Agenda item:
Title: WFD CIS ECOSTAT WG A Report “Comparison of Limits of River Basin District Specific Pollutants set for the Demands of the European Water Framework Directive in Streams and Rivers”
Version no.: 1.0 Date: 29.02.2012
Author(s): Ulrich Claussen, ECOSTAT WG A, Colead Germany Patrick Müller, Annika Merkelbag & Tobias Garling & Jens Arle (Federal Environment Agency, Germany)
Summary
This Report for the period of October 2010 to December 2011 presented by the ECOSTAT WG A, Colead Germany is based on the adhoc activity laid down in the work program of the WG A on River Basin District Specific Pollutants set for the Demands of the European Water Framework Directive in Streams and Rivers set by the MSs in order to meet “good ecological status” of surface waters pursuant to the European Water Framework Directive. The report shall provide a brief overview on EQS to the Water Directors as a basis for discussion and further harmonization.
This progress report is available on the WFD CIRCA system in the ECOSTAT WG A meeting documents folder.
Water Directors are invited to:
ü take note of the progress of the adhoc activity on River Basin District Specific Pollutants in the Working Group A ECOSTAT
ü decide on the necessity for further harmonisation among MS of EQSs for River Basin District Specific Pollutants, with the aim to achieve comparability of ecological status assessment as required by the WFD
Contact: &
Table of Contents
Inhalt
Introduction 3
Methods 3
Results 6
Discussion & Conclusions 9
Acknowledgements 10
References 10
Introduction
The European Water Framework Directive (WFD, EC 2000), which was adopted in 2000, fundamentally changed water management in all Member States of the European Union (EU), putting aquatic ecology at the base of management decisions. The WFD has precipitated a fundamental change in management objectives from merely pollution control to ensuring ecosystem integrity as a whole. Deterioration and improvement of ‘ecological quality’ is defined by the response of biota, rather than by changes in physical or chemical parameters. However, physical and chemical parameters (= variables) are used as supportive quality elements in assessments to define surface water types and to describe reference conditions. River Basin District Specific Pollutants (RBDSPs) are the second group of supportive quality elements used to insure the “good ecological status”. The aim of this study is to evaluate and compare environmental quality standards (EQSs) of RBDSPs set by European Member States (MSs) for “good ecological status” of surface waters as stipulated by the WFD.
Methods
EQSs of RBDSPs of the different MSs were evaluated by analysing data sheets provided by the MSs during the official WISE – reporting in March 2010. The data submitted by the MSs are available on http://cdr.eionet.europa.eu/ as HTML factsheets (SWMethods – files). Data were copied from these HTML factsheets, pasted into Microsoft Excel files and saved separately. For the analyses the structure of data tables was modified by inserting additional columns (e.g. adding country codes and water categories). The results of our analyses of EQSs of RBDSPs (including environmental quality objectives, maximum tolerable concentrations, maximum allowable concentrations, threshold values, water quality targets defined for good-moderate-boundaries by individual MSs) are provided as tables allowing the direct comparison of values.
We analysed data of HTML factsheets of 18 MSs (compare Table 1) which had reported to WISE until October 2010. UK reported to WISE by citation of three background documents, which were checked for EQSs by the authors of this report. SE, EE, FI and NL did not report EQSs in their official WISE data sheets. Twelve of the remaining thirteen MSs had not yet reported to WISE. To increase our database we added further data on EQSs based on outcomes of a 2010 - workshop of the WG E
(see: http://circa.europa.eu/Members/irc/env/wfd/library?l=/working_groups/priority_substances/specific_pollutants/pollutantseqs_countries&vm=detailed&sb=Title&cookie=1).
Furthermore we added data on EQSs provided by MSs after email-contacts following the presentation of an earlier draft of this document in March 2011 (30.-31. March 2011, ECOSTAT WG A meeting). Table 1 provides an overview of the data sources used for analysis.
Table 1 Data sources used for analysis. X = data were available. a data were not used because of a request of the MS; b data were not used because of linguistic problems (missing translation); * provided by the MS via email contact.
Member State / WISE-Data / WG E - Data / Additional data*AT - Austria / X / X
BE – Belgium / X / X
BG - Bulgaria / X
CY - Cyprus / X / X
CZ – Czech Republic / X / X
DK - Denmark / X
EE - Estonia / X
FI - Finland / X / X
FR - France / X / X
DE - Germany / X
GR - Greece
HU - Hungaria
IC - Iceland
IE - Ireland / X / X
IT – Italy / Xa / X / X
LV - Latvia / X
LI - Liechtenstein
LT - Lithuania / X / X
LU - Luxembourg / X
MT - Malta / X
NL - Netherlands / X / X
NO - Norway / X
PL - Poland / X / X
PT - Portugal
RO - Romania / X / X
SK - Slovakia / X
SV - Slovenia / X
ES - Spain / X
SE - Sweden / X / X / Xb
SW - Switzerland
TU - Turkey
UK – United Kingdom / X / X
All data are summarized in a MS-Excel data sheet. Substances were identified by their CAS.-No., if available, or by the name. EQSs are expressed as µg l-1(medium: water, if not otherwise denoted - (cf. Appendix 1 and MS Excel version of the data matrix which can be accessed from the authors). The EQSs are commonly expressed as annual average (AA-EQS) or as average of multiple measurements - (if not otherwise denoted – (cf. Appendix 1 and MS Excel Version of the data matrix attached to this report).
We compared the maximum and minimum limits of EQSs recorded for each substance between the MS (only limits with comparable statistical expression e.g. annual averages or means of multiple measurements were used) using the following equation:
(Maximum Limit MS X / Minimum Limit MS Y) x 100 % = Relative difference of EQSs between MSs (%)
A Detrended Correspondence Analysis (DCA) (Jongman et al. 1995) was conducted to analyse and visualise similarities and differences in the lists of EQSs selected by the MSs. For this, the primary MS EXCEL matrix was transformed into an absence – presence matrix (1 stands for EQS of RBDSP was reported, 0 means that no value was reported). Ordination statistics were calculated with the program Canoco Version 4 (ter Braak & Smilauer 1998).
Furthermore, we ranked the MSs according to the “mean strictness” of their EQSs. For this, a reduced data-set was formed with the number of limits of EQSs, which were found to be the highest reported value in comparison to values of all other MSs for the same substances. This data set contained only substances for which at least 5 MSs had reported and a minimum of 4 EQSs were available for each of the substances. The relative contribution of maximum limit values was determined using the following equation:
(Number of Maximum EQS MS X / Total number of EQS MS X) x 100 % = relative proportion of Maximum EQSs of a MS (%).
PNEC freshwater -values (Predicted No Effect Concentrations) were used as a common benchmark for the comparison of EQSs among MSs. PNEC freshwater -values are available at the official web - page of the European Chemicals Agency (ECHA) which is an integral part of the implementation process of the EC-Directive No. 1907/2006 of 1. June 2007 (REACH, (EC 2007)) (compare
http://apps.echa.europa.eu/registered/registered-sub.aspx#search).
For each substance of our data-set we browsed the ECHA database for a PNEC freshwater – value based on CAS-No.`s. Afterwards we compared the official PNEC freshwater – values with the EQSs in our data-set.
Results
The complete data - matrix underlying this report is given in Appendix 1 and in the MS Excel version of the data matrix attached to this report. All in all, 366 substances were recorded (314 thereof with CAS.-No.). 223 substances were reported by at least 2 MSs and could therefore be compared. For 143 substances, only one MS reported limit–values which makes further comparison impossible. Figure 1 shows the number of substances reported by different MSs.
Fig. 1 Number of River Basin District Specific Pollutants (listed substances) with EQSs recorded for different MSs.
Comparing the numbers of EQSs used by the MSs very large differences were observed. Maximum numbers were recorded for Czech Republic (169) and the Netherlands (162) and minimum numbers for Cyprus (3) and Estonia (0). Average number of substances was 46,5.
The MSs assessed a large variety of substances (Fig. 2). For 143 substances only one MS reported limit–values, whereas only two substances (zinc & copper) were assessed by 20 MSs.
Fig. 2 Number of River Basin District Specific Pollutants (listed substances) with EQSs assessed by one or more than one MS.
Figure 3 shows the most common substances for which the MSs reported EQSs. The comparison of maximum and minimum EQSs revealed very large differences for a number of substances among the MSs. Generally we found more differences than similarities between MSs in our data-set (compare Appendix 1).
Fig. 3 Most common substances for which MSs defined EQSs. MCPA = 2-methyl-4-chlorophenoxyacetic acid.
In the DCA the first two axes explained only 18.3% of the variability in composition of RBDSPs selected by the MSs (Figure 4). The low variability explained by the first two axes indicated that there was no strong continuous gradient in the data-set, which could be explained by completely different assumptions on the importance of relevant substances. However, the DCA - plot is useful to visualise similarities and differences in the composition of RBDSPs selected by the MSs. This is because the position of a MS in the plot is a result of similarities and differences of the composition of RBDSPs of the MS relative to all other MSs. MSs in close proximity to another in the plot indicate higher similarities, whereas larger distances between MSs indicate larger differences in the composition of RBDSPs selected by the MSs for implementation of the WFD.
Fig.4 DCA - plot showing similarities and differences in the composition of RBDSPs selected by the MSs. Triangles mark the position of MSs in the plot. The position of a MS in the plot is a result of similarities and differences of the composition of RBDSPs of the MS relative to all other MSs. Triangles in close proximity to another indicate higher similarities, whereas larger distances between MS-triangles indicate larger differences in the composition of RBDSPs selected by the MSs for implementation of the WFD.
The proportions of maximum EQSs relative to the total number of RBDSPs is given in Table 2. A number of MSs (for instance CY, BG & PL) were found to have high proportions of maximum EQSs in relation to the total number RBDSPs whereas others (for instance RO, LU & CZ) have lower ones.
Table 2 Ranking of the proportions of maximum EQSs of MSs relative to the total number of RBDSPs.
MS / Total number of EQSs limits / Number of maximum limit values / Relative proportion of maximum limit values of a MS (5)CY / 3 / 2 / 66,67
BG / 4 / 2 / 50,00
PL / 11 / 5 / 45,45
ES / 14 / 5 / 35,71
LV / 6 / 2 / 33,33
UK / 18 / 6 / 33,33
SE / 9 / 3 / 33,33
SK / 13 / 4 / 30,77
FR / 7 / 2 / 28,57
AT / 20 / 5 / 25,00
NL / 76 / 17 / 22,37
DE / 64 / 14 / 21,88
BE / 72 / 15 / 20,83
IT / 45 / 9 / 20,00
FI / 5 / 1 / 20,00
RO / 67 / 13 / 19,40
LU / 33 / 4 / 12,12
CZ / 54 / 2 / 3,70
IE / 11 / 0 / 0
LT / 5 / 0 / 0
DK / 6 / 0 / 0
NO / 0 / 0 / 0
SV / 0 / 0 / 0
MT / 0 / 0 / 0
EE / 0 / 0 / 0
For only 71 (19,4 %) of the 366 RBDSPs in our data - set PNEC freshwater –values were detected in the database of the ECHA. The comparison of official PNEC freshwater – values and EQSs of RBDSPs reported by the MS revealed large differences. Low correspondence (low correlations) between PNEC freshwater – values and EQSs of RBDSPs reported by the MSs were observed. (compare Appendix 2)
Discussion & Conclusions
As expected, the MS´s used a large number of RBDSPs in order to describe good / moderate boundaries of ecological status. A substantial number of EQSs of RBDSPs was compared among MSs and unexpected high differences in limits were observed.