Environmental risk assessment of zinc in European freshwaters: A critical appraisal

Science of the Total Environment 407 (2009) 5373–5391

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Science of the Total Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c i t o t e n v

Environmental risk assessment of zinc in European freshwaters: A critical appraisal P.A. Van Sprang a,⁎, F.A.M. Verdonck a, F. Van Assche b, L. Regoli b, K.A.C. De Schamphelaere c a b c

ARCADIS-EURAS, Kortrijksesteenweg 302, B-9000, Gent, Belgium International Zinc Association - Europe, Avenue de Tervueren 168, Box 4, 1150 Brussels, Belgium Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, B-9000 Gent, Belgium

a r t i c l e

i n f o

Article history: Received 16 January 2009 Received in revised form 3 June 2009 Accepted 23 June 2009 Available online 24 July 2009 Keywords: Zinc Environmental risk assessment Ecotoxicity Freshwater Bioavailability Exposure Monitoring

a b s t r a c t A risk assessment report (RAR) on zinc and zinc compounds has recently been prepared in the framework of the European Union (EU) Council Regulation 793/93/EEC on Existing Chemicals. The EU Scientific Committee on Human and Environmental Risks (SCHER) has, however, expressed some fundamental, science-based concerns about the approach followed and the conclusions. The main objective of the present study was to assess the potential environmental risks associated with current use patterns of Zn in nine EU river basins in Germany, France and Belgium, thereby using more advanced methodologies which are largely in line with the recommendations made by SCHER. This included (i) avoiding working with measured Zn concentrations from monitoring stations that were potentially influenced by point sources and/or historical contamination, (ii) the full bioavailability normalization of all chronic ecotoxicity data to river basin specific physico-chemistry using biotic ligand models (BLM), prior to deriving predicted no effect concentrations (PNEC) with the species sensitivity distribution (SSD) approach, and (iii) the use of a probabilistic framework for risk characterization. Further, a total risk approach instead of an added risk approach was used, and the PNEC was equated to the HC5-50 without an additional assessment factor. Based on monitoring data we estimated predicted environmental concentrations (PEC) for the different EU river basins between 1.3 and 14.6 µg dissolved Zn/L. PNEC values varied between 22.1 and 46.1 µg dissolved Zn/L. This resulted in deterministic risk characterization ratios (RCR) that were below 1 in all river basins, suggesting that there is no deterministic regional risk associated with current use patterns of Zn in these river basins. With the probabilistic approach we identified rather limited risks, i.e., between b 0.4 and 18.3%. When the EU RAR approach was applied to the same monitoring datasets, deterministic risks were found in different river basins. A detailed analysis showed that this different deterministic conclusion of risk is mainly due to the fact that the EU RAR (i) uses an additional assessment factor of 2 to derive the PNEC and (ii) uses a more conservative approach for implementing bioavailability (BioF approach). We argue that the larger conservatism in the EU RAR mainly originates from decisions made to deal in a pragmatic way with (i) uncertainty related to the across-species extrapolation of BLMs and (ii) the relatively high sensitivity of some multi-species toxicity studies. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Zinc metal (Zn) and five zinc compounds, i.e., zinc oxide (ZnO), zinc chloride (ZnCl2), zinc sulphate (ZnSO4), zinc phosphate (Zn3 (PO4)2 and zinc distearate ((C18H35O2)2Zn) were prioritized under EU Regulation EEC/793/93 in September 1995. This implied that a full risk assessment for Zn needed to be carried out, following the guidelines detailed in the Technical Guidance Document (TGD) on Risk Assessment for New and Existing Substances (EU, 2003). The draft final European Union Risk Assessment Report (EU RAR) on zinc (environmental part) has become available recently (EU, 2006). Zinc was the first natural element to be assessed under this regulation and it became apparent quickly that the standard methodology of the TGD, which was mainly developed for man-made organic compounds, was less suitable for an essential element like zinc (Bodar et al., 2005). This ⁎ Corresponding author. Tel.: +32 9 241 77 45; fax: + 32 9 241 77 01. E-mail address: [email protected] (P.A. Van Sprang). 0048-9697/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2009.06.029

was mainly due to three aspects which deserved specific recognition because these aspects differentiate zinc from most organic compounds. First, as zinc is an essential element for many metabolic functions in most organisms, organisms may be conditioned (acclimated or adapted) to the prevailing Zn concentration. Since this conditioning may influence an organism's tolerance to Zn itself, the Zn-tolerance of an organism in the field may depend on the local natural background concentration of Zn and, likewise, the Zn-tolerance as determined with ecotoxicity tests may depend on the Zn concentration in the culture medium in which the organism is maintained in the laboratory (Muyssen and Janssen, 2001a, b). From this, one could easily argue that natural background and culture media zinc concentrations should be considered in risk assessment, if possible. The EU RAR (EU, 2006) dealt with natural backgrounds and essentiality by adopting the added risk approach (Crommentuijn et al., 2000; see also Section 2.1), and by setting acceptable ranges for background applied in ecotoxicity testing. Second, it is now generally recognized by regulators, industry and academia that total or dissolved concentrations of metals such as Zn, are

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not good predictors of toxic effects and that metal bioavailability needs to be taken into account to accurately assess the potential impact of metals on aquatic ecosystems (Bergman and Dorward-King, 1997; Janssen et al., 2000; Paquin et al., 2002; Waeterschoot et al., 2003; Nigoyi and Wood, 2004; Fairbrother et al., 2007). Indeed, numerous detailed studies have demonstrated the protective effects of complexation by DOC (e.g., Paulauskis and Winner, 1988; Heijerick et al., 2003), and competing cations like Ca2+, Mg2+, Na+, and H+ (e.g., Paulauskis and Winner, 1988; Heijerick et al., 2002a,b; De Schamphelaere and Janssen, 2004; De Schamphelaere et al., 2005a; Heijerick et al., 2005) on Zn toxicity to fish, crustaceans and algae. This research has culminated into the development of the biotic ligand model (BLM), which can accurately predict the toxicity of some metals, including Zn, as a function of the physicochemistry of the water (Fig. 1; Paquin et al., 2002, Nigoyi and Wood, 2004). The BLM methodology is now considered as state-of-the-art for predicting metal bioavailability because it integrates existing knowledge about metal speciation in the solution surrounding the organism and the interactions between metal ions and competing ions at binding sites on the organism-water interface (e.g., epithelial cells of gill tissue). Since chronic Zn-BLMs now exist for organisms from three trophic levels, i.e., the primary producing green alga Pseudokirchneriella subcapitata (De Schamphelaere et al., 2005a); the herbivore crustacean D. magna (Heijerick et al., 2005) and the fish Oncorhynchus mykiss (De Schamphelaere and Janssen, 2004), it is obvious that these models should be incorporated in risk assessment. The EU RAR (EU, 2006) made use of these BLMs by means of the so-called “bioavailability factor” approach (BioF approach) (see Section 3.3.3 for details). Third, zinc is very “data rich”. Compared to many organic compounds, a very large amount of exposure and effects data exists for zinc. These data are of variable quality and relevancy for risk assessment. However, the need for adequate screening of datasets such that only relevant and reliable data are used for environmental risk assessment is often not recognized by users of these data. In addition, in the case where such large datasets exist, increased attention is currently given to (i) the use of statistical extrapolation methods to derive HC5 (and PNEC) values from species sensitivity distributions (SSD) and PEC values from environ-

mental concentration distributions (ECD) and (ii) the use of probabilistic risk assessment techniques (Van Sprang et al., 2004; Verdonck et al., 2002). The EU RAR (EU, 2006) has integrated some (but not all) of these aspects of large-dataset handling. As for all other substances assessed under regulation 793/93/EC, the risk assessment report for Zn resulted from discussions between member state experts in the EU “Technical Committee on New and Existing Substances” (TCNES). The outcome of such a broad discussion process is obviously influenced by the capacity of the different members to integrate new scientific concepts – such as those described above – for regulatory purposes. This is, logically, a process which lags behind the scientific developments as such. Consequently, “EU risk assessments, although built on scientific principles, are unarguably mixed with some pragmatic elements and the zinc risk assessment is, by no means, an exception” (Bodar et al., 2005). After the draft EU RAR was closed by the TCNES process (EU, 2006), the EU Scientific Committee for Human and Environmental Risks (SCHER) provided its opinion and concerns on the scientific quality of the report (SCHER, 2007). Major science-based concerns of SCHER are related to (i) the use of the added risk approach (see Section 2.1 for more detail) for dealing with natural backgrounds and essentiality, (ii) the BioF approach for taking into account bioavailability, (iii) the rather arbitrary setting of PNECs using assessment factors, and (iv) the fact that no probabilistic risk assessment was carried out, The main objective of the present study was therefore to assess the potential environmental risks to selected EU freshwater river basins figuring in the EU RAR associated with the current production and use patterns of zinc and zinc compounds on a regional scale (i.e., excluding locations influenced by point sources and historical contamination), thereby using methodologies and concepts that are more advanced with regard to bioavailability modeling and large dataset handling (including probabilistic approaches) and thereby also following closely the recommendations made by SCHER (2007). Whenever appropriate, we will compare the outcomes of our analyses with those reported in the EU RAR (EU, 2006) and provide explanations for differences. We will also transparently describe the assumptions made and identify the remaining data gaps that require further research.

Fig. 1. Schematic overview of the biotic ligand mode exemplified for Zn (redrawn from De Schamphelaere et al., 2005b). Dashed arrows represent speciation of Zn, full lines represent competition with Zn for the biotic ligand. The amount of Zn bound to the biotic ligand determines the toxic response, independent of water chemistry (intrinsic sensitivity). See main text, Section 2.3.2 for more detail and explanation of symbols used.

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2. Materials and methods A schematic overview of the methodology followed for exposure assessment (Section 2.2) and effect assessment (Section 2.3) and for risk characterization (Section 2.4) is given in Fig. 2.

2.1. Dealing with essentiality and natural backgrounds In contrast to the EU RAR, who adopted the added risk approach we have used the total risk approach (see Struijs et al., 1997 and Crommentuijn et al., 2000 for description of both approaches). The added risk concept was developed to deal with elements that have a natural background concentration in the environment. Such approach implies that only the anthropogenic part, i.e., the amount added to the natural background concentration is considered relevant for the effect assessment of the metal. In other words, this approach suggests that the effects due to the (bioavailable fraction of the) metal background concentration are neglected. Alternatively, the total risk approach considers that both the natural background concentration and the anthropogenic part of the metal can contribute to the observed effects. While the added risk approach requires reliable information about (i) Zn concentrations in culture media of ecotoxicological test organisms and (ii) about region-specific natural

Fig. 2. Schematic overview of the methodology used for the risk assessment of Zn per river basin. Octagonal boxes represent the different steps of data collection, data handling and calculations, while rectangular boxes represent outcomes of calculations. Reference is given to the relevant sections, tables, figures and supplementary material.

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background concentrations of Zn, the total risk approach does not. The information required for the added risk approach is very often not available in studies reporting on the ecotoxicity of Zn. In addition, the SCHER (2007) – and earlier also the Scientific Committee for Toxicity, Ecotoxicity and the Environment (CSTEE, 2004) – states that the current knowledge on region-specific metal background concentrations in aquatic systems is insufficient to correctly implement the added risk approach and that, in general, the added risk approach may increase the uncertainty associated with the risk assessment. For these reasons, we adopted the total risk approach in the present study and recommend that essentiality and natural backgrounds should be dealt with in the risk management phase. This means that in regions (or locations) where potential risks are identified (i.e., PEC N PNEC), it should be investigated whether this is due to a high natural background or to other sources of Zn. With the total risk approach, we follow the recommendation of SCHER (2007). 2.2. Exposure assessment 2.2.1. Data collection Zinc is a metal which is incorporated in several regional and national monitoring programs. In the present study, both (i) zinc monitoring data and (ii) data on the physico-chemical properties (i.e., Cs (suspended solids), pH, Ca, Mg and DOC (dissolved organic carbon)) were gathered from nine river basins in total, four in Germany (Weser, Elbe, Rhine, and Danube), two in Belgium (Scheldt and Meuse–Seine) and three in France (Rhône– Méditérranée, Seine–Normandie, and Rhine–Meuse). Zn monitoring data on the German, Belgian and French river basins were obtained from (monitoring institutions and monitoring year in parentheses): http://www.umweltbundesamt.de/luft/immissionssituation/hid/index.htm (Umweltbundesamt, UBA, 2001), http://environnement.wallonie.be (Direction Générale des Resources Naturelles et de l' Environnement, DGRNE, 2001) and http://sandre.eaufrance.fr (Réseau National des Données sur l'Eau, RNDE, 2000, 2001, and 2002), respectively. Physico-chemical properties for German and French river basins were retrieved from online databases (website and monitoring year considered in parentheses): Weser (http://www.arge-weser.de, 2000), Elbe (http://www.arge-elbe.de, 2000), Rhine (http://www.dk-rhein.de, 2000), Danube (http://www.icpdr. org, 2001), Rhône–Méditérranée (http://www.eaurmc.fr, 2000), Seine–Normandie (http://www.eau-seine-normandie.fr, 1996) and Rhine–Meuse (http://www.eau-rhin-meuse.fr, 2000) basins. Physico-chemical properties of the Belgian river basins were retrieved from DGRNE (http:// environnement.wallonie.be, 2000). All Zn monitoring data were reported as total Zn concentrations (µg/L). Physico-chemical properties were collected together with information about total Zn concentrations because knowledge of total Zn concentrations alone is not sufficient for proper risk characterization. First, the concentration of suspended solids (Cs) was required to translate the measured total Zn concentrations to dissolved measured Zn concentrations using the equilibrium partitioning methodology (see Section 2.2.3). Second, the bioavailability-based normalization (see Section 2.3.2) of the chronic Zn toxicity data to the physico-chemical conditions prevailing in a river basin required the collection of information on the most important abiotic factors that mitigate chronic zinc toxicity. Therefore monitoring data on pH, Ca (mg/L), Mg (mg/L) and DOC (mg/L) were compiled. An overview of the rivers and sampling stations used for extracting total zinc concentrations, Cs, pH, Ca, Mg and DOC is provided as supplementary material (S1). 2.2.2. Data handling and ECD on the basis of total Zn All reported monitoring data on total zinc concentrations were first scrutinized to ensure that they are useful for the scope of the present study, i.e., for assessing the risks associated with the current use patterns of Zn in the EU. First, zinc concentrations as reported in monitoring databases may be influenced by a number of factors, including industrial point sources, diffuse emissions, local natural background, and historical contamination. Zinc is extensively monitored in EU waters and, according to the TGD, Section 2.2. (EU, 2003), such monitored data should be assigned to local or regional scenarios, based on the relation between the emission source and the sampling site. In this respect, the TGD stipulates that “samples taken at sites directly influenced by an (industrial) emission should be used to describe the local scenario, while samples taken at larger distances may represent the regional concentrations”. Regarding the allocation of measured data to the local or regional scale, the TGD further specifies that “if there is no spatial proximity between the sampling site and point sources of emission (e.g., from rural regions), the data represent a regional concentration (PEC-regional) that has to be added to the calculated PEC-local. If the measured concentrations reflect the releases into the environment through point sources, they are of the PEC-local-type (TGD Section 2.2.2. (EU, 2003). We used specific emission information e.g., from the European Pollutant Emission Register (http://eper.eea.europa.eu/ eper/) to analyse, where possible, the relationship between monitored data and industrial point source emissions. In those cases were a clear relationship could be established and documented, the measured concentration was considered as a local concentration and not used in the regional data analysis. Secondly, the monitored data of the EU risk assessment and the present paper are mainly from countries with a long history of industrial activity (e.g., Germany, France, Belgium). This long history has resulted in a significant historical contamination of some of the river basins assessed in this paper. Risk assessments made under Regulation 793/93/CE assess the risks related to the present day production and use of given substances. For this reason, monitored data from identified areas with historical pollution were taken out of the regional database and assessed separately since they are not related to the present day production and use of zinc. However, it is recognized that scientifically more robust methods/criteria should be developed for assigning influence from point source and/or historical pollution. In summary, monitored data clearly influenced by industrial point source emission and by historical contamination were not further considered for constructing environmental concentration distributions (ECD) (see Section 2.2.3). This approach deviates from the one used in the EU Zn RAR (EU, 2006), in which no detailed analysis of the influences of point sources and historical contamination was carried out, and both these influences were included in the database. Second, initial inspection of the monitoring databases revealed a rather broad range of detection limits (DL) for Zn, i.e., ranging between 1 and 50 µg/L. In the event where values were reported as bDL, they were treated as if the true measured values were 1/2 DL, as long as the DL was not higher than 25 µg/L. Because one half of the detection limit (12.5 µg/L) is in the range of the lowest HC5–50 values (see Section 3.2.3) found, using below detection limit data of higher than 25 µg/L could result in upwardly biased risk characterization ratios (see 2.3). Finally, in accordance with the TGD, the 90th percentile of all reported total Zn concentrations per sampling station was determined by means of non-parametric percentile calculation with Excel® (Microsoft, Redmond, WA, USA) software. A log-normally distributed ECD (on the basis of total Zn) for a river basin was fitted to the 90th percentiles of all sampling stations within that basin.

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2.2.3. Determination of ECD and deterministic PEC on the basis of dissolved Zn Since risk characterization (see Section 2.4) of Zn involves a comparison of effects with exposure on the basis of a dissolved Zn concentration, a transformation of the ECD on the basis of total Zn to an ECD on the basis of dissolved Zn was required per basin. This was performed with the equilibrium partitioning approach, using the formula: ½Zndissolved  =


½Zntotal   1 + Kd  Cs  10 − 6

ð1Þ

where [Zndissolved] and [Zntotal] are the dissolved and total Zn concentration (µg/L), Kd is the equilibrium partitioning coefficient (L/kg), and Cs is the concentration of suspended solids (mg/L). Both Kd and Cs are known to exhibit variation among different water bodies and this variation was taken into account – next to the variation of total Zn – in a probabilistic manner to obtain basin-specific ECDs for dissolved Zn. We calculated 250 Monte Carlo estimates of dissolved Zn, by drawing 250 independent samples from (i) the log-normal ECD for total Zn, (ii) a basin-specific log-normal distribution function of Cs (fitted to Cs concentrations reported in the monitoring databases), and (iii) a log-normal frequency distribution of Kd (fitted to 47 Kd values reported by US EPA, 2005) with mean (log10 Kd) = 5.0 and standard deviation (log10 Kd) = 0.5. The EU RAR used a similar mean log Kd of 5.04. Applying Eq. (1) to these 250 individual samples yielded the basin-specific ECD on the basis of dissolved Zn (consisting of 250 estimates of dissolved Zn). The 50th percentile of this ECD was considered the deterministic predicted environmental concentration on the basis of dissolved Zn (PECdissolved). The whole ECD was used for probabilistic risk characterization (see 2.4). 2.3. Effects assessment 2.3.1. Compilation of chronic toxicity data for Zn The datasets reported in the final draft EU RAR for zinc (EU, 2006) and in the study of Van Sprang et al. (2004) were used as the basis for the compilation of chronic freshwater toxicity data with Zn. All these data were originally retrieved from peer-reviewed literature and published study reports and only relevant and reliable chronic toxicity data were included in these databases. Reliability covers the inherent quality of a test relating to test methodology and the way that the performance and results of the test are described. Relevance covers the extent to which a test is appropriate for a particular risk assessment. According to the TGD (EU, 2003) the test media used in the toxicity tests should be ‘representative of the environmental compartment being studied’. In that respect, in Van Sprang et al. (2004) and in the EU Zn RAR (EU, 2006) only data obtained in test media with pH between 6 and 9 and hardness between 24 and 250 mg CaCO3/L were retained. Additionally, if the zinc concentration in the culture media of the test organism was below 1 µg/L, the study was not retained either. Indeed, according to Zuurdeeg et al. (1992), the natural background concentration of zinc in European surface waters varied between 5–40 µg/L (10th–90th percentile of total Zn). Considering the partitioning of zinc between the soluble phase and the fraction bound to suspended matter, a cut-off of 1 µg dissolved Zn/L was proposed in the EU RAR for zinc (EU, 2006) as a relevancy criterion for the selection of the ecotoxicity data. This criterion is applied also in this manuscript. This was to avoid using ecotoxicity data from organisms that might have become overly sensitive to Zn due to acclimation to low Zn concentrations, which are not representative for natural background Zn concentrations in Europe. As a first step in our data compilation, the Van Sprang et al. (2004) and the EU RAR (2006) data – which exhibited considerable overlap – were merged. In a second step, we applied an additional selection criterion to this dataset, related to the use of BLMs in the effects assessment (see Section 2.3.2). Only those toxicity data were retained which were obtained in test media with physico-chemistry within the demonstrated workable range of the BLMs, i.e., under conditions for which the BLMs have been demonstrated to accurately predict chronic zinc toxicity. De Schamphelaere et al. (2005a) have suggested separate workable ranges for pH and Ca concentration for the Daphnia magna, Oncorhynchus mykiss and Pseudokirchneriella Table 1 Workable ranges of pH and Ca and parameters of the biotic ligand models (BLM) for Daphnia magna, Oncorhynchus mykiss and Pseudokirchneriella subcapitata. Daphnia magna BLM pH range Ca range (mg/L) Stability constantsa Log KCaBL Log KMgBL Log KNaBL Log KHBL Log KZnBL Species i D. polymorpha P. jenkinsi C. dubia D. magna D. longispina H. azteca E. virgo A. fissa B. rubens

Oncorhynchus mykiss BLM 6–8.4 5–160 D. magnab 3.2 2.7 1.9 5.8 5.3 100 × fZnBL,NOEC,i 7.85 (1) 1.30 (1) 1.27 ± 0.61 (6) 7.29 ± 3.50 (32) 9.77 ± 5.43 (2) 1.47 (1) 19.4 (1) 4.80 (1) 4.80 (1)

pH range Ca range (mg/L) Stability constantsa Log KCaBL Log KMgBL Log KNaBL Log KHBL Log KZnBL Species i D. rerio J. floridae P. phoxinus P. promelas O. mykiss S. fontinalis S. trutta C. bairdii

Pseudokirchneriella subcapitata BLM 5.7–8.1 5–160 O. mykissc 3.6 3.1 2.4 6.3 5.5 100 × fZnBL,NOEC,i 41.0 ± 27.9 (9) 2.42 (1) 3.87 (1) 8.19 (1) 12.8 ± 7.12 (19) 43.2 (1) 8.43 ± 7.35 (2) 5.92 ± 4.75 (2)

pH range Ca range (mg/L) Model constantd SpH

5.7–8.0 No limitation P. subcapitata − 0.754

Species i P. subcapitata C. vulgaris

QNOEC,i − 1.435 ± 0.257 (30) − 1.037 ± 0.165 (5)

Species-specific ‘intrinsic sensitivities’ (fZnBL,NOEC,i or QNOEC,i) for other invertebrate, fish and algae species, calculated from the ecotoxicity database (Supplementary material, S2) of chronic no observed effect concentrations (NOEC) as explained in Section 2.3.2, are given as mean ± standard deviation (n). a Biotic Ligand Model stability constants for XBL are defined as KXBL = [XBL]/{(X) × [BL]}, where parentheses (X) denotes chemical activity of X, [XBL] is the concentration of X bound a to the BL, and [BL] is the concentration of unoccupied BL sites. b Taken from Heijerick et al. (2005). c Taken from De Schamphelaere and Janssen (2004). d Taken from De Schamphelaere et al. (2005a).

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Fig. 3. Schematic overview of the procedure for normalization of ecotoxicity data with biotic ligand models (BLM), for normalization of NOEC values obtained in exposure medium x to the physico-chemistry of target water y (see main text, Section 2.3.2 for more detail).

subcapitata bioavailability models (Table 1). Slight differences between these three BLMs exist – due to different range of exposure media that were used for BLM development and validation – but all models are workable for pH between 6 and 8 and for Ca between 5 and 160 mg/L. Only toxicity data obtained in test media with pH and Ca within these boundaries were retained in our ecotoxicity database. No criterion was applied to the DOC concentration, as the effect of DOC on chronic Zn toxicity has been shown to be solely due to effects on Zn speciation and as the effect of DOC on Znspeciation is fully captured by the speciation-model WHAM-Model V (which is an integral part of all BLMs, see Section 2.3.2). The latter model has been calibrated over a large range of chemical conditions and DOC concentrations (Tipping, 1994; Cheng et al., 2005). In a third step, the toxicity data retained so far were subjected once more to a profound reliability and relevance analysis. The details of this analysis are given as supplementary material (S2). In a fourth step, an additional literature search was performed to update the chronic ecotoxicity database with new valuable chronic toxicity data for Zn that were not yet included in the databases of Van Sprang et al. (2004) or the EU RAR (EU, 2006). The new chronic toxicity data were subjected to the same selection criteria as mentioned above with the same level of stringency. A final step in the construction of the ecotoxicity database was the compilation of the physico-chemistry of the exposure media in which chronic ecotoxicity data were obtained in the original studies. Indeed, when chronic toxicity data of Zn, obtained in a given exposure medium, are to be normalized to the specific water chemistry of a river basin, it is required that the most critical physicochemical characteristics of the original test medium are known or can be estimated. The most critical variables for accurately predicting chronic Zn bioavailability and toxicity are pH, Ca and DOC (De Schamphelaere et al., 2005a). Other input variables for the bioavailability models are concentrations of Mg, Na, K, total alkalinity, sulfate, and chloride. In cases where one or more of these variables were not reported in the original publication, the values of the missing variables were estimated based on, e.g., (i) monitoring data (e.g., in cases where test media were natural waters), (ii) published test guidelines (e.g., in cases where only a reference to a standard medium published in a standard testing guideline was given), (iii) charge balance and ionic strength considerations, etc. A detailed overview of the reported and the estimated physico-chemical variables of each individual exposure medium are provided as Supplementary material (S2).

2.3.2. Normalization of chronic Zn toxicity data to river-basin-specific physico-chemistry using BLMs Since chronic toxicity of Zn depends on the physico-chemistry of the test medium, all chronic toxicity data (i.e., NOEC values) from the database (see S2) were normalized to river-basin specific physico-chemistry before being used as input data for the calculation of river-basin specific HC5–50 values (see Section 2.3.3). Furthermore, the within river basin-variability of the physico-chemistry was propagated using Monte-Carlo simulation. First, mean values per sampling station were calculated from the collected monitoring data (see Section 2.2.1). For each river basin, cumulative distribution functions were then fitted to these mean values: a normal distribution for pH and a log-normal distribution for DOC, Ca and Mg. Then, 250 independent random samples were drawn from each distribution to generate 250 combinations of river-basin specific water chemistry. The − other input variables for the BLMs, i.e., Na, K, SO2− 4 , Cl and alkalinity were estimated from their correlation with Ca as determined from monitoring data on large EU rivers (i.e., from the United Nations ‘Global Environmental Monitoring System’, GEMS, monitoring years 1988–1996, www. gemswater.org). Correlation coefficients were between 0.51 and 0.96 for all variables. Details of these correlations are given as Supplementary material (S3). All chronic toxicity data where then normalized to each of these 250 ‘sampled’ physico-chemistries. This procedure yielded the input NOEC values (250 per original NOEC value in the ecotoxicity database reported in S2) for the subsequent calculation of 250 HC5–50 values per river basin and, ultimately for the estimation of the cumulative probability distribution of HC5–50 values per river basin (see Section 2.3.3). The normalization procedure itself takes into account (i) the speciation of Zn and (ii) competitive binding of Zn2+ and cations (Ca2+, Mg2+, Na+ and H+) at the biological membrane (biotic ligand) (see Fig. 3). The procedure consist of three steps and is explained in brief below for the normalization of a single NOECdissolved,i,x obtained for a species i in a given test medium x to a ‘target water’ with physico-chemistry y (Eq. (2)). A simplified example will consider a NOEC for D. magna that will be normalized using the D. magna BLM. The first step is to calculate the socalled “intrinsic sensitivity” for D. magna. In accordance with the BLM concept, this is fNOEC ZnBL , a dimensionless parameter which is the fraction of the biotic ligand sites that is occupied by Zn at a Zn concentration equal to the NOECdissolved,i,x. To this end, the chemical activity of the free Zn2+ ion at the NOEC, i.e., NOECZn2+,i,x (mol/L) is calculated, as well as the chemical activities of the competing cations. This was performed using the BLM software (version 2.1.2 for Windows®; Hydroqual, Mahwah, NJ, USA), which incorporates WHAM-Model V (Tipping, 1994) as the speciation module. Stability constants for the formation of inorganic Zn-complexes were taken from the National Institute for Standardization (NIST) (Smith et al., 2004). We assumed that natural organic matter consisted of 61% active fulvic acid unless a more precise estimate was available for a given natural test medium (see Cheng et al., 2005 and Table S2.1 for more detail). The fZnBL,NOEC,i,x is then obtained as follows:

fZnBL;NOEC;i;x =

KZnBL  NOECZn2þ ;i;x

 1 + KZnBL  NOECZn2 +;i;x + KCaBL  Ca2 + i;x + KMgBL  Mg2+ i;x + KNaBL  ðNaþ Þi;x + KHBL  ðH þ Þi;x

ð2Þ

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where KZnBL, KCaBL, KMgBL, KNaBL, and KHBL (L/mol) are stability constants for competitive binding of Zn2+, Ca2+, Mg2+, Na+ and H+ to the biotic ligand (BL), respectively (Table 1) and where (Ca2+)i,x, (Mg2+)i,x, (Na+)i,x and (H+)i,x are the chemical activities (as mol//L) of Ca2+, Mg2+, Na+ and H+ in exposure medium x, respectively. The second step is to use this fZnBL,NOEC,i,x, to compute the Zn2+ activity in ‘target water’ y, with another physico-chemistry would be:           fZnBL;NOEC;i;x 2+ 2+ þ þ  NOECZn2 +;i;x;y =   1 + KCaBL  Ca + KMgBL  Mg + KNaBL  Na + KHBL  H y y y y 1 − fZnBL;NOEC;i;x  KZnBL

ð3Þ

where (Ca2+)y, (Mg2+)y, (Na+)y and (H+)y are the chemical activities of Ca2+, Mg2+, Na+ and H+ in ‘target water’ y, respectively. These activities are calculated from solution chemistry of target water y with WHAM-Model V. The third step is to transform this NOECZn2+,i,x,y to the final normalized dissolved NOEC in ‘target water’ y, i.e., the NOECdissolved,i,x,y, using WHAM-Model V. In practice, with the BLM software steps two and three can be performed simultaneously. In the context of the D. magna example we have now reached the stage where we have normalized a single D. magna NOEC value from the ecotoxicity database to the physicochemistry of the ‘target water’. The same procedure can be followed for all other D. magna NOEC values in the database as well as for all NOEC values for O. mykiss in the database, excepting that the specific BLM stability constants for O. mykiss (see Table 1) are used in Eqs. (2) and (3). The procedure for normalizing NOEC data obtained with P. subcapitata is founded on the same principle as the procedure for D. magna and O. mykiss, except that a different formula describes the competitive interactions between Zn2+ and other cations. Heijerick et al. (2002a) and De Schamphelaere et al. (2005a) showed that the effect of pH on the toxicity of Zn2+ to P. subcapitata could not be fully explained by H+ competing with Zn2+ for a single unidentate metal-binding site and that the protective effects of Ca2+, Mg2+ and Na+ on the toxicity of Zn2+ were much less important than the pH effect (Heijerick et al., 2002a). Therefore, an alternative model has been suggested – until competitive effects are better understood – which consists of a simple linear regression model between the log10-transformed NOEC (expressed as free Zn2+-activity) and pH (De Schamphelaere et al., 2005a). Under this model, the following equation is valid for any given NOECdissolved,i,x: QNOEC;i;x = Log10 NOECZn2 +;i;x − SpH × pHi;x

ð4Þ

where SpH is the slope of the above-mentioned linear regression model (SpH = −0.754 for P. subcapitata; Table 1, see De Schamphelaere et al., 2005a) and QNOEC,i,x is a measure of the ‘intrinsic sensitivity’. SpH is conceptually identical to the log KHBL of D. magna and O. mykiss; QNOEC,i,x is conceptually identical to fZnBL,NOEC,i,x (see De Schamphelaere et al., 2006 for a detailed motivation). In the same way as described for D. magna, the QNOEC,i,x is then used in the next step of the normalization to obtain the NOECZn2+,i,x,y for a ‘target water’ y: Log10 NOECZn2 +;i;x;y = QNOEC;i;x + SpH × pHy

ð5Þ

The final step in the normalization of a NOEC for P. subcapitata is to translate the NOECZn2+,i,x,y to the NOECdissolved,i,x,y with WHAM-Model V. In practice, the normalization of the algae NOEC data can also be carried out with the BLM-software. It is intuitive that the D. magna, O. mykiss and P. subcapitata BLM are used for normalizing toxicity data of D. magna, O. mykiss and P. subcapitata, respectively. However, the chronic toxicity database contains NOEC values for other species than just those three for which specific BLMs have been developed. Ideally, a separate species-specific BLM should be developed for every single species, but this is considered not realistic. Thus an interim alternative was to assume that competitive interactions between Zn2+ and cations (Ca2+, Mg2+, Na+ and H+) at the biological membrane (biotic ligand) are the same among related species (within broad taxonomic groups (algae, crustaceans, fish)). In other words, the BLM stability constants KZnBL, KCaBL, KMgBL, KNaBL, and KHBL as well as SpH are assumed the same among related species. The only difference between related species is assumed to be their intrinsic sensitivity (fZnBL,NOEC or QNOEC). Following these assumptions, all invertebrate NOEC values will be normalized with the D. magna BLM, all vertebrate (fish) NOEC values with the O. mykiss BLM and all algae NOEC values with the P. subcapitata BLM. A similar assumption has been made earlier in (i) the EU RAR on Cd (EU, 2004) – where a single hardness correction function has been applied to the NOEC values for all species represented in the ecotoxicity database (including invertebrates, vertebrates and algae) and (ii) in the derivation with BLM of site-specific water quality criteria for Cu – where a single BLM is used for all invertebrates and vertebrates in the ecotoxicity database (US EPA, 2007). The approach we follow in the present study with Zn should be considered a refinement of the latter two approaches, as it acknowledges the existence of differences among three groups of organisms (vertebrates, invertebrates and algae). 2.3.3. Data aggregation, SSD construction, and estimation of HC5–50 and its distribution within a river basin When the normalization procedure as explained in Section 2.3.2, is completed for all NOEC values in the ecotoxicity database for a given target physico-chemistry y, multiple NOEC values may be available for a single species, while only one NOEC per species can be entered into the SSD. In such a case, the NOEC entered into the SSD is the geometric mean of all available normalized NOEC values for that species. When data on multiple endpoints are available for the same species the lowest geometric mean NOEC (most sensitive endpoint) is used for the construction of the SSD. This approach is recommended by the TGD (EU, 2003). Further on, these NOEC values will be referred to as “geomean NOEC values”. Eight parametric distributions (Normal, Logistic, Pearson, Weibull, Log-Normal, Gamma, Exponential and Cauchy) were fitted to the logtransformed geomean NOEC values. The best fitting distribution was determined based on the Kolmogorov–Smirnov (K/S) goodness-of-fit statistic (Stephens, 1982; Gan et al., 1991; Cullen and Frey, 1999). The hazardous concentration for 5% of the species (HC5) was then estimated. However, due to sampling uncertainty, this estimation bears uncertainty and according to the TGD (EU, 2003) the HC5 at its median estimate (or at its 50% confidence level) should be used for risk assessment. The median HC5 is further denoted as “HC5-50”. The normal distribution extrapolation constants as developed by Aldenberg and Jaworska (2000) were used for obtaining the HC5–50 from a normal distribution (of logtransformed NOEC values). The sampling uncertainty for the other seven distributions was taken into account using parametric bootstrap simulation of geomean NOEC values with replacement (Davison and Hinkley, 1997). The median of the HC5 values obtained from 4000 independent Monte Carlo runs was considered the HC5–50.

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For every river basin, this fitting procedure was repeated for all 250 simulated combinations of the river basin-specific physico-chemistry (see Section 2.3.2). The median of these 250 simulated HC5–50 values was considered to be the deterministic PNEC, while all 250 HC5–50 values formed the probability distribution of HC5–50 values that was used for the probabilistic risk characterization (see Section 2.4). 2.4. Risk characterisation The potential risks of Zn in the different EU river basins were estimated using (i) a deterministic approach and (ii) a probabilistic approach. Following the deterministic approach a deterministic risk characterisation ratio (RCR) was calculated as the quotient of single values representing exposure and effects, i.e., deterministic PECdissolved (see Section 2.2.3)/deterministic PNEC (see Section 2.3.3). Zn is present at concentrations that may pose a risk to aquatic ecosystems in the river basin in case the RCR N 1. Following the probabilistic approach, a probabilistic risk quotient distribution was obtained as the non-parametric distribution of ratios of random values from the 250 simulated dissolved PEC values (see Section 2.2.3) and the 250 simulated HC5–50 values (see Section 2.3.3). The risk is defined as the probability that the 90th percentile of the total Zn concentration at a randomly selected station in a river basin yields – after binding to suspended solids – a dissolved Zn concentration that exceeds some randomly selected HC5–50 from the probability distribution of HC5–50 that is representative for the physico-chemistry of that river basin. This probability is equal to the probability that the RCR (PEC/HC5–50 = PEC/PNEC) is N1 and can be regarded as a measure of risks (Verdonck et al., 2002). 2.5. Validation of the HC5–50 with results from multi-species studies A PNEC, in this study equated to the HC5–50, is the concentration below which an unacceptable effect to the environment is assumed not to occur, i.e., below which structure and functioning of ecosystems are assumed not to be adversely affected (EU, 2003). Versteeg et al. (1999) demonstrated, based on a meta-analysis of published toxicity data, that the HC5–50 derived from toxicity experiment in the laboratory is indeed usually lower than the ecosystem-level NOEC for model ecosystems, i.e., multi-species toxicity experiments. The EU RAR, however, mentions that some adverse effects of Zn on multi-species systems were found “at or (slightly) below the HC5–50” of the SSD (EU, 2006; Bodar et al., 2005). This analysis, however, did not take into account differences in chemical composition – and hence differences in bioavailability – among test media used for obtaining toxicity data for single-species and multi-species test systems. Without prior normalization of all toxicity data for bioavailability differences, the comparison between the HC5–50 and multi-species NOEC values is of little relevance. In this study we performed the same analysis while taking into account bioavailability. Briefly, we reevaluated eleven multi-species studies cited in the EU RAR as well as two additional studies identified via a Web of Science® search. A summary of all these studies is provided as Supplementary material (S4). An initial quality screen reduced the database to eight studies (see S4 for explanation). From these studies ecosystem-level lowest observed effect concentrations (LOEC) were retrieved as detailed in S4. We grouped the wealth of endpoints reported in the different studies into three categories: biomass/abundance endpoints, structural endpoints (e.g., number of species) and functional endpoints (e.g., community respiration). If the LOEC was reported as a total Zn concentration, a probability distribution of dissolved Zn was generated by Monte Carlo simulation (250 samples) using total Zn, Cs and the Kd distribution for Zn (cf. Section 2.2.3). Next, HC5–50 values, normalized for bioavailability, were calculated for the specific chemical composition of the experimental test media used in each study, following the methodology detailed in Section 2.3.2 and 2.3.3. Here too, Monte Carlo simulation (250 samples) was used to propagate variability and/or uncertainty about the pH, Ca, and DOC to yield a probability distribution of the HC5–50 value for each study. In many cases not all physico-chemistry was reported and had to be estimated such as explained above (see Section 2.3.2). Details about the physico-chemistry and the assumptions made are given as supplementary material (S4). We then compared the median LOEC with the HC5–50 and also calculated the probability that the LOEC was higher than the HC5–50, i.e., p (LOEC N HC5–50). If p(LOEC N HC5–50) is low, this means that a significant toxic effect has occurred in a model ecosystem exposed to a Zn concentration that is likely below the HC5–50 and that the HC5–50 is not conservative for that model ecosystem. 2.6. Software used All distribution fittings and Monte Carlo simulations were performed with Bestfit software package (Palisade Inc., Newfield, USA). All bioavailability normalizations with the BLM were carried out with publicly available BLM software (version 2.1.2, Hydroqual, Mahwah, NJ, USA, http://www.hydroqual.com/wr_blm.html). The BLM parameter files (extension ⁎.dat), originally delivered with the software were modified to include the stability constants and species-specific intrinsic sensitivities mentioned in Table 1. The original thermodynamic database (extension ⁎. dbs) was modified to reflect stability constants for inorganic complexes from NIST (Smith et al., 2004). All statistical analyses were carried with Statistica® 6 software (StatSoft, Tulsa, OK, USA).

Table 2 Summary of the exposure assessment for nine EU river basins. Germany

# stations (all) # stations (no influence of point source and/or historical contamination) Median of 90th % of total Zn (µg/L) (all stations) Median of 90th % of total Zn (µg/L) (only stations with no influence of point source and/or historical contamination) PECtotal (reported in EU RAR) (µg/L) 10th % of ECD for dissolved Zn (µg/L) 50th % of ECD for dissolved Zn (µg/L) = PECdissolved 90th % of ECD for dissolved Zn (µg/L)

Belgium

Danube

Elbe

Rhine

14 14

44 38

41 34

12.3 12.3

22.1 18.0

20.1 0.6 3.5 13.1

72.0 0.8 4.3 22.9

Weser

France Meuse− Seine

Scheldt

Rhine− Meuse

Rhône− Méditteranée

Seine− Normandie

9 5

115 109

29 23

36 36

14 14

3 3

25.7 21.5

71.4 42.6

20.0 19.7

51.6 35.0

16.0 16.0

6.0 6.0

5.4 5.4

46.0 1.8 6.4 24.9

181.0 4.3 14.6 39.2

106.0–129.0 1.3 6.1 24.7

– 1.1 6.0 30.0

99.0 1.5 7.3 22.6

– 0.4 1.3 5.1

30.0 0.6 2.1 4.1

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3. Results and discussion 3.1. Exposure assessment A detailed description of the different monitoring datasets considered is given as Supplementary material (S1). The PEC values for the nine river basins, as total zinc concentrations, are reported in Table 2. Before removal of monitoring stations with influence of historical pollution and/or point sources the median total concentrations varied between 5.4 µg/L (Seine-Normandie basin) and 71.4 µg/L (Weser basin). In five river basins sampling stations with likely influences from historical pollution (mainly due to mining or metallurgical activities) and/or point source pollution were identified (see Supplementary material (S1) for a detailed description of sampling stations omitted from the analysis). The ECDs for total Zn, which were obtained following removal of these sampling stations, are presented in Fig. 4. The PEC values, as total zinc concentrations, were up to a factor 1.7 lower, i.e., ranging between 5.4 µg/L (Seine–Normandie basin) and 42.6 µg/L (Weser basin) (Table 2). For seven of the nine considered river basins, the EU RAR, in which removal of point source and historical influences was not accounted for, reports PEC values, as total zinc concentrations, ranging between 30.0 µg/L (Seine–Normandie basin) and 181 µg/L (Weser basin) (Table 2) (EU, 2006). Comparing the PEC values for these seven basins shows that the EU RAR (EU, 2006) reports consistently higher values, i.e., between two to sixfold higher. It is clear that eliminating sampling stations with historical and point source influences may have a very important impact on the assessment of regional risks associated with current production and use patterns of Zn. Next, river-basin specific distributions of Cs (suspended solids) were used, together with a Kd distribution to obtain ECDs on the basis of dissolved Zn. Across all river basins investigated, median suspended solid concentrations varied by about 3.5-fold, i.e., between 9.9 mg/L (Rhine–Meuse basin) and 36.6 mg/L (Scheldt basin) (Table 3). The 10th percentiles varied between 4.6 and 17.9 mg/L and the 90th percentiles between 21.3 and 74.9 mg/L (Table 3). The calculations ultimately led to the ECDs presented in Fig. 4, with the 50th percentiles of these ECDs (PECdissolved) between 1.3 µg/L (Rhine–Méditerranée basin) and 14.6 µg/L (Weser basin), and 90th percentiles between 5.1 µg/L (Rhine–Méditerranée basin) and 39.2 µg/L (Weser basin) (Table 2). These ECDs will be used further on in the risk characterization (see Section 3.3). 3.2. Effects assessment 3.2.1. Data compilation First, recently generated chronic toxicity data that were considered relevant and reliable were added to the combined ecotoxicity databases reported in the EU Zn RAR (EU, 2006) and by Van Sprang et al. (2004). Only data from studies which reported measured Zn concentrations (rather than just nominal) were included. New data were added for P. subcapitata (De Schamphelaere et al., 2003, 2005a), D. magna (Heijerick et al., 2005; De Schamphelaere et al., 2005a), and O. mykiss (De Schamphelaere and Janssen, 2004; De Schamphelaere et al., 2005a). In addition, chronic toxicity data for six new species were added, i.e., Chlorella sp. (unicellular alga, Wilde et al., 2006), Daphnia longispina (cladoceran, Muyssen et al., 2003), Anuraeopsis fissa and Brachionus rubens (two rotifer species, Azuara-Garcia et al., 2006), Cottus bairdii (fish, Woodling et al., 2002; Brinkman and Woodling, 2005), and Salmo trutta (fish, Källqvist et al., 2003). Re-evaluation of reliability and relevance of the toxicity data reported by Van Sprang et al. (2004) and in the EU RAR (EU, 2006) resulted in the exclusion of several chronic toxicity data from our final database for various reasons, with the major ones being (i) the lack of reported measured Zn concentrations, (it is noted that the EU RAR made use of NOECs reported as nominal concentrations in an added risk approach), and (ii) not having used (or reported) sound statistical analysis for deriving

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NOEC values. A detailed description of why NOEC values were not retained in our final database is given as Supplementary material (S2). The retained data in our final ecotoxicity database, together with the estimated physico-chemistry of the test media and some additional background information is given as Supplementary material (S2). An overview of the species included in the final ecotoxicity database is given in Table 4. The final freshwater Zn ecotoxicity database contains high quality chronic ecotoxicity data for 19 different species belonging to different taxonomic groups, i.e., fish (eight different species covering four families), invertebrates (four crustaceans, one insect, two rotifers, two mollusks) and algae (two different green algae species). The database complies with the TGD's requirements of minimum sample size, i.e., “at least 10 NOECs (preferably more than 15) for different species” (EU, 2003). It also complies with seven of eight requirements of taxonomic composition, the only exception being the absence of a NOEC for a higher aquatic plant. The EU RAR cites a few studies suggesting that higher plants are not very sensitive to Zn and that the absence of NOEC for a higher plant should therefore not prohibit the use of the SSD approach for deriving the PNEC. Further, all toxicity data cover ecologically relevant endpoints, i.e., mortality, reproduction, hatching and growth, and were considered as representing ‘true chronic’ exposure times. The latter is debatable for the insect E. virgo where a relatively short term exposure time of 10 days is noticed. It is currently unknown of this is sufficiently long to yield a NOEC that is conservative for the full life-cycle of insects (see also Section 3.4). 3.2.2. Normalization of NOEC data to river basin specific physicochemistry The normalization of chronic toxicity data required knowledge about physico-chemistry of the river basins. The 10th, 50th (median) and 90th percentiles of pH, DOC, Ca and Mg for the different river basins are reported in Table 3. All nine considered river basins exhibit similar pH, with median pH values between 7.7 and 8.1, 10th percentiles between 7.0 and 7.9 and 90th percentiles between 7.9 and 8.4. A larger inter-basin variation of DOC concentrations was observed. The Weser (5.2 mg/L) and the Scheldt basin (6.1 mg/L) exhibited clearly higher DOC concentrations than the other seven river basins (median DOC between 2.0 and 3.5 mg/L). Overall, the 10th and 90th percentiles of DOC varied were 1.4 to 4.3 mg/L and 2.3 to 13.4 mg/L, respectively. Median Ca concentrations varied by about twofold among river basins, i.e., between 49.5 mg/L (Danube basin) and 114.7 mg/L (Scheldt basin), while the 10th and 90th percentiles varied from 11.7 mg/L to 98.9 mg/L and from 55.9 mg/L to 218.8 mg/L, respectively. The distributions fitted to these data were sufficient to draw 250 random samples of physico-chemistry for each of the nine river basins (2250 samples in total). All sampled combinations of physico chemistry are given as Supplementary material (S2). Out of the 2250 randomly drawn Monte Carlo samples of physicochemistry (250 per basin × 9 basins), 36.6% had chemistry outside the ‘workable range’ of the BLM, with 34.0% having a pH N 8 and 3.7% having a Ca N 160 mg/L (upper boundaries). None of the samples had pH b 6.0 and only 0.3% had Ca b 5 mg/L (lower boundaries). This introduces uncertainty in the normalizations of toxicity data and, consequently, into the estimation of HC5–50 and RCR. The % of samples above the upper pH boundary of 8.0 varies strongly among river basins: 1.2% for the Seine–Normandie, 10% for the Scheldt, 22.4% for the Meuse–Seine, 24.8% for the Rhine, 25.2% for the Rhine–Meuse, 34% for the Weser, 58% for the Danube, and Elbe, and 72.4% for the Rhône– Méditteranée basin. The degree of uncertainty of the assessment will obviously increase with increasing % of samples with pH N 8. Extending the workable ranges of the BLMs to pH N 8, based on new ecotoxicity experiments is therefore highly recommended. We recommend that this would be performed in the first place for green algae for two reasons. First, because the chronic BLMs for D. magna and O. mykiss have been validated up to a slightly higher upper pH, i.e., 8.4 and 8.13

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Fig. 4. Environmental concentration distributions of total zinc and probabilistically modeled dissolved zinc concentrations in different EU river basins. The solid line is the modeled log-normal distribution function fitted to the 90th percentile values of the total Zn concentration measured at individual monitoring stations (squares). The dashed line represents the modeled distribution of the dissolved Zn concentrations.

respectively, with a considerably lower % of Monte Carlo samples above these values, i.e., 2% and 15%, respectively. Second, because green algae become increasingly more sensitive than invertebrates and fish at increasing pH (see Fig. 5). For the remaining part of this

paper, we will assume that the BLMs can be extrapolated slightly outside their upper pH boundaries. Under this assumption, the 250 Monte Carlo samples of physicochemistry per basin were used for normalizing all NOEC values from

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Table 3 Overview of the physico-chemistry in nine EU river basins. Country/river basin

Cs (mg/L)

pH

DOC (mg/L)

Germany Danube Elbe Rhine Weser

Ca (mg/L)

Mg (mg/L)

35.6 (13.6–53.0) 28.2 (16.4–48.5) 17.0 (11.1–26.1) 17.5 (9.7–31.4)

8.0 (7.9–8.1) 8.0 (7.7–8.3) 7.9 (7.6–8.1) 7.9 (7.7–8.2)

2.3 (2.1–2.5) 5.2 (4.3–6.2) 2.5 (1.6–4.2) 3.5 (2.2–5.7)

49.5 (43.0–55.9) 72.3 (41.3–126.5) 55.5 (22.2–139.2) 70.1 (43.7–112.5)

Belgium Meuse–Seine Scheldt

16.7 (7.9–35.6) 36.6 (17.9–74.9)

7.7 (7.3–8.2) 7.7 (7.4–8.0)

3.1 (1.9–5.2) 6.1 (2.7–13.4)

67.7 (35.7–128.3) 114.7 (98.9–133.1)

8.3 (4.6–15.2) 13.6 (10.1–18.2)

France Rhine–Meuse Rhône–Médittéranée Seine–Normandie

9.9 (4.6–21.3) 27.8 (16.8–45.8) 15.8 (9.6–26.1)

7.7 (7.0–8.4) 8.1 (7.9–8.2) 7.7 (7.5–7.9)

2.9 (2.0–4.4) 2.0 (1.4–2.9) 2.9 (2.1–3.9)

50.5 (11.7–218.8) 66.6 (54.0–82.0) 79.6 (68.1–92.9)

5.9 (2.2–15.7) 6.4 (4.4–9.5) 16.1 (13.8–18.8)

10.8 (8.7–12.8) 16.1 (4.6–57.1) 13.3 (8.1–22.0) 42.3 (10.7–166.8)

The pH, and concentrations of dissolved organic carbon (DOC), suspended solids (Cs), Ca, and Mg are presented as the 50th percentile and 10th and 90th percentiles are between parentheses.

it is called “typical” because this sample has an HC5–50 of 28.1 µg Zn/L, which is the median value of all 2250 Monte Carlo samples. The lowest NOEC values for green algae, invertebrates and fish in this typical water are found for P. subcapitata (14.4 µg/L), C. dubia (42.9 µg/L) and J. floridae (90.9 µg/L), respectively. When all Monte Carlo samples are considered, P. subcapitata is the most sensitive species in 97.6% of the samples, while C. dubia is the most sensitive in 2.4% of the samples. The latter occurs only in samples with pH b 7.5 and this suggests that relative sensitivities of species may shift with changes in physico-chemistry. The latter is also shown in Fig. 5. This figure shows how the algae become increasingly more sensitive than invertebrates and fish with increasing pH. This is mainly the result of using considerably different BLM for algae vs. daphnia and fish (see Section 2.3.2). At this point, it is instructive to discuss the reality of the central assumption which we have made for the normalizations, i.e., that Zn BLMs developed for one species can be used for normalizing toxicity data for another species (P. subcapitata BLM for all algae, D. magna BLM for all invertebrates, and O. mykiss BLM for all vertebrates). First, for both cladocerans and fish, the mechanism of acute and chronic Zn toxicity appears to be related to ionoregulatory disturbance

the toxicity database (Table S3.1) and geomean NOEC values per species were calculated. The mean intrinsic sensitivities calculated from the database are given in Table 1. Within a taxonomic group (invertebrates, fish or algae) lower values of fZnBL,NOEC or Q indicate a higher intrinsic sensitivity. Thus, the most sensitive green alga in the database is P. subcapitata, the most sensitive invertebrate is the cladoceran C. dubia followed closely by the snail P. jenkinsi and the amphipod H. azteca, and the most sensitive fish is J. floridae. The high sensitivity of C. dubia relative to most other invertebrate species is in agreement with the results of a meta-analysis of acute toxicity of metals conducted by Von Der Ohe and Liess (2004). Freshwater snails are also well-known to be among the taxa that are most sensitive to metals in chronic exposures, especially to metals (such as Zn, Co, Pb) that are believed to competitively inhibit uptake of Ca, which is a limiting factor for shell growth (Borgmann et al., 1978; Grosell et al., 2006; De Schamphelaere et al., 2008). The same relative sensitivities can be inferred from the normalized geomean NOEC values. As an example, normalized geomean NOEC values for a single Monte Carlo sample with ‘typical’ physico-chemistry are given in Table 4. The physico-chemistry of this sample (Danube, sample #5 in S5) is as follows: pH 7.95, DOC = 2.6 mg/L, Ca = 45.9 mg/L, Mg= 12.1 mg/L and

Table 4 Overview of phyla, families and species included in the final ecotoxicity database (see Supplementary material, S2), with an indication of exposure duration, endpoints considered, most sensitive endpoint (the one followed by ⁎), number of retained NOEC data of the most sensitive endpoint per species (N), and the normalized NOEC data for a typical EU water. Species

Algae Pseudokircheneriella subcapitata Chlorella sp. Invertebrates Dreissena polymorpha Potamopyrghus jenkinsi Ceriodaphnia dubia Daphnia magna Daphnia longispina Hyalella azteca Ephoron virgo Anuraeopsis fissa Brachionus rubens Vertebrates (only fish) Danio rerio Jordanella floridae Phoxinus phoxinus Pimephales promelas Oncorhynchus mykiss Salvelinus fontinalis Salmo trutta Cottus bairdi a

Family

Exposure Endpoints considered duration (most sensitive endpoint underlined) (days)

Common name

Phylum

Green algae Green algae

Chlorophyta Chlorellaceae Chlorophyta Chlorellaceae

3 3

Growth rate Growth rate

Mussel Snail Crustacean Crustacean Crustacean Crustacean Insect Rotifer Rotifer

Mollusca Mollusca Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Rotifera Rotifera

Dreissenidae Hydrobiidae Daphniidae Daphniidae Daphniidae Daphniidae Polymitarcyidae Brachionidae Brachionidae

70 112 7 21 21 70 10 25 25

Survival⁎, Growth Growth Reproduction Survival, Reproduction⁎ Reproduction Survival⁎, reproduction, growth Survival⁎, growth Population growth rate Population growth rate

Zebra fish Flag fish Eurasian minnow Fathead minnow

Chordata Chordata Chordata Chordata

Cyprinidae Cyprinodontidae Cyprinidae Cyprinidae

14 98 150 240

Rainbow trout Brook trout Brown trout Mottled sculpin

Chordata Chordata Chordata Chordata

Salmonidae Salmonidae Salmonidae Cottidae

30 3 years 116–119 30

Geomean NOEC Geomean NOEC not-normalized normalizeda (µg Zn/L) (µg Zn/L) 14.5 50.3

14.4 31.8

382.0 72.0 39.7 105.4 137.9 42.0 718.0 50.0 50.0

209.2 48.5 42.9 178.4 238.8 53.4 498.3 137.8 137.8

Survival, hatching⁎ 666.1 Survival, growth⁎, hatching, reproduction 26.0 Survival⁎, growth 50.0 Survival, growth, hatching, reproduction⁎, 78.0 development (embryonal and larval) Survival⁎, growth 158.6 Survival, growth, hatching⁎, reproduction 534.0 Survival, hatching⁎, growth 119.4 Survival⁎, growth 68.1

1378.0 90.9 132.4 249.9

NOEC values normalized to a typical water chemistry (see Danube Monte Carlo sample #5): pH 7.95, DOC = 2.6 mg/L, Ca = 45.9 mg/L.

338.1 1657.6 214.5 162.9

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Fig. 5. Relative sensitivity of algae compared to invertebrates and fish as shown by the ratio of the geometric mean of normalized NOECs (based on 2250 Monte Carlo samples, see S5).

resulting in decreased Ca levels in the blood, also known as hypocalcaemia (Hogstrand et al., 1995; Muyssen et al., 2006). This may eventually result in mortality (O. mykiss and D. magna) but also in reduced feeding rate, growth and reproduction (D. magna). The decrease of Ca in the blood/haemolymph may result from the fact that Ca and Zn share common uptake routes through the ionoregulatory epithelia (Hogstrand et al., 1995, 1996) for which Ca2+ and Zn2+ may compete. Hence, the strong protective effect of Ca on Zn toxicity, as well as the lower protective effect of Mg and Na can be explained on a purely physiological basis. The protective effect of Mg and Na may result from non-specific competition for anionic sites at the ionoregulatory surface (Alsop and Wood, 1999). It is also intriguing that the log K values for competitive interactions between Zn2+, Ca2+, Mg2+, Na+, and H+ are all very comparable between fish and Daphnia (Table 1). Furthermore, the log KCaBL for O. mykiss (3.6) is very similar to the inversed Michaelis–Menten halfsaturation constant for apical uptake through the gill-epithelium for the same species (Hogstrand et al., 1995; De Schamphelaere and Janssen, 2004). This further supports the physiological explanation for the protective effect of Ca on Zn toxicity. Also, the fact that the epithelial Ca-channel is highly conserved among vertebrates suggests that the Ca2+ competition concept is an ubiquitous phenomenon in fish and that the values of log KCaBL will probably also be very similar for most fish species (De Schamphelaere and Janssen, 2004). The similarity of constants in fish and Daphnia suggests that this may also apply to other invertebrate taxa. Thus, the uncertainty associated with extrapolating the O. mykiss and D. magna BLMs to other vertebrate and invertebrate species can be assumed relatively small. Unfortunately, there is an lack of chronic ecotoxicity data on Zn in the literature to support this statement quantitatively. Only for one fish species, i.e., Cottus bairdii, chronic Zn toxicity data are available for two test media with different water hardness (Woodling et al., 2002; Brinkman and Woodling, 2005). These authors observed NOEC values of 27 µg Zn/L and 172 µg Zn/L at hardness levels of 46 and 154 mg CaCO3/L, respectively (see ecotoxicity database, S2). The observed variability of NOEC values is thus sixfold, with a coefficient of variation (CV) of 103%. We can now investigate if this variability is reduced after normalization of both NOEC values to a single target physico-chemistry, e.g., the ‘typical’ target chemistry mentioned in Table 4. We then find normalized NOEC values of 83 and 256 µg/L, which represents a threefold difference and a CV of 70% (see Danube #5 in S5 for all normalized NOEC values). A similar exercise was performed for the six selected NOEC values for C. dubia from two studies (Belanger and Cherry, 1990; Masters et al., 1991) and obtained in waters with varying DOC (3.7–4.7 mg/L), Ca (20– 42 mg/L) and pH (6–8). The NOEC values varied between 25 and 100 µg/L, representing a fourfold variability and a coefficient of variation of 64%. After normalization, NOEC values varied only between 21 and 68 µg/L, i.e., by threefold and with a CV of 40%. Hence, the limited number of available data indicates that extrapolation of the O. mykiss BLM to C. bairdii and of the D. magna BLM to C. dubia reduces the part of

the variability of NOEC values that is due to differences in bioavailability among test media. This reduction of variability supports our central assumption. Similar observations are usually made when bioavailability models for Cu and Ni are extrapolated across vertebrate and invertebrate species. Examples include successful extrapolations of the acute Cu BLM to many species of cladocerans (Bossuyt et al., 2004), of the chronic Cu BLM for D. magna to the rotifer B. calyciflorus (De Schamphelaere et al., 2006) and the Ni BLM for O. mykiss to the fathead minnow Pimephales promelas (Deleebeeck et al., 2007). With regard to algal species, the evidence in support of our central assumption is even more convincing. Although the physiological mechanisms of Zn toxicity and Zn bioavailability to algae are largely unknown, it appears that the effect of pH on the toxicity of Zn2+ ion is of much greater importance than protective effects of Ca2+, Mg2+, and Na+ (Heijerick et al., 2002a; De Schamphelaere et al., 2005a). The log EC10 expressed as Zn2+ activity decreases by 0.754 log-units (=SpH) with a pH increase of one pH unit (De Schamphelaere et al., 2005a). A limited number of examples from the literature suggest that this SpH might be applicable to other algal species too. Wilde et al. (2006) exposed Chlorella sp. to Zn in five artificial media with pH ranging from 6 to 8 and found NOEC values decreasing from 350 to 5.9 µg Zn/L (59-fold difference, CV of 122%). Calculation of NOEC values expressed as free Zn2+ activities from their data – as explained in Section 2.3.2 – revealed a decrease from 2890 nmol Zn2+/L (pH 6) to 62.9 nmol Zn2+/L (pH 8), and an estimated SpH equal to −0.820 (linear regression, n=5), which is very close to the earlier-mentioned SpH for P. subcapitata (consequently, normalization of the EC10 values to the typical physico-chemistry resulted in normalized EC10 values between 25 and 53 µg/L, meaning that the variability was considerably reduced, i.e., to only a 2.2-fold variation CV of 35%). This seems to support our assumption that the algal Zn BLM may be

Fig. 6. Cumulative probability plot of normalized NOEC values and fitted SSD curve (Danube #5) (hazen plotting).

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Table 5 Summary of the probability distributions of HC5–50 (µg Zn/L) and RCR values and the probability that the PECdissolved exceeds the HC5–50 based on the best fitting and the log-normal species sensitivity distribution. Germany Log–normal HC5–50 (µg/L) RCR % RCR N 1 Best fit HC5–50 (µg/L) RCR % RCR N 1

Belgium

France

Danube

Elbe

Rhine

Weser

Meuse–Seine

Scheldt

Rhine–Meuse

Rhône–Méditteranée

Seine–Normandie

19.8 (17.2–23.1) 0.18 2.6

35.9 (30.5–44.8) 0.12 4.9

22.2 (15.1–31.4) 0.29 12.1

26.8 (20.4–37.4) 0.54 21.1

25.7 (18.4–36.1) 0.24 9.2

40.2 (26.8–71.0) 0.15 6.1

23.3 (15.8–38.9) 0.31 9.5

19.9 (17.1–24.9) 0.07 0

23.0 (19.9–27.4) 0.09 0

25.5 (20.6–29.4) 0.14 1.3

41.6 (32.9–53.9) 0.10 4.5

25.6 (18.2–39.1) 0.25 9.4

29.4 (22.3–43.2) 0.50 18.3

30.2 (21.8–44.6) 0.20 5.9

46.1 (27.9–86.6) 0.13 5.7

28.3 (21.3–45.8) 0.26 6.4

22.1 (17.9–29.9) 0.06 0.0

24.1 (20.7–30.6) 0.09 0.0

Data are presented as the 50th percentiles with the 10th and 90th percentiles between parentheses.

extrapolated to other algal species without much uncertainty remaining. Further support is provided in De Schamphelaere and Janssen (2006), who showed that the toxicity of the free Cu2+ to green algae was influenced by pH to an almost identical degree for four different species. Thus, based on the evidence presented above, our central assumption, i.e., that Zn BLMs may be extrapolated to other species than for which they were originally developed, does not seem to be unrealistic or unreasonable. However, it is recommended that further research be carried out to test this assumption for other species. Given the lack of quantitative data for invertebrates other than crustaceans, priority should probably be given to species from phyla other than arthropods (e.g., rotifers, molluscs) or from arthropod taxa other than crustaceans (e.g., insects). Only such research will allow to quantify the uncertainty associated with the central assumption. 3.2.3. Construction of SSD and calculation of HC5 The geometric means of the normalized NOEC values (see S5) were used to construct SSDs (one for each of the 250 Monte Carlo samples per river basin). An example of an SSD is given in Fig. 6 based on the normalized NOEC values given in Table 4. This figure visualizes the greater sensitivity of the algae compared to the invertebrates and the fish, which was mentioned earlier (Section 3.2.1). SSDs that are representative for each individual river basin are also given as supportive information (S5). The probability distributions that best fitted to the set of log-transformed geomean NOEC values were the lognormal distribution (47.2% of the

2,250 Monte Carlo samples), the normal distribution (36.7%) and the logistic distribution (16.0%) (see S5). From the fitted distributions, 250 simulated HC5–50 values per river basin were estimated and 10th, 50th and 90th percentiles of the HC5–50 for each river basin are given in Table 5. The median HC5–50 values (based on the best fitting distribution) varied by about twofold among the different river basins, i.e., between 22.1 µg Zn/L (dissolved Zn) (Rhône–Méditerranée basin) and 46.1 µg Zn/L (Scheldt basin); the 10th percentile varied between 17.9 µg/L and 32.9 µg Zn/L; the 90th percentile between 29.4 µg Zn/L and 86.6 µg Zn/L. If the conventional normal distribution was fitted to all Monte Carlo samples (i.e., no selection of best fitting distribution), median HC5–50 values would have varied between 19.8 µg Zn/L (Danube basin) and 40.2 µg Zn/L (Scheldt basin) (Table 5). Those values are 5% to 22% lower than those derived based on the best fit distribution. Thus, if one chooses to consistently apply the conventional normal distribution, as was done in the EU RAR of Zn (Bodar et al., 2005; EU, 2006,) this would result in slightly higher risk estimates. Van der Hoeven (2001) stated that each assumption about the actual distribution of species sensitivities is completely arbitrary. Newman et al. (2000) have investigated species sensitivity data for a large number of chemicals and concluded that none of the usually applied distributions (e.g., log–logistic or log–normal) was supported by the data. Therefore, in the present study, preference was given to the selection of the best fitting species sensitivity distribution from all investigated distributions instead of using the log–normal distribution by default (as in the EU RAR).

Fig. 7. The effects of DOC concentrations (mg/L) on the HC5–50 values for Zn (µg Zn/L).

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Furthermore, linear regression analysis based on the calculated HC5–50 values for all 2250 Mont Carlo samples showed that DOC was the main variable affecting the HC5–50 (r = 0.91, r2 = 0.83) (Fig. 7) (see S5 for the 2250 data points used to perform this regression). Linear regressions with the other independent variables have weak (r2 = 0.14, r = 0.38 for the regression with Ca) to no explanatory power (r2 b 0.001 for pH and Mg). The low explanatory power of Ca is due to the fact that a strong protective effect of Ca2+ on chronic Zn toxicity has only been observed for D. magna and O. mykiss, and not for P. subcapitata (see Section 2.3.2 and Table 1), which is the most sensitive species (see Section 3.2.2). The non-existent relation with Mg is due to the fact that the effect of Mg on chronic Zn toxicity to D. magna and O. mykiss is even much weaker than the effect of Ca (De Schamphelaere and Janssen, 2004; Heijerick et al., 2005, and Table 1). The non-existent effect of pH can probably be explained by counteracting mechanisms of complexation (more Zn2+ at lower pH) and competition (more competition with H+ at lower pH). It must be stressed, however, that these observations should not be extrapolated to river basins with different physico-chemistry than those considered here. For example, 95% of the Monte Carlo samples had pH N 7.4 while there are certainly EU regions which have waters with more acidic pH (e.g., large parts of Scandinavia, Salminen et al., 2005, http://www.gsf.fi/publ/foregsatlas/maps/Water/w_field_ph_edit.pdf). A rigorous sensitivity analysis of how chemistry affects the HC5–50 over a wider range of physicochemistry is therefore recommended.

3.3. Risk characterization 3.3.1. Deterministic approach Deterministic RCR values for the nine river basins were calculated as the ratio of the median PECdissolved (see Table 2) to the median of the HC5–50 values estimated using the best fit distribution (see Table 5). Fig. 8 shows that the deterministic RCRs are ≤0.5 for all river basins considered, i.e., between 0.06 (Rhône–Méditerranée basin) and 0.50 (Weser basin). This means that there should be no deterministic ecological risk (RCR≤ 1) for freshwater associated with the current use patterns of Zn in the investigated EU river basins. This is in contrast with the conclusion made in the EU RAR that RCRN 1 for most river basins and individual rivers considered, including the Rhine–Meuse, the Elbe, the Weser and several other rivers which are located in one of the river basins investigated here (see Table 3.4.70 of EU RAR; EU, 2006). We investigated if these contrasting conclusions might be due to different risk characterization approaches followed. To this end, we focused on three main differences between the approach used in our

study and the one used in the EU RAR: (i) an additional assessment factor of two was applied on the HC5–50 to establish the PNEC in the EU RAR, while we equated the PNEC with the HC5–50, (ii) monitoring data of total Zn were not as thoroughly screened for potential influences from historical contamination and point source in the EU RAR as compared to our study, (iii) a very different approach for taking into account bioavailability in the risk characterization was followed in the EU RAR, i.e., the ‘bioavailability factor’ approach or BioF approach (see Section 3.3.3). When we applied the EU RAR risk characterization method approach to the monitoring data collected here (see Section 2.2.1 and S1), deterministic risks were identified in the river basins of the Weser in Germany (RCR= 4.6), the Meuse–Seine in Belgium (RCR = 1.1), the Rhine–Meuse in France (RCR = 1.1) and the Rhine in Germany (RCR= 1.8) (Fig. 8). No risk was identified for the Danube (RCR= 0.8) the river Elbe basin (RCR = 0.8) in Germany, for the river Scheldt basin (RCR= 0.8) in Belgium, or for the Rhône–Méditteranée (RCR= 0.3) and the river Seine–Normandie basin (RCR= 0.3) in France. In the previous calculations we used the median of the 250 BioF values for the 250 simulated combinations of physico-chemistry for each river basin (see S5). If the use of the additional assessment factor of two is ignored, the RCR is reduced by a factor of two and risk only remains in the river Weser basin in Germany (RCR = 2.3). The impact of not removing data from monitoring stations with suspected influence from historical pollution or point sources is usually much lower than a factor of two, i.e., from no influence (Danube, Seine–Normandie, Rhine–Meuse, Rhône–Méditteranée) to a factor of 1.8 (Weser) (Fig. 8). The effect of using the BioF approach instead of the full SSD normalization approach followed here is of the same order of the assessment factor, i.e., between factor 2.0 and 2.8. This exercise suggests that the differences between RCR values obtained from the EU RAR approach and those derived in the present study can most likely be attributed to the use of an additional assessment factor of two and to the different approach of taking into account bioavailability. Bodar et al. (2005) recognize – and we concur – that such an assessment factor only represents “a regulatory decision to build in additional conservatism” and “ignores the original principles of SSDs” (Bodar et al., 2005). There is currently no robust scientific method to determine the magnitude of an assessment factor. SCHER (2007) also recognized “that this selection of this factor is a matter of judgment and is not based on definitive scientific evidence”. A more detailed discussion of the BioF approach is provided further on (see Section 3.3.3).

3.3.2. Probabilistic approach A further estimation of the probability of risks is provided in a probabilistic framework. Based on selecting the best fitting SSD for the

Fig. 8. Deterministic risk characterization ratio (RCR) for the different EU river basins calculated with increasing levels of conservatism (AF: assessment factor used for calculation; PEC-all stations: calculations take into account all stations those stations with influences of point sources and/or historical contamination; PEC-selected stations: only stations with no influence of point sources and/or historical contamination were taken into account; BioF HC5–50: RCR calculated using the bioavailability factor approach; Full normalization HC5–50: HC5–50 value calculated using the full normalization approach).

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analysis, the probability that the PEC exceeds the PNEC varied between b0.4% (none of the 250 Monte Carlo samples gave an RCR N 1) for the Rhône–Méditérranée and Seine–Normandie basins in France up to 9.4% for the Rhine and 18.4% for the Weser river basins in Germany (Table 5). When the conventional log–normal SSD is used, slightly higher risk probabilities were found, i.e., still b0.4% for Rhône–Méditérranée and Seine–Normandie river basins and between 2.6% and 21.1% for the other basins (Table 5). These analyses clearly point to the Weser river basin as the basin with the highest risk probability. 3.3.3. A critical comparison of the BioF approach and the full normalization approach We have shown above that the BioF approach results in more conservative risk estimates (higher RCR) than the full normalization approach followed here (see Section 3.2.1). Here below we provide a critical comparison of both approaches. We have implemented bioavailability on the effects side, i.e., by normalizing all individual NOEC values to the target physico-chemistry, and then divide the PEC by the bioavailability-normalized HC5–50 to obtain the RCR (as explained in Sections 2.3 and 2.4). The EU RAR implements bioavailability on the exposure side, i.e., by first correcting the PEC for bioavailability (by multiplying the PEC with a bioavailability factor, BioF) and then dividing this by a ‘generic’ PNEC to obtain the RCR. The method is briefly summarized below. First, the ‘generic’ HC5–50 is derived by fitting a log-normal SSD to a set of NOEC values that are not normalized for bioavailability prior to the SSD fitting. It is then assumed that the resulting ‘generic’ HC5–50 is representative for EU waters with high bioavailability. Then, three separate BioFs are calculated, each based on calculations made with one of the three available BLMs i.e., for P. subcapitata, D. magna and for O. mykiss: BioFi;y = NOECi;ref = NOECi;y

ð6Þ

where BioFi,y = the bioavailability factor for species i in target water y, NOECi,ref = the NOEC for species i in a reference water with high bioavailability (predicted with the BLM for species i), and NOECi,y = the NOEC for species i in target water y (also predicted with the BLM for species i). The highest of those three BioFs is selected as the final BioFy for target water x and the RCR is calculated: n o RCRy = BioFy × PEC = PNECgeneric

ð7Þ

By selecting the highest BioFy the smallest bioavailability correction is carried out and it is assumed that this results in sufficiently conservative risk estimates (EU, 2006). We have three major objections to this BioF approach.

5387

The first one is that the BLM is a model that predicts effects (and not exposure) and the application of the BLM on the exposure side seems an artificial construct. The application of the BLM on the exposure side is also not in line with recent metals regulations that incorporate bioavailability. Examples include the EQS setting for Cd in the EU Water Framework Directive, where a single hardness correction has been applied to the PNEC and the US WQC for copper which uses a single BLM for normalizing copper toxicity data to arrive at a BLM-calculated WQC which depends on water chemistry (EU, 2004; US EPA, 2007). In its recent opinion, SCHER (2007) also stated that “a method (i.e., BLM) designed to account for bioavailability at the effect side (i.e., the biological/organism side) should not be applied at the exposure side”. According to the EU RAR “the main reason for correcting the PEC is that no BLMs are available for each individual organism from the ecotoxicity database” (EU, 2006). We agree that having a BLM for every single species in the SSD would indeed be a more ideal situation. However, this is not strictly needed, if one assumes that BLMs are like among like species and if existing BLMs are subsequently extrapolated across species at the effects side by normalizing all NOEC values from the ecotoxicity database. We further argued that this assumption is not unreasonable or unrealistic (see Section 3.2.2). Thus, the use of the BioF approach constitutes a pragmatic choice which was intended to result in generally conservative risk estimates. And this objective has indeed been reached, since for the 2250 Monte Carlo samples considered, RCRs obtained with the BioF approach are in 97% of the cases higher than those obtained with the full normalization approach based on the use of the log–normal distribution (median value of 2.1-fold higher, up to 7.2fold higher) (Fig. 9). The second problem identified in the BioF approach is that it requires the calculation of reference NOEC values for P. subcapitata, D. magna and O. mykiss with the BLMs for physicochemical conditions of high bioavailability, the setting of which is arbitrary and may influence the ultimate RCR. The approach followed in the present study has the advantage that it does not require the setting of such arbitrary reference conditions. A third problem we have with the BioF approach relates to the use of the generic SSD and the assumption that the resulting HC5–50 is representative for EU waters with high bioavailability. The generic SSD in the EU RAR contains non-normalized NOEC values that are obtained in a variety of test media, not all of which are necessarily test media with high bioavailability. For example, the NOEC value of 72 µg/L for the snail P. jenkinsi was originally obtained in a test medium with pH 8, DOC 4.3 mg/L and Ca 63.6 mg/L. A considerable fraction of EU waters would have physico-chemical conditions with higher bioavailability (i.e., lower pH, lower DOC and lower Ca). An example of such a water is that mentioned in Table 4 (pH = 7.95, DOC = 2.6 mg/L, Ca =45.9 mg/L) and normalization of this NOEC to this water gives a lower normalized NOEC

Fig. 9. Comparison of the estimation of the RCR values using the BioF and the full normalization approach.

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of 48.5 µg/L (Table 4). This example illustrates that the non-normalized NOEC for this species is not necessarily representative for conditions of high bioavailability. Other species for which the normalized NOEC (to the same water chemistry) is lower than the non-normalized NOEC are Chlorella sp., D. polymorpha and Ephoron virgo (see Table 4). For all other species, the normalized NOEC is higher than the non-normalized NOEC (at least for this example physico-chemistry). As a result, not normalizing NOEC values results in subjective ordination of species sensitivities. In other words, the order of species sensitivities within the SSD (i.e., interspecies variability) may be artificially changed based on the differences in physico-chemistry in test media in which NOEC values were obtained. As such, a change in species order may influence the shape of the SSD and result in a higher or lower HC5–50 value. Additionally, a more sensitive species tested in a low bioavailability medium may have a higher nonnormalized NOEC than a less sensitive species tested in a high bioavailability medium. Thus an intrinsically more sensitive species may appear less sensitive. For example, based on the non-normalized NOEC Jordanella floridae (NOEC of 26 µg/L) ‘appears’ more sensitive than P. jenkinsi (NOEC of 72 µg/L), while on the basis of the normalized NOEC P. jenkinsi (NOEC of 48.5 µg/L) is clearly more sensitive than J. floridae (NOEC of 90.9 µg/L; see Table 4). This example illustrates that a nonnormalized SSD is clearly less informative than a normalized SSD, because the former contains a set of NOEC values obtained in a variety of test waters from low to high bioavailability. It seems therefore that it has been only by chance that the end-result of the generic SSD has provided a generic HC5–50 of 15.6 µg/L that is relevant for a condition of high bioavailability (Bodar et al., 2005; EU, 2006). This value is indeed slightly lower than the lowest 10th percentile of the normalized HC5–50 of 17.9 µg/L which was found for the Rhône– Méditerranée basin (Table 5). This means that the generic HC5–50 from the EU RAR is with N90% probability conservative for all river basins considered here. Thus, the combination of this fortuitously conservative generic HC5–50 with the most conservative bioavailability correction has resulted in (overly) conservative risk estimates compared to the approach followed here. Additional research may help to relax the uncertainty related to the extrapolation of BLMs to like species and thus take away the main concern of regulators which was the primary cause for using the overly conservative BioF approach instead of the scientifically more refined approach followed here. 3.4. Further validation of the HC5–50 using results from multi-species studies Median pH, Ca, hardness and DOC, LOECdissolved and HC5–50 for the eight considered studies are given in Table 6. The latter two values are also given in Fig. 10, together with their 95% confidence interval. Raw output data of the Monte Carlo simulations is given as supplementary material (S6). It is noticed that the median pH of 8.3 for three studies,

i.e., Marshall et al. (1983), Genter et al. (1987) and Belanger et al. (1986) is outside the BLM boundary for algae. The studies were nevertheless considered because we thought it could give information about the degree of conservatism of the HC5–50 outside these boundaries. Median HC5–50 values for the different studies are between 11.7 and 36.1 µg dissolved Zn/L; two are lower than the generic PNEC from the EU RAR (15.6 µg/L), six are higher. The median LOEC for at least one type of endpoint (biomass/abundance, structure, or function) was below the median HC5–50 in half of the studies (Table 6, Fig. 10). For those four studies p(LOEC N HC5–50) varied between 0% and 17.6%, suggesting that in those studies significant effects were observed at Zn concentrations that are with great certainty below the HC5–50 (Table 6). This occurred in the study with the lake planktonic community (Marshall et al.,1983), for two out of five studies which investigated periphyton responses (Pratt et al., 1987, Genter et al., 1987) and for the study with the clam Corbicula sp. (Belanger et al., 1986). Below we discuss in more detail the outcome of the comparisons and suggest possible areas where further research would be welcome. The planktonic community from Lake Michigan investigated by Marshall et al. (1983) experienced significant adverse effects following a 14-day exposure to a dissolved Zn concentration of 16 µg/L, i.e., 1.5-fold below the HC5–50 of 24 µg/L (Table 6). Adverse effects were observed on all taxonomic groups investigated, i.e., phytoplankton, cladocerans, rotifers and copepods (see S4). Interestingly, a closer look at all normalized single-species NOEC values in Lake Michigan water (given in S6) determined that the most sensitive algae (P. subcapitata, median NOEC = 10.5 µg/L) is about 4.5-fold more sensitive than the most sensitive cladoceran (C. dubia, median NOEC = 45.4 µg/L) and 13-fold more sensitive than the two rotifer species (A. fissa and B. rubens, median NOEC = 140.7 µg/L). Comparing these values with the ecosystem-level LOEC of 16.0 µg/L suggests that the adverse effects of Zn on the rotifers and cladocerans in Lake Michigan are due to an indirect effect of reduced food availability caused by reduced primary productivity. Despite these indications that the Lake Michigan planktonic community might indeed be relatively sensitive, one should avoid overemphasizing this observation for three reasons. First, we had to estimate all important bioavailability modifying chemistry variables (pH, DOC, and Ca) from secondary sources (see S4) – none was reported by Marshall et al. (1983) – and this leaves a great deal of uncertainty in the assessment of this system. Second, with an estimated pH between 7.9 and 8.7 (median 8.3), this test system is outside the upper pH boundary of the validity of the Daphnia (pH N 8.4) and algae (pH N 8) BLMs. Third, the natural background concentration of Zn in Lake Michigan was quite low, i.e., 0.6 µg/L (Marshall et al., 1983). This means that the resident planktonic community could be naturally more sensitive to increasing Zn concentrations, than communities residing in higher backgrounds. Natural backgrounds in most EU waters are usually estimated much

Table 6 Summary of multi-species studies in terms of type of community investigated, exposure duration, water chemistry (median of 250 Monte Carlo samples of DOC, pH, Ca, and hardness), median of lowest observed effects concentration (LOEC, as µg dissolved Zn/L) on the basis of biomass or abundance (B/A); structural (S) or functional (F) endpoints, median of simulated HC5–50 values (µg dissolved Zn/L), and probabilities that the LOEC is higher than the HC5–50, i.e., p(LOEC N HC5–50) (in %). Chemistry Study

Communities

Duration

pH

days Marshall et al. (1983) Clements (2004) Kashian et al. (2004) Paulsson et al. (2000) Pratt et al. (1987) Niederlehner and Cairns (1993) Genter et al. (1987) Belanger et al. (1986) a b c

Planktonic Insect Insect + periphyton Periphyton Periphyton Periphyton Periphyton Molluscc

14 10 10 7 30 300 30 30

8.3 7.9 7.9 6.6 7.4 7.4 8.3 8.3

p(LOEC N HC5)

LOEC

Hardness

DOC

Ca

HC5–50

B/A

S

F

mg CaCO3/L

mg/L

mg/L

µg/L

µg/L

µg/L

µg/L

133 22.5 41.0 24.9 72.7 72.7 79.5 79.5

2.1 2.1 2.1 4.2 0.40 0.40 3.61 3.61

35.0 7.0 12.8 7.3 17.9 17.9 14.4 14.4

24.0 23.4 21.8 18.9 11.7 11.7 36.1 36.1

16.0 700.3 445.7b 28.9 4.2 N172.0 24.3 24.3

16.0 700.3 N 445.7 28.9 29.8 172.0 24.3 ND

16.0 NDa N 445.7 69.9 ND 73.0 ND ND

ND = not determined. Only abundance of Heptageniidae was affected. Other insect taxa and periphyton biomass were not affected. A single mollusk species was investigated, but it fed on naturally suspended phytoplankton and periphyton, of which no endpoints were reported.

B/A

S

F

1.5 100 100 83.4 0 100 17.6 17.6

1.5 100 100 83.4 100 100 17.6 17.6

1.5 ND 100 100 ND 100 ND ND

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Fig. 10. Median of the simulated HC5–50 and lowest observed effect concentrations (LOEC) of Zn in multi-species studies for biomass/abundance, structural and functional endpoints. Bottom and top of error bars represent the 5th and 95th percentile of the simulated HC5–50 and LOEC values. A “N” mark means that no effect was observed at the lowest tested concentration, meaning that the true LOEC is at a higher concentrations than the plotted one. ⁎ means that only the abundance of (some) macro-invertebrates was affected, but not the biomass of the periphyton.

higher, i.e., 10th and 90th percentiles of 1.0 and 10.2 µg/L, respectively (FOREGS Geochemical Baseline Programme (FGBP); http://www.gsf.fi/ publ/foregsatlas/index.php, Salminen et al., 2005). Hence, the Lake Michigan study is not necessarily representative for typical EU waters, although waters with such low background do exist, for instance in Northern Scandinavia (Salminen et al., 2005). Given the uncertainties associated with this study it is recommended to conduct additional experiments in which planktonic communities from waters with a more typical background Zn are investigated in exposure media within the BLM boundaries and of which all relevant chemistry is measured. The stream insect communities investigated by Clements (2004) and Kashian et al. (2004) appeared very insensitive to Zn, with the insect community only being affected at 700 µg/L and 446 µg/L, respectively, i.e., 30 and 20-fold the HC5–50, respectively (Table 6). None of the investigated taxa (mayflies, caddisflies, stoneflies, Ephemeroptera, Plecoptera and Trichoptera), except Heptagenidae, suffered adverse effects up to 479 µg/L (Table 6, see also S4 for more detail about individual taxa). The low sensitivity of insect communities is in accordance with the low sensitivity of the only insect representative in the ecotoxicity database i.e., the mayfly Ephoron virgo (Van der Geest, 2001), which has a median normalized NOEC of 244 and 260 µg/L (see S6) in the test media of Clements (2004) and Kashian et al. (2004), respectively, and which is the least sensitive invertebrate in the ecotoxicity database (see Table 4). It must be noted, however, that both community-level studies as well as the study with larvae of E. virgo involved only 10 days of exposure and it is unknown if this is sufficiently long to yield NOEC estimates that are conservative for the full life cycle of insects. Additional data on the sensitivity of insect(s) to Zn over their full life-cycle is therefore recommended. The sensitivity of periphyton communities appears to vary widely among the five available studies. Based on biomass related endpoints, both Kashian et al. (2004) and Niederlehner and Cairns (1993) did not observe significant effects at Zn concentrations up to 15-fold the HC5–50 (Fig. 10, Table 6). Paulsson et al. (2000) demonstrated significant effects at twofold the HC5–50. Pratt et al. (1987) and Genter et al. (1987) found significant effects at median Zn concentrations three and 1.5 times below the HC5–50, respectively, but the latter study was performed in water with pH outside the BLM boundary for algae. The contrast between Pratt et al. (1987) and Niederlehner and Cairns (1993) is particularly interesting. Both studies are from the same research group and used the same natural periphyton community and similar test procedures. The only difference was the much longer exposure duration

in Niederlehner and Cairns (1993), i.e., 300 days compared to Pratt et al. (1987), i.e., 21 days. This suggests that the much lower sensitivity of the periphyton community in Niederlehner and Cairns (1993) might be caused by the fact that periphyton communities are able to acclimate to and recover from initial Zn-stress. This is not surprising as it has been shown that several species, including primary producers (e.g., Muyssen and Janssen, 2001a), can acclimate to Zn stress during longer-term exposures. All other periphyton studies applied exposure times wellbelow 300 days, i.e., between seven and 30 days. It is concluded that the short-term (7–30 days) response of periphyton communities is highly variable among different studies, and that acclimation and recovery from Zn stress is likely in longer exposures. Finally, the mollusk Corbicula sp. tested in a stream under field conditions and feeding on periphyton and naturally suspended phytoplankton (Belanger et al., 1986) experienced a significantly reduced growth at 24.3 µg/L, which is 1.5-fold below the HC5–50. Either Corbicula sp. is more sensitive to Zn than the most sensitive mollusk in the ecotoxicity database, i.e., P. jenkinsi (median normalized NOEC =64.1 µg/ L, see S6) or the adverse effect is due to reduced food availability. The latter is not implausible, since periphyton growth was reduced at the same Zn concentration in the parallel study (Genter et al., 1987, see previous paragraph) and since the normalized NOEC for the most sensitive alga from the database, i.e., P. subcapitata was 19.5 µg/L (see S6) and thus below 24.3 µg/L. Here too, the pH was clearly outside the boundaries of the algae BLM (pHN 8). It is concluded that in some test systems adverse effects were observed below the HC5–50, while in others no effects are observed up to 30-fold the HC5–50. However, based on the considerations above, it is difficult to draw definitive conclusions as to whether or not the HC5–50 is conservative enough to protect ecosystems. This is due to several reasons including (i) the use of relatively short exposure times (30 days or less) in most studies, (ii) considerable uncertainty about estimates of water chemistry of exposure media, (iii) and/or water chemistry (notably pH) outside the BLM boundaries (media being outside), (iv) high variability among sensitivity observed measured in different studies (periphyton), (v) testing of communities that may be intrinsically sensitive due to low background concentrations, which are not necessarily relevant for most EU waters, and (vi) the possible acclimation/recovery of communities during longer exposure. It is therefore recommended that additional exposures of multi-species systems (or model ecosystem) to Zn be carried out and that the above-mentioned aspects are considered in the design of these experiments. Finally, the fact that the three studies with

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pH outside the BLM boundary revealed adverse effects of Zn below the HC5–50 calls for caution when interpreting the results of risk characterization for waters outside these boundaries. It also again stresses the need for further research to extend the application boundaries of the BLMs (as already mentioned in Section 3.3.2).

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.scitotenv.2009.06.029. References

4. Conclusions and research recommendations We have conducted a regional environmental risk assessment of Zn for nine EU river basins following a state-of-the-science methodology. The deterministic PECs for the different river basins were between 1.3 µg dissolved Zn/L (Rhône–Méditerranée basin) and 14.6 µg dissolved Zn/L (Weser basin). The deterministic PNECs varied between 22.1 µg dissolved Zn/L (Rhône–Méditerranée basin) and 46.1 µg dissolved Zn/L (Scheldt basin). Deterministic RCRs were below one in all river basins, i.e., between 0.06 (Rhône–Méditerranée basin) and 0.50 (Weser basin), suggesting that there is no deterministic risk associated with current use patterns of Zn in these river basins. Using the probabilistic approach we found that the probability of the PEC exceeding the PNEC was limited from b0.4% in the Seine–Normandie and Rhône–Méditerranée basins to a maximum of 18.3% in the Weser basin. When the EU RAR approach was applied, however, deterministic risks were found in the Weser basin (RCR=2.6) and in the Rhine basins in Germany (RCR=1.2) and France (RCR=1.1). A detailed analysis demonstrated that this different deterministic conclusion of risk (RCRN 1) is mainly due to the fact that the EU RAR contains more built-in conservatism. First, the EU RAR (2006) included sampling points which were clearly influenced by historical contamination and/or point sources which were outside the scope of a risk assessment at a regional scale. However, our detailed analysis has clearly demonstrated that it is very difficult to allocate monitoring data to the local or regional scale. Therefore, it is recognized that scientifically more robust methods/criteria should be developed for assigning influence from point source and/or historical pollution. Second, the EU RAR (2006) has followed a more conservative approach for implementing bioavailability (BioF approach), in order to deal in a pragmatic way with the uncertainty related to the across-species extrapolation of BLMs. Although our initial analysis of published literature data with some species suggests that such extrapolation is not unrealistic, it is recommended that further research be carried out to test the reality of cross-species extrapolation with more species. At the same time, it is also recommended to extend – with new ecotoxicity experiments – the workable pH range of the existing BLMs, with special attention for increasing the upper pH boundary of 8.0 that is now applicable to the algae BLM. This will decrease the number of sampling stations that are outside the workable ranges of the BLMs and this will further decrease the uncertainty of the risk assessment. Third, the EU RAR (2006) has used an additional assessment factor of two to derive the PNEC from the HC5–50, mainly inspired by the relatively high sensitivity of some multi-species toxicity studies. Our detailed analysis and interpretation of these studies suggests that it is very difficult to draw – due to the limitations of the studies published so far – definitive conclusions as to whether or not the HC5–50 is conservative enough to protect ecosystems. It is therefore recommended that additional exposures of multi-species systems (or model ecosystems) to Zn be carried out, with special attention for the experimental design. Carrying out additional research on acrossspecies extrapolation of BLMs and effects of Zn on multi-species systems could relax the uncertainties which have led to some pragmatic decisions and more conservatism in the EU RAR (2006) and thus increase the regulatory acceptance of the more science-based risk assessment methodologies followed in the present study.

Acknowledgment Karel De Schamphelaere is a senior research assistant of the Flanders Research Foundation (FWO-Vlaanderen).

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