 

1 

7 

2 

26 

11 

104 

5 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 1.2. Number of national freshwater types that could be assigned or not to a broad type following 
checking by Member States.  

Lakes 

Rivers 

not 
assigned 
(LW-00) 

assigned 

“All” type 
used 

not 
assigned 
(RW-00) 

assigned 

“All” type 
used 

Country 

Austria 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czechia 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Netherlands 

Norway 

Poland 

Portugal 

Romania 

Slovenia 

Slovakia 

6 

3 

3 

8 

3 

8 

20 

8 

4 

5 

1 

4 

4 

1 

2 

10 

Spain 

30 

Sweden 

39 

9 

5 

5 

9 

5 

9 

3 

2 

5 

6 

33 

2 

47 

1 

27 

28 

12 

23 

35 

7 

8 

1 

21 

10 

6 

5 

6 

12 

74 

15 

7 

52 

72 

22 

11 

yes 

yes 

yes 

yes 

yes 

no 

no 

no 

yes 

no 

yes 

no 

yes 

yes 

no 

no 

yes 

no 

no 

no 

yes 

no 

no 

no 

no 

no 

7 

3 

8 

6 

8 

6 

5 

10 

6 

4 

16 

8 

3 

5 

62 

6 

2 

5 

2 

12 

no 

no 

no 

yes 

yes 

no 

no 

no 

no 

no 

no 

yes 

no 

no 

no 

no 

no 

no 

yes 

no 

no 

no 

no 

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
UK 

5 

9 

yes 

8 

yes 

1.2.3  Values applied to “All” water types 

Many standards were not linked to a specific national type, instead the entry for type was recorded 
as “All”. These records were assumed to apply to all national types for that River Basin District (RBD).  
An  “ALL”  grouping  category  has  been  included,  which  can  be  used  to  assess  which  countries 
reported this approach and how the range of values compared to those for the identified broad 
types. 

1.2.4 

Summarising data for presentation 

The original data extracted from WISE had type specific good/moderate boundary values for each 
RBD, so for some countries the same type specific boundary was replicated several times. To avoid 
giving additional weight to these values only a single national type specific boundary value was used 
when  plotting  and  summarising  values  (we  refer  to  these  as  “discrete  records”  in  section  3). 
Additionally, boundary values were not always consistent within each national type; some countries 
reported different values for the same broad type for different RBDs. As the number of national 
types also varied between countries, we present graphs showing the range of values within a broad 
type, with separate dots for each national type that correspond to that broad type.   Red and blue 
dots are used when a standard was reported as a range with upper and lower thresholds rather 
than a single value. 

We have then calculated the median, 75th and 90th percentiles for all standards that measure the 
central tendency (mean, median, see 2.2) included in the comparison and plotted these values as 
horizontal  dotted  lines  on  the  graphs  to  give  a  quick  visual  insight  into  how  individual  national 
standards compare to an EU-wide consensus (in some cases sample size results in percentiles having 
the same numeric value and thus not all lines are shown in some figures).   Where standards were 
reported as a range and we were not able to split these into clear lower and upper standards (see 
2.3.2) we include both reported values if each measured the central tendency. We would expect 
national standards that measure the central tendency of the supporting element to cluster around 
the median whilst those based on higher percentiles should lie closer to the 75th and 90th percentile 
lines.    Graphs  show  these  results aggregated  by  both  Member  State  and  Broad  Type  (although 
percentiles are only included where more than two Member States have contributed standards to 
the same Broad Type) but these need to be interpreted with care.   Although national standards 
falling above the median value for a type may be less likely to protect good ecological status for 
sensitive BQEs than standards falling below the median value for the type, the opposite can also 
happen  because  each broad type  encompasses a  wide  range of  national  water body types  and 
national decision-making processes use standards in different ways (see 1.1).     We recommend 
readers focus on the big picture: a Member State that has national standards that are consistently 
more lenient than those of near-neighbours or those sharing similar water body types should regard 
this report as an opportunity to ask questions to ensure that its standards are sufficiently protective.    

1.2.5 

Linking the standards to sensitive biological quality elements 

The  relationship  between  summary  statistics  for  the  reported  standards  (median,  75 th  and  90th 
percentiles) and concentrations reported for the relevant supporting elements at monitoring sites 
where the biota is reported to be in good or better status is shown in boxplots.  These are based 
on national records of both physico-chemical parameters (e.g. ortho-phosphate concentration in 
rivers) and biological quality element (BQE) data (e.g. normalised Ecological Quality Ratios (nEQR) 
and status class for phytobenthos in rivers) submitted annually to EEA and stored in the WISE-State-

7 

 
 
 
of-Environment database. The results are either based on all data for parameters where the data 
are insufficient to split by type (e.g. oxygen and pH) or are shown separately for aggregated broad 
types (e.g. lowland, calcareous, medium-large rivers) according to Lyche Solheim et al. 2019 (see 
also Tables 1.3 and 1.4 below) for parameters with sufficient data (e.g. nutrients). For all physico-
chemical  parameters  except  pH  we  used  EQRs  that  were  sensitive  to  eutrophication  or  general 
degradation (EQR_E and EQR_G). For pH we compared the upper standards with these EQRs, but 
the lower ones with EQRs sensitive to acidification (invertebrates only). 

There will be variations in the way that samples are collected and analysed between countries, but 
status assessments for the BQEs have been harmonised via the WFD intercalibration exercise.   If 
the summary statistics of the reported standards are appreciably above the concentrations given by 
the “box” for high and good status for the relevant BQE then this suggests that national standards 
for that supporting element are generally too relaxed and may not be protecting good status.  Once 
again, we should stress that there may be good reasons why any individual standard deviates from 
this global picture, but this comparison should enable ECOSTAT to decide where its attention should 
be focussed over the coming years.   

Table 1.3. Translation of broad types of lakes to aggregated broad types according to Lyche Solheim 
et al. 2019, supplementary material table S5 and S6). 

Broad 
Type 
code 

L-01 

L-04 

Broad Type Name  

Aggregated 
broad type 
code 

Aggregated Broad Type 

Name 

Very large lakes, shallow or deep and 
stratified (all Europe) 

LA-01 

Very large lakes, stratified 

Lowland, calcareous/mixed, very 
shallow/unstratified 

LA-02 

Lowland, calcareous, very 
shallow, unstratified 

L-03 

Lowland, calcareous/mixed, stratified 

L-06 

L-08 

L-10 

L-05 

Lowland organic (humic) and 
calcareous/mixed 
Mid-altitude, calcareous/mixed 

Mid-altitude, organic (humic) and 
calcareous/mixed 
Lowland organic (humic) and siliceous 

L-09 

Mid-altitude, organic (humic) and siliceous 

LA-03 

Lowland-mid-altitude, calcareous 
(incl. humic), shallow, stratified 

LA-04 

Lowland-mid-altitude, humic (& 
siliceous) 

L-02 

Lowland, siliceous 

LA-05 

Lowland, siliceous 

L-07 

Mid-altitude, siliceous 

LA-06 

Mid-altitude, siliceous 

L-11 

Highland, siliceous (all Europe), incl. 
organic (humic) 

8 

 
 
 
L-12 

Highland, calcareous/mixed (all Europe), 
incl. organic (humic) 

LA-07 

Highland 

L-13 

Mediterranean, small-large, siliceous 

L-14 

Mediterranean, small-large, 
calcareous/mixed 

LA-08 

Mediterranean, small-large, 
siliceous and calcareous 

9 

 
 
 
Table 1.4. Translation of broad types of rivers to aggregated broad types according to Lyche Solheim 
et al. 2019, supplementary material table S5 and S6). 

Broad 
Type 
code 

Broad Type Name  

Aggregated 
Broad Type 
code 

Aggregated Broad Type  

Name 

R-01 

Very large rivers 

RA-01 

Very large rivers 

R-04 

Lowland, calcareous or mixed, medium-
large 

R-07 

Lowland, organic and calcareous/mixed 

RA-02 

Lowland, calcareous or mixed, 
medium-large 

R-05 

Lowland, calcareous or mixed, very 
small-small 

RA-03 

Lowland, calcareous or mixed, 
very small-small 

R-02 

Lowland, siliceous, medium-large 

R-06b 

Lowland, organic and siliceous, 
medium-large 

R-03 

Lowland, siliceous, very small-small 

R-06a 

Lowland, organic and siliceous, very 
small-small 

RA-04 

Lowland, siliceous incl organic, 
medium-large 

RA-05 

Lowland, siliceous incl organic, 
very small-small 

R-10 

R-13 

R-11 

Mid-altitude, calcareous or mixed, 
medium-large 

Mid-altitude, organic and 
calcareous/mixed 

RA-06 

Mid-altitude, calcareous, incl. 
organic, medium-large 

Mid-altitude, calcareous or mixed, very 
small-small 

RA-07 

Mid-altitude, calcareous or 
mixed, very small-small 

R-08 

Mid-altitude, siliceous, medium-large 

R-12b 

Mid-altitude, organic and siliceous, 
medium-large 

R-09 

Mid-altitude, siliceous, very small-small 

R-12a 

Mid-altitude, organic and siliceous, very 
small-small 

R-14 

Highland (all Europe), siliceous, incl. 
organic (humic) 

R-15 

Highland (all Europe), calcareous/mixed 

RA-08 

Mid-altitude, siliceous incl 
organic, medium-large 

RA-09 

Mid-altitude, siliceous incl 
organic, very small-small 

RA-10 

Highland and glacial 

10 

 
 
R-16 

Glacial rivers (all Europe)  

R-17 

R-18 

R-19 

R-20 

Mediterranean, lowland, medium-large, 
perennial 

Mediterranean, mid-altitude, medium-
large, perennial 

Mediterranean, very small-small, 
perennial 

Mediterranean, temporary/intermittent 
streams 

RA-11 

Mediterranean perennial 

RA-12 

Mediterranean  temporary  and 
very small 

For rivers, we have focussed on phytobenthos and benthic macroinvertebrates, the BQEs for which 
the most data are available, whilst for lakes we have used phytoplankton and, in a few instances, 
macrophytes.   Readers should be aware that not all BQEs are sensitive to all supporting elements 
and that, in some cases, we have been unable to use the BQE that is most sensitive to a particular 
supporting element.   In particular, there are no data on fish in the WISE-SoE-database, so none of 
the  supporting  elements  could  be  linked  to  fish,  although  that  would  have  been  relevant  for 
supporting elements such as dissolved oxygen or BOD.    

We have also included an analysis of variance to indicate the strength of the differences (country 
and broad type) between supporting element good/moderate boundary concentrations.  This is a 
type III analysis of variance, recommended for unbalanced designs (Fox et al., 2020) with “country” 
and “broad type” included as treatments.   It was implemented using the “car” library within R.   To 
assess the relative importance of country and type effects we calculate the partial omega squared 
value which is a bias corrected estimate of the proportion of variance explained by each variable, 
using the “sjstats” library in R.  Bias correction, which adjusts for the difference between the sample 
and population estimate of this variance, is important as sample size is relatively small and thus the 
effect  size  can  be  overestimated.    However,  when  the  variance  explained  is  low  it  can  result  in 
negative values. Following the advice of (Okada 2017) we report these negative values in the analysis 
of variance summary tables, but translate this to zero in the text.   These analyses should be treated 
as broad indications of the extent to which variation amongst national standards is determined by 
factors other than the pressure in question. Overview of reported physico-chemical quality elements 
and metrics. 

11 

 
 
 
2  Overview of reported physico-chemical quality elements and metrics 

2.1  Which physico-chemical quality elements are used?  

The supporting elements used by the different countries for the different water categories are given 
in Table 2.1 and ETC-ICM (2019) based on data reported by countries to WISE with the 2nd River 
Basin Management Plans (RBMPs).  

The principle used to select the most commonly-used supporting elements for further analyses in 
this  report  are  those  that  are  used  by  at  least  seven  countries  and  which  are  also  ecologically 
relevant.  Based  on  these  principles  the  supporting  elements  included  in  this  report  are:  
conductivity; 
Rivers:  BOD,  O2,  pH, 
Lakes: Transparency, O2, pH, TP, TN; 

TP,  PO4-P,  TN,  NO3-N,  NH4-N; 

chloride, 

2.2  Metrics used for most commonly used quality elements  

The  most  commonly  used  means  of  aggregating  data  are  annual  averages  (AA-EQS),  annual 
maximum  concentration  (MAC-EQS)  or  various  high  or  low  percentiles  (Table  2.2).  A  full  list  of 
summary metrics reported is shown in Table 2.3.  

12 

 
 
 
Table 2.1. General physico-chemical quality elements reported to WISE, summarised by number of 
countries  per  water  category  (ETC-ICM  2019).  Quality  elements  selected  for  further  analysis  are 
highlighted in blue. Data includes all EU Member States and Norway, except EL and LT, as their data 
were not available in WISE at the time the data were compiled in 2019.  

Code 

Quality element name 

Rivers 

Lakes  Parameter specifications 

QE3-1-1-1 

Secchi disk depth 

QE3-1-2-1  Water temperature (Celsius) 

QE3-1-3-1  Oxygen saturation (%) 

QE3-1-3-2  Dissolved oxygen (mg/l) 

QE3-1-3-3 

Other 
oxygenation conditions 

determinands 

for 

QE3-1-4-1 

Practical salinity units 

QE3-1-4-2  Other determinand for salinity 

QE3-1-5-1  Acid neutralising capacity 

QE3-1-5-2  pH 

QE3-1-5-3 

Other 
acidification status 

determinands 

for 

QE3-1-6-1-1  Nitrate 

Nitrite 

Non-ionised ammonia 

3 

12 

10 

15 

16 

1 

8 

7 

21 

5 

18 

10 

4 

15 

6 

9 

10 

3 

2 

2 

4 

15 

3 

8 

3 

1 

BOD, COD, TOC etc. 

Conductivity, chloride 

Alkalinity, 
aluminium concentration 

inorganic 

QE3-1-6-1-
2 

QE3-1-6-1-
3 

QE3-1-6-1-
4 

QE3-1-6-1-
5 

Ammonium 

18 

10 

Total nitrogen 

QE3-1-6-4 

Total inorganic nitrogen 

QE3-1-6-2-
1 

QE3-1-6-2-
2 

Orthophosphate 

Total phosphorus 

QE3-1-6-3 

Silicate 

14 

1 

16 

21 

1 

13 

1 

5 

22 

0 

QE3-1-6-4 

Other  determinands  for  nutrient 
status 

Kjeldahl  N,  SRP,  N/P,  TOC, 
BOD,  suspended  solids,  TRIX, 
UMeco et 

13 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  2.2:  Overview  of  parameters  and  metrics  used.    Numbers  refer  to  the  number  of  countries 
reporting standards that use a particular parameter/metric combination.  Metrics have been split into 
those that measure the central tendency (e.g. mean, median) and those measuring a more extreme 
statistic (e.g. percentiles, maximum and minimum). 

Supporting element 

Lakes 

Dissolved oxygen 

% oxygen saturation 

Secchi disk depth 

pH 

TN 

TP 

Rivers 

Dissolved oxygen 

% oxygen saturation 

pH 

BOD5 

Ammonium N 

Nitrate as N 

TN 

Orthophosphate 

TP 

Chloride 

Conductivity 

Central Tendency 

annual 

seasonal 

Percentile 

Other 

1 

1 

2 

1 

2 

1 

1 

3 

3 

13 

9 

7 

19 

9 

3 

8 

13 

15 

13 

10 

13 

19 

6 

6 

3 

1 

1 

2 

2 

2 

7 

8 

15 

7 

7 

8 

2 

6 

5 

2 

3 

3 

1 

2 

2 

4 

5 

1 

1 

2 

2 

1 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2.3: List of reported metrics (as shown in legends to subsequent figures in section 3). 

Metric  measures 

central 
tendency 

an 
extreme 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

Reported metric 

Assumed explanation 

AA-EQS 

Annual average 

Maximum 

Maximum value 

Minimum 

GS Mean 

Median 

Minimum value 

Growth season mean 

Median value 

95th percentile 

95th percentile value 

Individual values 

Individual values value 

MAC-EQS 

Maximum acceptable concentration 

Perc80/m x 

not known 

winter 

winter summary metric (not known) 

90th percentile 

90th percentile value 

5th percentile 

5th percentile value 

summer 

summer summary metric (not known) 

98th percentile 

98th percentile value 

seasonal average 

seasonal average value 

yes 

10th percentile 

10th percentile value 

10&90thper 

10 & 90th percentile values (for a range) 

MinA-EQS 

not known 

MinA-MaxA 
year) 

(per 

not known 

Summer max 

Summer max value 

Winter max 

Winter max value 

Min/Jahr 
Max/Jahr 

- 

Minimum  and  maximum  January  values  (for  a 
range) 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.3  Challenges encountered whilst collating and comparing data 

We recognize that countries are free to adopt their own approaches to implement the WFD and, 
as a result, this exercise has worked with the data that are supplied.   Consistency in data collection 
within  a  country  allows  historical  comparisons  which  may  reveal  trends  in  water  quality  and 
ecological status.   However, this creates some challenges when attempting to compare national 
approaches.   

Two principal challenges were encountered whilst compiling this report. The first is where different 
parameters,  depth  strata  (in  lakes)  and  statistical  summary  metrics  were  applied  to  the  same 
supporting element and the second is ambiguity in data reporting of ranges of values for a standard.   
Most cases of the latter should have been picked up through our consultations with national experts; 
however, we encourage everyone to ensure that any further changes that are needed are made 
directly onto the WISE database in order to improve these outputs in the future. 

2.3.1  Different parameters, depth strata and statistical summary metrics  

A good example of differences in parameters is the use of either concentration of dissolved oxygen 
or  percent  saturation.    The  two  values  are,  in  theory,  interchangeable  (if  the  temperature  was 
recorded  at  the time  that  the  measurement  was made);  however, this  is  rarely possible  with the 
high-level aggregations of data used in this exercise.   Both approaches are valid means of assessing 
the  oxygenation  conditions  of  water  bodies  but  national  standards  can  only  be  compared  with 
others that use the same parameter. 

A similar issue was encountered in the use of ammonium-N and unionized ammonia.   Although 
most countries use ammonium-N, toxicity is exerted through the action of unionized ammonia on 
cells and the equilibrium between ammonium and ammonia depends upon pH.   In practice, too 
few countries measure unionized ammonia for this to be the basis of EU-wide comparisons.   

We have not, at this stage, differentiated between standards set using samples collected from the 
top or bottom (lake hypolimnion) of the water column and those set using samples that integrate 
the entire water column.   This will need to be considered when making detailed comparisons of 
standards for individual variables. 

Finally,  a  range  of  statistical  summary  metrics  are  used,  including  annual  averages,  seasonal 
averages,  upper and  lower percentiles  (Table 2.2).     There  will  be,  in many  cases, good  reasons 
behind these choices, which may be reflected in the decision-making process. However, there are 
also, we suspect, national  conventions that predate the WFD and which may benefit from being 
revisited.   

2.3.2  Ambiguity with data reporting as ranges of values: 

Many countries report ranges, which can mean different things: 

- If reported for “All” types, the range can be for many national types (often spanning several 
broad types).  There may, indeed, be no reason to expect variation between types for some 
supporting elements; 
- 
values; 
- Some supporting elements can have ‘two-tailed’ effects where either both low and high 
values  can  impede  good  ecological  status  (e.g.  pH)  or  where  a  low  value  may  directly 

If reported for one national type, the range can be site (or waterbody)-specific limit 

16 

 
 
 
influence ecological status whilst a high value indicates secondary effects (dissolved oxygen 
and, in some cases, pH).  We have attempted to split records for those supporting elements 
where lower and upper thresholds were provided into upper and lower standards to allow 
for more meaningful comparisons.  However, this was not always possible due to the original 
reporting  format,  and  sometimes  we  have  had  to  make  assumptions  about  whether  a 
reported standard was a lower or upper threshold.  

17 

 
 
3 

Boundary values for supporting elements within and between common types 

This  chapter  presents  a  comparison  of  results  for  those  quality  elements  (QEs)  that  are  most 
commonly used in inland waters (chapter 2.1). We summarise the results for each, with further details 
given  in the  appendices.    Where there  were  sufficient  data,  we  also  present  analysis  of  variance 
tables to partition the variability of the reported standards between countries and water body broad 
types. Additionally, we compare the reported standards with observed concentrations using box 
plots showing the range of mean concentrations from water bodies categorised as Good or better 
and Moderate of worse for different relevant BQEs, overlain by horizontal dotted lines indicating 
percentiles summarising the range of the reported standards.  The box plots provide a provisional 
indication of whether the standards will support good ecological status for the relevant BQEs and 
can be used as a basis for selecting standards that may need further investigation.  

3.1  Oxygen  

Dissolved oxygen (DO) in waters are reported as either percentage saturation or as a concentration, 
which complicates the comparison of standards as it is not possible to convert between the two 
parameters without also having the temperature at the time of measurement.  We thus compare 
standards for each parameter separately. Dissolved oxygen has “two tailed” effects, with low values 
indicating  potential  threats  to  fish  and  invertebrates,  while  high  values  indicate  excess  oxygen 
production from plant or algal growth. For this supporting element, where possible, we therefore 
compare upper and lower standards separately, and interpret good status to be at risk for values 
below the lower value and above the upper value, e.g. 80-120% oxygen saturation.  

3.1.1  Oxygen (lakes) 

For lakes, similar numbers of countries report DO using concentration (6) and percentage (6). 

Oxygen concentrations in lakes are influenced by many natural factors and, as a result, comparisons 
are  difficult.      The  wide  variability  in  national  standards  is  as  likely  to  represent  differences  in 
approaches to collecting data as it is to how standards are set.    

3.1.1.1  Dissolved oxygen concentration (lakes) 

There were 75 records of dissolved oxygen standards from 6 countries (Figure 3.1). Many countries 
use the annual mean (“AA-EQS”) as a summary metric. 5 countries (BE, BG, CY, PT, RO, UK) use a 
single value for each national type and 1 country (PT) present standards as a range. 

The data could be linked to 7 broad types (Figure 3.2 & Table 3.2). 1 of these only (LW-00) had type 
specific values from more than 2 countries and thus not allowing the range of standards in broad 
types to be compared. 3 countries (CY, PT, UK) apply the same standard to all national types found 
in one or more of their river basin districts. 

The standards ranged from 4 mg/L (BE) to 11 mg/L (PT) with an interquartile range of 6 to 7 (Figure 
3.2). 

A one-way analysis of variance was used to test the significance of country as there were too few 
types to test the effect of type (Table 3.1). The importance of the country effect can be judged from 
the Omega squared values which represent the proportion of variance explained by country (71.1%).    

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of dissolved oxygen based on classifications 

18 

 
 
for  phytoplankton  and  macrophytes  (Figure  3.3)  using  data  taken  from  the  EEA-WISE-SoE-
database. 

More information in Annex A1.1. 

Figure 3.1: Dissolved oxygen standards by country (single value black, minimum blue, maximum red); 
dotted  lines  show  interquartile  range  of  mean  or  median  values,  (10th  percentile=red,  25th 
percentile=orange, 50th percentile = blue (overlain by 25th as values are the same). 

19 

 
 
 
 
 
 
 
Figure 3.2: Dissolved oxygen standards by broad type (single value black, minimum blue, maximum 
red).  

Table 3.1: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for dissolved oxygen (Including main and partial effect sizes and Omega squared – 
see 1.2.5).  

Omega sq, 

Sum sq 

Df 

Intercept 

Country 

0.71 

Residuals 

3.2 

0.3 

0.1 

1 

3 

6 

F 
value 

Prob. 

258.15 

0.00000 

9.18 

0.01164 

20 

 
 
 
 
 
 
 
 
Figure 3.3: Lake Dissolved oxygen standards (dotted lines) overlain on box plots showing the range 
of Dissolved oxygen concentration for sites classified by phytoplankton and macrophytes. (10th 
percentile=red, 25th percentile=orange, median=blue overlain by 25th as values are the same) 
Significance of difference between groups, (phytoplankton) Wilcoxon p=0.112 (macrophytes) 
Wilcoxon p= <0.001 

21 

 
 
 
Table 3.2: Overview of common broad types for which standards are available, showing the number 
of countries/national types/distinct standards for Dissolved oxygen concentration. 

Code 

Broad type 

All 

All 

LW-00  Not assigned 

LW-01  Very large lakes shallow or deep and stratified 

(all Europe) 

LW-03 

Lowland stratified calcareous or mixed 

LW-04 

Lowland calcareous or mixed very shallow or 
unstratified 

LW-04  Not assigned 

LW-06 

Lowland organic (humic) and calcareous or 
mixed 

LW-07  Mid-altitude siliceous 

LW-08  Mid-altitude calcareous or mixed 

LW-11  Highland Siliceous (all Europe) incl. organic 

(humic) 

Country  Type  Standard 

3 

4 

1 

2 

2 

2 

1 

1 

1 

1 

1 

15 

1 

2 

4 

4 

2 

1 

2 

2 

3 

4 

1 

1 

1 

1 

1 

1 

1 

1 

3.1.1.2 

Percent oxygen saturation (lakes) 

There were 57 records of percent oxygen saturation standards from 6 countries (Figure 3.4). Many 
countries  use  the  annual  mean  (“AA-EQS”)  as  a  summary  metric,  but  minimum,  maximum  and 
higher/lower percentile were also frequently used. 3 countries (AT, IT, SI) use a single value for each 
national type while 3 countries (IE, NL, PT) present standards as a range. 

Of  these  none  specify  that  the  ranges  represent  sub-types  or  RBD  specific  boundaries  and  it  is 
assumed that the range represents upper and lower boundary values, as % oxygen saturation is a 
QE that might be expected to have a two tailed effect. 

The standards ranged from 30% (AT) to 140% (PT), with an interquartile range of 40% to 90% (Figure 
3.5).  Given the bimodal  distribution of the  boundaries  the data  were  split  into  upper  and  lower 
values, with values > 100% being placed in the upper group. The lower threshold values ranged 
from 30% to 80% with a median value of 60% while the upper boundaries ranged from 120% to 
140% with a median value of 120%. 

The data could be linked to 10 broad types (Figure 3.5 & Table 3.3), 2 countries (IE, PT) apply the 
same standard to all national types found in one or more of their river basin districts. Only 3 of these 
groups  (LW-00,  LW-04,  LW-08)  had  types  from  more  than  2  countries  restricting  comparisons 
between types. 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.3).  The  relative  importance  of  the  country  and  type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each  of  country  and  type  (lower boundary values  country 23.2%  and  type  0%;  upper boundary 
values country -36.4% and type 0%).  In both cases, data did not satisfy all assumptions of normality 
so these results need to be interpreted with care.  

22 

 
 
 
In order to link the reported standards to relevant BQEs their interquartile ranges are  marked as 
horizontal lines on box plots showing the distribution of % oxygen saturation based on classifications 
for  phytoplankton  and  macrophytes  (Figure  3.6)  using  data  taken  from  the  EEA-WISE-SoE-
database. For both phytoplankton and macrophytes EQRs sensitive to eutrophication and general 
degradation were used. The percentiles shown relate to the upper groups as phytoplankton and 
macrophytes are related to eutrophication. 

More information in Annex A1.1. 

Figure 3.4: % oxygen saturation standards by country (single value black, minimum blue, maximum 
red); dotted lines show median values for the upper (red) and lower (blue) groups. 

23 

 
 
 
 
 
 
Figure  3.5:  %  oxygen  saturation  standards  by  broad  type  (single  value  black,  minimum  blue, 
maximum red). “All” are standards that are not type-specific, but reported for all types in a RBD, while 
LW-00  are  type-specific  standards  for  national  types  that  do  not  match  any  of  the  broad  types. 
Horizontal lines mark the 25th (green) and 75th (red) percentiles for each group of standards). 

24 

 
 
 
 
Table 3.3: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for lower and upper groups of log % oxygen saturation boundaries (Including 
main and partial effect sizes and Omega squared – see 1.2.5.) 

Lower Boundaries 

Omega sq, 

p Omega sq  Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

0.23 

0.17 

CodeBT 

-0.38 

-0.50 

Residuals 

Upper Boundaries 

133.35 

0.000 

2.09 

0.150 

0.11 

0.995 

11.4 

0.9 

0.2 

0.9 

1 

5 

11 

10 

Omega sq, 

p Omega sq  Sum sq  Df 

F value 

Prob. 

Intercept 

Country 

-0.36 

CodeBT 

-1.09 

-0.17 

-0.80 

Residuals 

1929.1 

0.001 

0.33 

0.750 

0.00 

1.000 

22.90 

0.0 

0.0 

0.0 

1 

2 

4 

2 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.6: Upper and lower boundaries for lake % oxygen saturation standards (dotted lines) 
overlain on box plots showing the range of % oxygen saturation concentration for sites classified by 
phytoplankton and macrophytes. (For the upper boundaries lines show are the 90th percentile=red 
and median=blue and for the lower boundaries they are the 10th percentile=red, and 
median=blue). Phytoplankton and macrophytes used EQRs sensitive to eutrophication and general 
degradation with the percentiles of the upper boundaries. Significance of difference between groups, 
(phytoplankton) Wilcoxon p= 0.469 (macrophytes) Wilcoxon p= 0.005 (Percentiles were calculated 
from all summary metrics as majority of values were based on extreme percentiles) (n.b. 75th/25th 
percentiles are hidden as they are the same as the median). 

26 

 
 
 
 
Table 3.4: Overview of common broad types for which standards are available, showing the number 
of countries/national types/distinct standards for % oxygen saturation. 

Code 

Broad type 

All 

Not assigned 

All 

LW-
00 

LW-01  Very large lakes shallow or deep and stratified 

(all Europe) 

Lowland stratified calcareous or mixed 

Lowland calcareous or mixed very shallow or 
unstratified 

Not assigned 

Lowland organic (humic) and calcareous or 
mixed 

Mid-altitude siliceous 

Mid-altitude calcareous or mixed 

LW-
03 

LW-
04 

LW-
04 

LW-
06 

LW-
07 

LW-
08 

LW-11  Highland Siliceous (all Europe) incl. organic 

(humic) 

LW-12  Highland calcareous or mixed (all Europe) 

incl. organic (humic) 

LW-13  Mediterranean small-large siliceous 

LW-14  Mediterranean small-large calcareous or mixed 

Country  Type  Standard 

2 

4 

2 

2 

3 

3 

1 

2 

3 

1 

2 

1 

1 

1 

17 

2 

2 

5 

5 

1 

2 

5 

2 

5 

2 

4 

2 

5 

2 

2 

3 

3 

1 

2 

3 

1 

3 

1 

1 

3.1.2  Oxygen (rivers) 

For rivers there are more countries reporting DO using concentration (15) than percentage (11) 

3.1.2.1  Dissolved oxygen concentration (rivers) 

There were 341 records of dissolved oxygen standards from 15 countries (Figure 3.7). Many countries 
use the annual mean (“AA-EQS”) as a summary metric. 15 countries (BE, BG, CY, DE, ES, FR, GR, HU, 
LT,  LU,  LV, PL,  PT,  RO,  SK) use  a  single  value for  each national  type and  1  country  (BG)  present 
standards as a range. 

The data could be linked to 14 broad types (Figure 3.8 & Table 3.6). 8 of these (RW-00, RW-01, RW-
04,  RW-05, RW-08,  RW-09,  RW-10, RW-11)  had  type  specific  values  from  more  than 2  countries 
allowing the range of standards in these types to be compared. 5 countries (BG, CY, FR, GR, PT) 
apply the same standard to all national types found in one or more of their river basin districts. 

The  standards  ranged  from  3  mg/L  (PT)  to 9.1  mg/L  (PL)  with  an  interquartile  range  of  6 to  7.5 
(Figure 3.8). 

27 

 
 
 
A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.5).  The  relative  importance  of  the  country  and  type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (46.9%) and type (7.9%) 

To  link  the  reported  standards  with  relevant  BQEs,  their  interquartile  ranges  are  marked  as 
horizontal lines on box plots showing the distribution of Dissolved oxygen based on classifications 
for phytobenthos and macro-invertebrates (Figure 3.9) using data taken from the EEA-WISE-SoE. 

More information in Annex A1.2.1. 

Figure 3.7: Comparison of river Dissolved oxygen standards by country. (single value black, 
minimum blue, maximum red symbols, dotted lines show interquartile range of mean or median 
values (10th percentile=red, 25th percentile=orange, 50th percentile = blue.) 

28 

 
 
 
 
 
 
 
Figure 3.8: Dissolved oxygen standards by broad type (single value black, minimum blue, maximum 
red). “All” are standards that are not type-specific, but reported for all types in a RBD, while RW-00 
are type-specific standards for national types that do not match any of the broad types. Horizontal 
lines mark the 25th (green) and 75th (red) percentiles. 

Table 3.5: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for Dissolved oxygen (Including main and partial effect sizes and Omega squared 
– see 1.2.5).  

Omega sq, 

p Omega sq 

Sum sq  Df 

F value 

Prob. 

Intercept 

Country 

0.47 

CodeBT 

0.08 

Residuals 

0.51 

0.15 

416.4 

0.0000 

7.68 

0.0000 

2.69 

0.0265 

116.2 

19.3 

4.5 

1 

9 

6 

11.7 

42 

29 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.9: River Dissolved oxygen standards (dotted lines) overlain on box plots showing the range 
of Dissolved oxygen concentration for sites classified by phytobenthos and macro-invertebrates. 
(90th percentile=red, 75th percentile=orange, median=blue). Significance of difference between 
groups, (phytobenthos) Wilcoxon p=<0.001 (invertebrates) Wilcoxon p= <0.001. 

30 

 
 
 
 
Table 3.6: Overview of common broad types for which standards are available, showing the number 
of countries/national types/distinct standards for dissolved oxygen. 

Code 

Broad type 

All 

All 

RW-00  Not assigned 

RW-01  Very large rivers 

RW-02 

Lowland siliceous medium-large 

RW-03 

Lowland siliceous very small-small 

RW-04 

RW-05 

RW-
06a 

RW-
06b 

Lowland calcareous or mixed medium-
large 

Lowland calcareous or mixed very small-
small 

Lowland organic and siliceous very small-
small 

Lowland organic and siliceous medium-
large 

RW-08  Mid-altitude siliceous medium-large 

RW-09  Mid-altitude siliceous very small-small 

RW-10  Mid-altitude calcareous or mixed 

medium-large 

RW-11  Mid-altitude calcareous or mixed very 

small-small 

RW-14  Highland (all Europe) siliceous 
incl. organic (humic) 

RW-15  Highland (all Europe) calcareous or mixed 

RW-20  Mediterranean temporary or intermittent 

streams 

Country  Type  Standard 

5 

8 

6 

2 

2 

8 

7 

1 

1 

5 

5 

6 

6 

2 

2 

2 

1 

60 

16 

3 

5 

43 

13 

1 

1 

7 

9 

28 

25 

3 

3 

10 

4 

9 

3 

3 

4 

5 

3 

1 

1 

4 

3 

6 

5 

2 

3 

4 

3.1.2.2 

Percent oxygen saturation (rivers) 

There were 159 records of percent oxygen saturation standards from 11 countries (Figure 3.10). Many 
countries  use  the  annual  mean  (“AA-EQS”)  as  a  summary  metric,  but  minimum,  maximum  and 
higher/lower percentile were also frequently used. 7 countries (CZ, EE, FR, HU, IT, PT, UK) use a 
single value for each national type while 5 countries (AT, ES, IE, NL, PT) present standards as a range. 

Of  these  none  specify  that  the  ranges  represent  sub-types  or  RBD  specific  boundaries  and  it  is 
assumed that the range represents upper and lower boundary values, as percent oxygen saturation 
is a QE that might be expected to have a two tailed effect. 

The standards ranged from 50% (EE, NL) to 140% (PT), with an interquartile range of 70% to 120% 
(Figure 3.11). Given the bimodal distribution of the boundaries the data were split into two groups 
of upper and lower values, with values > 100% being placed in the upper group. The lower threshold 

31 

 
 
 
values ranged from 50% to 100% with a median value of 70% while the upper boundaries ranged 
from 110% to 140% with a median value of 120%. 

The data could be linked to 7 broad types (Figure 3.11 & Table 3.7), 6 countries (AT, FR, IE, IT, PT, 
UK) apply the same standard to all national types found in one or more of their river basin districts. 
Only 4 of these groups (RW-00, RW-01, RW-04, RW-05) had types from more than 2 countries, 
preventing comparisons between types. 

A  two-way  analysis  of  variance  was  used  to  test  the  significance  of  country  and  type  for  lower 
boundaries but only a one way test for the upper boundaries as there were too few types to test 
the effect of type (Table 3.7). The importance of the country effect can be judged from the Omega 
squared values which represent the proportion of variance explained by country (lower boundary 
values country -5.7% and type -24.5%; upper boundary values, country effect 9.7%). 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of % oxygen saturation based on classifications 
for phytobenthos and macro-invertebrates (Figure 3.12) using data taken from the EEA-WISE-SOE-
DATABASE. For both phytobenthos and macro-invertebrates EQRs sensitive to eutrophication and 
general degradation were used. The percentiles shown only relate to the lower % oxygen saturation 
boundary groups as there were too few upper standards to justify inclusion. 

More information in Annex A1.2.2 

32 

 
 
 
 
 
Figure 3.10: Comparison of river % oxygen saturation standards by country. (single value black, 
minimum blue, maximum red symbols, dotted lines show median values for the upper (red) and 
lower (blue) groups). 

Figure 3.11: River percent oxygen saturation standards by broad type (single value black, minimum 
blue, maximum red, Horizontal lines mark the 25th (green) and 75th (red) percentiles for the lower 
(blue dots) group of standards, (there were too few upper standards to justify inclusion of similar 
percentiles for this group).  

Table 3.7: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for lower and upper groups of log % oxygen saturation boundaries (Including 
main and partial effect and Omega squared – see 1.2.5). 

Lower 
Boundaries 

Intercept 

Country 

CodeBT 

Residuals 

Omega sq, 

Sum sq 

Df 

F value 

Prob. 

-0.06 

-0.25 

19.20 

0.35 

0.04 

0.80 

453.64 

0.00 

0.83 

0.6 

0.11 

1.0 

1 

10 

8 

19 

33 

 
 
 
 
 
 
 
 
 
 
Upper 
Boundaries 

Intercept 

Country 

0.1 

Residuals 

Omega sq, 

Sum sq  Df 

F value 

Prob. 

11439.13 

0.00 

1.45 

0.26 

22.92 

0.00 

1 

5 

0.03 

15 

Figure 3.12: Lower boundaries for river % oxygen saturation standards (dotted lines) overlain on box 
plots showing the range of % oxygen saturation concentration for sites classified by phytobenthos 
and macroinvertebrates. (10th percentile=red, 25th percentile=orange, median=blue). Phytobenthos 
and macrophytes used EQRs sensitive to eutrophication and general degradation.  Significance of 
difference between groups: phytobenthos - Wilcoxon p= <0.001; macroinvertebrates: Wilcoxon p= 
<0.001 (Percentiles were calculated from all summary metrics as majority of values were based on 
extreme percentiles) 

Table 3.8: Overview of common broad types for which standards are available, showing the number 
of countries/national types/distinct standards for % oxygen saturation. 

Code 

Broad type 

Country  Type  Standard 

34 

 
 
 
 
 
 
 
 
 
 
All 

All 

RW-00 

Not assigned 

RW-01 

Very large rivers 

RW-03 

Lowland siliceous very small-small 

RW-04 

Lowland calcareous or mixed medium-large 

RW-05 

RW-10 

RW-11 

Lowland calcareous or mixed very small-
small 

Mid-altitude calcareous or mixed medium-
large 

Mid-altitude calcareous or mixed very small-
small 

RW-15 

Highland (all Europe) calcareous or mixed 

6 

5 

3 

1 

3 

3 

1 

1 

1 

3 

31 

3 

3 

13 

7 

5 

6 

4 

4 

5 

2 

2 

6 

5 

3 

3 

2 

3.1.3  Dissolved oxygen: synthesis 

For both lakes and rivers there are several clear differences in parameters used, with countries using 
either or both of concentration and percent saturation and using both upper and lower thresholds.  
For  lakes,  in  particular,  comparison  is  further  complicated  because  it  is  not  clear  whether  the 
epilimnion,  hypolimnion or the  entire  water  column  is being  assessed.   Some  countries  that use 
percent  saturation  have  separate  standards  specifying  upper  and  lower  thresholds.      Where 
concentration  or  percent  saturation  depend  upon  spot  measurements,  the  time  of  the 
measurement may also influence whether upper or lower thresholds are more informative, as upper 
thresholds will be relevant only when primary production is high.  Whilst the lower threshold is of 
more direct interest to regulators, as this will influence the survival of fish and invertebrates, the 
upper threshold also reflects the impact of the pressure on primary production and may serve as a 
warning that direct impacts arising from low oxygen saturation are likely.    

Dissolved oxygen is unlikely to directly influence algal or plant communities, but may reflect water 
body status determined by them. EEA data on measured oxygen concentrations in water bodies 
classified by different BQEs show no differences in the range of mean oxygen for phytoplankton in 
lakes (Figs. 3.3 and 3.6), while for lake macrophytes (Figs. 3.3 and 3.6), and river phytobenthos (Figs 
3.9 and 3.12) the oxygen concentrations are lower in water bodies at worse than good status. In 
contrast  invertebrates  may  be  directly  influenced  by  low  dissolved  oxygen  and  water  bodies 
classified using this BQE showed the biggest differences (Figs 3.9 and 3.12). It was only possible to 
split the reported standards into upper and lower thresholds for percentage oxygen saturation.   It 
is possible that this high-level analysis overlooks nuances that make such thresholds valuable within 
individual countries, but in general upper standard values tended to be at higher values than the 
reported data, suggesting that they represent fairly extreme conditions. 

Lower standards for dissolved oxygen are likely to be set to protect fish. For both lakes and rivers, 
several  countries  continue  to use  the  6  mg/L  threshold  set  by  the  Freshwater  Fish  Directive.    A 
comparison with dissolved oxygen concentrations in water bodies classified by macroinvertebrate 
status  (Figs 3.9 & 3.12)  suggests  that  these thresholds are  generally  too  lenient to  protect  good 
status.  Interpretation of these results should also bear in mind that fish are likely to be more sensitive 
to low oxygen than invertebrates but it was not possible to show this BQE in a box plot.   We also 
note that humic lakes may have naturally low DO concentrations (sometimes even anoxic) in  the 

35 

 
 
 
hypolimnion during stratification, even at reference conditions.   This emphasises the need for local 
knowledge to inform how standards are set and used.  A further concern is that changing thermal 
regimes (Kraemer et al., 2021) will affect the solubility of oxygen (Jane et al., 2021), meaning that 
trends in “saturation” may not reflect the actual availability of oxygen to organisms.    

Development of more protective standards will also need to consider how samples are collected 
and, in lakes, whether the epi- or hypolimnion, or whole water column is considered) as well as 
appropriate summary metrics.    

3.2 

Transparency  

3.2.1 

Secchi depth (lakes) 

Data for Secchi disk depth provided 222 records from 16 countries (Figure 3.13). Many countries use 
the annual mean (“AA-EQS”) as a summary metric. 13 countries (AT, BG, CZ, EE, ES, HR, IT, LT, LV, 
NL, PL, PT, SI) use a single value for each national type and 7 countries (AT, BG, DE, FR, HR, IT, NO) 
present standards as a range. 

The data could be linked to 13 broad types (Figure 3.14 & Table 3.10). 9 of these (LW-00, LW-01, 
LW-02, LW-03, LW-04, LW-07, LW-08, LW-11, LW-14) had type specific values from more than 2 
countries allowing the range of standards in these types to be compared. 1 country (HR) apply the 
same standard to all national types found in one or more of their river basin districts. 

The standards ranged from 0.5 m (PL) to 10.6 m (NA) with an interquartile range of 1.5 to 3.3 (Figure 
3.14). 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.9).  The  relative  importance  of  the  country  and  type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (38.49%) and type (3.9%) 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of Secchi disk depth based on classifications 
for  phytobenthos  and  macrophytes  (Figure  3.15)  using  data  taken  from  the  EEA-WISE-SOE-
DATABASE. 

More information in Annex A2.1 

36 

 
 
Figure 3.13:  Secchi disk depth standards by country (single value black, minimum blue, maximum 
red).  Dotted  lines  show  interquartile  range  of  mean  or  median  values,  (10th  percentile=red,  25th 
percentile=orange, 50th percentile = blue.) 

Figure 3.14: Secchi disk depth standards by broad type (single value black, minimum blue, maximum 
red), Horizontal lines mark the 25th (green) and 75th (red) percentiles. “All” are standards that are not 

37 

 
 
 
 
 
type-specific, but reported for all types in a RBD, while LW-00 are type-specific standards for national 
types that do not match any of the broad types. 

Table 3.9: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for log Secchi disk depth (Including main and partial effect sizes and Omega 
squared – see 1.2.5) Secchi disk depth 

Omega sq, 

p Omega sq  Sum sq 

Df 

F value 

Prob. 

Intercept 

5.7 

1 

31.02 

0.0000 

Country 

0.38 

CodeBT 

0.04 

0.40 

0.06 

14.4 

14 

5.55 

0.0000 

2.5 

7 

1.92 

0.07874 

Residuals 

13.7 

74 

Figure 3.15: Lake Secchi disk depth standards (dotted lines) overlain on box plots showing the range 
of Secchi disk depth concentration for sites classified by phytoplankton and macrophytes. (10th 

38 

 
 
 
 
 
 
 
 
 
 
 
  
 
 
percentile=red, 25th percentile=orange, median=blue).  Significance of difference between groups, 
(phytoplankton) Wilcoxon p=<0.001 (macrophytes) Wilcoxon p= <0.001 

39 

 
 
 
 
 
Table 3.10: Overview of common broad types for which standards are available, showing the 
number of countries/national types/distinct standards for Secchi depth in lakes. 

Code 

Broad type 

All 

Not assigned 

All 

LW-
00 

LW-01  Very large lakes shallow or deep and stratified 

(all Europe) 

Lowland siliceous 

Country  Type  Standard 

1 

11 

4 

4 

1 

65 

2 

26 

6 

7 

5 

5 

Lowland stratified calcareous or mixed 

10 

21 

10 

LW-
02 

LW-
03 

LW-
04 

LW-
05 

LW-
06 

LW-
07 

LW-
08 

LW-
09 

Lowland calcareous or mixed very shallow or 
unstratified 

Lowland organic (humic) and siliceous 

Lowland organic (humic) and calcareous or 
mixed 

Mid-altitude siliceous 

Mid-altitude calcareous or mixed 

Mid-altitude organic (humic) and Siliceous 

LW-11  Highland Siliceous (all Europe) incl. organic 

(humic) 

LW-12  Highland calcareous or mixed (all Europe) 

incl. organic (humic) 

LW-13  Mediterranean small-large siliceous 

LW-14  Mediterranean small-large calcareous or mixed 

9 

1 

2 

7 

8 

1 

5 

2 

1 

3 

13 

1 

4 

14 

8 

1 

2 

6 

16 

14 

2 

8 

5 

2 

9 

1 

5 

3 

2 

6 

3.2.2 

Secchi depth: synthesis 

Secchi  depth  is  a  straightforward  measurement  that  offers  useful  supplementary  information  to 
BQEs and other supporting elements.   Light limitation is the most important driver of macrophyte 
status and thus Secchi depth provides important information on the way that two key BQEs interact 
in the presence of elevated nutrients.  Interpretation of results is, however, likely to be complicated 
by  the  way  it  is  combined  with  BQEs  and  other  supporting  elements  in  order  to  make  a  final 
assessment.    

EEA data showed strong relationships between Secchi depth associated with different status classes 
for both phytoplankton and macrophytes (Fig. 3.15).   For phytoplankton, the median boundary is 
below the 25th percentile for sites in good or better status but roughly in line with the 75th percentile 

40 

 
 
 
for those not in good status.  This suggests that current standards are broadly consistent with good 
status.    The  same  is  true  for  macrophytes:  the  median  boundary  is  greater  than  both  the  25 th 
percentile  for  “good  and  better”  and  the  75th  percentile  for  “less  than  good”.      These  simplistic 
comparisons  however  take  no  account  of  complicating  factors  such  as  humic  substances  and 
inorganic turbidity (clay particles from the catchment or resuspended sediment in very shallow lakes. 
However,  there  are  strong  differences  between  countries  (Table  3.9)  and  those  with  particularly 
lenient  thresholds  (  1  m)  should  be  encouraged  to  check  these  against  national  data  on 
relationships  with  sensitive  BQEs  to  validate  whether  these  are  sufficiently  protective.      It  is  also 
important be recognise that some shallow lakes are naturally highly turbid (e.g. due to suspension 
of particulates by wind/wave action) and, as a result, Secchi depth may not be a suitable indicator 
of ecological status. 

3.3  pH  

3.3.1 

pH (lakes) 

Data for pH provided 177 records from 14 countries (Figure 3.16). Many countries use the annual 
mean (“AA-EQS”) as a summary metric, but minimum, maximum and higher/lower percentile were 
also frequently used. For pH 4 countries (CZ, HU, NO, RO) use a single value for each national type 
while 12 countries (AT, BE, BG, EE, ES, HU, IE, NL, NO, PL, PT, SI) present standards as a range. 

Of these only AT and NO specify that the ranges represent sub-types or RBD specific boundaries 
and it is assumed that in other cases the range represents upper and lower boundary values, as pH 
is a QE that might be expected to have a two tailed effect. 

The standards ranged from 3 (EE) to 10.5 (ES), with an interquartile range of 6.5 to 8.7 (Figure 3.17). 
Given the bimodal distribution of the boundaries the data were split into two groups of upper and 
lower values, with values > 7.0 being placed in the upper group. The lower threshold values ranged 
from 3 to 7 with a median value of 6 while the upper boundaries ranged from 7.3 to 10.5 with a 
median value of 8.5. 

The data could be linked to 11 broad types (Figure 3.17 & Table 3.11), only 3 countries (CY, IE, PT) 
apply the same standard to all national types found in one or more of their river basin districts. 7 of 
these groups (LW-00, LW-01, LW-02, LW-03, LW-04, LW-06, LW-08) had types from more than 2 
countries enabling comparisons between types to be made. 

A two-way analysis of variance was used to test the significance of country and type  differences 
between  standards  (Table  3.11).  The  relative  importance  of  the  country  and  type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each  of  country  (lower  boundary  values:  0%;  upper  boundary  values:  5.1%)  and  type  (lower 
boundary values: 20.1%; upper boundary values: 0%). 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  pH  based  on  classifications  for 
phytoplankton  and  macrophytes  (Figure  3.18)  using  data  taken  from  the  EEA-WISE-SOE-
DATABASE.  For  phytoplankton  EQRs  sensitive  to  acidification  were  used,  for  macrophytes  EQRs 
sensitive to eutrophication and general degradation were used, with the percentiles shown relating 
to the lower and upper pH boundary groups respectively. 

More information in Annex A3.1 

41 

 
 
Figure 3.16: Comparison of lake pH standards by country. (single value black, minimum blue, 
maximum red symbols, dotted lines show median values for the upper (red) and lower (blue) 
groups. 

42 

 
 
 
 
 
Figure 3.17: Lake pH standards by broad type (single value black, minimum blue, maximum red, 
Horizontal lines mark the 25th (green) and 75th (red) percentiles for the lower (blue dots) and higher 
(red dots) groups of standards respectively.) 

Table 3.11: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for lower and upper groups of pH boundaries (Including main and partial effect 
sizes, Omega squared – see 1.2.5). 

Lower Boundaries 

Omega sq, 

p Omega sq 

Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

CodeBT 

Residuals 

-0.11 

0.20 

-0.14 

0.18 

61.7 

1.3 

8.2 

1 

6 

6 

100.04 

0.000 

0.34 

0.908 

2.22 

0.084 

12.3 

20 

43 

 
 
 
 
 
 
 
 
 
 
Upper Boundaries 

Omega sq, 

p Omega 
sq 

Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

0.05 

0.05 

CodeBT 

-0.04 

-0.05 

756.3 

6.5 

1.4 

1 

9 

6 

1459.02 

0.000 

1.4 

0.210 

0.47 

0.831 

Residuals 

28.5 

55 

Figure 3.18: Upper and lower boundaries for lake pH standards (dotted lines) overlain on box plots 
showing the range of pH concentration for sites classified by phytoplankton and macrophytes. (For 
the upper boundaries lines show are the 90th percentile=red, 75th percentile=orange, median=blue 
and for the lower boundaries they are the 10th percentile=red, 25th percentile=orange, 
median=blue). Phytoplankton and macrophytes used EQRs sensitive to acidification or 
eutrophication and general degradation respectively. Significance of difference between groups, 
(phytoplankton) Wilcoxon p= <0.001 (macrophytes) Wilcoxon p= 0.001 (Percentiles were calculated 
from all summary metrics as many of values were based on extreme percentiles). 

44 

 
 
 
 
 
 
 
 
 
 
 
 
Table 3.12: Overview of common broad types for which standards are available, showing the 
number of countries/national types/distinct standards for H in lakes. 

Code 

Broad type 

All 

Not assigned 

All 

LW-
00 

LW-01  Very large lakes shallow or deep and stratified 

(all Europe) 

Lowland siliceous 

Lowland stratified calcareous or mixed 

Lowland calcareous or mixed very shallow or 
unstratified 

Lowland organic (humic) and siliceous 

Lowland organic (humic) and calcareous or 
mixed 

Mid-altitude siliceous 

Mid-altitude calcareous or mixed 

Mid-altitude organic (humic) and Siliceous 

LW-
02 

LW-
03 

LW-
04 

LW-
05 

LW-
06 

LW-
07 

LW-
08 

LW-
09 

LW-11  Highland Siliceous (all Europe) incl. organic 

(humic) 

LW-12  Highland calcareous or mixed (all Europe) 

incl. organic (humic) 

Country  Type  Standard 

3 

9 

4 

3 

4 

6 

1 

3 

2 

3 

1 

2 

1 

1 

62 

5 

4 

7 

12 

1 

4 

11 

6 

2 

5 

2 

3 

11 

5 

2 

2 

8 

1 

3 

3 

2 

1 

3 

1 

3.3.2  pH (rivers) 

There were 573 records of pH standards from 20 countries (Figure 3.19). Many countries use the 
annual mean (“AA-EQS”) as a summary metric, but minimum, maximum and higher/lower percentile 
were also frequently used. 10 countries (BE, CZ, DE, FI, HR, HU, NO, PT, RO, UK) use a single value 
for each national type while 16 countries (AT, BE, BG, EE, ES, FR, HR, HU, IE, LU, NL, NO, PT, SE, SK, 
UK) present standards as a range. 

Of these only NO and SE specify that the ranges represent sub-types or RBD specific boundaries 
and it is assumed that in other cases the range represents upper and lower boundary values, as pH 
might be expected to have a two tailed effect. 

The standards ranged from 3.9 (SE) to 9 (HR, CZ, EE, HU, BE), with an interquartile range of 6 to 8.5 
(Figure 3.20). Given the bimodal distribution of the boundaries the data were split into two groups 
of upper and lower values, with values > 7.0 being placed in the upper group. The lower threshold 

45 

 
 
 
values ranged from 3.86 to 7 with a median value of 6 while the upper boundaries ranged from 8 
to 9 with a median value of 8.5. 

The data could be linked to 20 broad types (Figure 3.20 & Table 3.13); only 7 countries (AT, BG, FR, 
HR, IE, PT, UK) apply the same standard to all national types found in one or more of their river 
basin districts. 11 of these groups (RW-00, RW-01, RW-02, RW-03, RW-04, RW-05, RW-08, RW-09, 
RW-10, RW-11, RW-15) had types from more than 2 countries enabling comparisons between types 
to be made. 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.13).  The  relative  importance  of the  country  and  type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (lower boundary values: 44.5%; upper boundary values: 45.4%) and type (lower 
boundary values: 0% and upper boundary values: 0%) and type (-0.5%).  In both cases, data did 
not satisfy all assumptions of normality so these results need to be interpreted with care. 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  pH  based  on  classifications  for 
phytobenthos  and  macroinvertebrates  (Figure  3.21)  using  data  taken  from  the  EEA-WISE-SOE-
DATABASE. For phytobenthos EQRs sensitive to eutrophication and general degradation were used, 
for macroinvertebrates EQRs sensitive to acidification were used, with the percentiles shown relating 
to the upper and lower pH boundary groups respectively. 

More information in Annex A3.2. 

Figure 3.19: Comparison of river pH standards by country (single value black, minimum blue, 
maximum red). Dotted lines show median values for the upper (red) and lower (blue) groups. 

46 

 
 
 
Figure 3.20: River pH standards by broad type (single value black, minimum blue, maximum red). 
“All” are standards that are not type-specific, but reported for all types in a RBD, while RW-00 are 
type-specific standards for national types that do not match any of the broad types. Horizontal lines 
mark the 25th (green) and 75th (red) percentiles for the lower (blue symbols) and upper (red 
symbols) group of standards respectively.) 

Table 3.13: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for lower and upper groups of pH boundaries (Including main and partial effect 
sizes, Omega squared – see 1.2.5). 

Lower Boundaries 

Omega sq, 

p Omega sq 

Sum sq 

Df 

F value 

Prob. 

0.44 

-0.01 

0.44 

-0.02 

203.5 

1 

977.02 

0.000 

17.2 

1.5 

12 

9 

14.2 

68 

6.89 

0.000 

0.81 

0.607 

Intercept 

Country 

CodeBT 

Residuals 

Upper Boundaries 

Omega sq, 

p Omega sq  Sum sq  Df 

F value 

Prob. 

47 

 
 
 
 
 
 
 
 
 
 
 
Intercept 

398.0 

1 

7507.59 

0.000 

Country 

0.45 

0.44 

3.5 

10 

6.57 

0.000 

CodeBT 

-0.04 

-0.08 

0.2 

9 

0.43 

0.911 

Residuals 

2.8 

52 

Figure 3.21: Upper and lower boundaries for river pH standards (dotted lines) overlain on box plots 
showing the range of pH for sites classified by phytobenthos and invertebrates. (For the upper 
boundaries lines show are the 90th percentile=red, 75th percentile=orange (overlain by 90th 
percentile line), median=blue and for the lower boundaries they are the 10th percentile=red, 25th 
percentile=orange, median=blue). Phytobenthos and macrophytes used EQRs sensitive to 
eutrophication and general degradation with the percentiles of the upper boundaries. Significance of 
difference between groups, (phytoplankton) Wilcoxon p= 0.068 (macrophytes) Wilcoxon p= <0.001 
(Percentiles were calculated from all summary metrics as majority of values were based on extreme 
percentiles). 

48 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.14: Overview of common broad types for which standards are available, showing the 
number of countries/national types/distinct standards for pH in rivers. 

Code 

Broad type 

All 

All 

RW-00  Not assigned 

RW-01  Very large rivers 

RW-02 

Lowland siliceous medium-large 

RW-03 

Lowland siliceous very small-small 

RW-04  Lowland calcareous or mixed medium-

large 

RW-05 

Lowland calcareous or mixed very small-
small 

RW-
06a 

RW-
06b 

Lowland organic and siliceous very 
small-small 

Lowland organic and siliceous medium-
large 

RW-08  Mid-altitude siliceous medium-large 

RW-09  Mid-altitude siliceous very small-small 

RW-10  Mid-altitude calcareous or mixed 

medium-large 

RW-11  Mid-altitude calcareous or mixed very 

small-small 

RW-
12a 

RW-
12b 

Mid-altitude organic and siliceous very 
small-small 

Mid-altitude organic and siliceous 
medium-large 

RW-13  Mid-altitude organic and calcareous or 

mixed 

RW-14  Highland (all Europe) siliceous 
incl. organic (humic) 

RW-15  Highland (all Europe) calcareous or 

mixed 

RW-17  Mediterranean lowland medium-large 

perennial 

RW-18  Mediterranean mid-altitude medium-

large perennial 

RW-19  Mediterranean very small-small perennial 

RW-20  Mediterranean temporary or intermittent 

streams 

49 

Country  Type  Standard 

7 

11 

10 

3 

5 

11 

10 

2 

1 

5 

5 

8 

8 

2 

1 

1 

2 

3 

1 

1 

1 

2 

3 

66 

23 

3 

8 

53 

21 

2 

1 

13 

11 

36 

33 

9 

2 

1 

13 

7 

3 

1 

4 

10 

5 

11 

8 

3 

4 

8 

8 

2 

1 

6 

6 

9 

9 

3 

1 

1 

4 

4 

1 

1 

1 

2 

 
 
 
3.3.3  pH: synthesis 

pH is another physico-chemical supporting element for which several countries present both lower 
and upper standards.   The former will protect against acidification whilst the latter may also be an 
additional indicator of eutrophication (where  removal of inorganic carbon leads to an increase in 
pH due to a shift in the equilibrium of the carbonate-bicarbonate buffer system).   In both cases, we 
should expect a strong type-specific effect, with the standards likely to be most relevant in soft water 
lake  and  stream  types.    Acid  neutralizing  capacity  is  also  used  to  indicate  acidification  in  such 
circumstances, but the number of countries reporting this PCSE was too low to permit comparisons. 

Relatively  few  EEA  data,  however,  are  available  for metrics  that  are  sensitive  to  acidification  and 
there is no particular reason to expect most metrics for lake phytoplankton and macrophytes or 
river phytobenthos to be sensitive to this pressure (acid-sensitive metrics for river phytobenthos are 
available in a few countries but were not intercalibrated).  For lakes we include results from a single 
country (SE) who have an acid sensitive phytoplankton metric. For this metric, the median lower 
boundary  falls  between  the  “good  and  better”  and  “less  than  good”  distributions  (Fig.  3.18) 
suggesting it is broadly consistent with good status. For rivers there are, however, sufficient data for 
acid-sensitive  invertebrate metrics  to  permit  a more  meaningful  comparison  between  standards: 
again the median value and the 25th percentile falls between the “good and better” and “less than 
good”  distributions  (Fig.  3.21),  suggesting  adequate  boundaries.        However,  as  fewer  countries 
contributed data for acid-sensitive metrics, these interpretations need to be treated with caution.  

By  contrast,  upper  pH  standards  should  align  with  eutrophication-sensitive  metrics:  Fig.  3.18, 
however, suggests that these may be too lenient although there is considerable overlap between 
the  “good  and  better”  and  “less  than  good”  distributions.    The  relevance  of  these  upper  pH 
standards, particularly for well-buffered waters, and the extent to which they are based on empirical 
evidence rather than expert judgement still needs to be assessed.    

3.4  BOD  

3.4.1 

BOD (rivers) 

There were 429 records of BOD standards from 15 countries (Figure 3.22). Many countries use the 
annual mean (“AA-EQS”) as a summary metric. 18 countries (AT, BE, BG, CY, CZ, DE, FR, HR, HU, IE, 
LT, LU, LV, PL, PT, RO, SI, SK) use a single value for each national type and none presents standards 
as a range. 

The data could be linked to 19 broad types (Figure 3.23 & Table 3.16). 12 of these (RW-00, RW-01, 
RW-02, RW-03, RW-04, RW-05, RW-08, RW-09, RW-10, RW-11, RW-14, RW-15) had type specific 
values from more than 2 countries allowing the range of standards in these types to be compared. 
4 countries (CY, FR, IE, PT) apply the same standard to all national types found in one or more of 
their river basin districts. 

The standards ranged from 1.5 mg/L (CZ) to 8 mg/L (BE), with an interquartile range of 2.9 to 5 
(Figure 3.23). 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.15).  The  relative  importance  of the  country  and  type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (52.6%) and type (9.3%) 

50 

 
 
 
In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  BOD5  based  on  classifications  for 
phytobenthos  and  macroinvertebrates  (Figure  3.24)  using  data  for  EQR  metrics  responding  to 
general degradation and eutrophication taken from the EEA SoE database (Waterbase). 

More information in Annex A4.1  

Figure 3.22: Comparison of BOD5 standards by country (single value black, minimum blue, 
maximum red). Dotted lines show interquartile range of mean or median values, (90th 
percentile=red, 75th percentile=orange, 50th percentile = blue) 

51 

 
 
 
 
 
Figure 3.23: River BOD5 standards by broad type (single value black, minimum blue, maximum 
red). “All” are standards that are not type-specific, but reported for all types in a RBD, while RW-00 
are type-specific standards for national types that do not match any of the broad types. Horizontal 
lines mark the 25th (green) and 75th (red) percentiles). 

Table 3.15: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for BOD5 (Including main and partial effect sizes, Omega squared – see 1.2.5).  

Omega sq, 

p Omega sq  Sum sq  Df 

F value 

Prob. 

Intercept 

Country 

0.53 

CodeBT 

0.09 

Residuals 

81.0 

1 

120.82 

0.00000 

121.60 

13 

13.95 

0.00000 

26.7 

10 

3.97 

0.00014 

0.58 

0.20 

65.7 

98 

52 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.24: River BOD5 standards (dotted lines) overlain on box plots showing the range of BOD5 
concentration for sites classified by phytobenthos and macro-invertebrates. (90th percentile=red, 
75th percentile=orange, median=blue) Significance of difference between groups, (phytobenthos) 
Wilcoxon p=<0.001 (invertebrates) Wilcoxon p= <0.001. 

53 

 
 
 
 
 
Table 3.16: Overview of common broad types showing the number of countries/national 
types/distinct standards for BOD5 in rivers. 

Code 

Broad type 

All 

All 

RW-00  Not assigned 

RW-01  Very large rivers 

RW-02 

Lowland siliceous medium-large 

RW-03 

Lowland siliceous very small-small 

RW-04  Lowland calcareous or mixed medium-

12 

large 

Country  Type  Standard 

4 

8 

8 

3 

3 

1 

48 

19 

4 

6 

61 

3 

15 

7 

4 

3 

11 

RW-05 

Lowland calcareous or mixed very small-
small 

10 

24 

8 

RW-
06a 

RW-
06b 

Lowland organic and siliceous very 
small-small 

Lowland organic and siliceous medium-
large 

RW-08  Mid-altitude siliceous medium-large 

RW-09  Mid-altitude siliceous very small-small 

RW-10  Mid-altitude calcareous or mixed 

1 

1 

7 

7 

10 

medium-large 

1 

1 

18 

15 

61 

1 

1 

8 

7 

13 

RW-11  Mid-altitude calcareous or mixed very 

10 

59 

13 

small-small 

RW-
12a 

Mid-altitude organic and siliceous very 
small-small 

RW-14  Highland (all Europe) siliceous 
incl. organic (humic) 

RW-15  Highland (all Europe) calcareous or 

mixed 

RW-16  Glacial rivers (all Europe) 

RW-17  Mediterranean lowland medium-large 

perennial 

RW-18  Mediterranean mid-altitude medium-

large perennial 

RW-19  Mediterranean very small-small perennial 

RW-20  Mediterranean temporary or intermittent 

1 

3 

5 

1 

2 

1 

1 

2 

streams 

1 

4 

14 

3 

4 

1 

4 

10 

1 

3 

7 

3 

3 

1 

3 

3 

3.4.2  BOD: synthesis 

5-day BOD is widely used as a measure of the scale of organic pollution impacts in rivers, mostly as 
annual  averages.    A  broad  trend  was  apparent  with  lowland  rivers  generally  having  higher 

54 

 
 
 
thresholds  than  mid-altitude  rivers  which,  in  turn,  had  higher  thresholds  than  highland  and 
Mediterranean rivers (Fig. 3.23).  Despite this, Member State still had a greater influence on threshold 
values than type (Table 3.15).    

Both phytobenthos and macroinvertebrates respond to BOD5, although the effect in the latter was 
stronger than that in the former (Fig. 3.24).   However, the median position of national standards in 
relation to these data (above the distributions of even the worse than good sites) suggests that they 
many may be too lenient.   

3.5  Ammonium-N  

3.5.1  Ammonium-N (rivers)  

There were 457 records of BOD standards from 21 countries (Figure 3.25). Many countries use the 
annual mean (“AA-EQS”) as a summary metric. 21 countries (BE, BG, CY, CZ, DE, EE, ES, FR, GR, HR, 
HU, IT, LT, LU, LV, NO, PL, PT, RO, SK, UK) use a single value for each national type and 1 country 
(PT) present standards as a range. 

The data could be linked to 19 broad types (Figure 3.26 & Table 3.18). 12 of these (RW-00, RW-01, 
RW-02, RW-03, RW-04, RW-05, RW-08, RW-09, RW-10, RW-11, RW-14, RW-15) had type specific 
values from more than 2 countries allowing the range of standards in these types to be compared. 
5 countries (CY, FR, GR, IT, PT) apply the same standard to all national types found in one or more 
of their river basin districts. 

The standards ranged from 0.05 mg N/L (HR) to 1.4 mg N/L (RO) with an interquartile range of 0.1 
to 0.47 mg N/L (Figure 3.26). 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards (Table  3.17).  The  relative  importance  of the  country and  type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (81.2%) and type (4%) 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of Ammonium N based on classifications for 
phytobenthos  and  macroinvertebrates  (Figure  3.27)  using  data  taken  from  the  EEA-WISE-SOE-
DATABASE. 

More information in Annex A5.1. 

55 

 
 
 
 
Figure 3.25: Ammonium-N standards by country (single value black, minimum blue, maximum red 
symbols, dotted lines show interquartile range of mean or median values, (90th percentile=red, 75th 
percentile=orange, 50th percentile = blue). 

56 

 
 
 
 
Figure 3.26: Ammonium-N standards by broad type (single value black, minimum blue, maximum 
red). “All” are standards that are not type-specific, but reported for all types in a RBD, while RW-00 
are type-specific standards for national types that do not match any of the broad types.  Horizontal 
lines mark the 25th (green) and 75th (red) percentiles. 

Table 3.17: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for log Ammonium-N (Including main and partial effect sizes, Omega squared – 
see 1.2.5.)  

Intercept 

Country 

CodeBT 

Residuals 

Omega sq, 

p Omega sq 

Sum sq  Df 

F value 

Prob. 

0.5 

1 

5.3 

0.02402 

0.81 

0.04 

0.85 

0.21 

53.7 

14 

41.33 

0.00000 

3.5 

10 

3.75 

0.00038 

7.28 

78 

57 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.27: River Ammonium-N standards (dotted lines) overlain on box plots showing the range of 
Ammonium N concentration for sites classified by phytobenthos and macro-invertebrates. (90th 
percentile=red, 75th percentile=orange, median=blue).  Significance of difference between groups, 
(phytobenthos) Wilcoxon p=<0.001 (invertebrates) Wilcoxon p= <0.001. 

58 

 
 
 
 
Table 3.18: Overview of common broad types showing the number of countries/national 
types/distinct standards for Ammonium-N in rivers. 

Code 

Broad type 

All 

All 

RW-00  Not assigned 

RW-01  Very large rivers 

RW-02 

Lowland siliceous medium-large 

RW-03 

Lowland siliceous very small-small 

RW-04  Lowland calcareous or mixed medium-

large 

RW-05 

Lowland calcareous or mixed very small-
small 

RW-
06a 

RW-
06b 

Lowland organic and siliceous very 
small-small 

Lowland organic and siliceous medium-
large 

RW-08  Mid-altitude siliceous medium-large 

RW-09  Mid-altitude siliceous very small-small 

RW-10  Mid-altitude calcareous or mixed 

medium-large 

RW-11  Mid-altitude calcareous or mixed very 

small-small 

RW-
12a 

RW-
12b 

Mid-altitude organic and siliceous very 
small-small 

Mid-altitude organic and siliceous 
medium-large 

RW-14  Highland (all Europe) siliceous 
incl. organic (humic) 

RW-15  Highland (all Europe) calcareous or 

mixed 

RW-17  Mediterranean lowland medium-large 

perennial 

RW-18  Mediterranean mid-altitude medium-

large perennial 

RW-19  Mediterranean very small-small perennial 

RW-20  Mediterranean temporary or intermittent 

streams 

Country  Type  Standard 

5 

9 

10 

3 

3 

11 

10 

1 

1 

6 

6 

7 

8 

2 

1 

3 

3 

1 

1 

1 

2 

1 

75 

21 

4 

6 

53 

19 

1 

1 

10 

11 

35 

36 

3 

1 

4 

7 

3 

1 

4 

10 

4 

19 

12 

4 

4 

13 

10 

1 

1 

5 

6 

9 

11 

2 

1 

3 

4 

2 

1 

2 

4 

59 

 
 
 
3.5.2  Ammonium-N: synthesis 

Ammonium-N is another widely used supporting element in rivers.  It is not, itself, toxic but toxicity 
is  exerted  through  the  action  of  unionized  ammonia  on  cells  and  the  equilibrium  between 
ammonium  and  ammonia  depends  upon  pH  (more  ammonia  at  high  pH).      However,  too  few 
countries  measure  unionized  ammonia  for  this  to  be  the  basis  of  EU-wide  comparisons. 
Ammonium-N  can  also,  directly  or  indirectly  (via  nitrification),  be  a  source  of  nitrogen  with  the 
potential to stimulate plant and algal growth. 

Although type accounted for only a small amount of variation in ammonium-N standards relative 
to country (Table 3.17), lowland rivers tended to have higher ammonium standards than mid altitude 
and highland rivers.   

There  was  a  significant  difference  in  ammonium-N  concentrations  between  sites  with  river 
phytobenthos  and  invertebrates  at  “good  or  better”  status  compared  with  “moderate or  worse” 
(Figure  3.27).    The  effect  was  more  pronounced  for  invertebrates.  However,  in  both  cases,  the 
median  standards  were  relatively  lenient  compared  with  the  data  distributions  which,  for  both 
phytobenthos and invertebrates, suggested thresholds of less than 0.1 mg/L. 

3.6  Nitrogen  

As well as the information included in previous sections, this section also presents a comparison 
with results presented in earlier reports in order to show any changes in nutrient standards that 
have occurred during this period.   

3.6.1  Nitrate  

There  were  484  records  of  nitrate  standards  (2009  dataset)  from  18  countries  which  can  be 
compared with the values reported in a 2014 survey (Phillips and Pitt 2015) (Figure 3.28). The majority 
of countries use the annual mean (“AA-EQS”) as a summary metric. 18 countries (AT, BE, BG, CY, 
CZ, ES, FR, GR, HR, IT, LT, LU, LV, PL, PT, RO, SI, SK) use a single value for each national type and 2 
countries (BG, PT) present standards as a range. 

The data could be linked to 18 broad types (Figure 3.29 & Table 3.19). 12 of these (RW-00, RW-01, 
RW-04, RW-05, RW-08,  RW-09,  RW-10,  RW-11, RW-14,  RW-15, RW-17, RW-20) had type  specific 
values from more than 2 countries allowing the range of standards in these types to be compared. 
6 countries (BG, CY, FR, GR, IT, PT) apply the same standard to all national types found in one or 
more of their river basin districts. 

The standards ranged from 0.05 mg N/L (BG) to 11.29 mg N/L (FR) with an interquartile range of 1.3 
to 5.1 mg N/L (Figure 3.29). 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.19).  The  relative  importance  of the  country  and  type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (69.6%) and type (8.1%). 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of Nitrate as N based on classifications for 
phytobenthos (Figure 3.30) using data taken from the EEA-WISE-SOE-DATABASE. 

More information in Annex A6.1. 

60 

 
 
 
Figure 3.28: Comparison of river Nitrate as N standards by country. Green symbols represent values 
reported in 2014, others colours those reported in 2019 (single value black, minimum blue, 
maximum red symbols;   dotted lines show interquartile range of mean or median values (90th 
percentile=red, 75th percentile=orange, 50th percentile = blue) 

61 

 
 
 
 
 
 
Figure 3.29: Nitrate as N standards by broad type (single value black, minimum blue, maximum 
red). “All” are standards that are not type-specific, but reported for all types in a RBD, while RW-00 
are type-specific standards for national types that do not match any of the broad types. Horizontal 
lines mark 25th (green) and 75th (red) percentile. 

Table 3.19: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for Nitrate as N (Including main and partial effect sizes, Omega squared – see 
1.2.5). 

Omega sq, 

p Omega sq 

Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

CodeBT 

Residuals 

0.70 

0.08 

0.76 

0.27 

139.6 

262.3 

36.9 

1 

12 

10 

190.40 

29.81 

5.033 

0 

0 

0 

64.5 

88 

62 

 
 
 
 
 
 
 
 
 
 
   
Figure 3.30 (previous page): River Nitrate (as N) standards (dotted lines) overlain on box plots 
showing the range of Nitrate as N concentration for sites classified by phytobenthos and macro-
invertebrates as good status or better (left-hand box) or moderate status and worse (right hand 
box). (90th percentile=red, 75th percentile=orange, median=blue, grouped by aggregated river type 
(see Methods section 1.2.5, Table 1.2 for translation between the broad types shown in 3.29 and the 
aggregated broad types shown here). Wilcoxon test statistic shows significance of type specific 
differences in distribution of observed data. 

63 

 
 
 
Table 3.20: Overview of common broad types and their associated aggregated types showing the 
number of countries/national types/distinct standards for Nitrate as N in rivers. 

Associated 
aggregated 
type code  Country  Type  Standard 

All 

RA-00 

RA-01 

RA-04 

Code 

Broad type 

All 

Not assigned 

All 

RW-
00 

RW-01  Very large rivers 

RW-
02 

RW-
03 

RW-
04 

RW-
05 

RW-
06a 

RW-
06b 

RW-
08 

RW-
09 

Lowland siliceous medium-large 

Lowland siliceous very small-small 

RA-05 

Lowland calcareous or mixed 
medium-large 

Lowland calcareous or mixed very 
small-small 

Lowland organic and siliceous very 
small-small 

Lowland organic and siliceous 
medium-large 

RA-02 

RA-03 

RA-05 

RA-04 

Mid-altitude siliceous medium-large 

RA-08 

Mid-altitude siliceous very small-
small 

RA-09 

RW-10  Mid-altitude calcareous or mixed 

RA-06 

medium-large 

RW-11  Mid-altitude calcareous or mixed 

RA-07 

very small-small 

RW-14  Highland (all Europe) siliceous 
incl. organic (humic) 

RA-10 

RW-15  Highland (all Europe) calcareous or 

RA-10 

mixed 

RW-16  Glacial rivers (all Europe) 

RW-17  Mediterranean lowland medium-

large perennial 

RW-18  Mediterranean mid-altitude 

medium-large perennial 

RA-10 

RA-11 

RA-11 

RW-19  Mediterranean very small-small 

RA-12 

perennial 

64 

6 

9 

6 

2 

2 

11 

9 

1 

1 

6 

6 

8 

8 

3 

5 

1 

3 

1 

2 

1 

86 

13 

3 

4 

51 

17 

1 

1 

17 

13 

56 

50 

4 

14 

3 

7 

1 

6 

5 

22 

10 

2 

1 

15 

10 

1 

1 

8 

8 

15 

18 

3 

7 

2 

4 

1 

3 

 
 
 
RW-
20 

Mediterranean temporary or 
intermittent streams 

RA-10 

3 

9 

5 

3.6.2  Total N  

3.6.2.1 

Total N (lakes) 

There were 335 records of Total N (TN) standards from 12 countries (2019 dataset) which can be 
compared  with  the  values  reported  in  a  2014  survey  (Phillips  and  Pitt  2015)  (Figure  3.31).  Most 
countries use the annual mean (“AA-EQS”) as a summary metric. 12 countries (BE, BG, EE, FI, HU, 
LT, LV, NL, NO, PL, PT, RO) use a single value for each national type and none presents standards 
as a range. 

The data could be linked to 9 broad types (Figure 3.32 & Table 3.24). 7 of these (LW-00, LW-01, 
LW-02, LW-03, LW-04, LW-05, LW-06) had type specific values from more than 2 countries allowing 
the range of standards in these types to be compared.  

The standards ranged from 0.2 mg N/L (NO) to 5 mg N/L (RO) with an interquartile range of 0.7 to 
1.5 mg N/L (Figure 3.32). 

A two-way analysis of variance was used to test the significance of country and type differences 
between standards  (Table 3.24). The  relative  importance  of the  country  and  type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (40.9%) and type (9.1%). 

In order to link the  reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  TN  based  on  classifications  for 
phytoplankton (Figure 3.33) using data taken from the EEA-WISE-SOE-DATABASE. 

More information in Annex A6.2.  

65 

 
 
 
 
Figure 3.31 Comparison of lake TN standards by country. Green symbols represent values reported 
in 2014, others colours those reported in 2019 (single value black, minimum blue, maximum red 
symbols; dotted lines show interquartile range of mean or median values, (90th percentile=red, 75th 
percentile=orange, 50th percentile = blue). 

66 

 
 
 
 
Figure 3.32: TN standards by broad type (single value black, minimum blue, maximum red). “All” 
are standards that are not type-specific, but reported for all types in a RBD, while RW-00 are type-
specific standards for national types that do not match any of the broad types.  Horizontal lines 
mark 25th (green) and 75th (red) percentiles. 

Table 3.21: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for log TN (Including main and partial effect sizes, Omega squared – see 1.2.5.) 

Omega sq, 

p Omega sq 

Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

CodeBT 

Residuals 

0.41 

0.09 

0.45 

0.15 

0.1 

8.5 

2.5 

1 

0.582 

0.453 

10 

4.191 

0.002 

5 

2.414 

0.067 

4.7 

23 

67 

 
 
 
 
 
 
 
 
 
 
  
Figure 3.33: Lake TN standards (dotted lines) overlain on box plots showing the range of TN 
concentration for sites classified by phytoplankton and macrophytes. (90th percentile=red, 75th 
percentile=orange, median=blue), including standards based on percentiles, AA-EQS, GS Mean and 
seasonal mean, grouped by aggregated lake type (see Methods section 1.2.5, Table 1.2 for 
translation between the broad types shown in 3.32 and the aggregated broad types shown here). 
Wilcocon test statistic shows significance of type specific differences in distribution of observed data. 

Table 3.22: Overview of common broad types and their associated aggregated types showing the 
number of Member State/national types/distinct standards for TN. 

68 

 
 
 
 
Associated 
aggregated 
type code 

Country  Type  Standard 

Broad type 

Broad 
type 
code 
All 
All 
LW-00  Not assigned 
LW-01 

LW-04 

LW-02 
LW-03 

Very  large  lakes  shallow  or  deep 
and stratified (all Europe) 
Lowland siliceous 
Lowland  stratified  calcareous  or 
mixed 
Lowland calcareous or mixed very 
shallow or unstratified 
Lowland  organic 
siliceous 
Lowland  organic 
calcareous or mixed 
LW-07  Mid-altitude siliceous 
LW-08  Mid-altitude calcareous or mixed 
LW-09  Mid-altitude  organic  (humic)  and 

(humic)  and 

(humic)  and 

LW-06 

LW-05 

All 
LA-00 
LA-01 

LA-05 
LA-03 

LA-02 

LA-04 

LA-03 

LA-06 
LA-03 
LA-04 

LW-11 

Siliceous 
Highland  Siliceous 
incl. organic (humic) 

(all  Europe) 

LA-07 

10 
3 

4 
8 

8 

3 

5 

1 
1 
1 

2 

32 
4 

5 
15 

16 

6 

7 

2 
2 
1 

4 

18 
4 

3 
9 

6 

5 

4 

2 
1 
1 

3 

3.6.2.2  Total N (rivers) 

There were 410 records of TN standards for rivers from 13 countries in the 2019 dataset which can 
be compared with the values reported in a 2014 survey  (Phillips and Pitt 2015) (Figure 3.31). The 
majority of countries use the annual mean (“AA-EQS”) as a summary metric. 13 countries (BE, BG, 
EE, FI, HR, HU, LT, LV, NL, NO, PL, RO, SK) use a single value for each national type and 1 country 
(BG) presents standards as a range. 

The data could be linked to 20 broad types (Figure 3.32 & Table 3.23). 13 of these (RW-00, RW-01, 
RW-02, RW-03, RW-04, RW-05, RW-06a, RW-06b, RW-08, RW-09, RW-10, RW-11, RW-14) had type 
specific values from more than 2 countries allowing the range of standards in these types to be 
compared. 1 country (BG) applies the same standard to all national types found in one or more of 
their river basin districts. 

The standards ranged from 0.2 mg N/L (BG) to 35 mg N/L (RO) with an interquartile range of 0.9 
to 3.5 mg N/L (Figure 3.32). 

A two-way analysis of variance was used to test the significance of country and type differences 
between standards  (Table  3.23).  The  relative  importance  of the  country  and type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (74.1%) and type (4.7%).  However, this result should be interpreted with caution 
because data do not fulfil all requirements of normality.  

69 

 
 
 
 
 
 
In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  TN  based  on  classifications  for 
phytobenthos (Figure 3.33) using data taken from the EEA-WISE-SOE-DATABASE. 

More information in Annex A6.2.2. 

Figure 3.34: Comparison of river TN standards by country. Green symbols represent values reported 
in  2014,  others  colours  those  reported  in  2019  (single  value  black,  minimum  blue,  maximum  red 
symbols; dotted lines show interquartile range of mean or median values, (90th percentile=red, 75th 
percentile=orange, 50th percentile = blue). 

70 

 
 
 
 
 
 
Figure 3.35: TN standards by broad type (single value black, minimum blue, maximum red). “All” 
are standards that are not type-specific, but reported for all types in a RBD, while RW-00 are type-
specific standards for national types that do not match any of the broad types. Horizontal lines 
mark 25th (green) and 75th (red) percentiles. 

Table 3.23: Analysis of variance for factorial model relating country and broad type to Member 
State boundary values for log TN (Including main and partial effect sizes, Omega squared – see 
1.2.5). 

Omega sq, 

p Omega sq 

Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

CodeBT 

Residuals 

0.74 

0.05 

0.78 

0.18 

2.4 

48.2 

4.4 

1 

12 

11 

17.800 

0.000 

29.830 

0.000 

2.978 

0.002 

10.1 

75 

71 

 
 
 
 
 
 
 
 
 
 
  
Figure 3.36 (previous page): River TN standards (dotted lines) overlain on box plots showing the 
range of TN concentration for sites classified by phytobenthos and macro-invertebrates. (90th 
percentile=red, 75th percentile=orange, median=blue), including standards based on AA-EQS, 
Median, Seasonal average, summer (??) percentile, grouped by aggregated river type (see Methods 
section 1.2.5, Table 1.2 for translation between the broad types shown in 3.35 and the aggregated 
broad types shown here). Wilcocon test statistic shows significance of type specific differences in 
distribution of observed data. 

72 

 
 
 
Table 3.24: Overview of common broad types and their associated aggregated types showing the 
number of countries/national types/distinct standards for r TN in rivers. 

Associated 
type code  Country  Type  Standards 

Code 

Broad type 

All 

All 

RW-00  Not assigned 

RW-01 

Very large rivers 

RW-02 

Lowland siliceous medium-large 

RW-03 

RW-04 

RW-05 

Lowland siliceous very small-
small 

Lowland calcareous or mixed 
medium-large 

Lowland calcareous or mixed 
very small-small 

All 

RA-00 

RA-01 

RA-04 

RA-05 

RA-02 

RA-03 

RW-06a  Lowland organic and siliceous 

RA-05 

very small-small 

RW-06b  Lowland organic and siliceous 

RA-04 

medium-large 

RW-08  Mid-altitude siliceous medium-

RA-08 

large 

RW-09  Mid-altitude siliceous very small-

RA-09 

small 

RW-10  Mid-altitude calcareous or mixed 

RA-06 

medium-large 

RW-11  Mid-altitude calcareous or mixed 

RA-07 

very small-small 

RW-12a  Mid-altitude organic and 
siliceous very small-small 

RA-09 

RW-12b  Mid-altitude organic and 

RA-08 

siliceous medium-large 

RW-13  Mid-altitude organic and 

RA-06 

calcareous or mixed 

RW-14 

RW-15 

Highland (all Europe) siliceous 
incl. organic (humic) 

Highland (all Europe) calcareous 
or mixed 

RA-10 

RA-10 

RW-17  Mediterranean lowland medium-

RA-11 

large perennial 

RW-18  Mediterranean mid-altitude 

RA-11 

medium-large perennial 

73 

1 

8 

10 

5 

6 

8 

8 

3 

3 

3 

3 

6 

5 

1 

1 

1 

3 

2 

1 

1 

1 

26 

25 

7 

14 

46 

18 

7 

3 

9 

13 

21 

18 

13 

2 

1 

10 

3 

3 

1 

1 

13 

13 

7 

7 

12 

8 

4 

3 

4 

4 

7 

9 

3 

1 

1 

3 

3 

2 

1 

 
 
RW-19  Mediterranean very small-small 

RA-12 

perennial 

RW-20  Mediterranean temporary or 
intermittent streams 

RA-12 

1 

2 

4 

10 

2 

3 

3.6.3 

Synthesis: nitrogen 

Whilst  total  nitrogen  (TN)  is  the  preferred  variable  for  assessing  nitrogen  in  lakes,  there  is  less 
consensus on appropriate variables for assessing nitrogen in rivers.  10 countries use TN whilst three 
use nitrate-N and nine have standards for both TN and nitrate-N.  

For  lakes,  type  effects  standards  (Fig.  3.32),  with  lowland  calcareous  lakes  generally  having  the 
highest thresholds, followed by lowland siliceous humic lakes whilst siliceous clear lakes generally 
had the lowest thresholds.  There was, however, a strong country effect overriding the significance 
of the effect of type (Table 3.24).  Standards showed better agreement with the EEA data than was 
the case for many other supporting elements although median values for standards for a few types 
appear to be too lenient.  This is particularly apparent for LA-04 (lowland-mid-altitude, humic and 
siliceous) but possibly also relevant for LA-02 (lowland, calcareous, very shallow and LA-07 (highland 
lakes) (Fig. 3.33).   

Type also has an effect on TN and nitrate-N in rivers (Figs 3.32 & 3.29) though less obviously than 
was the case for lakes.   Once again, calcareous rivers tend to have higher standards than those 
draining catchments with silicious bedrocks.  Type seems to determine the extent to which higher 
nitrogen is associated with lower status (Fig. 3.35): strong effects are seen in RA-03, RA-04, RA-05 
(lowland) and RA-09 (mid-altitude) for TN, for example, but not in the RA-01 (large rivers), RA-07 & 
RA-08  (mid-altitude  types),  amongst  others.    For  nitrate-N,  Types  RA-04,  RA-05  and  RA-12 
(Mediterranean) indicate strong effects whilst RA-01, RA-02, RA-07 and RA-08 do not (Fig. 3.30).  
These results highlight the need for caution when interpreting the role of nitrogen and we suspect 
that interactions with phosphorus are the most likely reason for these differences.  As for lakes, N 
standards are mostly in line with the evidence from the EEA data though there are exceptions (RA-
05 for TN; RA-09, RA-10 for nitrate-N).    

Most lake total nitrogen boundary values have remained unchanged since the 2014 report although 
one value from BE was higher (5 mg N/L) than the value reported in 2014 (4 mg N/L). One country 
(PT) reported lower values, while two (BE, RO) reported higher values. 

The range of river nitrogen standards reported in this survey was similar to that reported in 2014, 
with high maximum values of 11.3 mg N/L for nitrate nitrogen and 35 mg N/L for total nitrogen.   
For nitrate most countries reported the same boundary values as in 2014, 3 countries (GR, PL, SK) 
reported lower boundary values while 2 (AT, RO) included additional higher values.  There were 
very few changes for total nitrogen, with only PL reporting lower boundary values while BE and HU 
reported additional higher boundary values. 

74 

 
 
 
 
3.7 

Phosphorus  

3.7.1  Orthophosphate (rivers) 

There  were  404  records from  18  countries  in  the 2019  dataset  which  can  be  compared  with the 
values reported in a 2014 survey (Phillips and Pitt 2015) (Figure 3.37). The majority of countries use 
the annual mean (“AA-EQS”) as a summary metric. 18 countries (AT, BE, BG, CY, CZ, DE, ES, FR, GR, 
HR,  HU,  IE,  LT,  LU,  LV,  PL,  RO,  SK)  use a single  value  for  each national  type and  1  country  (BG) 
present standards as a range. 

The data could be linked to 18 broad types (Figure 3.38 & Table 3.26). 10 of these (RW-00, RW-01, 
RW-03, RW-04, RW-05, RW-08, RW-09, RW-10, RW-11, RW-15) had type specific values from more 
than 2 countries allowing the range of standards in these types to be compared. 5 countries (BG, 
CY, FR, GR, IE) apply the same standard to all national types found in one or more of their river 
basin districts. 

The standards ranged from 10 µg P/L (BG) to 500 µg P/L (FR) with an interquartile range of 60 to 
160 µg P/L(Figure 3.38). 

A two-way analysis of variance was used to test the significance of country and type differences 
between standards  (Table  3.25).  The  relative  importance  of the  country  and type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each  of  country  (51.5%)  and  type  (9%).    However,  this  result  should  be  interpreted  with  caution 
because data do not fulfil all requirements of normality.  

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of Orthophosphate based on classifications 
for phytobenthos (Figure 3.39) using data taken from the EEA-WISE-SOE-DATABASE. 

More information in Annex A6.3.  

75 

 
 
 
Figure  3.37:  Comparison  of  river  Orthophosphate  standards  by  country.  Green  symbols  represent 
values  reported  in 2014,  others colours  those  reported  in 2019  (single value  black,  minimum  blue, 
maximum  red  symbols.  Dotted  lines  show  interquartile  range  of  mean  or  median  values,  (90th 
percentile=red, 75th percentile=orange, 50th percentile = blue). 

76 

 
 
 
 
Figure 3.38: Orthophosphate standards by broad type (single value black, minimum blue, 
maximum red). “All” are standards that are not type-specific, but reported for all types in a RBD, 
while RW-00 are type-specific standards for national types that do not match any of the broad 
types. Horizontal lines mark 25th (green) and 75th (red) percentiles, (including standards based on 
percentiles, AA-EQS and Median). 

Table 3.25: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for Orthophosphate (Including main and partial effect sizes, Omega squared – see 
1.2.5). 

Omega sq,  p Omega sq 

Sum sq 

Df 

Intercept 

Country 

0.52 

CodeBT 

0.09 

0.57 

0.19 

Residuals 

335188.9 

408722.2 

92623.6 

214710.7 

1 

11 

8 

62 

F 
value 

Prob. 

96.789  0.000 

10.729  0.000 

3.343 

0.003 

77 

 
 
 
 
 
 
 
 
 
 
  
Figure 3.39: River Orthophosphate standards (dotted lines) overlain on box plots showing the range 
of Orthophosphate concentration for sites classified by phytobenthos. (90th percentile=red, 75th 
percentile=orange, median=blue), including standards based on percentile, AA-EQS and Median, 
grouped by aggregated river type (see Methods section 1.2.5, Table 1.2 for translation between the 

78 

 
 
 
broad types shown in 3.38 and the aggregated broad types shown here). Wilcocon test statistic 
shows significance of type specific differences in distribution of observed data. 

Table 3.26: Overview of common broad types and their associated aggregated types showing the 
number of countries/national types/distinct standards for Orthophosphate in rivers. 

Associated 
aggregated 
type code 

All 

RA-00 

RA-01 

RA-04 

Code 

Broad type 

All 

Not assigned 

All 

RW-
00 

RW-01  Very large rivers 

RW-
02 

RW-
03 

RW-
04 

RW-
05 

RW-
06a 

RW-
06b 

RW-
08 

RW-
09 

Lowland siliceous medium-large 

Lowland siliceous very small-small 

RA-05 

Lowland calcareous or mixed 
medium-large 

Lowland calcareous or mixed very 
small-small 

Lowland organic and siliceous very 
small-small 

Lowland organic and siliceous 
medium-large 

RA-02 

RA-03 

RA-05 

RA-04 

Mid-altitude siliceous medium-large 

RA-08 

Mid-altitude siliceous very small-
small 

RA-09 

RW-10  Mid-altitude calcareous or mixed 

RA-06 

medium-large 

RW-11  Mid-altitude calcareous or mixed 

RA-07 

very small-small 

RW-
12a 

Mid-altitude organic and siliceous 
very small-small 

RW-14  Highland (all Europe) siliceous 
incl. organic (humic) 

RA-09 

RA-10 

RW-15  Highland (all Europe) calcareous or 

RA-10 

mixed 

RW-17  Mediterranean lowland medium-

RA-11 

large perennial 

RW-18  Mediterranean mid-altitude 

RA-11 

medium-large perennial 

79 

Country  Type  Standard 

5 

8 

7 

2 

3 

1 

99 

16 

3 

6 

5 

22 

9 

3 

5 

10 

37 

12 

9 

1 

1 

5 

5 

8 

8 

1 

2 

3 

1 

1 

15 

1 

1 

7 

9 

33 

33 

1 

3 

7 

3 

1 

8 

1 

1 

4 

5 

14 

11 

1 

2 

2 

2 

1 

 
 
RW-19  Mediterranean very small-small 

RA-12 

perennial 

RW-
20 

Mediterranean temporary or 
intermittent streams 

RA-12 

1 

2 

4 

7 

2 

4 

3.7.2  Total P  

3.7.2.1 

Total P (lakes) 

There were 866 records from 24 countries in the 2019 dataset which can be compared with the 
values reported in a 2014 survey (Phillips and Pitt 2015) (Figure 3.40). The majority of countries use 
the annual mean (“AA-EQS”) as a summary metric. 20 countries (AT, BE, BG, CY, CZ, EE, ES, FI, HR, 
HU, IE, LT, LV, NL, NO, PL, PT, RO, SI, UK) use a single value for each national type and 7 countries 
(AT,  DE, FR, HR, IT, SE, UK) present standards as a range. 

The data could be linked to 14 broad types (Figure 3.41 & Table 3.28). 12 of these (LW-00, LW-01, 
LW-02,  LW-03,  LW-04,  LW-05,  LW-06,  LW-07,  LW-08,  LW-11,  LW-12,  LW-14)  had  type  specific 
values from more than 2 countries allowing the range of standards in these types to be compared. 
5 countries (CY, HR, IE, PT, UK) apply the same standard to all national types found in one or more 
of their river basin districts. 

The standards ranged from 3 µg P/L (SE) to 400 µg P/L (HU) with an interquartile range of 15 to 45 
µg P/L (Figure 3.41). 

A two-way analysis of variance was used to test the significance of country and type differences 
between standards (Table  3.27). The  relative  importance  of the  country and type  effects  can  be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (46.6%) and type (16.5%). 

In order to link the reported standards to relevant BQEs their interquartile  ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  TP  based  on  classifications  for 
phytoplankton (Figure 3.42) using data taken from the EEA-WISE-SOE-DATABASE. 

More information in Annex A6.4.  

80 

 
 
 
 
 
 
Figure 3.40: Comparison of lake TP standards by country. Green symbols represent values reported 
in 2014, others colours those reported in 2019 (single value black, minimum blue, maximum red 
symbols. Dotted lines show interquartile range of mean or median values, (90th percentile=red, 
75th percentile=orange, 50th percentile = blue). 

81 

 
 
 
 
Figure 3.41: TP standards by broad type (single value black, minimum blue, maximum red). “All” are 
standards that are not type-specific, but reported for all types in a RBD, while LW-00 are type-
specific standards for national types that do not match any of the broad types. Horizontal lines 
mark 25th (green) and 75th (red) percentiles, (including standards based on AA-EQS, Median, GS 
Mean, seasonal average and percentile). 

Table 3.27: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for log TP (Including main and partial effect sizes and mega squared – see 1.2.5.) 

Omega sq, 

p Omega sq  Sum sq  Df 

F value 

Prob. 

Intercept 

Country 

0.47 

CodeBT 

0.17 

Residuals 

33.5 

1 

212.806 

0.56 

0.31 

36.0 

20 

11.433 

13.2 

10 

8.401 

0 

0 

0 

21.1 

134 

82 

 
 
 
 
 
 
 
 
 
 
  
Figure 3.42: Lake TP standards (dotted lines) overlain on box plots showing the range of TP 
concentration for sites classified by phytoplankton and macrophytes. (90th percentile=red, 75th 
percentile=orange, median=blue), including standards based on AA-EQS, Median, GS Mean, 
seasonal average and percentiles, grouped by aggregated lake type (see Methods section 1.2.5, 
Table 1.2 for translation between the broad types shown in 3.41 and the aggregated broad types 
shown here). Wilcoxon test statistic shows significance of type specific differences in distribution of 
observed data. 

Table 3.28: Overview of common broad types and their associated aggregated types showing the 
number of countries/national types/distinct standards for TP in lakes. 

83 

 
 
 
 
All 

LW-
00 

LW-
01 

LW-
02 

LW-
03 

LW-
04 

LW-
05 

LW-
06 

LW-
07 

LW-
08 

LW-
09 

LW-
10 

Code  Broad type 

All 

Not assigned 

Very large lakes shallow or deep and stratified 
(all Europe) 

Lowland siliceous 

Lowland stratified calcareous or mixed 

LA-03 

Lowland calcareous or mixed very shallow or 
unstratified 

LA-02 

Lowland organic (humic) and siliceous 

LA-04 

Lowland organic (humic) and calcareous or 
mixed 

Mid-altitude siliceous 

LA-03 

LA-06 

Associated 
aggregated 
type code 

All 

LA-00 

LA-01 

LA-05 

Country  Type  Standard 

5 

18 

7 

7 

12 

14 

4 

6 

8 

1 

6 

109 

48 

13 

9 

15 

11 

23 

14 

24 

15 

9 

7 

19 

5 

6 

8 

Mid-altitude calcareous or mixed 

LA-03 

10 

24 

16 

Mid-altitude organic (humic) and Siliceous 

LA-04 

Mid-altitude organic (humic) and calcareous or 
mixed 

LA-03 

LW-11  Highland Siliceous (all Europe) incl. organic 

LA-07 

(humic) 

Highland calcareous or mixed (all Europe) 
incl. organic (humic) 

Mediterranean small-large siliceous 

LA-07 

LA-08 

Mediterranean small-large calcareous or mixed 

LA-08 

LW-
12 

LW-
13 

LW-
14 

3.7.2.2 

Total P (rivers) 

2 

2 

6 

3 

1 

3 

4 

3 

18 

6 

2 

9 

2 

1 

8 

5 

2 

6 

There were 615 records  from 23 countries in the 2019 dataset which can be compared with the 
values reported in a 2014 survey (Phillips and Pitt 2015) (Figure 3.43). The majority of countries use 
the annual mean (“AA-EQS”) as a summary metric. 22 countries (BE, BG, CY, CZ, DE, EE, FI, FR, GR, 
HR, HU, IT, LT, LU, LV, NL, NO, PL, PT, RO, SI, SK) use a single value for each national type and 2 
countries (BG, SE) present standards as a range, although they do not provide a reason. 

The data could be linked to 20 broad types (Figure 3.44 & Table 3.30). 17 of these (RW-00, RW-01, 
RW-02, RW-03, RW-04, RW-05, RW-06a, RW-06b, RW-08, RW-09, RW-10, RW-11, RW-12a, RW-14, 

84 

 
 
RW-15, RW-17, RW-20) had type specific values from more than 2 countries allowing the range of 
standards  in  these  types  to  be  compared.  6  countries  (BG,  CY,  FR,  GR,  IT,  PT)  apply  the  same 
standard to all national types found in one or more of their river basin districts. 

The standards ranged from 6 µg P/L (SE) to 1000 µg P/L (BE) with an interquartile range of 44 to 
200 µg P/L (Figure 3.44). 

A two-way analysis of variance was used to test the significance of country and type differences 
between standards  (Table  3.29).  The  relative  importance  of the  country  and type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (68.5%) and type (4.8%). However, this result should be interpreted with caution 
because data do not fulfil all requirements of normality.  

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of TP based on classifications for phytobenthos 
(Figure 3.45) using data taken from the EEA-WISE-SOE-DATABASE. 

More information in Annex A6.4. 

Figure 3.43: Comparison of river TP standards by country. Green symbols represent values reported 
in  2014,  others  colours  those  reported  in  2019  (single  value  black,  minimum  blue,  maximum  red 
symbols. Dotted lines show interquartile range of mean or median values, (90th percentile=red, 75th 
percentile=orange, 50th percentile = blue). 

85 

 
 
 
 
 
 
Figure 3.44: TP standards by broad type (single value black, minimum blue, maximum red).  
Horizontal lines mark 25th (green) and 75th (red) percentiles, (including standards based on 
percentiles, AA-EQS, Median, seasonal average, summer (??). 

Table 3.29: Analysis of variance for factorial model relating country and broad type to Member 
State boundary values for TP (Including main and partial effect sizes, Omega squared – see 1.2.5). 

Omega 
sq, 

p Omega 
sq 

Sum sq 

Df 

F value  Prob. 

Intercept 

Country 

0.68 

0.72 

CodeBT 

0.05 

0.15 

Residuals 

759270.1 

2101520.3 

210484.8 

633263.7 

166.658 

25.627 

3.080 

0 

0 

0 

1 

18 

15 

139 

86 

 
 
 
 
 
 
 
 
 
 
  
Figure 3.45 (previous page): River TP standards (dotted lines) overlain on box plots showing the 
range of TP concentration for sites classified by phytobenthos and macro-invertebrates. (90th 
percentile=red, 75th percentile=orange, median=blue), including standards based on AA-EQS, 
Median, seasonal average, summer (??), percentiles, grouped by aggregated river type (see 
Methods section 1.2.5, Table 1.2 for translation between the broad types shown in 3.44 and the 

87 

 
 
 
aggregated broad types shown here). Wilcocon test statistic shows significance of type specific 
differences in distribution of observed data. 

Table 3.30: Overview of common broad types and their associated aggregated types for which 
standards are available, showing the number of countries/national types/distinct standards for TP. 

Code  Broad type 

Associated 
aggregated 
type code  Country  Type  Standard 

All 

RW-
00 

RW-
01 

RW-
02 

RW-
03 

RW-
04 

RW-
05 

RW-
06a 

RW-
06b 

RW-
08 

RW-
09 

RW-
10 

RW-
11 

RW-
12a 

RW-
12b 

RW-
13 

RW-
14 

All 

Not assigned 

Very large rivers 

All 

RA-00 

RA-01 

Lowland siliceous medium-large 

RA-04 

Lowland siliceous very small-small 

RA-05 

Lowland calcareous or mixed 
medium-large 

Lowland calcareous or mixed very 
small-small 

Lowland organic and siliceous very 
small-small 

Lowland organic and siliceous 
medium-large 

RA-02 

RA-03 

RA-05 

RA-04 

Mid-altitude siliceous medium-large 

RA-08 

Mid-altitude siliceous very small-small 

RA-09 

Mid-altitude calcareous or mixed 
medium-large 

Mid-altitude calcareous or mixed very 
small-small 

Mid-altitude organic and siliceous 
very small-small 

Mid-altitude organic and siliceous 
medium-large 

Mid-altitude organic and calcareous 
or mixed 

Highland (all Europe) siliceous 
incl. organic (humic) 

RA-06 

RA-07 

RA-09 

RA-08 

RA-06 

RA-10 

88 

6 

10 

13 

7 

6 

14 

13 

4 

4 

8 

8 

10 

10 

3 

2 

1 

4 

1 

43 

28 

10 

14 

68 

29 

7 

4 

22 

24 

55 

54 

15 

3 

1 

11 

6 

15 

15 

10 

9 

16 

13 

5 

4 

8 

10 

12 

11 

5 

2 

1 

4 

 
 
RW-
15 

RW-
17 

RW-
18 

RW-
19 

RW-
20 

Highland (all Europe) calcareous or 
mixed 

Mediterranean lowland medium-large 
perennial 

Mediterranean mid-altitude medium-
large perennial 

Mediterranean very small-small 
perennial 

Mediterranean temporary or 
intermittent streams 

RA-10 

RA-11 

RA-11 

RA-12 

RA-12 

4 

3 

1 

2 

3 

8 

7 

1 

6 

12 

4 

4 

1 

3 

5 

3.7.3  Phosphorus: synthesis 

Total phosphorus (TP) is widely used in lakes whilst both TP and “PO4-P” are used in rivers either 
separately (10 countries) or together (15 countries). 

As for TN in lakes, type appears to have a strong effect on TP standards (Fig. 3.41), with lowland 
calcareous lakes generally having the highest thresholds and siliceous lakes generally having the 
lowest thresholds.  Once again, however, a strong country effect is overriding the effect of type 
(Table 3.24).   When compared with EEA data some types seem to have standards that are likely to 
be adequate (LA-01 very large, LA-03 shallow lowland) whilst, for others, many standards still appear 
to be too lenient (LA-02 very shallow, LA-07 highland).  

Strong effects related to type are also seen in rivers with similar patterns to lakes: rivers draining 
silicious catchments generally having lower thresholds than rivers draining calcareous ones.  Many 
of these types show strong differences in concentrations associated with “good or better” versus 
“moderate or worse” status.  The effectiveness of standards associated with different types varies: 
while the median values for both phosphate and TP are generally closer to the upper quartiles of 
sites in good or better status than for quality elements such as ammonium the majority are higher 
than the mid-point of the distributions of sites not in good status, particularly for PO4-P. There are 
exceptions, the lowland siliceous river types (RA-04, RA-05) and mid-altitude siliceous (RA-09) but 
the  overall  conclusion  is  that  river  phosphorus  boundaries  for  many  types/countries  still  require 
review. 

There  were  very  few  changes  in  lake  total  phosphorus  boundary  values  compared  with  values 
reported in 2014, with only a slight reduction in the maximum reported value, 400 µg P/L, compared 
with 500 µg P/L in 2014. Two countries (CY, RO) reported additional higher values, with one country 
(PL) reporting a lower range of boundaries. 

As  for  nitrogen,  there  were  few  changes  in  river  phosphorus  boundaries,  with  the  same  high 
maximum  values  of  500  µg/L  for  ortho-phosphorus,  but  a  higher  value  (1000  µg/L)  for  total 
phosphorus (compared with a maximum of 660 µg/L in 2014). Most boundary values remained the 
same as those reported in 2014 with only one country (PL) reporting a lower range of boundary 
values (both for ortho-phosphorus and total phosphorus). Some countries (BG, CY, PT) reported 
additional lower values for ortho-phosphorus, and total phosphorus. However, others (BE, FR, RO) 
reported additional higher values for ortho-phosphorus, and (CY, FR, NL, RO) for total phosphorus. 

89 

 
 
 
3.8 

Salinity  

3.8.1 

Salinity (rivers)  

13  countries  have  standards  for  parameters  that  relate  to  the  salinisation  of  inland  waters.      4 
countries (BG, CY, PL, RO) use conductivity for this purpose and 5 (AT, CZ, DE, LU, NL) use chloride 
concentration.  3 countries (BE, ES, HU) report standards for both, whilst MT uses Practical Salinity 
Units.   Salinity can also be a component of typology in countries where there are naturally brackish 
lakes (e.g. NL).   

3.8.1.1  Chloride (rivers) 

There were 306 records of chloride standards from 7 countries (Figure 3.46). Many countries use 
the annual mean (“AA-EQS”) as a summary metric. 6 countries (AT, BE, DE, HU, LU, NL) use a single 
value for each national type and 2 countries (ES, HU) present standards as a range. 

The data could be linked to 9 broad types (Figure 3.47 & Table 3.32). 3 of these (RW-01, RW-04, 
RW-05)  had  type specific  values  from  more  than 2 countries  allowing  the  range  of  standards  in 
these types to be compared. 3 countries (AT, BE, LU) apply the same standard to all national types 
found in one or more of their river basin districts. 

The standards ranged from 20 mg/L (HU) to 600 mg/L (AT), with an interquartile range of 50 to 200 
mg/L (Figure 3.47). 

A two-way analysis of variance was used to test the significance of country and type differences 
between  standards  (Table  3.31).  The  relative  importance  of the  country  and  type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (38.6%) and type (0%) 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal  lines  on  box  plots  showing  the  distribution  of  Chloride  based  on  classifications  for 
phytobenthos  and  macroinvertebrates  (Figure  3.48)  using  data  taken  from  the  EEA-WISE-SOE-
DATABASE. 

More information in Annex A5.1. 

90 

 
 
 
 
Figure 3.46. Comparison of river Chloride standards by country. (single value black, minimum blue, 
maximum  red  symbols,  dotted  lines  show  interquartile  range  of  mean  or  median  values,  (90th 
percentile=red, 75th percentile=orange (overlain by 90th percentile), 50th percentile = blue). 

91 

 
 
 
 
Figure 3.47: River Chloride standards by broad type (single value black, minimum blue, maximum 
red, Horizontal lines mark the 25th (green) and 75th (red) percentiles). 

Table 3.31: Analysis of variance for factorial model relating country and broad type to Member State 
boundary values for log Chloride (Including main and partial effect sizes, Omega squared – see 
1.2.5).  

Omega sq, 

p Omega sq 

Sum sq 

Df 

F value 

Prob. 

Intercept 

Country 

0.39 

CodeBT 

-0.06 

0.36 

-0.1 

Residuals 

28.1 

5.0 

0.2 

3.4 

1 

4 

2 

9 

74.26 

0.00001 

3.29 

0.06357 

0.30 

0.74630 

92 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.48: River Chloride standards (dotted lines) overlain on box plots showing the range of 
Chloride concentration for sites classified by phytobenthos and macro-invertebrates. (90th 
percentile=red, 75th percentile=orange (overlain by 90th percentile), median=blue). Significance of 
difference between groups, (phytobenthos) Wilcoxon p=<0.001 (invertebrates) Wilcoxon p= <0.001/ 

93 

 
 
 
 
 
Table  3.32  Overview  of  common  types  and  number  of  Member  State/national  types/distinct 
standards 

Country  Type  Standard 
1 
6 
7 
3 
5 
10 
8 

3 
2 
4 
2 
2 
3 
3 

3 
2 
5 
3 
3 
4 
3 

2 
2 
2 

2 

2 
4 
5 

7 

1 
1 
2 

3 

Lowland siliceous medium-large 
Lowland siliceous very small-small 
Lowland calcareous or mixed medium-large 
Lowland calcareous or mixed very small-small 
Lowland organic and siliceous very small-small 

Code 
Broad type 
All 
All 
RW-00  Not assigned 
RW-01  Very large rivers 
RW-02 
RW-03 
RW-04 
RW-05 
RW-
06a 
RW-
06b 
RW-08  Mid-altitude siliceous medium-large 
RW-09  Mid-altitude siliceous very small-small 
RW-10  Mid-altitude  calcareous  or  mixed  medium-

Lowland organic and siliceous medium-large 

large 

RW-11  Mid-altitude  calcareous  or  mixed  very  small-

small 

RW-12a  Mid-altitude organic and siliceous very small-

small 
Mid-altitude  organic  and  siliceous  medium-
large 

RW-
12b 
RW-13  Mid-altitude organic and calcareous or mixed 
RW-14  Highland  (all  Europe)  siliceous  incl. organic 

(humic) 

RW-15  Highland (all Europe) calcareous or mixed 
RW-16  Glacial rivers (all Europe) 
RW-17  Mediterranean 

lowland  medium-large 

perennial 

RW-18  Mediterranean  mid-altitude  medium-large 

perennial 

RW-19  Mediterranean very small-small perennial 
RW-20  Mediterranean 

temporary  or 

intermittent 

streams 

3.8.1.2  Conductivity (rivers) 

There were 208 records of conductivity standards from 7 countries (Figure 3.49).  Many countries 
use the annual mean (“AA-EQS”) as a summary metric. 7 countries (BE, BG, CY, ES, HU, PL, RO) use 
a single value for each national type and 2 countries (ES, HU) present standards as a range. 

The data could be linked to 18 broad types (Figure 3.50 & Table 3.34). 5 of these (RW-00, RW-01, 
RW-02, RW-10, RW-11) had type specific values from more than 2 countries allowing the range of 
standards  in  these  types  to  be  compared.  2  countries  (BG,  CY)  apply  the  same  standard  to  all 
national types found in one or more of their river basin districts. 

94 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The standards ranged from 20 µS/cm (ES) to 60,000 µS/cm (ES), with an interquartile range of 450 
to 1000 (Figure 3.50). 

A two-way analysis of variance was used to test the significance of country and type differences 
between standards  (Table  3.33).  The  relative  importance  of the  country  and type  effects  can be 
judged from the Omega squared values which represent the proportion of variance explained by 
each of country (26.7%) and type (12.4%) 

In order to link the reported standards to relevant BQEs their interquartile ranges are marked as 
horizontal lines on box plots showing the distribution of Conductivity based on classifications for 
phytobenthos  and  macroinvertebrates  (Figure  3.51)  using  data  taken  from  the  EEA-WISE-SOE-
DATABASE. 

More information in Annex A5.1. 

Figure 3.49: Comparison of river Conductivity standards by country. (single value black, minimum 
blue, maximum red symbols, dotted lines show interquartile range of mean or median values, (90th 
percentile=red, 75th percentile=orange, 50th percentile = blue). 

95 

 
 
 
 
 
Figure 3.50: River Conductivity standards by broad type (single value black, minimum blue, 
maximum red, Horizontal lines mark the 25th (green) and 75th (red) percentiles, (including 
standards based on AA-EQS, percentiles). 

Table 3.33: Analysis of variance for factorial model relating country and broad type to Member 
State boundary values for log Conductivity (Including main and partial effect sizes and Omega 
squared – see 1.2.5).  

Omega sq, 

p Omega sq 

Sum sq  Df 

F value 

Prob. 

Intercept 

Country 

0.27 

CodeBT 

0.12 

Residuals 

0.31 

0.17 

81.9 

1.8 

1.1 

1 

3 

3 

522.98 

0.00000 

3.93 

0.03371 

2.36 

0.11866 

2.0 

13 

96 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.51: River Conductivity standards (dotted lines) overlain on box plots showing the range of 
Conductivity concentration for sites classified by phytobenthos and macro-invertebrates. (90th 
percentile=red, 75th percentile=orange, median=blue). Significance of difference between groups, 
(phytobenthos) Wilcoxon p=<0.001 (invertebrates) Wilcoxon p= <0.001 

97 

 
 
 
 
 
 
 
Table 3.34: Overview of common types for which conductivity standards are available, showing the 
number of Member State/national types/distinct standards for lakes.  

Code 
Broad Type 
All 
All 
RW-00  Not assigned 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-
06a 
RW-
06b 
RW-08  Mid-altitude siliceous medium-large 
RW-09  Mid-altitude siliceous very small-small 
RW-10  Mid-altitude  calcareous  or  mixed  medium-

Very large rivers 
Lowland siliceous medium-large 
Lowland siliceous very small-small 
Lowland calcareous or mixed medium-large 
Lowland calcareous or mixed very small-small 
Lowland  organic  and  siliceous  very  small-
small 
Lowland organic and siliceous medium-large 

large 

RW-11  Mid-altitude calcareous or mixed very small-

small 

RW-14  Highland  (all  Europe)  siliceous  incl. organic 

(humic) 

RW-15  Highland (all Europe) calcareous or mixed 
RW-20  Mediterranean 

temporary  or 

intermittent 

Country  Type  Standard 
1 
33 
10 
5 
5 
25 
4 
1 

1 
17 
5 
5 
4 
3 
1 
1 

2 
5 
4 
3 
2 
2 
1 
1 

1 

2 
2 
3 

3 

2 

1 
2 

1 

2 
3 
8 

13 

3 

1 
10 

1 

2 
3 
3 

5 

2 

1 
2 

streams 

3.8.2 

Salinity: synthesis 

Just  under  half  of all  countries provided  information  on standards  relating to salinity,  with  three 
different approaches being adopted, one of which (Practical Salinity Units) was only used by one 
country so could not be analysed further.   

None  of  the  metrics  available  for  analysis  were  specifically  calibrated  against  salinity  gradients 
although some “general degradation” measures for river phytobenthos, at least, have been shown 
to correlated with salinity.   

With these caveats in mind, chloride standards, especially, appear to be particularly lenient although 
the median position of  conductivity standards, too, is also above the 75th percentile of sites at “good 
or better” status. 

98 

 
 
 
 
4  Discussion 

4.1  General comments 

The previous chapter catalogued standards used by Member States for different physico-chemical 
supporting  elements  and made  some  tentative  comparisons between  countries,  between  broad 
types and with available biological and physico-chemical data from monitoring sites in WISE.   In 
some cases, there are clear differences between countries.  These might be due to the way that the 
standard was set (e.g. expert judgment rather than derived from empirical data) but might also be 
due to differences in the methods used to collect data (which part of the water column was sampled, 
for example, in the case of oxygen) and the summary statistics used.   All of this will complicate 
direct comparisons of national standards (Skarbøvik et al., 2020; Fölster et al., 2021).    

Note that the comments offer an EU-wide overview of the situation for each supporting element, in 
order to focus the work of ECOSTAT and should not be interpreted as endorsing any particular 
national standard.  That responsibility remains with Member States, although the EU Commission 
may ask the Member States to demonstrate the ability of the standards to support good ecological 
status for relevant sensitive biological quality elements.   

Many of these supporting elements do not act in isolation: they may form part of a “cocktail” of 
stressors produced by a single pressure (e.g. urban waste water or diffuse pollution from agriculture 
causing elevated levels of nutrients and BOD, as well as reduced Secchi depth and reduced oxygen). 
The supporting elements may, themselves, also reflect the action of other stressors on one BQE 
(e.g.  Secchi  depth  reflects  the  effect  of  phosphorus  on  phytoplankton)  whilst,  simultaneously, 
exerting a direct effect on another BQE (e.g. light limitation – indicated by Secchi depth – on lake 
macrophytes).  The remainder of this section will evaluate these interactions in freshwaters, leading 
to a proposed plan of action for ECOSTAT to take this work forward. 

4.2  Changes in nutrient standards 

Given the attention that has been given to nutrients since the original report highlighting the extent 
of differences between national standards (Phillips & Pitt 2015) it might have been expected that 
this survey would have shown changes to the boundary criteria used by Member States. In general, 
this was not the case. Although differences in the coding of national types meant that it was not 
possible to make a direct comparison of national boundary values, Figures 3.28, 3.31, 3.34, 3.37, 
3.40 & 3.43 demonstrate little evidence of any substantial change in the reported boundary values.  
For  both  nitrogen  and  phosphorus,  the  majority  of  the  boundary  values  remain  the  same,  with 
several countries still having relatively high values and a few even reporting additional higher values.  
One  reason for this may  be the  lag  in  the  reporting  systems  and the timing  of  the  river  basing 
planning process. The data reported in 2015 would have represented boundary values for the 1 st 
WFD planning cycle whilst those in this report reflect the 2nd cycle, with values potentially established 
prior to the completion of the nutrient guidance document in 2018. However, the current conclusion 
is  that  nutrient  boundary  values  in  some  countries  are  considerably  higher  than  the  nutrient 
concentrations  found  in  waterbodies  reported  to  be  in  good  status  for  sensitive  BQEs  (see  4.3 
below),  and  therefore  require  further  consideration  in  order  to  ensure  that  they  support  good 
ecological quality. 

99 

 
 
4.3  Challenges involved in setting threshold values  

The  current  guidance  on  establishing  nutrient  boundaries  recommends  the  use  of  pressure 
response relationships between measures of biological status, such as EQR or WFD status class and 
nutrient  concentrations.  This  approach  can  in  theory  be  extended  to  include  a  wider  range  of 
supporting element variables. However, from preliminary work designed to test such an approach, 
a number of issues are starting to emerge. There are currently no clear solutions to these issues, 
but we highlight some of these in the following section to stimulate further discussion.  

In order to test the application of the nutrient toolkit methods we used a pan-European data set 
extracted  from  the  WISE-SoE-database  to  generate  pressure  response  relationships  between 
normalized EQRs and various supporting elements. Given the growing recognition of the role of 
multiple  pressures  we  specifically  considered  the  application  of  more  complex  multivariate 
relationships which included multiple pressure variables. However, one of the key requirements of 
regression modelling is the independence of predictor variables and the first issue that emerges is 
that  in  many  cases  the  supporting  element  variables  for  which  we  require  boundary  values  are 
significantly correlated with each other (Figure 4.1).  In this respect, the present work differs from 
much research on multiple pressures, which assumes independent effects, albeit with the potential 
to interact.   

100 

 
 
 
 
Issue 1. Correlation of supporting element variables.  How does this affect the development and 
interpretation of pressure response models? 

Figure 4.1: Correlations, distribution and scatter plot showing relationship between variables available 
for modelling. 

Clearly, we would expect high correlations between some variables, for example ortho-phosphate 
and total phosphorus, but we also see that ammonium nitrogen is correlated with phosphorus (total 
and ortho-phosphorus), and also with biochemical oxygen demand. Thus, if there is a significant 
pressure-response  relationship  between  total  phosphorus  and  EQR,  there  will  inevitably  be  a 
similarly good relationship between ammonium nitrogen and EQR and boundary values taken from 
the intersection of the good moderate EQR boundaries for each of these relationships will also, as 
a result, be related. However, the issue of causality cannot be determined: all that can be concluded 
is  the  most  likely  concentrations  of  both  phosphorus  and  ammonium  at  the  good  moderate 
boundary.  

Issue 2. Which are the most important variables for explaining variation in ecological quality? 

101 

 
 
 
 
One potentially useful method of investigating the importance of different variables that themselves 
have a degree of correlation are boosted regression trees (BRTs: Elith et al., 2008; Elith & Leathwick, 
2017). This is a powerful technique, that differs from traditional regression methods which produce 
a single “best” model as they combine a large number or simple tree models to create a final model 
which is less influenced by correlations between variables. The approach can be used to determine 
which  combination  of  predictor  variables  best  predict  EQRs  and  are  thus  likely  to  be  important 
supporting elements for the BQE under consideration.   At this stage, we are not proposing BRTs as 
a means of setting standards; only as a means of understanding the factors responsible for shaping 
ecological communities and for identifying the dominant stressors. 

This was explored using BRTs to predict EQR for river phytobenthos, river macro-invertebrates, lake 
phytoplankton  and  lake  macrophytes  were  fitted  to  a  common  suite  (except  that  transparency 
replaced DO for lake macrophytes) of variables. These models need further work, as it is important 
to simplify the models using pruning techniques, but the initial results illustrate the effect of using 
multiple variables to reduce uncertainty and the relative importance of each. The models explain 
70-87% of the variability (Figure 4.2), a much higher value than simple univariate models. The model 
for phytoplankton EQR explains the most variance (87%) and that for river phytobenthos the least 
(70%). Total phosphorus is the most important predictor variable for both lake phytoplankton and 
river phytobenthos (Fig 4.2.3), but transparency (Secchi disk depth) is the most important factor for 
lake macrophytes and ammonium nitrogen and total phosphorus are equally important for river 
macro-invertebrates. This reflects our current understanding of the importance of phosphorus for 
the algal based metrics, but suggests that light availability is the key factor for lake macrophytes and 
that river macro-invertebrates are influenced by a more general organic pressure characterized by 
nutrients and ammonium nitrogen. Had we omitted TP from the phytoplankton and phytobenthos 
models the influence of ammonium N would have been substantially higher. 

Figure 4.2 Proportion of total variance in EQR-values explained by boosted regression tree models 
for different BQEs 

102 

 
 
 
Figure 4.3: Relative contribution of predictor variable to boosted regression tree models for different 
BQEs.  Cntry = Country; Lat = latitude; lon = longitude; SD = Secchi depth; type = broad type 

Issue 3.  Country specific and regional variation, should we expect consistent relationships between 
pressure variables and ecological quality? 

The extent of the correlation between variables is not always consistent across different countries. 
Our initial examination of the data suggests that TP, ammonium-nitrogen and BOD are generally 
correlated with each other in all countries; however the relationship between these variables and 
nitrate-nitrogen is more variable. A scatter plot of river nitrate nitrogen and total phosphorus shows 
a  wedge-shaped  relationship.  Nitrate-nitrogen  concentrations  are  usually  high  at  high 
concentrations  of  phosphorus  whilst  at  lower  levels  of  phosphorus  a  wide  range  of  nitrate 
concentrations occur (Figure 4.4). However, our preliminary investigations of the data indicates that 
sites from different countries occupy different parts of the data cloud.  For example sites in BE tend 
to  always  have  high  nitrate-nitrogen  regardless  of  the  TP  concentration  whilst  Scandinavian 
countries tend to have lower nitrate-nitrogen concentrations for equivalent concentrations of TP. 
This probably reflects different pressures, but it represents an additional challenge for modelling. 
Clearly, model predictions which include both TP and nitrate-nitrogen using data from countries 
with  different  ranges  of  these  supporting  elements  are  likely  to  generate  different  results.  This 
illustrates both the potential advantage of using pan-European data sets, by expanding the range 
of pressure variables, but also the difficulties of interpretation. 

103 

 
 
 
 
Figure  4.4:  Scatter  plots  showing  relationship  between  total  phosphorus  (TP  mg/L)  and  nitrate 
nitrogen  (NO3-N  mg/L)  in  European  rivers,  illustrating  overall  shape  of  the  relationship  and how 
different countries occupy contrasting parts of the data cloud. (grey points all data, red points data 
from BE, blue points data from SE and FI). 

In the BRT models mentioned above both country and water body type (Broad Type) were included 
in the models. In all cases typology contributed a relatively small amount to the explained variation 
(<10%),  while  for  macro-invertebrates,  country  accounted  for  almost  as  much  variability  as 
ammonium-N  and  TP.  The  reason  for  this  is  not  clear,  particularly  as  the  models  also  included 
latitude and  longitude  variables to allow  for  regional  variation  and normalised  EQR  values  were 
used.   Country effects were greatest for river invertebrates and this may reflect different scales of 
pressures such as hydromorphological alteration between countries as well as differing sensitivities 
of invertebrate metrics to this pressure.   Diatom metrics, by contrast, are more consistent between 
countries and are mostly calibrated against chemical pressures, so it is not surprising that country 
effects are smaller; nor is it a surprise that country has less effect on lake phytoplankton assessments 
where all methods are calibrated against a eutrophication gradient 

Given that multivariate models can explain significantly more variation than simple univariate models 
further consideration needs to be given to their use for establishing boundary values. One potential 
issue is that predictions from the model depends on the values of other variables. Thus, a range of 
boundary values emerges, each contingent on values of other predictors. This may explain why type 
is not an important predictor in these multivariate models and allow for different combinations of 
boundary values in different water body types.  

Issue 4.  Which BQEs should be selected when developing pressure response relationships? 

Finally, it is important to recognise the different sensitivities of the BQEs to pressure. Because of this, 
we recommend that the most sensitive BQE to a supporting element is selected to set the threshold 

104 

 
 
 
 
for that supporting element.  Thus, models which use macro-invertebrates is likely to be more useful 
when establishing supporting element boundaries for quality elements reflecting organic pressures, 
whilst the plant based metrics are better suited to reflect the impact of nutrients. Dissolved oxygen 
and free ammonia, which itself is related to pH and total ammonia, may need to be related to the 
status of fish which requires access to other data, as fish are not yet adequately represented in the 
WISE-SOE data set. 

4.4 

Synthesis 

A recurring theme in section 3 is that variation in standards between countries overrides differences 
between  broad  types.    One  key  question,  then,  is  the  extent  to  which  differences  in  national 
standards  can  be  explained  by  biogeographical  variation  rather  than  as  evidence  that  some 
countries have standards that are less effective at protecting good status.   Even after acknowledging 
the relatively simple nature of the analyses presented in section 3, it appears that many standards 
for supporting elements are not set at levels that are likely to protect good status and, therefore, 
that the likelihood of achieving WFD aims is reduced.  

In contrast, the comparison of standards in section 3 highlights the apparent lack of regional or 
even water body type specific differences in pressure-response relationships, suggesting that more 
uniformity might be expected. However, the clear differences in the relative importance of different 
pressures  is  clearly  a  complicating  issue,  as  are  differences  in  monitoring  approach.  Supporting 
elements can be divided into those that are direct drivers of status through primary effects on BQEs 
and those that are secondary effects, providing supplementary information that reinforces decision 
making.      Ammonium-N  and  oxygen  conditions,  for  example,  reflect  properties  that  directly 
influence  fish and  invertebrates,  whilst  Secchi  depth  and upper  pH  thresholds  could be  seen  as 
secondary  effects  of  nutrients  (caused  by  high  phytoplankton  biomass  and  primary  production) 
when determining risks to water bodies. Secchi depth is also affected by humic substances and by 
inorganic turbidity, which poses challenges when setting boundaries that protect against nutrient 
enrichment.  Secchi  depth  can  however  directly  influence  macrophytes  and  is  also  an  important 
parameter for human well-being through its effect on cultural ecosystem services, such as recreation 
and bathing water.  

In terms of practical actions to support Member States, the following conclusions can be drawn: 

1.  Supporting element standards should not be considered in isolation but rather as part of an 

integrated decision-making system that enables Member States to make effective decisions 

about appropriate management strategies.    

2.  Considerable effort has already been spent on promoting best practice for nutrients, but this 

has not yet been translated into more effective standards (3.6.3 & 3.7.3).   Generally, the 

process is in place although some Member States may need support as they apply this to their 

own situations.  In this regard, the links shown in this report between the standards and the 

sensitive BQEs may help to identify a range of standards per broad type that is compatible 

with good status for the BQEs, and also to identify those standards that seem to be too lenient 

and which may need to be validated. ECOSTAT should also recognise that the democratic 

safeguards mean that the translation of science into policy may take some years to realise.  

We also recognise that setting effective N or P standards in situations where there are 

interactions from other stressors that may conceal the impact of nutrients still needs additional 

105 

 
 
work.  These other stressors include siltation causing light limitation for river phytobenthos or 

macrophytes or habitat deterioration for fish and macroinvertebrates; the presence of 

pesticides degrading the BQEs due to toxicity; or hydromorphological pressures, such as water 

level fluctuations degrading the habitats of the littoral zone. This does not mean, however, 

that nutrients should not be managed to a level that would support good status in absence of 

the other pressures/stressors. 

3.  Low oxygen concentrations, BOD and ammonium-N often combine to form a “cocktail” of 

stressors (along with nutrients) associated with organic pollution.  Evidence in this report 

suggests that current standards for several of these are too lenient. Organic pollution (which is 

still an important pressure across much of Europe) would be an obvious focus for ECOSTAT 

going forward.   Some of the work on nutrients will be directly relevant to these stressors; but 

are also important in areas exposed to diffuse pollution from agriculture, which affects twice as 

many water bodies in Europe as the organic pollution from point sources (EEA 2018 report). 

However, the development of realistic standards should facilitate reduction of both organic 

pollution and diffuse nutrient pollution.  Guidance on managing water resources subject to 

multiple stressors can be found in Schinegger et al. (2018). The main recommendation is to 

always prioritise measures to reduce the primary/dominant stressor first. This is, in many cases, 

nutrients/organic pollution and/or hydromorphological pressures.     

4. 

In some cases (e.g. dissolved oxygen – 4.2.1), comparisons between countries are complicated 

by different approaches to monitoring and classification; in others, there are differences in 

how data are summarised.   These issues will need to be considered when individual 

supporting elements are discussed in more detail.  Whilst there are situations where a 

measure of central tendency is most appropriate (e.g. for nutrients, where the mean may be 

regarded as a useful proxy for the load to which the biota is exposed), there are also 

circumstances where an upper or lower percentile will be more relevant. Generally, those 

supporting elements that exert a toxic or fatal effect (e.g. pH, dissolved oxygen, free ammonia) 

should use upper/lower percentiles, reflecting the most extreme cases to which the biota is 

exposed.  Questions about data aggregation lead naturally onto questions of appropriate 

sampling frequencies.   

5.  Following on from this, the present work cannot be considered in isolation from the broader 

process of classification.  Whilst our mandate does not extend to formal interpretation of 

classification rules there is a need to make a scientific case for appropriate combinations of 

variables within a physico-chemical supporting element (i.e. when it is appropriate to include a 

nitrogen standard within “nutrient conditions” for inland waters).   Again, the work on nutrients 

has suggested that a nuanced approach is necessary in order to give Member States 

confidence that programmes of measures are based on secure scientific foundations.     

6.  Lower pH thresholds reflect acidification, a key pressure that applies in some regions of 

Europe but is less relevant in others.  Further work on this supporting element is probably best 

106 

 
 
devolved to a subgroup of countries with vulnerable water bodies, and which have metrics 

tuned to evaluate this pressure and should not duplicate efforts performed under the auspices 

of the LRTAP-convention.  

7.  Some countries are still using standards inherited from pre-WFD legislation: dissolved oxygen 

from Freshwater Fish Directive and nitrate-N from Nitrates Directive (see 4.2).   In both cases, 

several countries continue to use these values without presenting evidence that they are 

appropriate for the more stringent requirements of the WFD.   These both merit further 

evaluation using pan-European data. 

8.  Although not discussed in this report, we recognise the importance of having a suite of 

variables that will permit a pan-European approach to detecting effects of climate change.  

This suggests a need for a co-ordinated approach to variables that are likely to change as a 

result and which in turn will impact the biota. Scientific evidence suggests that climate change 

is likely to aggravate eutrophication problems and reduce oxygen concentrations, both due to 

increasing water temperature, as well as to increasing frequency of extreme rain events 

flushing nutrients and organic pollution from the catchment into rivers and lakes (Ho et al. 

2019, Jane et al. 2021). This interaction raises a need to consider more protective nutrient 

standards and BOD and oxygen concentration standards, as well as more effective mitigation 

and adaptation measures.   

4.5 

The way forward 

The workplan approved by ECOSTAT in October 2020 is as follows: 

• 
Work on nutrient boundaries (2020-2021): Analysis of current nutrient boundaries, including 
comparison  with  previous  boundaries  (collated  2014);  Identification  of  remaining  issues  and 
problems: are there still issues of comparability?  Are there still Member States that are “outliers”? 
Do  we  still  need to  work on  particular  water  categories  /  issues/  MS?  Solving    remaining  issues: 
provide tailored support to individual Member States using IC and Member State datasets 

These issues are largely covered by this report and were discussed in more detail at the workshop 
in  April  2021.      It  is  clear  that  some  countries  remain  as  “outliers”  with  respect  to  nutrient 
boundaries, and these will need tailored support. 

• 
Work  on  physico-chemical  boundaries  (2020-2021):  Comparisons  within  broad  types; 
Validation  of  boundary  values:  testing  with  EEA/Member  State  datasets  to  derive  appropriate 
boundaries for physico-chemical elements; Extracting best practice from Member State case studies. 

The first part of this is covered by this report, and this includes some preliminary testing with 
EEA data, illustrating possible ranges of boundaries that will be compatible with good status.   
This has uncovered several instances where boundaries may be too lenient to protect good 
status; however, section 4.3 also indicates some of the problems that will be encountered when 
attempting to derive appropriate boundaries.   More detailed analysis of at regional level, as 
well as Member State case studies will complement analyses performed at a pan-European 
level.  We also need to look at how different combination rules can affect outcomes (does this 

107 

 
 
explain apparently lenient standard setting in some cases?), using the ETC-ICM 2020 
Comparability report as a basis. 

• 
Updating  the  statistical  tool-kit  and  guidance  to  derive  boundaries  for  physico-chemical 
elements  (as  some  supporting  elements  may  require  approaches  not  covered  by  the  current 
versions of the tool-kit and guidance) (2020-2021); 

Some of the methods included in the current version of the tool-kit will be applicable to other 
physico-chemcial  elements.    However,  the  statistical  tool-kit  and  guidance  recognised  the 
problems associated with setting appropriate boundaries in multiple stressor situations but was 
not  able  to  offer  a  comprehensive  range  of  methods  to  address  these.    Ongoing  work  (see 
previous point) as well as guidance from the MARS project should provide some further options 
for dealing with multiple stressor situations.   

108 

 
 
5 

References 

Elith, J., Leathwick, J.R. and Hastie, T. (2008). A working guide to boosted regression trees. 

Journal of Animal Ecology 77: 802-813. 

Elith, J. and Leathwick, J.R. (2017). Boosted Regression Trees for ecological modeling. 

Vignette for R dismo package, R Project for Statistical Computing, Vienna, Austria. 

ETC/ICM (2015). TC biological indicators: data analysis of reporting on types characteristics for 

coastal and transitional waters. (EEA activity:1.6.1 f; ETC/ICM task, milestone: 4) 

ETC/ICM (2019).   Summary note on standards for physico-chemical quality elements reported 

by Member States with their 2nd RBMPs (ETC-ICM task 1.5.1.1., subtask 3 Comparability issues) 

ETC/ICM  (2020).  Lyche  Solheim,  A.,  Austnes,  K.,  Kristensen,  P.,  Birk,  S.  Comparison  of 
ecological status between countries and river basin management plans (RBMP) cycles. Approaches 
used by countries for rivers and lakes, consequences for comparison for the 3rd RBMP assessment. 
ETC/ICM 
https://www.eionet.europa.eu/etcs/etc-
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icm/products/etc-icm-report-4-2029-comparison-of-ecological-status-between-countries-and-
river-basin-management-plans-rbmp-cycles    

Technical 

4/2020: 

Report 

pp. 

Fölster, J., Garmo, Ø. A., Carlson, P., Johnson, R., Velle, G., Austnes, K., Hallstan, S., Holmgren, 
K., Schartau, A. K., Moldan, F. & Aroviita, J. (2021). Acidified or not? A comparison of Nordic systems 
for  classification  of  physicochemical  acidification  status  and  suggestions  towards  a  harmonised 
system. SLU, Vatten och miljö: Rapport 2021:1. 

Fox, J., Weisberg, S., Price, B. et al. (2020).  ‘car’.   R package version 3.0-10. https://cran.r-

project.org/web/packages/car/index.html 

Ho,  J.C.,  Michalak,  A.M.,  Pahlevan,  N.  2019.  Widespread  global  increase  in  intense  lake 

phytoplankton blooms since the 1980s. Nature, doi:10.1038/s41586-019-1648-7. 

Jane, S.F., Hansen, G.J.A., Kraemer, B.M. et al. (2021). Widespread deoxygenation of temperate 

lakes. Nature 594, 66–70 https://doi.org/10.1038/s41586-021-03550-y 

Kraemer, B.M., Pilla, R.M., Woolway, R.I. et al. (2021). Climate change drives widespread shifts 

in lake thermal habitat.  Nature Climate Change https://doi.org/10.1038/s-41558-021-01060-3 

Lyche Solheim, A., Globevnik, L., Austnes, K., Kristensen, P., Moe, S. J., Persson, J., Phillips, G., 
Poikane, S., van der Bund, W. & Birk, S. (2019). A new broad typology for rivers and lakes in Europe: 
Development  and  application  for  large-scale  environmental  assessments.  Science  of  The  Total 
Environment, 697, 134043. https://doi.org/10.1016/J.SCITOTENV.2019.134043 

MARS  guidance  for  river  basin  managers,  2018.  Schinegger,  R.,  Schülting,  L.,  Schmutz,  S., 
Lyche  Solheim,  A.,  Birk,  S.,  Feld,  C.,  Hering,  D.,  Kuijper,  M.,  Chrzanowski,  C.,  Buijse,  T.,  Borja,  A., 
Venohr, M., Globevnik, L.: MARS Recommendations on how to best assess and mitigate impacts of 
multiple stressors in aquatic ecosystems. www.mars-project.eu . 

Okada,  K.  (2017)  Negative  estimate  of  variance-accounted-for  effect  size:  How  often  it  is 

obtained, and what happens if it is treated as zero. Behav Res Methods 49(3), 979-987. 

Phillips, G., Kelly, M., Teixeira, Salas, F., Free, G., Leujak, W., Pitt, J.-A.., Solheim, A.L., Várbíró, 
G.  &  Poikane  (2019).    Best  practice  for  establishing  nutrient  concentrations  to  support  good 
ecological  status.    European  Commission  Joint  Research  Centre  Science  for  Policy  Report, 
Publications Office of the European Union, Luxembourg. 

Phillips, G. and Pitt, J. (2015) A comparison of European freshwater nutrient boundaries used 

for the Water Framework Directive: A draft report to ECOSTAT, October 2015. 

Skarbøvik, E., Aroviita, J., Fölster, J., Lyche Solheim, A., Kyllmar, K., Rankinen, K. & Kronvang, B. 
(2020).  Comparing  nutrient  reference  concentrations  in  Nordic  countries  with  focus  on  lowland 
rivers. Ambio 49: 1771-1783. 

109 

 
 
110 

 
 
 
 
 
 
List of abbreviations 

BOD – biochemical oxygen demand  

BQE – Biological Quality Element 

COD - chemical oxygen demand 

EQR – Ecological Quality Ratio  

RBD – River Basin District 

RBMP – River Basic Management Plan 

SRP – soluble reactive phosphorus 

TN - total nitrogen  

TP – total phosphorus 

WFD – Water Framework Directive  

WISE – Water Information System for Europe 

111 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendices 

Extra information on each supporting element is 
provided in the charts and tables below. 

110 

112 

114 

116 

112 

108 

Transparency 

Oxygen (lakes and rivers)   108 

Contents 
A1 
A1.1  Oxygen Lakes 
A1.1.1 Dissolved oxygen concentration (lakes)  108 
A1.1.2 % oxygen saturation concentration (lakes)  
A1.2  Oxygen (rivers) 
A1.2.1 Dissolved oxygen concentration (rivers)  
A1.2.2 Percent oxygen saturation (rivers)  
A2 
A2.1  Secchi disk depth (lakes)   116 
A3 
119 
pH 
119 
A3.1  pH (lakes) 
A3.2  pH (rivers)  122 
A4 
BOD  126 
A4.1  BOD5 concentration (rivers)  
A5 
A5.1  Ammonium N concentration (rivers)  
132 
Nutrients 
A6 
A6.1  Nitrate (rivers) 
135 
A6.2  Total Nitrogen (lakes)  
137 
A6.3  Total Nitrogen (rivers)  
A6.4  Orthophosphate (Rivers)   140 
A6.5  Total phosphorus (lakes)   143 
A6.6  Total P (rivers) 
A7 
151 
Salinity 
A7.1.1 Chloride concentration (rivers)   151 
A7.2  Conductivity (rivers)  

Ammonium-N 

147 

132 

129 

129 

153 

126 

112 

 
 
 
 
 
 
 
Oxygen (lakes and rivers) 
Oxygen Lakes 
Dissolved oxygen concentration (lakes) 

Figure A1 Dissolved oxygen standards by country and broad type (single value black, 
minimum blue, maximum red), horizontal lines mark 25th and 75th quantiles for types with 
2 or more countries contributing to the type. 

113 

 
 
 
 
 
 
Table A1 Dissolved oxygen metrics used by country 
AA-
5th 
EQS 
percentile 
0 
0 
6 
0 
1 
0 
0 
0 
0 
0 
1 
1 

95th 
percentile 
0 
0 
0 
1 
0 
0 

10th 
percentile 
3 
0 
0 
0 
0 
0 

BE 
BG 
CY 
PT 
RO 
UK 

GS 
Mean 
0 
0 
0 
0 
4 
0 

Perc80/m x 
0 
0 
0 
1 
0 
0 

summer  winter 

0 
0 
0 
0 
0 
2 

0 
0 
0 
0 
1 
0 

Table A2 records where Dissolved oxygen was reported as a value or a range 
Country  value 

range 

BE 
BG 
CY 
PT 
RO 
UK 

3 
18 
1 
8 
15 
29 

1 

Table A3 Number of different Dissolved oxygen standards by country and broad type 

BE  BG  CY  PT  RO  UK  Sum 
4 
1 
8 
0 
1 
0 
2 
0 
2 
0 
1 
0 
1 
1 
1 
0 
1 
0 
21 
2 

1 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
2 
0 
1 
0 
0 
0 
0 
0 
3 

2 
2 
0 
0 
0 
0 
0 
0 
0 
4 

0 
3 
0 
0 
1 
1 
0 
0 
0 
5 

0 
1 
1 
1 
1 
0 
0 
1 
1 
6 

All 
LW-00 
LW-01 
LW-03 
LW-04 
LW-06 
LW-07 
LW-08 
LW-11 
Sum 

Data set used Ver12b (2021-05-18) 

114 

 
 
 
 
 
 
 
 
 
 
 
 
 
% oxygen saturation concentration (lakes) 

Figure A2 % oxygen saturation standards by country and broad type (single value black, 
minimum blue, maximum red). Horizontal dotted lines show the median values for the 
uppder (red) and lower (blue) groups of standards 

115 

 
 
 
 
 
 
Table A5 % oxygen saturation metrics used by country 

95th percentile  AA-EQS  Perc80/m x 

AT 
IE 
IT 
NL 
PT 
SI 

0 
1 
0 
0 
0 
0 

10 
0 
10 
0 
0 
1 

0 
0 
0 
0 
4 
0 

seasonal average 
0 
0 
0 
4 
0 
0 

Table A6 records where % oxygen saturation was reported as a value or a range 
Country  value 

range 

AT 
IE 
IT 
NL 
PT 
SI 

13 

21 

2 

3 

9 
9 

Table A7 Number of different % oxygen saturation standards by country and broad type 

IT  NL  PT  SI  Sum 
IE 
AT 
2 
0 
1 
0 
2 
1 
0 
3 
0 
1 
0 
1 
0 
1 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
1 
0 
1 
0 
1 
0 
2 
0 
1 
0 
0 
0 
1 
0 
2 
0 
1 
0 
0 
0 
1 
0 
0 
4 
10  1  10 

3 
7 
2 
2 
3 
1 
2 
4 
1 
3 
1 
1 
30 

0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
1 

0 
1 
0 
1 
1 
1 
0 
0 
0 
0 
0 
0 
4 

All 
LW-00 
LW-01 
LW-03 
LW-04 
LW-06 
LW-07 
LW-08 
LW-11 
LW-12 
LW-13 
LW-14 
Sum 

Data set used Ver12b (2021-05-18) 

116 

 
 
 
 
 
 
 
 
 
 
 
 
 
Oxygen (rivers) 
Dissolved oxygen concentration (rivers) 

Figure A3 Dissolved oxygen standards by country and broad type (single value black, 
minimum blue, maximum red) 

117 

 
 
 
 
Table A7 Dissolved oxygen metrics used by country 

10th percentile  95th percentile  AA-EQS  Median  MinA-EQS  Minimum  Perc80/m x 

BE 
BG 
CY 
DE 
ES 
FR 
GR 
HU 
LT 
LU 
LV 
PL 
PT 
RO 
SK 

10 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
11 
10 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 

2 
9 
1 
0 
3 
0 
0 
6 
3 
0 
6 
16 
0 
0 
0 

0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
12 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
4 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 

Table A8 records where Dissolved oxygen was reported as a value or a range 
Country  value 

range 

1 

BE 
BG 
CY 
DE 
ES 
FR 
GR 
HU 
LT 
LU 
LV 
PL 
PT 
RO 
SK 

40 
17 
1 
30 
37 
11 
1 
10 
5 
7 
23 
65 
10 
55 
28 

118 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A9 Number of different Dissolved oxygen standards by country and broad type 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-14 
RW-15 
RW-20 
Sum 

BE  BG  CY  DE  ES  FR  GR  HU 
0 
0 
0 
5 
1 
1 
1 
0 
2 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
1 
0 
1 
1 
1 
1 
1 
0 
0 
0 
0 
0 
0 
6 
12 

0 
3 
1 
0 
0 
1 
1 
0 
0 
1 
1 
2 
2 
0 
0 
0 
12 

1 
1 
0 
0 
0 
2 
0 
0 
0 
1 
1 
0 
0 
1 
1 
1 
9 

1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

LT  LU  LV  PL  PT  RO  SK  Sum 
0 
0 
0 
0 
0 
2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

0 
0 
0 
1 
1 
1 
0 
0 
0 
0 
2 
1 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
3 
2 
1 
2 
0 
0 
2 
0 
3 
0 
11  10 

0 
2 
1 
2 
2 
0 
0 
1 
1 
1 
2 
1 
2 
1 
0 
0 
16 

5 
17 
6 
3 
4 
14 
8 
1 
1 
5 
6 
10 
9 
2 
3 
4 
98 

0 
0 
0 
0 
0 
4 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 

1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

0 
0 
0 
0 
0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
4 

Data set used Ver12b (2021-05-18) 

Percent oxygen saturation (rivers) 

Figure A4 % oxygen saturation standards by country and broad type (single value black, 
minimum blue, maximum red). Horizontal dotted lines show the median values for the 
uppder (red) and lower (blue) groups of standards 

119 

 
 
 
 
 
Table A10 % oxygen saturation metrics used by country 

10th 
percentile 
0 
0 
5 
0 
2 
0 
0 
0 
0 
0 
3 

AT 
CZ 
EE 
ES 
FR 
HU 
IE 
IT 
NL 
PT 
UK 

90th 
percentile 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

95th 
percentile 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 

AA-
EQS  Maximum  Minimum  Perc80/m x 

0 
0 
0 
1 
0 
1 
0 
1 
0 
0 
0 

0 
7 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
5 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 

seasonal 
average 
0 
0 
0 
0 
0 
0 
0 
0 
11 
0 
0 

Table A11 records where % oxygen saturation was reported as a value or a range 
Country  value 

range 
3 

42 
16 

11 
1 

1 

1 
36 

20 

3 

15 
10 

AT 
CZ 
EE 
ES 
FR 
HU 
IE 
IT 
NL 
PT 
UK 

Table A12 Number of different % oxygen saturation standards by country and broad type 

AT  CZ  EE  ES  FR  HU 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
1 

0 
0 
0 
0 
2 
2 
3 
3 
2 
12 

1 
1 
0 
0 
0 
0 
0 
0 
0 
2 

0 
1 
0 
0 
0 
0 
0 
0 
0 
1 

0 
0 
1 
0 
2 
2 
0 
0 
0 
5 

IE 
1 
0 
0 
0 
0 
0 
0 
0 
0 
1 

IT  NL  PT  UK  Sum 
0 
1 
3 
0 
1 
0 
2 
0 
3 
0 
2 
0 
0 
0 
0 
0 
0 
0 
1  11 

8 
8 
3 
2 
7 
6 
3 
3 
2 
42 

3 
1 
0 
0 
0 
0 
0 
0 
0 
4 

1 
2 
0 
0 
0 
0 
0 
0 
0 
3 

All 
RW-00 
RW-01 
RW-03 
RW-04 
RW-05 
RW-10 
RW-11 
RW-15 
Sum 

Data set used Ver12b (2021-05-18) 

120 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Transparency 
Secchi disk depth (lakes) 

Figure A5 Secchi disk depth standards by country and broad type (single value black, 
minimum blue, maximum red) 

121 

 
 
 
 
 
Table A13 Secchi disk depth metrics used by country 
95th percentile  AA-EQS  GS Mean  Median 

AT 
BG 
CZ 
DE 
EE 
ES 
FR 
HR 
IT 
LT 
LV 
NL 
NO 
PL 
PT 
SI 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 

10 
7 
7 
0 
7 
3 
0 
7 
17 
2 
5 
0 
9 
7 
0 
2 

0 
0 
0 
9 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
30 
0 
0 
0 
0 
0 
0 
0 
0 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 
0 
0 
0 
0 

Table A14 records where Secchi disk depth was reported as a value or a range 
Country  value 

range 
6 
2 

AT 
BG 
CZ 
DE 
EE 
ES 
FR 
HR 
IT 
LT 
LV 
NL 
NO 
PL 
PT 
SI 

8 
18 
16 

13 
11 

6 
18 
3 
14 
9 

23 
1 
2 

14 

30 
2 
3 

23 

122 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A15 Number of different Secchi disk depth standards by country and broad type 

AT  BG  CZ  DE  EE  ES  FR  HR 
2 
0 
0 
4 
0 
1 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
1 
2 
1 
0 
0 
0 
0 
0 
2 
0 
0 
3 
0 
7 
10 

0 
20 
0 
0 
2 
0 
0 
0 
1 
5 
0 
1 
0 
0 
1 
30 

0 
1 
1 
0 
2 
1 
0 
0 
0 
1 
0 
1 
0 
0 
0 
7 

0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

0 
2 
0 
1 
3 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
7 

0 
3 
0 
0 
2 
2 
0 
0 
1 
1 
0 
0 
0 
0 
0 
9 

0 
3 
0 
1 
0 
0 
0 
0 
1 
1 
0 
1 
0 
0 
0 
7 

IT 
0 
3 
1 
0 
1 
1 
0 
0 
1 
1 
0 
2 
3 
2 
2 
17 

All 
LW-00 
LW-01 
LW-02 
LW-03 
LW-04 
LW-05 
LW-06 
LW-07 
LW-08 
LW-09 
LW-11 
LW-12 
LW-13 
LW-14 
Sum 

LT  LV  NL  NO  PL  PT  SI  Sum 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

2 
42 
5 
5 
18 
11 
1 
2 
7 
14 
1 
6 
5 
2 
6 
127 

0 
0 
2 
1 
1 
0 
1 
1 
1 
0 
1 
1 
0 
0 
0 
9 

0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
2 

0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
1 

0 
1 
0 
0 
1 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
4 

0 
1 
0 
2 
2 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
7 

0 
1 
0 
0 
3 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
5 

Data set used Ver12b (2021-05-18) 

123 

 
 
 
 
pH 
pH (lakes) 

Figure A6 pH standards by country and broad type (single value black, minimum blue, 
maximum red). Horizontal dotted lines show the median values for the uppder (red) and 
lower (blue) groups of standards 

124 

 
 
 
 
Table A16 pH metrics used by country 

AA-EQS  GS Mean 

Individual values  MAC-EQS  Minimum  Perc80/m x 

AT 
BE 
BG 
CY 
EE 
ES 
HU 
IE 
NL 
NO 
PL 
PT 
RO 
SI 

10 
0 
6 
1 
7 
9 
8 
0 
0 
10 
1 
0 
0 
1 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 

0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 

0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
0 
0 
8 
0 
0 
0 
0 
0 

Table A17 records where pH was reported as a value or a range 

Country 
AT 
BE 
BG 
CY 
EE 
ES 
HU 
IE 
NL 
NO 
PL 
PT 
RO 
SI 

value 

range 
14 
8 
31 

1 

1 

9 

15 

15 
30 
7 
6 
9 
17 
4 
8 

2 

125 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A18 Number of different pH standards by country and broad type 

AT  BE  BG  CY  EE  ES  HU 
0 
1 
0 
4 
0 
4 
1 
0 
1 
0 
0 
0 
0 
0 
0 
3 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
2 
8 
1 
10 

0 
4 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
6 

0 
2 
0 
1 
2 
1 
0 
1 
0 
0 
0 
0 
0 
7 

0 
9 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
9 

0 
1 
1 
0 
1 
1 
0 
0 
0 
1 
0 
1 
0 
6 

IE  NL  NO  PL  PT  RO  SI  Sum 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

0 
1 
2 
1 
0 
0 
1 
0 
2 
0 
1 
2 
0 
10 

4 
30 
5 
3 
6 
9 
1 
3 
3 
3 
1 
3 
2 
73 

0 
4 
0 
0 
1 
2 
0 
1 
0 
0 
0 
0 
0 
8 

0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
1 

0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
1 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
3 

All 
LW-00 
LW-01 
LW-02 
LW-03 
LW-04 
LW-05 
LW-06 
LW-07 
LW-08 
LW-09 
LW-11 
LW-12 
Sum 

Data set used Ver12b (2021-05-18) 

126 

 
 
 
 
pH (rivers) 

Figure A7 pH standards by country and broad type (single value black, minimum blue, 
maximum red). Horizontal dotted lines show the median values for the uppder (red) and 
lower (blue) groups of standards 

127 

 
 
 
 
Table A19 pH metrics used by country 

10&90thper 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

AT 
BE 
BG 
CZ 
DE 
EE 
ES 
FI 
FR 
HR 
HU 
IE 
LU 
NL 
NO 
PT 
RO 
SE 
SK 
UK 

10th 
percentile 
0 
8 
0 
0 
0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

90th 
percentile 
1 
8 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
7 
0 
8 
0 

95th 
percentile 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 

AA-
EQS 
0 
1 
8 
0 
0 
0 
2 
0 
0 
0 
7 
0 
0 
0 
11 
0 
0 
6 
0 
1 

Individual 
values 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 

seasonal 
average 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
13 
0 
0 
0 
0 
0 
0 

MAC-
EQS  MaxA  Maximum  Median 

Min/Jahr - 
Max/Jahr  MinA  Minimum  Perc80/m x 

0 
0 
0 
11 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
11 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
11 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
5 
0 
13 
0 
0 
0 
10 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 

0 
5 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
9 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

128 

 
 
 
Table A20 records where pH was reported as a value or a range 
Country  value 

range 
3 
11 
31 

AT 
BE 
BG 
CZ 
DE 
EE 
ES 
FI 
FR 
HR 
HU 
IE 
LU 
NL 
NO 
PT 
RO 
SE 
SK 
UK 

66 

42 
66 

61 

2 
1 

36 
1 
57 

4 

16 
35 

10 
28 
9 
6 
7 
16 
10 
10 

6 
28 
11 

129 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A21 Number of different pH standards by country and broad type 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-12a 
RW-12b 
RW-13 
RW-14 
RW-15 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

AT  BE  BG  CZ  DE  EE  ES 
0 
0 
0 
1 
5 
2 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
4 
0 
3 
0 
4 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
2 
0 
0 
0 
2 
0 
6 
0 
2 
0 
7 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
24  20 
1 

0 
9 
4 
0 
2 
2 
2 
0 
0 
2 
2 
2 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
27 

1 
1 
0 
0 
0 
1 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
1 
8 

0 
0 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

FI 
0 
2 
2 
1 
1 
0 
0 
1 
1 
0 
0 
1 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
10 

FR  HR  HU 
0 
1 
1 
0 
1 
1 
2 
1 
0 
1 
0 
0 
1 
1 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
1 
0 
2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
7 
11 
2 

IE 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

LU  NL  NO  PT  RO  SE  SK  UK  Sum 
10 
0 
0 
27 
1 
0 
16 
1 
0 
3 
0 
0 
8 
0 
0 
20 
1 
1 
17 
1 
1 
2 
0 
0 
1 
0 
0 
7 
0 
1 
8 
0 
1 
16 
1 
0 
17 
1 
0 
3 
0 
0 
1 
0 
0 
1 
0 
0 
4 
0 
0 
5 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
2 
1 
0 
171 
7 
4 

0 
0 
1 
1 
0 
0 
0 
0 
0 
1 
2 
0 
0 
2 
1 
0 
3 
0 
0 
0 
0 
0 
11 

0 
3 
1 
0 
3 
4 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
13 

0 
0 
1 
0 
0 
2 
1 
0 
0 
0 
0 
2 
1 
0 
0 
0 
0 
1 
0 
0 
0 
0 
8 

2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

0 
1 
0 
0 
0 
1 
1 
1 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
6 

Data set used Ver12b (2021-05-18) 

130 

 
 
 
 
BOD  
BOD5 concentration (rivers) 

Figure A8 BOD5 standards by country and broad type (single value black, minimum blue, 
maximum red) 
Table A22 BOD5 metrics used by country 

131 

 
 
 
90th percentile  AA-EQS  Average of March-October  Median  Perc80/m x 

AT 
BE 
BG 
CY 
CZ 
DE 
FR 
HR 
HU 
IE 
LT 
LU 
LV 
PL 
PT 
RO 
SI 
SK 

22 
9 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
10 
21 
12 

0 
1 
8 
1 
0 
12 
0 
0 
7 
1 
2 
5 
5 
16 
0 
1 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 

0 
0 
0 
0 
7 
0 
0 
21 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 

Table A23 records where BOD5 was reported as a value or a range 

range 

Country 
AT 
BE 
BG 
CY 
CZ 
DE 
FR 
HR 
HU 
IE 
LT 
LU 
LV 
PL 
PT 
RO 
SI 
SK 

value 
55 
35 
17 
1 
21 
31 
9 
28 
10 
3 
5 
7 
23 
65 
8 
71 
74 
27 

132 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A24 Number of different BOD5 standards by country and broad type 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-12a 
RW-14 
RW-15 
RW-16 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

AT  BE  BG  CY  CZ  DE 
0 
0 
3 
5 
2 
0 
0 
0 
0 
0 
1 
1 
2 
0 
0 
0 
0 
0 
1 
3 
1 
1 
1 
3 
1 
3 
0 
0 
0 
0 
0 
3 
0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
12 
22 

0 
2 
1 
0 
0 
1 
1 
0 
0 
1 
1 
1 
1 
1 
0 
0 
0 
0 
0 
0 
0 
10 

0 
1 
0 
0 
0 
2 
0 
0 
0 
1 
1 
0 
0 
0 
1 
1 
0 
0 
0 
0 
1 
8 

0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
2 
2 
0 
0 
1 
0 
0 
0 
0 
0 
7 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

FR  HR  HU 
0 
0 
1 
0 
4 
0 
2 
3 
0 
1 
0 
0 
1 
1 
0 
0 
2 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
1 
0 
2 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
1 
0 
0 
3 
0 
0 
0 
0 
7 
21 
1 

IE 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

LT 
0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

LU 
0 
0 
0 
0 
0 
2 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
5 

LV 
0 
0 
0 
0 
0 
3 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
5 

PL 
0 
2 
1 
2 
2 
0 
0 
1 
1 
1 
2 
1 
2 
0 
1 
0 
0 
0 
0 
0 
0 
16 

PT  RO 
0 
1 
4 
0 
1 
0 
0 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
13 
1 

SK 
SI 
0 
0 
0 
2 
1 
1 
0 
1 
0 
0 
3 
3 
1 
1 
0 
0 
0 
0 
0 
2 
0 
2 
4 
3 
1 
3 
0 
0 
0 
1 
2 
1 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
21  12 

Sum 
4 
23 
12 
4 
4 
21 
13 
1 
1 
10 
9 
19 
19 
1 
3 
8 
3 
3 
1 
3 
3 
165 

Data set used Ver12b (2021-05-18) 

133 

 
 
 
 
Ammonium-N 
Ammonium N concentration (rivers) 

Figure A9 Ammonium N standards by country and broad type (single value black, minimum 
blue, maximum red) 
Table A25 Ammonium N metrics used by country 

134 

 
 
 
75th percentile  90th percentile  AA-EQS  Average of March-October  Median  Perc80/m x 

BE 
BG 
CY 
CZ 
DE 
EE 
ES 
FR 
GR 
HR 
HU 
IT 
LT 
LU 
LV 
NO 
PL 
PT 
RO 
SK 
UK 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 
0 

10 
0 
0 
0 
0 
3 
0 
4 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
15 
11 
2 

0 
8 
1 
0 
12 
0 
4 
0 
0 
0 
8 
1 
2 
4 
6 
7 
16 
0 
1 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 

0 
0 
0 
7 
0 
0 
0 
0 
1 
18 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 

Table A26 records where Ammonium N was reported as a value or a range 
Country  value 

range 

BE 
BG 
CY 
CZ 
DE 
EE 
ES 
FR 
GR 
HR 
HU 
IT 
LT 
LU 
LV 
NO 
PL 
PT 
RO 
SK 
UK 

34 
17 
1 
21 
35 
16 
32 
10 
1 
28 
10 
1 
5 
7 
23 
13 
65 
8 
69 
28 
32 

1 

Table A27 Number of different Ammonium N standards by country and broad type 

135 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-12a 
RW-12b 
RW-14 
RW-15 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

BE  BG  CY  CZ  DE  EE  ES 
0 
0 
0 
4 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
1 
1 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 
2 
2 
0 
2 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 
7 
10 

0 
3 
2 
0 
0 
1 
2 
0 
0 
1 
1 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
12 

0 
1 
0 
0 
0 
2 
0 
0 
0 
1 
1 
0 
0 
0 
0 
1 
1 
0 
0 
0 
1 
8 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
0 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

FR  GR  HR  HU 
0 
1 
0 
2 
2 
1 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
8 
4 

0 
3 
2 
0 
1 
2 
2 
0 
0 
0 
0 
1 
2 
0 
0 
0 
0 
2 
1 
2 
0 
18 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

IT 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

LT 
0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

LU 
0 
0 
0 
0 
0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 

LV  NO  PL  PT  RO 
0 
0 
0 
7 
2 
0 
3 
1 
0 
0 
2 
0 
0 
2 
0 
2 
0 
4 
2 
0 
2 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
2 
0 
2 
1 
0 
2 
2 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 
0 
21 
16 
6 

2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

0 
0 
1 
1 
0 
0 
0 
0 
0 
1 
1 
0 
0 
1 
1 
1 
0 
0 
0 
0 
0 
7 

SK  UK 
0 
0 
2 
0 
0 
1 
0 
0 
0 
0 
0 
2 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 
2 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
2 
11 

Sum 
6 
25 
15 
4 
4 
18 
14 
1 
1 
6 
7 
13 
15 
2 
1 
3 
4 
2 
1 
2 
4 
148 

Data set used Ver12b (2021-05-18) 

136 

 
 
 
 
Nutrients 
Nitrate (rivers) 

Figure A10 Nitrate as N standards by country and broad type (single value black, minimum 
blue, maximum red). Horizontal lines mark 25th (green) and 75th (red) quantiles. 

137 

 
 
 
 
 
Table A28 Nitrate as N metrics used by country 

90th percentile  95th percentile  AA-EQS  Maximum  Median 

AT 
BE 
BG 
CY 
CZ 
ES 
FR 
GR 
HR 
IT 
LT 
LU 
LV 
PL 
PT 
RO 
SI 
SK 

14 
8 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
1 
20 
0 
13 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 

0 
0 
9 
1 
0 
3 
0 
0 
0 
1 
2 
4 
2 
14 
5 
0 
0 
0 

0 
0 
0 
0 
7 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
7 
0 
0 
1 
20 
0 
0 
0 
0 
0 
0 
0 
21 
0 

Table A29 Nitrate as N number of records where standard was reported as a value or a 
range 
Country  value 

range 

AT 
BE 
BG 
CY 
CZ 
ES 
FR 
GR 
HR 
IT 
LT 
LU 
LV 
PL 
PT 
RO 
SI 
SK 

52 
34 
17 
1 
42 
37 
9 
1 
28 
1 
5 
7 
7 
65 
18 
56 
74 
28 

1 

1 

138 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A30 Number of different Nitrate as N standards by country and broad type 
FR  GR  HR 
0 
1 
1 
3 
0 
0 
3 
0 
0 
0 
0 
0 
1 
0 
0 
2 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
1 
0 
0 
2 
0 
0 
0 
0 
0 
20 
1 
1 

AT  BE  BG  CY  CZ  ES 
0 
0 
3 
3 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
2 
0 
1 
0 
2 
0 
2 
0 
0 
0 
1 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
3 
14 

LV  PL  PT  RO 
0 
0 
8 
0 
3 
0 
0 
0 
0 
0 
1 
1 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 
20 
2 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-14 
RW-15 
RW-16 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

SK 
SI 
0 
0 
0 
2 
1 
1 
0 
1 
0 
0 
3 
2 
1 
2 
0 
0 
0 
0 
0 
3 
0 
2 
4 
3 
2 
2 
0 
1 
2 
1 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
21  13 

LU 
0 
0 
0 
0 
0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 

0 
2 
1 
1 
1 
0 
0 
1 
1 
1 
2 
1 
2 
1 
0 
0 
0 
0 
0 
0 
14 

0 
0 
0 
0 
0 
2 
2 
0 
0 
0 
0 
4 
4 
0 
2 
0 
0 
0 
0 
0 
14 

LT 
0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

IT 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

1 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
1 
1 
7 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
1 
1 
0 
0 
1 
1 
0 
0 
1 
1 
1 
1 
0 
0 
0 
0 
0 
0 
0 
8 

1 
1 
0 
0 
0 
2 
0 
0 
0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
1 
9 

Sum 
6 
26 
10 
2 
2 
17 
12 
1 
1 
9 
8 
18 
18 
3 
7 
2 
4 
1 
3 
5 
155 

Data set used Ver12b (2021-05-18) 

139 

 
 
 
 
 
 
Total Nitrogen (lakes) 

Figure A11 TN standards by country and broad type (single value black, minimum blue, 
maximum red). Horizontal lines mark 25th (green) and 75th (red) quantiles. 

140 

 
 
 
 
 
Table A31 TN metrics used by country 

90th percentile  95th percentile  AA-EQS  GS Mean 

BE 
BG 
EE 
FI 
HU 
LT 
LV 
NL 
NO 
PL 
PT 
RO 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 

0 
6 
8 
0 
5 
2 
6 
0 
9 
6 
0 
0 

3 
0 
0 
11 
0 
0 
0 
0 
0 
0 
0 
3 

seasonal average 
0 
0 
0 
0 
0 
0 
0 
5 
0 
0 
0 
0 

Table A32 TN number of records where standard was reported as a value or a range 
Country  value 

range 

BE 
BG 
EE 
FI 
HU 
LT 
LV 
NL 
NO 
PL 
PT 
RO 

4 
18 
15 
197 
7 
6 
26 
9 
13 
23 
2 
15 

Table A33 Number of different TN standards by country and broad type 

BE  BG  EE 
2 
1 
1 
0 
1 
0 
1 
0 
0 
3 
1 
2 
1 
1 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
1 
0 
8 
6 
3 

FI  HU 
3 
6 
1 
2 
0 
1 
0 
0 
1 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
5 
11 

LT 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
2 

LW-00 
LW-01 
LW-02 
LW-03 
LW-04 
LW-05 
LW-06 
LW-07 
LW-08 
LW-09 
LW-11 
Sum 

LV  NL  NO  PL  PT  RO  Sum 
23 
1 
1 
4 
0 
0 
4 
1 
0 
12 
2 
1 
9 
2 
1 
5 
0 
2 
5 
0 
1 
2 
0 
0 
1 
0 
0 
1 
0 
0 
3 
0 
0 
69 
6 
6 

2 
0 
0 
1 
1 
0 
1 
0 
0 
0 
0 
5 

4 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
6 

2 
0 
1 
1 
0 
1 
1 
0 
0 
1 
2 
9 

0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
2 

Data set used Ver12b (2021-05-18) 

141 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Total Nitrogen (rivers) 

Figure A12 TN standards by country and broad type (single value black, minimum blue, 
maximum red). Horizontal lines mark 25th (green) and 75th (red) quantiles. 

142 

 
 
 
 
 
Table A34 TN metrics used by country 

90th percentile  AA-EQS  Median 

BE 
BG 
EE 
FI 
HR 
HU 
LT 
LV 
NL 
NO 
PL 
RO 
SK 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
12 
14 

1 
8 
3 
10 
0 
7 
2 
5 
0 
22 
16 
0 
0 

0 
0 
0 
0 
20 
0 
0 
0 
0 
0 
0 
0 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
0 
0 
6 
0 
0 
0 
0 

summer 
4 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

Table A35 TN number of records where standard was reported as a value or a range 
Country  value 

range 

1 

BE 
BG 
EE 
FI 
HR 
HU 
LT 
LV 
NL 
NO 
PL 
RO 
SK 

5 
17 
16 
61 
28 
10 
10 
23 
14 
75 
65 
57 
28 

143 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A36 Number of different TN standards by country and broad type 
LV  NL  NO  PL  RO  SK  Sum 
0 
0 
2 
0 
1 
0 
2 
0 
2 
0 
0 
3 
0 
2 
1 
0 
1 
0 
1 
0 
2 
0 
1 
0 
2 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
5 

0 
0 
1 
1 
1 
4 
0 
2 
0 
2 
2 
0 
2 
1 
0 
2 
0 
1 
0 
2 
0 
2 
2 
0 
2 
0 
0 
3 
0 
1 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
22  16  12  14  130 

FI  HR  HU  LT 
0 
0 
0 
3 
0 
2 
3 
0 
2 
0 
0 
1 
1 
0 
1 
2 
1 
0 
2 
1 
0 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
1 
0 
1 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
2 
0 
0 
1 
0 
0 
0 
2 
0 
0 
0 
0 
2 
10  20 

BE  BG  EE 
0 
1 
0 
0 
1 
3 
1 
0 
1 
0 
0 
1 
0 
0 
0 
1 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
3 
8 
5 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-12a 
RW-12b 
RW-13 
RW-14 
RW-15 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

1 
14 
16 
7 
8 
17 
11 
4 
3 
4 
5 
9 
12 
3 
1 
1 
3 
3 
2 
1 
2 
3 

0 
1 
1 
0 
1 
2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 

0 
0 
1 
1 
1 
0 
0 
0 
0 
0 
0 
1 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
7 

0 
0 
1 
0 
0 
5 
1 
0 
0 
0 
0 
3 
2 
0 
0 
0 
0 
2 
0 
0 
0 
0 

Data set used Ver12b (2021-05-18) 

144 

 
 
 
 
Orthophosphate (Rivers) 

Figure A13 Orthophosphate standards by country and broad type (single value black, 
minimum blue, maximum red). Horizontal lines mark 25th (green) and 75th (red) quantiles. 

Table A37 Orthophosphate metrics used by country 

145 

 
 
 
 
90th percentile  95th percentile  AA-EQS  Median 
10 
10 

AT 
BE 
BG  0 
0 
CY 
0 
CZ 
0 
DE 
0 
ES 
FR 
1 
GR  0 
HR  0 
HU  0 
0 
IE 
0 
LT 
0 
LU 
0 
LV 
PL 
0 
RO  21 
11 
SK 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 

0 
0 
9 
1 
0 
14 
2 
0 
0 
0 
6 
1 
2 
4 
2 
16 
0 
0 

0 
0 
0 
0 
7 
0 
0 
0 
1 
17 
0 
0 
0 
0 
0 
0 
0 
0 

Table A38 Orthophosphate number of records where standard was reported as a value or a 
range 
Country  value 

range 

1 

AT 
BE 
BG 
CY 
CZ 
DE 
ES 
FR 
GR 
HR 
HU 
IE 
LT 
LU 
LV 
PL 
RO 
SK 

45 
34 
17 
1 
21 
31 
35 
9 
2 
28 
10 
6 
10 
7 
7 
65 
48 
27 

146 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A39 Number of different Orthophosphate standards by country and broad type 
IE  LT  LU  LV  PL  RO  SK 
0 
0 
2 
0 
0 
0 
0 
0 
9 
2 
1 
0 
0 
3 
1 
0 
0 
0 
0 
2 
0 
0 
0 
0 
2 
3 
1 
0 
1 
0 
1 
1 
0 
1 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
1 
0 
0 
0 
0 
1 
0 
0 
0 
0 
2 
4 
0 
0 
2 
1 
1 
0 
0 
2 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
0 
16  21  11 
2 
2 

AT  BE  BG  CY  CZ  DE  ES  FR  GR  HR  HU 
0 
0 
0 
0 
1 
0 
4 
10 
1 
1 
1 
0 
0 
1 
0 
0 
0 
2 
0 
0 
1 
0 
1 
0 
1 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
1 
0 
1 
0 
1 
0 
2 
1 
2 
0 
1 
1 
2 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 
14 
10  10 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-12a 
RW-14 
RW-15 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

0 
2 
2 
0 
1 
2 
2 
0 
0 
0 
0 
1 
2 
0 
0 
0 
2 
1 
2 
0 
17 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

1 
1 
0 
0 
0 
2 
0 
0 
0 
1 
1 
0 
0 
0 
1 
1 
0 
0 
0 
1 
9 

0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
2 
2 
0 
0 
1 
0 
0 
0 
0 
7 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 

0 
0 
0 
0 
0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

Sum 
6 
31 
10 
3 
5 
14 
11 
1 
1 
5 
6 
15 
13 
1 
2 
3 
2 
1 
2 
4 
136 

Data set used Ver12b (2021-05-18) 

147 

 
 
 
 
 
Total phosphorus (lakes) 

Figure A14 TP standards by country and broad type (single value black, minimum blue, 
maximum red). Horizontal lines mark 25th (green) and 75th (red) quantiles. 

148 

 
 
 
 
 
Table A40 TP metrics used by country 

95th percentile  AA-EQS  GS Mean  MAC-EQS  Median 

AT 
BE 
BG 
CY 
CZ 
DE 
EE 
ES 
FI 
FR 
HR 
HU 
IE 
IT 
LT 
LV 
NL 
NO 
PL 
PT 
RO 
SE 
SI 
UK 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 

12 
0 
6 
1 
12 
0 
8 
11 
0 
0 
7 
6 
1 
17 
2 
8 
0 
15 
6 
4 
0 
12 
2 
13 

0 
5 
0 
0 
0 
10 
0 
0 
10 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
12 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
30 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 
0 
0 
0 
0 
0 
0 
0 

149 

 
 
 
 
Table A41 TP number of records where standard was reported as a value or a range 
Country  value 

range 
4 

14 

30 
1 

21 

8 
6 
18 
1 
16 

16 
28 
197 

7 
7 
3 

3 
26 
9 
66 
23 
11 
15 

AT 
BE 
BG 
CY 
CZ 
DE 
EE 
ES 
FI 
FR 
HR 
HU 
IE 
IT 
LT 
LV 
NL 
NO 
PL 
PT 
RO 
SE 
SI 
UK 

12 

3 

2 
319 

150 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A42 Number of different TP standards by country and broad type 

FR  HR  HU 
FI 
AT  BE  BG  CY  CZ  DE  EE  ES 
0 
2 
0 
0 
0 
0 
3 
0 
20 
6 
13 
5 
1 
0 
0 
2 
0 
1 
0 
0 
0 
1 
0 
0 
0 
0 
2 
0 
0 
0 
2 
0 
0 
0 
0 
1 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
1 
0 
1 
5 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
4 
0 
0 
1 
0 
6 
7 
13  10  30 
12 

0 
0 
4 
4 
0 
0 
0 
1 
2 
0 
2 
0 
0 
0 
0 
0 
1 
3 
1 
2 
0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
12  10 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

0 
2 
0 
0 
2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
5 

0 
1 
1 
0 
1 
1 
0 
0 
0 
1 
0 
0 
1 
0 
0 
0 
6 

0 
3 
0 
1 
2 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
8 

IT 
IE 
0 
1 
3 
0 
1 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
2 
0 
3 
0 
2 
0 
0 
2 
1  17 

LT  LV  NL  NO  PL  PT  RO  SE  SI  UK  Sum 
2 
0 
2 
0 
0 
0 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 
2 

0 
0 
0 
0 
0 
8 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
1 
3 
0 
1 
0 
0 
1 
1 
0 
1 
0 
2 
1 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
12  12  2 

7 
82 
10 
11 
21 
21 
5 
6 
11 
17 
2 
2 
10 
6 
2 
7 
220 

0 
1 
3 
2 
0 
0 
1 
1 
1 
1 
1 
1 
3 
0 
0 
0 
15 

1 
3 
0 
4 
3 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
13 

0 
2 
0 
0 
1 
2 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
6 

0 
1 
0 
0 
3 
1 
2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
8 

0 
1 
0 
1 
2 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 

All 
LW-00 
LW-01 
LW-02 
LW-03 
LW-04 
LW-05 
LW-06 
LW-07 
LW-08 
LW-09 
LW-10 
LW-11 
LW-12 
LW-13 
LW-14 
Sum 

Data set used Ver12b (2021-05-18) 

151 

 
 
 
 
 
 
Total P (rivers) 

Figure A15 TP standards by country and broad type (single value black, minimum blue, 
maximum red). Horizontal lines mark 25th (green) and 75th (red) quantiles. 

152 

 
 
 
 
 
Table A43 TP metrics used by country 

90th percentile  95th percentile  AA-EQS  Median 

BE 
BG 
CY 
CZ 
DE 
EE 
FI 
FR 
GR 
HR 
HU 
IT 
LT 
LU 
LV 
NL 
NO 
PL 
PT 
RO 
SE 
SI 
SK 

10 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
12 
0 
0 
10 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
0 
0 
0 

1 
8 
1 
0 
11 
3 
11 
0 
0 
0 
6 
1 
2 
4 
6 
0 
20 
16 
8 
0 
11 
0 
0 

0 
0 
0 
7 
0 
0 
0 
0 
1 
18 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
23 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 
0 
0 
0 
0 
0 
0 
0 

summer 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

153 

 
 
 
 
Table A44 TP number of records where standard was reported as a value or a range 
Country  value 

range 

BE 
BG 
CY 
CZ 
DE 
EE 
FI 
FR 
GR 
HR 
HU 
IT 
LT 
LU 
LV 
NL 
NO 
PL 
PT 
RO 
SE 
SI 
SK 

1 

11 

37 
17 
1 
21 
28 
16 
70 
9 
1 
28 
10 
1 
5 
7 
23 
14 
69 
65 
24 
55 

74 
28 

154 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A45 Number of different TP standards by country and broad type 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-12a 
RW-12b 
RW-13 
RW-14 
RW-15 
RW-17 
RW-18 
RW-19 
RW-20 
Sum 

BE  BG  CY  CZ  DE  EE 
0 
0 
0 
3 
1 
1 
0 
1 
0 
0 
1 
1 
1 
1 
0 
0 
0 
0 
0 
1 
0 
1 
0 
2 
0 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
3 
13 

0 
3 
1 
0 
0 
1 
2 
0 
0 
1 
1 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
11 

1 
1 
0 
0 
0 
1 
0 
0 
0 
1 
1 
0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
1 
8 

0 
0 
0 
0 
0 
1 
1 
0 
0 
0 
0 
2 
2 
0 
0 
0 
0 
1 
0 
0 
0 
0 
7 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

FI 
0 
3 
2 
1 
1 
0 
0 
1 
1 
0 
0 
1 
0 
0 
0 
1 
0 
0 
0 
0 
0 
0 
11 

FR  GR  HR  HU 
0 
1 
0 
0 
1 
0 
1 
0 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 
1 

0 
2 
2 
0 
1 
2 
2 
0 
0 
0 
0 
1 
2 
0 
0 
0 
0 
0 
2 
1 
3 
0 
18 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

IT 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

LT  LU  LV  NL  NO  PL  PT  RO  SE 
0 
0 
0 
0 
2 
0 
0 
0 
1 
1 
3 
0 
2 
1 
3 
0 
2 
0 
2 
0 
0 
1 
0 
1 
0 
1 
0 
1 
1 
1 
2 
0 
1 
1 
1 
0 
1 
1 
2 
0 
2 
1 
2 
0 
1 
0 
0 
0 
2 
1 
0 
0 
0 
1 
3 
0 
0 
1 
1 
0 
0 
0 
0 
0 
1 
0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
20  16 
2 

SI 
0 
0 
2 
1 
1 
2 
1 
0 
0 
0 
4 
1 
3 
1 
0 
0 
0 
0 
3 
0 
2 
0 
2 
2 
2 
2 
0 
0 
0 
0 
0 
0 
1 
0 
1 
0 
1 
0 
0 
0 
0 
0 
3 
0 
12  11  23  10  200 

SK  Sum 
0 
0 
1 
0 
0 
2 
1 
0 
0 
0 
0 
3 
1 
0 
0 
0 
0 
2 
0 
0 
0 
0 

8 
21 
18 
10 
9 
23 
18 
5 
4 
11 
11 
16 
15 
5 
2 
1 
4 
5 
4 
1 
4 
5 

3 
3 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 
0 
1 
1 
9 

0 
1 
1 
0 
1 
2 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 

0 
0 
0 
0 
0 
4 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
6 

0 
0 
0 
0 
0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 

Data set used Ver12b (2021-05-18) 

155 

 
 
 
 
Salinity 
Chloride concentration (rivers) 

Figure A16 Chloride standards by country and broad type (single value black, minimum blue, 
maximum red) 

Table A46 Chloride metrics used by country 

156 

 
 
90th percentile  AA-EQS  MAC-EQS 

AT 
BE 
DE 
ES 
HU 
LU 
NL 

0 
3 
0 
0 
0 
0 
0 

1 
1 
8 
1 
9 
5 
0 

1 
0 
0 
0 
0 
0 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
8 

Table A47 records where Chloride was reported as a value or a range 
Country  value 

range 

AT 
BE 
DE 
ES 
HU 
LU 
NL 

6 
8 
26 

9 
9 
246 

1 
1 

Table A48 Number of different Chloride standards by country and broad type 
LU  NL  Sum 
0 
1 
0 
0 
1 
0 
0 
0 
2 
0 
3 
1 
2 
1 
0 
1 
0 
1 
0 
0 
0 
0 
8 
5 

AT  BE  DE  ES  HU 
0 
0 
2 
0 
1 
0 
3 
1 
0 
1 
0 
0 
2 
0 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
1 
1 
0 
2 
1 
0 
9 
8 
2 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-08 
RW-09 
RW-10 
RW-11 
Sum 

4 
2 
6 
3 
4 
5 
4 
2 
2 
2 
3 
37 

1 
0 
1 
2 
0 
0 
0 
0 
0 
0 
0 
4 

0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

Data set used Ver12b (2021-05-18) 

157 

 
 
 
 
 
 
 
 
 
 
 
 
Conductivity (rivers) 

Figure A17 Conductivity standards by country and broad type (single value black, minimum 
blue, maximum red) (scale excludes one extreme value (60,000 us/cm ES) for clarity 

158 

 
 
 
Table A49 Conductivity metrics used by country 

90th percentile  AA-EQS  MAC-EQS 

BE 
BG 
CY 
ES 
HU 
PL 
RO 

3 
0 
0 
0 
0 
0 
7 

1 
9 
1 
8 
9 
16 
0 

0 
0 
0 
9 
0 
0 
0 

Table A50 records where Conductivity was reported as a value or a range 
Country  value 

range 

BE 
BG 
CY 
ES 
HU 
PL 
RO 

5 
31 
1 
27 
9 
65 
57 

12 
1 

Table A51 Number of different Conductivity standards by country and broad type 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-06a 
RW-06b 
RW-08 
RW-09 
RW-10 
RW-11 
RW-14 
RW-15 
RW-20 
Sum 

BE  BG  CY  ES  HU  PL  RO  Sum 
0 
1 
1 
2 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
4 

2 
22 
6 
5 
4 
3 
1 
1 
1 
2 
3 
3 
5 
2 
1 
2 
63 

0 
17 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
17 

0 
2 
1 
2 
2 
0 
0 
1 
1 
1 
2 
1 
2 
1 
0 
0 
16 

0 
0 
3 
1 
2 
0 
0 
0 
0 
0 
0 
1 
2 
0 
0 
0 
9 

0 
1 
1 
0 
0 
1 
1 
0 
0 
0 
0 
1 
1 
0 
0 
1 
7 

1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

1 
1 
0 
0 
0 
2 
0 
0 
0 
1 
1 
0 
0 
1 
1 
1 
9 

Data set used Ver12b (2021-05-18) 

159 

 
 
 
 
 
 
 
 
 
 
 
 
Figure A16 Chloride standards by country and broad type (single value black, minimum blue, 
maximum red) 

160 

 
 
Table A46 Chloride metrics used by country 
90th percentile  AA-EQS  MAC-EQS 

AT 
BE 
DE 
ES 
HU 
LU 
NL 

0 
3 
0 
0 
0 
0 
0 

1 
1 
8 
1 
9 
5 
0 

1 
0 
0 
0 
0 
0 
0 

seasonal average 
0 
0 
0 
0 
0 
0 
8 

Table A47 records where Chloride was reported as a value or a range 
Country  value 

range 

AT 
BE 
DE 
ES 
HU 
LU 
NL 

6 
8 
26 

9 
9 
246 

1 
1 

Table A48 Number of different Chloride standards by country and broad type 
LU  NL  Sum 
0 
1 
0 
0 
1 
0 
0 
0 
2 
0 
3 
1 
2 
1 
0 
1 
0 
1 
0 
0 
0 
0 
8 
5 

AT  BE  DE  ES  HU 
0 
0 
2 
0 
1 
0 
3 
1 
0 
1 
0 
0 
2 
0 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
0 
1 
0 
1 
1 
0 
2 
1 
0 
9 
8 
2 

All 
RW-00 
RW-01 
RW-02 
RW-03 
RW-04 
RW-05 
RW-08 
RW-09 
RW-10 
RW-11 
Sum 

4 
2 
6 
3 
4 
5 
4 
2 
2 
2 
3 
37 

0 
1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
1 

1 
0 
1 
2 
0 
0 
0 
0 
0 
0 
0 
4 

Data set used Ver12b (2021-05-18) 

161 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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doi:10.2760/470539 

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