Federal environmental quality guidelines - Copper
Official title: Federal environmental quality guidelines - Copper
Environment and Climate Change Canada
Federal Environmental Quality Guidelines (FEQGs) provide thresholds of acceptable quality of the ambient environment. They are based solely on the toxicological effects or hazards of specific substances or groups of substances. FEQGs serve three functions: first, they can be an aid to prevent pollution by providing targets for acceptable environmental quality; second, they can assist in evaluating the significance of concentrations of chemical substances currently found in the environment (monitoring of water, sediment, soil and biological tissue); and third, they can serve as performance measures to assess the effectiveness of risk management actions for the chemical substance. The use of FEQGs is voluntary unless prescribed in permits or other regulatory tools. Thus FEQGs, which apply to the ambient environment, are not effluent limits or “never-to-be-exceeded” values but may be used to derive effluent limits. The development of FEQGs is the responsibility of the Federal Minister of Environment under the Canadian Environmental Protection Act, 1999 (CEPA) (Government of Canada (GC) 1999). The intent is to develop FEQGs as an adjunct to the risk assessment/risk management of priority chemicals identified in the Chemicals Management Plan (CMP) or other federal initiatives. Where data permit, FEQGs are derived following CCME protocols. FEQGs are developed where there is a federal need for a guideline but where the CCME guidelines for the substance have not yet been developed or are not reasonably expected to be updated in the near future. More information on FEQGs is available here.
This factsheet describes the Federal Water Quality Guideline (FWQG) for the protection of freshwater aquatic life from adverse effects of copper (Table 1) and is based on the biotic ligand model (BLM) approach. The approach allows for the consideration of a diverse set of water quality parameters which are known to impact copper toxicity to aquatic life. The derivation of the FWQG here is largely based on the collection and evaluation of aquatic toxicity data published up to January 2018. No FEQGs have been developed for biological tissue, sediment or soil compartments at this time.
|Aquatic life||Guideline value (µg/L)|
|Freshwater||Variable – use Biotic Ligand Modela|
a The FWQG is for dissolved copper and is calculated using the Biotic Ligand Model Tool (ECCC 2021). The BLM tool and manual can be obtained by emailing: EC.RQE-EQG.EC@Canada.ca
b As an example, the site specific copper guideline of 0.41 µg/L is for a water temperature of 20oC, pH of 7.5, DOC of 0.5 mg/L and hardness of 50 mg/L CaCO3.
Copper (CAS 7440-50-8) is a naturally-occurring element commonly found in aquatic systems as a result of both natural and anthropogenic inputs (Nriagu 1979). Natural sources of copper include geological deposits, weathering and erosion of rocks and soils, and volcanic activity. The most important sources of copper in Canada are sulphide minerals such as chalcopyrite (CuFeS2) and bornite (Cu5FeS4). In oxic natural waters copper primarily occurs in Cu(II) oxidation state and may form a number of dissolved complexes with either inorganic or organic ligands. Despite constituting a small percentage of the total amount of copper in the water, Cu2+ is considered to be the major contributor to Cu toxicity. Cu2+ is highly reactive with both inorganic and organic chemicals in solution and in suspension, resulting in a multitude of chemical forms. Copper is a micronutrient and is an essential element to plants and animals (Kapustka et al. 2004). However, at higher concentrations it may become toxic to aquatic life (USEPA 2007).
In Canada, copper is always produced in conjunction with other metals, most often with nickel, zinc, lead, gold and molybdenum. Canada ranked 9th among the 15 top copper-producing countries and ranked 17th for refined copper output in 2014 (NRCan 2015). Canadian mines produced 697,503 tonnes (t) of copper in concentrate in 2014, an increase of 6.9% (652,595 t) from 2013. The increase was mostly attributed to higher production in British Columbia. Most of the Canadian mine production of copper is from British Columbia (47%) and Ontario (30%), followed by Newfoundland (8%), Quebec (6%) and Manitoba (3%). Refineries in Canada produced 325,352 t of refined copper in 2014, an increase of 1.2% (321,511 t) from 2013. Canada’s total copper exports in 2014 were $7.1 billion, a 14% increase from 2013 (NRCan 2015).
Copper is used in electrical wires and cables for its electrical conductivity and in plumbing, industrial machinery and construction materials due to its durability, machinability, corrosion resistance and ability to be cast with high precision and tolerances (NRCan 2015). Because copper does not degrade or lose chemical or physical properties in recycling processes, the International Copper Study Group estimated that in 2012 more than 30% of the world’s copper consumption came from recycled copper (ICSG 2014).
The National Guidelines and Standards Office of Environment and Climate Change Canada (ECCC) compiled the water quality monitoring datasets from ECCC and provinces and territories. The majority of copper monitoring data were for total copper and these data are summarized in Table 2. Total copper concentrations for Canadian jurisdictions varied among jurisdictions with an overall range from 0.002 (Ontario) to 5273 µg/L (Saskatchewan). However, the median (50th percentile) copper concentrations were less variable and ranged from 0.3 µg/L for Prince Edward Island to 1.8 µg/L for Manitoba. Canadian monitoring data for other parameters, including number of sites and samples are presented in ECCC (2021).
|Newfoundland and Labrador||0.02||0.2||0.3||0.4||1.4||2.2||30|
|Prince Edward Island||0.09||0.2||0.2||0.3||0.5||1.1||3.2|
Mode of action
As an essential element for life, copper is widely used by plants and animals for the normal functioning of enzymes and proteins (Owen 1982a,b; Linder 1991). Biological functions mediated by copper involve electron transfer catalysis by means of its two accessible oxidation states (Georgopoulos et al. 2002). High levels of copper can be detrimental to aquatic life. Grosell (2011) presents a detailed discussion on mechanism of copper toxicity on fish, including physiological, ion regulatory, behavioral and chemosensory impairments. In plants, elevated copper exposure causes reduced growth, photosynthesis, respiration and nitrogen fixation (Foster 1977; Rosko and Rachlin 1977; Laube et al. 1980).
Fate, behaviour and partitioning in the environment
In nature copper exists in one of four oxidation states: Cu (elemental copper), Cu+ cuprous ion, Cu2+ cupric ion and Cu3+ (Georgopoulos et al. 2002). Cupric ion is the most common oxidation state generally encountered in water. Cu+ is formed from Cu2+; however, it disappears within seconds under oxic conditions (Glazewski and Morrison 1996). Cu3+ is strongly oxidizing and occurs only in a few compounds of no environmental significance (ATSDR 2004). Processes that influence the availability of copper to biota are: precipitation, dissolution, adsorption, desorption, complexation and competition for biological adsorption sites (i.e., ligands) (EURAR 2008).
NOM: natural organic matter; DOC: dissolved organic carbon.
A schematic of various fractions of copper in surface waters is shown in Figure 1. It may not be possible to measure the absolute total copper concentration in water because of the limitations in routine sampling and analytical methods. The total recoverable copper is often used to represent the total copper concentration. Within the total recoverable fraction, herein referred to as total, both particulate (bound to or incorporated into suspended matter and minerals) and dissolved copper fractions are included. In surface waters, copper can exist as cupric ions (Cu2+), as simple inorganic complexes (e.g., CuCO3) and complexed with organic macromolecules (e.g., bound to humic substances), or may be sorbed onto inorganic and organic particulates (Miwa et al. 1988). Particulate-bound copper is subject to sedimentation. However, the fraction staying within the water column more strongly interacts with the dissolved fraction of total suspended solids (Kiaune and Singhasemanon 2011). The main distinction between particulate and dissolved forms is the filterability, with dissolved copper operationally defined as being able to pass through a filter with pore size of usually 0.45 μm (Paquin et al. 2002). It is important to note that often the dissolved copper fraction is referred as “total dissolved” because it includes metals bound to colloids that are able to pass through the filter, and therefore the dissolved fraction may not be truly dissolved (Paquin et al. 2002). This fraction could also include copper nanoparticles. The possibility that small particles and amorphous precipitates will pass a filter and be quantified as “dissolved” is a limitation of the operational nature of the definition of “dissolved” based on filtrations with a 0.45 μm filter.
The dissolved fraction includes organically-bound, inorganically-bound and free copper ions. Inorganically-bound copper complexes and free copper ions grouped together make up the labile fraction, which is defined by the frequent mobility between the two fractions (Pesavento et al. 2009). Depending on how the labile fraction is determined, it may also include some of the weakly bound organic complexes. The BLM available component is not a specific chemical fraction because bioavailability here refers to the degree to which it elicits toxic responses to exposed organisms, which integrates chemical and biological mechanisms. Generally, the free metal ions are among the most important forms that comprise the bioavailable fraction, being able to cross biological membranes using several transport mechanisms and hence causing toxic effects (Dragun et al. 2008). However for copper, research has shown that both the copper in the form of free ions (i.e., Cu2+) and some simple inorganic complexes (e.g., CuOH+) are able to pass biological membranes (Kramer et al. 2004). In contrast, organically-bound copper, presented as dissolved organic carbon (DOC), are considered less bioavailable to organisms (Kramer et al. 2004). Therefore, it is the free copper ion or labile fractions that are the most active copper species. Total copper concentrations are usually not directly related to ecological effects because exposure to organisms is influenced by processes that limit copper uptake (ICPS 1998).
Cu2+ typically binds to inorganic or organic ligands when it enters into water and sediments (EURAR 2008). The binding affinity of Cu2+ with inorganic and organic matter is dependent on pH and the oxidation-reduction potential in the local environment (EURAR 2008). With increasing pH, the dominant copper species change from Cu2+, CuCO3, Cu(CO3)22-, Cu(OH)3- to ultimately Cu(OH)42-. The relative proportion of Cu2+, copper hydroxide and carbonate complexes also depends on alkalinity and the magnitude of binding constants for the formation of complexes. Although pH and alkalinity vary substantially in freshwater systems, Cu2+, Cu(OH)+, Cu(OH)2 and Cu(CO3)22- make up 98% of dissolved inorganic copper, whereas other oxidation states of copper complexed with hydroxides, carbonates, chlorides, sulphate, ammonium and phosphates comprise <2% of dissolved inorganic copper (Nelson et al. 1986; Alberta Environment 1996).
In surface waters a major proportion of copper can be held in solution through complexation with naturally-occurring organic ligands. In water, copper binds to dissolved organic matter (e.g., humic or fulvic acids) by forming stable complexes with -NH2, -SH and, to a lesser extent, -OH groups (EURAR 2008). Affinity of copper varies among organic ligands, for example, affinity for humic acids is greater than for fulvic acids (Huang and Yang 1995). Cu2+ also binds with varying affinities to inorganic and organic components in sediments and soils. For example, Cu2+ binds strongly to hydrous manganese and iron oxides in clay and to humic acids, but much less strongly to aluminosilicates in sand (USEPA 2007).
A major fraction of total copper may be adsorbed within the first hour of entering the water column, and in most cases, equilibrium is reached within 24 hours (ATSDR 2004). Adsorption of copper to colloids, suspended particles, sediment and clay plays a major role in the behaviour of copper. The adsorption increases with increasing pH (Nelson et al. 1986; Al-Sabri et al. 1993) and desorption of copper can occur at pH less than 6 (O'Connor and Kester 1975). Organic ligands may increase or decrease adsorption of copper (Davis and Leckie 1978).
Copper that is taken up by fish from water is quickly transferred to blood plasma (Zia and McDonald 1994) and among organs, liver and gills accumulate the most copper (Brungs et al. 1973; Handy 1993; Svobodova et al. 1994). The uptake of copper in fish can vary, less is accumulated in bigger fish compared to smaller fish (Anderson and Spear 1980a,b). In crayfish highest concentrations were reported in the hepatopancreas and gills (Alikhan et al. 1990), whereas in a freshwater mollusk highest copper concentrations were found in gills and mantle (Tessier et al. 1984). Linear uptake of copper was observed in clam and depuration from gills was not observed, while in foot and mantle, concentrations decreased in a linear fashion (Salanki and V.-Balogh 1989). In daphnids reduced copper uptake is reported for waters of high hardness and high humic acid concentration (Winner 1985). The accumulation of copper in green algae initially increases by rapid adsorption followed by a slower intracellular uptake (Knauer et al. (1997). Wilde et al. (2006) reported both extracellular and intracellular copper increases in green algae with increasing copper concentration in water causing growth inhibition.
Copper in surface waters exists in different chemical forms due to several varying physico-chemical processes and abiotic factors, such as pH, alkalinity, hardness and DOC. Even though copper is present in water in many forms, the toxicity of copper to aquatic life has been shown to be related primarily to activity of the cupric ion, and possibly to some of the hydroxy complexes in fresh water, and bicarbonate complex for plants and algae (Andrew et al. 1977; Chapman and McCrady 1977; Howarth and Sprague 1978; Chakoumakos et al. 1979; Petersen 1982; Borgmann and Ralph 1983; Pagenkopf 1983; Rueter 1983; Allen and Hansen 1996) and marine waters (Zitko and Carson 1973; Sunda and Guillard 1976; Sunda and Lewis 1978; Anderson and Morel 1978). Many examples of classic response of organisms to cupric ion activity, as well as some limited exceptions, are reviewed by Campbell (1995).
A formal description of interactions between metals and organism, now commonly referred to as the Free Ion Activity Model (FIAM), was first provided by Morel (1983). Pagenkopf (1983), using a similar approach, developed the Gill Surface Interaction Model (GSIM) to predict metal effect levels over a range of water quality characteristics. The Biotic Ligand Model (BLM) further formalized these concepts in an easy-to-use tool that automates the calculation of chemical speciation and binding to sensitive receptors in aquatic biota (Playle et al. 1992; DiToro et al. 2001; Santore et al. 2001; Paquin et al. 2002; USEPA 2007). These sensitive receptors are physiologically active sites that correspond to the proximate site of action for toxicity and are collectively termed as the biotic ligand (BL) within the BLM.
Based on the mechanistic principles underlying the BLM, any changes in water quality that would be expected to affect the activity of the free copper ion would be expected to also decrease the bioavailability and hence toxicity of copper. For example, increases in pH, alkalinity and natural organic matter (NOM) would all tend to decrease copper bioavailability and would therefore result in increased copper effect concentrations (e.g., EC10s, or EC50s). The bioavailability of copper can also be modified by competitive interactions at the sensitive receptors (biotic ligand). Increased concentrations of sodium and calcium, for example, can result in reduced binding of copper to physiologically active gill binding sites and can thereby reduce copper bioavailability (Playle et al. 1992). Competition with protons is included in the copper model and could result in lower bioavailability at low pH. But these effects would occur at lower pH values than are typically used in toxicity tests and, as a result, the primary effect of changing pH is to decrease bioavailability at high pH by increasing the OH- and CO32- concentrations.
One important feature that is common to the BLM, FIAM and GSIM is the importance of the linkage between bioavailability and the chemical speciation of metals in aqueous systems (Figure 1). All these models assume equilibrium conditions in the water system. Chemical speciation within the BLM is simulated as an equilibrium system that includes complexation of inorganic ions and NOM. The chemical system for the original freshwater BLM is simulated by the Chemical Equilibria in Soils and Solutions (CHESS) model (Santore and Driscoll 1995), which also includes a description of metal interactions with NOM based on the Windermere Humic Aqueous Model (WHAM) (Tipping 1994).
While several versions of WHAM have been developed, the BLM has adopted reactions defined in the development of Model V (Tipping 1994) rather than adopting subsequent versions (e.g., Model VI (Tipping 1998) or Model VII (Tipping et al. 2011)). These latter versions improve the fit of the speciation model to speciation data at very low metal concentrations by increasing the number of strong binding sites that can interact with copper and other metals. While Model VI and Model VII predict copper speciation better than Model V, they are also much more complex and involve three to four times as many reactions as Model V and most of the improvement in predicted speciation occurs at copper concentrations that are lower than values associated with toxic effects. Most of the improvement, therefore, is not relevant in the range of copper concentrations that are of interest to bioavailability modeling. Furthermore, the improvements in Model VI and Model VII result from an increase in strong binding sites, and therefore result in lower estimates of bioavailability for a given copper and DOC concentration. The Model V is therefore selected for developing the FWQG because (i) the model is relatively simple; (ii) the model performs well in the range of concentrations relevant to bioavailability considerations; and (iii) the model produces results that are more protective than Model VI or Model VII.
The BLM includes reactions that describe the chemical interactions of copper and other cations to physiologically active sites (or “biotic ligands”) that correspond to the proximate site of action of toxicity. That is, the biotic ligand (BL) is represented by a single characteristic binding site density for the BL and by separate conditional stability constant for each of the dissolved chemical species with which it reacts (Figure 2). Predictions of metal toxicity are made by assuming that the dissolved metal toxicity, which varies with water chemistry, is always associated with a fixed critical level of metal accumulation at the biotic ligand. This fixed level of metal-biotic ligand accumulation at 50 percent mortality (or any other type of biological impairment, such as growth, reproduction or behavior) is referred to as the LA50 (or more generally critical accumulation may be more appropriate for a wide range of effects including sublethal chronic effects). It is assumed to be constant, regardless of the chemical characteristics of the site water. This combination of reactions that describe aqueous metal speciation and organism interactions allows the BLM to predict copper toxicity for a variety of organisms over a variety of water quality conditions (Santore et al. 2001).
Conceptual diagram showing relationship between chemical speciation and metal complexation, metal binding at Biotic Ligand (BL) sites and competition at these BL sites for other cations.
An advantage of the BLM is that most of the parameters are invariant for different organisms within a specified taxonomic group, despite the complexity of the modeling framework. All thermodynamic constants used to simulate inorganic and organic chemical equilibrium reactions are determined by characteristics of the metal and the available ligands. As such, the constants do not change for simulations involving different organisms within a specified taxonomic group. The ability of the BLM to apply a consistent mechanistic framework to metal bioavailability for organisms has been an important factor in the incorporation of the BLM approach in regulatory frameworks in USA (USEPA 2007) and European Union (EURAR 2008). For example, the US Environmental Protection Agency (USEPA) uses a single set of parameters for all aquatic organisms and in its approach, the copper BLM is used to normalize available toxicity data to a consistent set of exposure conditions. The normalized data are then used to develop a species sensitivity distribution (SSD), and the 5th percentile of the normalized SSD is determined using a truncated triangular distribution (Stephen et al. 1985; Erickson and Stephan 1988). The BLM calculates the final acute value (FAV) as if it was an aquatic organism whose sensitivity to copper was exactly at the 5th percentile (USEPA 2007). The acute water quality criterion is then calculated as the FAV divided by 2, and the chronic criterion as the FAV divided by the acute to chronic ratio (ACR) following the standard guidelines procedures of the USEPA (Stephan et al. 1985).
The BLM modeling framework used by the EU (EURAR 2008) is similar to that used by the USEPA is first used to normalize toxicity data to a consistent set of conditions in order to produce an SSD. However, there are also several differences in the EU approach. First, the toxicity data used by the USEPA are for acute exposures, while the database used by the EU is based on chronic exposures. Second, in addition to fish and invertebrates the EU approach also uses toxicity data for plants, whereas plants are not considered in the USEPA approach. Third, the EU approach uses a log-normal distribution to estimate the 5th percentile (Aldenberg and Jaworska 2000). Finally, the EU approach uses different BLM parameters for fish and invertebrates, and the approach used for plants is a simple regression on free copper ion. The use of different parameter sets in the EU approach requires that the entire SSD be recalculated for each target water body, rather than just calculating the 5th percentile in the USEPA approach.
Federal water quality guideline derivation
This FWQG is for dissolved copper because (i) dissolved copper is the most bioavailable form; (ii) concentrations of total copper in Canadian surface waters can be affected by non-bioavailable mineral forms; and (iii) most of the toxicity data used for deriving the guideline are based on exposures to dissolved copper. The FWQG also differs from previous guidelines in that it considers the effects of a broad suite of water quality factors on copper bioavailability (i.e., pH, organic matter, alkalinity and ionic content), whereas the existing CCME (CCREM 1987) copper guideline only considered hardness.
The BLM-based FWQG for copper involved compilation and evaluation of freshwater copper toxicity data, evaluation of model performance, utilization of the BLM to normalize the chronic toxicity dataset to site-specific conditions and construction of SSDs (Figure 3). The underlining approach and steps used here are similar to the USEPA (Santore et al. 2001); however, the differences result due to differences in guideline protocols, the parameters considered in the analysis, the details of the toxicity database (e.g., plant data considered, only uses chronic toxicity data) and the normalization of toxicity data in the SSD. Similar to the USEPA, in Canadian BLM a single set of BLM parameters (binding constants) are used for both fish and invertebrate species. Canadian approach is similar to that of the EU in that it is based on chronic toxicity data, considers plants in addition to fish and invertebrates, and utilizes multiple BLM parameters. However, the approach differs from the EU in that it uses a full BLM approach for plants rather than a regression on free copper ion.
The overall data processing steps required to derive the FWQG are shown in Figure 3. In Step 1, individual toxicity endpoints in the database are run through the BLM in speciation mode to calculate critical copper accumulation levels. In this step, the chemistry for the BLM, including temperature, pH, DOC, Ca2+, Mg2+, Na+, K+, SO42-, Cl- and alkalinity, is based on the exposure conditions used in toxicity test and for each toxicity endpoint a critical accumulation value is generated. This step is analogous to finding an intercept specific to a toxicity observation when using a hardness equation. In the case of the BLM, there are two model parameter sets that are used – one for both fish and invertebrates, and the second one for plants (Table 3). For the reactions in Table 3, reactions 2 and 3 represent the formation of bioavailable complexes for CuOH+ and CuHCO3+. Because these reactions involve more than two reactants, the log K, logarithm of the binding constant, values are not directly comparable with other reactions in Table 3. For example, the binding of CuHCO3+ to biotic ligand sites appears to be much stronger than the binding of free copper (compare the log K values for reactions 1 and 3), but the magnitude of the log K values cannot be directly compared due to the different stoichiometries of these two reactions. The reactions are written this way because that is the form required by the CHESS model (Santore and Driscoll 1995). To facilitate comparison, these two reactions are written again in an alternative form at the bottom of the table to show an overall log K value useful for comparing with other reactions.
Each data source or result is shown as green boxes and calculations are shown as numbered blue arrows. Step 1: raw toxicity data are processed with the BLM and the chemistry of the toxicity data exposure conditions to calculate critical accumulation levels. Step 2: critical accumulation levels are processed with the BLM using chemical conditions in the site water to produce normalized toxicity data, expressed as concentrations of copper in the exposure waters. Step 3: normalized concentrations of copper in the exposure waters are used to develop a SSD, and the 5th percentile of the SSD is the FWQG.
In Step 2, normalization process, each of the critical accumulation levels are run with the BLM again, but this time in toxicity mode and with the specified normalized water chemistry (e.g., temperature of 20oC, pH of 7.5, DOC of 0.5 mg/L and hardness of 50 mg/L). Toxicity mode takes the critical accumulation levels from Step 1 and determines the amount of dissolved copper in the exposure water that would be needed to result in the critical amount of accumulation on the BL of the organism. This needs to be done with the critical accumulation for each toxicity observation in the database, using each set of conditions of site chemistry. The results from this step are normalized concentrations of copper in the exposure waters at the toxicity endpoints. This step is analogous to traditional water quality guideline development when the toxicity data are converted to constant hardness conditions using the hardness slope. Although going from raw toxicity values to normalized toxicity values is often done in one step when using a hardness equation, the BLM needs to do the process in two steps because the BLM contains nonlinearities while the hardness equation is a simple linear relationship. Furthermore, having different parameter sets (log K values) for fish and invertebrates and plants means that the relative positions of organisms in the SSD can change with different chemical conditions, which in turn means that a full database normalization is required for each site’s chemistry. At the end of Step 2, the entire toxicity dataset has been normalized to a chosen site chemistry condition. In Step 3, the normalized concentrations of copper in exposure waters are processed into an SSD. For each species and each site, the effect concentrations are averaged (as a geometric mean) by endpoint and effect (only when endpoints are for the same species, same life stage and same duration), and the most sensitive endpoint is then used for that species in the site-specific SSD. The 5th percentile of the best fit SSD model is the site-specific FWQG.
|Reaction number||Reaction||Log K|
Fish & inverts
|1||BL – Cu = BL + Cu²⁺||5.4||7.4||toxic|
|2||BL - CuHCO₃ = BL = Cu²⁺ + H⁺ + CO₃²⁻||21.2||-||toxic|
|3||BL – CuOH = BL + Cu²⁺ + OH⁻||-3.3||-0.8||toxic|
|4||BL – H = BL + H⁺||7.2||6.4||competing|
|5||BL – Ca = BL + Ca²⁺||4.0||4.4||competing|
|6||BL – Mg = BL + Mg²⁺||3.5||4.4||competing|
|7||BL – Na = BL + Na⁺||-||4.0||competing|
|2 alternate||BL - CuHCO₃ = BL = CuHCO₃⁺||9.1||-||toxic|
|3 alternate||BL – CuOH = BL + CuOH⁺||4.2||6.7||toxic|
a The log K values are not given for certain reactions because bioavailability relationships for those taxonomic groups do not require specific interactions.
The EU’s chronic aquatic toxicity dataset collected for the risk assessment of copper (EURAR 2008) and the USEPA’s data compiled from an extensive copper toxicity data collection and evaluation exercise in 2015 formed the foundation of aquatic toxicity data considered for developing the BLM-based FWQG. ECCC further updated the toxicity dataset through collaboration with the British Columbia Ministry of Environment and Climate Change Strategy. A detailed review of acceptable studies from these sources was also performed by ECCC following the CCME (2007) guidance for data quality. Determinants of test acceptability included, but were not limited to, exposure duration, analytical determination of copper exposure concentrations and other water quality parameters, documentation of the control response, the use of suitable biological endpoints and the inclusion of appropriate statistical analyses of the data collected in the study.
A total of 355 chronic toxicity endpoints for 33 species were identified as acceptable. Many acceptable studies reported results for multiple effects (e.g., reproduction, growth, mortality) with multiple endpoints (e.g., NOEC, LOEC, ECx). Where multiple effects were available for a single species, only the most sensitive effect was selected following CCME (2007). In an effort to include data for preferred endpoints, if studies did not report an EC10, but reported enough information to develop a concentration-response curve, a response curve was fit to the data and EC10 values were calculated using the Toxicity Relationship Analysis Program (TRAP) (USEPA 2015). All 355 acceptable effect concentrations were normalized to temperature of 20oC, pH of 7.5, DOC of 0.5 mg/L and hardness of 50 mg/L. From the normalized toxicity dataset 83 preferred endpoints for 33 species (12 endpoints for 11 fish species, 39 endpoints for 17 invertebrate species and 32 endpoints for 5 plant species) were selected following CCME (2007). These 83 preferred endpoints along with the test water conditions inputted in BLM are listed in Appendix 1. Finally, geometric means of the effect concentrations of copper in the exposure waters were calculated for individual species if multiple endpoints for same effect and duration were available for them (i.e., one species, one vote).
For each chronic test, temperature, pH, alkalinity, hardness, DOC, %HA (humic acid fraction of DOC), Ca, Mg, Na, K, SO4 and Cl values were also compiled from studies or estimated. For all studies that required estimation of BLM chemical parameters a method similar to the simple chemistry estimation procedure described in the BLM user manual was used (ECCC 2021). If the chemistry could be inferred from information about the test design or source water, then that inferred chemistry was used (e.g., if the authors reported full chemistry for their control water and then added a known amount of a salt to achieve different levels of hardness). Alkalinity was first estimated when necessary from pH by assuming equilibrium with atmospheric CO2(g) with a pCO2 of 10-3.2 atm. Then, if ion concentrations needed to be estimated from hardness or other ions (e.g., if the authors reported Ca but nothing else), appropriate ion ratios were assigned based on the description of the test water source. Ion ratios were used in the same way as described in the BLM user manual (ECCC 2021). Concentrations of DOC were estimated with the best estimate achievable given the description of the test water, or as 0.3 mg/L in synthetic waters for which values near zero were expected. If toxicity studies reported total copper concentrations instead of dissolved concentrations, the USEPA (1996) conversion factor of 0.96 was applied to convert the total concentrations to dissolved concentrations.
For studies that did not provide any information about the distribution of DOC among humic and fulvic forms it was assumed that 10% of the DOC was in the form of humic acid and the rest as fulvic. This estimate is based on the assumption that most natural waters have a small (but non-zero) amount of humic acid, and that humic and fulvic acids together account for the entire measured DOC. These assumptions are consistent with the way the BLM was used to develop the USEPA criteria for copper (USEPA 2007).
Only 70 of the 355 toxicity observations reported HA fractions, 4 of which were in the SSD. This is not surprising because HA fraction does not typically vary significantly and has a relatively unimportant role in determining toxicity. Excluding instances where the HA fraction needed to be estimated, there were 115 toxicity observations in the database where chemistry needed to be estimated, 40 of which were observations considered for the SSD. Among the 83 endpoints considered for SSD, cation information was estimated for 31, anion information was estimated for 38, DOC values were estimated for 28 and alkalinity was estimated for 7 endpoints.
BLM model validation
The relationship between observed and BLM-predicted copper toxicity for 83 endpoints for 33 species (selected above) was examined to evaluate the performance of the copper BLM (Figure 4). Data from each study were run through the BLM in speciation mode using the test chemistry to get the critical accumulation. A study-specific critical accumulation was determined by taking the geometric mean of the critical accumulations for each study. These critical accumulations along with the test chemistry were run through the BLM in toxicity mode to get the BLM-predicted copper toxicity values. The results showed that the BLM model predicted 92% of the observed toxicity within a factor of 2 for the data used in FWQG. Among the 7 endpoints with greater than the factor of 2 value, 1 was for fish (Pimephales promelas), 4 were for invertebrates (Ceriodaphnia dubia and Villosa iris) and 2 were for plants (Chlorella protothecoides). These results demonstrate that the copper BLM model has performed well.
The solid line represents a 1:1 ratio of observed vs predicted toxicity and the dotted lines represent a factor of +/-2 from measured toxicity. For any datapoints above the dotted line, the model has a tendency to overestimate the effect-concentration (or underestimate potential Cu toxicity). The reverse is true for datapoints below the dotted line.
The factor of 2 has been used as a simple and generic way to evaluate the variation between predicted and measured toxicity values and comes from the observation that replicate toxicity tend to be within 2x of the mean (Figure 5). Given the inherent variability in toxicity testing outcomes, even a perfect model could on average only match variation in toxicity values as well as other replicate values, and therefore the boundaries of 2x around a line of perfect agreement shows how close the BLM should match individual observations (Santore et al. 2001). The variation in replicate values was further examined by Santore and Ryan (2015) where it was shown that 2x around the geometric mean corresponds to 1.5 standard deviations around the mean, and this conclusion was supported in a subsequent review by Meyer et al. (2018). With this definition in mind, replicate toxicity values should be within 2x of the mean 88% of the time.
Replicate values tend to fall within 2x of the geometric mean of all the data.
Federal Water Quality Guidelines (FWQGs) are preferably developed using the CCME (2007) protocol. In the case of copper, there were enough acceptable chronic toxicity data to meet the minimum data requirements for a CCME Type A guidelineFootnote 1 . The CCME (2007) protocol allows for the use of BLM for water quality guidelines, however, no Canadian water quality guideline has yet used this approach. It is to be noted that the BLM based FWQG for copper is for dissolved concentrations of copper. When guideline users only have total copper concentrations for their site, it is recommended that they first compare their total copper concentration to the dissolved copper guideline, and where there is an exceedance, re-sample the waterbody for dissolved copper.
The chronic toxicity dataset and the BLM were combined in a user-friendly BLM tool capable of calculating the site-specific FWQGs. Details of the required steps are given in the user’s guide and reference manual (ECCC 2021). In summary, the tool combines all steps of the BLM normalization process and estimation of the 5th percentile from the normalized SSD (as outlined in Figure 3). With the appropriate BLM parameter file, the BLM is applied to the dataset in speciation mode to determine the critical BL-Cu accumulation levels associated with each observation. Parameter files that contain all the thermodynamic constants that define the chemical speciation and metal binding to BL sites (Table 3) are supplied within the BLM tool. The BLM is then used in toxicity mode with the critical BL-Cu accumulation levels and the user-specified normalization chemistry (i.e., the site-specific chemistry). The predicted effect concentrations for the observations included in the SSD thus represent effect concentrations for each observation, as if each test were conducted in the same underlying water conditions as described by the user-entered site-specific chemistry. Within the BLM tool a geomean value is calculated for species that had multiple endpoints of similar effect and duration. Several cumulative distribution functions are fit to the data using regression methods and the best model is selected based on consideration of goodness-of-fit for each site-specific water chemistry. The 5th percentile value of the best model is the site-specific FWQG (Figure 6).
When the software is used to generate a FWQG for a given water quality condition (i.e. Step 2 in Figure 3), it checks that each of the chemistry input values are within the range of conditions that correspond to the calibration and validation data used in the development and evaluation of the model. When the input value for a chemical parameter is higher than the upper limit, the FWQG is determined with that parameter at the upper limit. Values below the lower limits are similarly replaced with the lower limit before deriving the FWQG.
Species sensitivity distribution derived 5th percentile (i.e., guideline) and confidence intervals for effect concentrations (Appendix 1) normalized to temperature of 20oC, pH of 7.5, DOC of 0.5 mg/L and hardness of 50 mg/L CaCO3. The BLM tool produces a similar SSD plot for the site-specific chemistry inputted by the user (see ECCC 2021).
A FWQG calculator is also provided for users. Since this calculator is intended to be a simple and conservative screening tool, the HC5 value reported by the look up table is the lowest of the neighboring guideline values. Users are always recommended to use the full BLM software.
Simplified versus full BLM
The BLM tool allows an option to use “simplified” chemistry that uses hardness and geochemical ion ratios to estimate most of the major ions, with alkalinity estimated from pH (ECCC 2021). Using this option reduces the number of input parameters from 10 to 4, and yet produced very similar results for the FWQG (Figure 7). The BLM tool includes average geochemical ion ratios for all of Canada, as well as averages for each province and each ecoregion (ECCC 2021). The tool also allows users to input their own ion ratios for a site. The accuracy of the result that is obtained with the simplified chemistry option can be improved by using geochemical ion ratios that most accurately reflect the site conditions, and therefore it is usually better to use ion ratios at the ecoregional level. For the comparison shown in Figure 7, Canada-wide averages for ion ratios were used and so this result is reflective of the minimal level of agreement that would be expected between the simplified and full chemistry options. Users should use the full chemistry calculation whenever possible, but if data availability does not support the full calculation, the simplified calculation provides an expedient way to overcome data limitations while providing nearly the same results.
Chemistry for this comparison was based on 10 samples selected at random from each of the twelve Canadian Provinces (120 samples in total).
Protectiveness of FWQG
To assess whether the FWQG for copper is sufficiently protective, the BLM-based FWQGs were calculated for each of the 355 acceptable endpoints for 33 species in the chronic toxicity dataset. The ratios of FWQG to effect concentrations were then compared to measured toxicity values at their associated water chemistry (Figure 8). Ratios of >1 indicate that the FWQG is protective of the toxicity value in that particular test, while ratios of <1 indicate that the FWQG is lower than the observed toxicity, and hence may require further evaluation.
The protectiveness assessment identified that 96% of acceptable toxicity data points were protected. To ensure protectiveness, each of the 13 endpoints with ratios <1 were further examined. Only 13 (4%) out of 355 observations fall below the 1:1 line and these endpoints included six endpoints for invertebrates (Ceriodaphnia dubia (2), Villosa iris (3) and Daphnia magna (1)); one for fish (Acipencer transmontanus); and six endpoints for plants (Lemna minor (3), Chlorella protothecoides (2), Raphidocelis subcapitata (1)). The mean species ratios for all 33 species in dataset were greater than 1. Ceriodephnia dubia endpoints (n=33) had a species mean ratio of 43, meaning on average the reported measured toxicity values were appproximately 43 times lower than the site-specifc FWQG. Similarly, for the other six species a greater number of endpoints had FWQG to effect concentration ratios of greater than one (11 for V. iris, 117 for D. magna; 4 for A. transmontanus, 5 for L. minor, 20 for C. protothecoides and 43 for Raphidocelis subcapitata).
Ratio of FWQG to chronic effect concentrations for all acceptable toxicity endpoints plotted against observed effect concentrations.
The FWQG was also compared with a database of olfactory effects in freshwater fish compiled by Meyer and Deforest (2018). In that review, the BLM-based water quality criteria developed by the USEPA (2007) was found to be protective for most (95.3%) of the compiled olfactory toxicity data for copper. The FEQG is generally more protective than the USEPA chronic criterion. Our FEQG results were protective to all of the olfactory toxicity data compiled by Meyer and Deforest (2018).
The overall assessment of the available data as discussed above indicates that the BLM-based FWQG for copper is protective (>95% protection) of aquatic life and thus is in alignment with the derivation of FWQG using the HC5 of the SSD.
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List of acronyms and abbreviations
acute to chronic ratio
Agency for Toxic Substances and Disease Registry
biotic ligand model
Canadian Council of Ministers of Environment
Canadian Council of Resources and Environment Ministers
Canadian Environmental Protection Act
chemical equilibria in soils and solutions
chemicals management plan
dissolved organic carbon
European Union risk assessment report
final acute value
free ion activity model
federal environmental quality guideline
federal water quality guideline
Government of Canada
gill surface interaction model
maximum acceptable toxicant concentration
natural organic matter
Natural Resources Canada
species sensitivity distribution
toxicity relationship analysis program
United States Environmental Protection Agency
Windermere humic aqueous model
|Species||Group||Duration ||Effect concentration (µg/L)||Normalized dissolved effect concentration (µg/L)||Temperature|
|Acipenser transmontanus (White sturgeon)||fish||24 d EC10 (growth)||1.02 D||0.62||15||8.0||0.41||100||Wang et al. 2014|
|Brachionus calyciflorus (Rotifer)||invertebrate||48 hr EC10 (growth)||7.55 D||5.24||25||6.0||4.91||100||De Schamphelaere et al. 2006|
|Campeloma decisum (Pointed snail)||invertebrate||42 d EC10 (survival)||6.85 T||1.17||15||7.7||2.65||45||Arthur & Leonard 1970|
|Campeloma decisum (Pointed snail)||invertebrate||42 d EC10 (survival)||9.21 T||1.56||15||7.7||2.65||45||Arthur & Leonard 1970|
|Ceriodaphnia dubia (Water flea)||invertebrate||7 d EC10 (reproduction)||11.0 D||4.44||25||8.4||0.40||105||Wang et al. 2011|
|Ceriodaphnia dubia (Water flea)||invertebrate||7 d EC10 (reproduction)||34.0 D||1.93||25||8.3||3.00||102||Wang et al. 2011|
|Ceriodaphnia dubia (Water flea)||invertebrate||7 d EC10 (reproduction)||29.0 D||0.78||25||8.3||5.80||106||Wang et al. 2011|
|Ceriodaphnia dubia (Water flea)||invertebrate||7 d EC10 (reproduction||25.0 D||0.39||25||8.3||10.00||106||Wang et al. 2011|
|Chlamydomonas reinhardtii (Green algae)||algae/plant||3 d EC10 (growth)||178.0 D||13.90||25||6.0||4.07||250||De Schamphelaere & Janssen 2006|
|Chlamydomonas reinhardtii (Green algae)||algae/plant||3 d EC10 (growth)||108.0 D||13.15||25||7.0||4.07||250||De Schamphelaere & Janssen 2006|
|Chlamydomonas reinhardtii (Green algae)||algae/plant||3 d EC10 (growth)||96.0 D||10.19||25||8.1||4.07||250||De Schamphelaere & Janssen 2006|
|Chlorella protothecoides (Green algae)||algae/plant||72 hr MATC (growth)||1.48 D||9.03||27||7.5||0.30||85||Franklin et al. 2002|
|Chlorella protothecoides (Green algae)||algae/plant||72 hr MATC (growth)||2.81 D||13.22||27||7.5||0.30||85||Franklin et al. 2002|
|Chlorella protothecoides (Green algae)||algae/plant||72 hr MATC (growth)||5.31 D||18.90||27||7.5||0.30||85||Franklin et al. 2002|
|Chlorella protothecoides (Green algae)||algae/plant||72 hr MATC (growth)||10.39 D||27.91||27||7.5||0.30||85||Franklin et al. 2002|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||108.0 D||15.56||25||6.0||2.14||100||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||407.0 D||20.87||25||6.0||6.41||100||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||56.0 D||10.61||25||7.9||2.09||400||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||36.0 D||25.80||25||7.0||0.62||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||173.0 D||10.40||25||8.0||6.55||400||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||99.0 D||10.53||25||7.0||4.48||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||85.0 D||8.23||25||7.0||4.15||500||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||162.0 D||12.66||25||8.8||4.10||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||283.0 D||18.03||25||7.1||7.90||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||188.0 D||21.15||25||6.0||2.08||400||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||510.0 D||24.48||25||6.1||6.31||400||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||31.0 D||8.45||25||7.9||2.20||100||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||188.0 D||16.38||25||7.9||6.48||100||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||404.0 D||15.80||25||5.5||4.25||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||159.0 D||24.38||25||7.1||4.25||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||84.0 D||9.05||25||7.0||4.48||250||De Schamphelaere & Janssen 2006|
|Chlorella vulgaris (Green algae)||algae/plant||3 d EC10 (growth)||132.0 D||15.25||25||7.0||4.24||250||De Schamphelaere & Janssen 2006|
|Clistoronia magnifica (Caddisfly)||invertebrate||8 month EC10 (life cycle)||5.74 T||5.67||15||7.3||0.70||26||Nebeker et al. 1984|
|Cottus bairdii (Mottled sculpin)||fish||28 d EC10 (biomass)||26.61 D||6.47||19||8.2||1.00||102||Besser et al. 2007|
|Cyprinella monacha (Spotfin chub)||fish||30 d IC10 (biomass)||23.0 T||9.50||25||8.3||0.30||162||Besser et al. 2001, 2005|
|Cyprinus carpio (Common carp)||fish||168 h MATC (survival)||31.0 T||11.71||23||7.6||1.00||100||Stouthart et al. 1996|
|Daphnia ambigua (Water flea)||invertebrate||10 d EC10 (reproduction)||23.99 T||20.00||21||8.0||0.30||63||Harmon et al. 2003|
|Daphnia magna (Water flea)||invertebrate||21 d EC10 (reproduction)||61.0 D||4.48||20||7.6||4.00||250||Muyssen & Janssen 2007|
|Daphnia magna (Water flea)||Invertebrate||21 d EC10 (reproduction)||56.1 D||0.75||20||7.7||5.00||180||Bossuyt & Janssen 2004|
|Daphnia magna (Water flea)||Invertebrate||21 d EC10 (reproduction)||57.1 D||0.77||20||7.7||5.00||180||Bossuyt & Janssen 2004|
|Daphnia magna (Water flea)||Invertebrate||21 d EC10 (reproduction)||46.5 D||0.62||20||7.7||5.00||180||Bossuyt & Janssen 2004|
|Daphnia magna (Water flea)||Invertebrate||21 d EC10 (reproduction)||50.2 D||0.67||20||7.7||5.00||180||Bossuyt & Janssen 2004|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||4.0 T||1.29||20||8.6||0.30||58||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||20.0 T||5.62||20||8.5||0.68||58||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||30.0 T||4.81||20||8.7||1.05||58||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||5.0 T||2.47||20||8.7||0.30||115||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||20.0 T||5.70||20||8.6||0.68||115||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||40.0 T||7.89||20||8.6||1.05||115||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||10.0 T||3.68||20||8.6||0.38||230||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||15.0 T||3.16||20||8.6||0.68||230||Winner 1985|
|Daphnia pulex (Water flea)||invertebrate||42 d NOEC (survival)||20.0 T||2.76||20||8.6||1.05||230||Winner 1985|
|Epioblasma capsaeformis (Oyster mussel)||invertebrate||28 d IC10 (survival)||3.1 D||1.61||20||8.2||0.30||162||Wang et al. 2007|
|Etheostoma fonticola (Fountain darter)||fish||30 d IC10 (survival)||9.3 T||4.64||25||8.1||0.30||163||Besser et al. 2001, 2005|
|Fluminicola sp. (Pebble snail)||Invertebrate||28 d EC20 (mortality)||11.1 D||1.21||20.35||8.4||1.00||166||Besser et al. 2009, 2016|
|Fontigens aldrichi (Spring snail)||invertebrate||28 d EC10 (survival)||10.4 D||1.12||20.35||8.4||1.00||166||Besser et al. 2009, 2016|
|Hyalella azteca (Amphipod)||invertebrate||10 d NOEC (survival)||30.0 D||37.01||20||7.0||0.70||10||Deaver & Rodgers 1996|
|Hyalella azteca (Amphipod)||invertebrate||10 d NOEC (survival)||82.0 D||74.64||20||7.5||0.70||22||Deaver & Rodgers 1996|
|Hyalella azteca (Amphipod)||invertebrate||10 d NOEC (survival)||50.0 D||31.91||20||7.7||0.70||36||Deaver & Rodgers 1996|
|Hyalella azteca (Amphipod)||invertebrate||10 d NOEC (survival)||50.0 D||25.84||20||7.8||0.70||50||Deaver & Rodgers 1996|
|Hyalella azteca (Amphipod)||invertebrate||10 d NOEC (survival)||82.0 D||30.65||20||8.1||0.70||64||Deaver & Rodgers 1996|
|Lampsilis siliquoidea (Fatnucket clam)||invertebrate||28 d IC10 (survival)||3.1 D||0.91||20||8.7||0.30||165||Wang et al. 2007|
|Lemna minor (Duckweed)||algae/plant||7 d EC10 (root length)||2.07 D||2.03||25||8.1||1.00||163||Antunes et al. 2012|
|Lemna minor (Duckweed)||algae/plant||7 d EC10 (root length)||0.72 D||0.79||25||8.3||1.00||187||Antunes et al. 2012|
|Lemna minor (Duckweed)||algae/plant||7 d EC10 (root length)||3.78 D||1.37||25||7.8||2.00||35||Antunes et al. 2012|
|Lemna minor (Duckweed)||algae/plant||7 d EC10 (root length)||5.92 D||1.70||25||7.6||2.00||96||Antunes et al. 2012|
|Lymnaea stagnalis (Pond snail)||invertebrate||30 d EC10 (growth)||1.8 D||0.34||24||7.8||1.20||56||Brix et al. 2011|
|Oncorhynchus mykiss (Rainbow trout))||fish||200 hr LC10 (survival)||7.0 T||3.96||12.2||7.2||1.40||24||Chapman 1978|
|Oncorhynchus tshawytscha (Chinook salmon)||fish||14 weeks EC10 (growth)||2.15||1.23||12.2||7.2||1.40||24||Chapman 1975, 1982|
|Perca fluviatilis (European perch))||fish||30 d MATC (growth)||29.29||7.13||17.5||7.8||1.00||194||Collvin 1985|
|Physa gyrina (Pouch snail)||invertebrate||28 d EC10 (growth)||12.0 D||2.39||20||8.3||0.58||170||Besser et al. 2016|
|Pimephales promelas (Fathead minnow)||fish||30 d IC10 (growth)||4 T||1.56||23||8.3||0.30||162||Besser et al. 2001, 2005|
|Pimephales promelas (Fathead minnow)||fish||30 d IC10 (growth)||16.0||6.73||25||8.3||0.30||162||Besser et al. 2001, 2005|
|Pyrgulopsis robusta (Spring snail)||invertebrate||28 d EC10 (growth)||5.9 D||0.61||20.6||8.2||1.10||169||Besser et al. 2009, 2016|
|Raphidocelis subcapitata (Green algae)||algae/plant||72 hr MATC (growth)||2.69 D||5.46||24||7.5||0.30||15||Franklin et al. 2002|
|Raphidocelis subcapitata (Green algae)||algae/plant||72 hr MATC (growth)||4.0 D||7.76||24||7.5||0.30||15||Franklin et al. 2002|
|Raphidocelis subcapitata (Green algae)||algae/plant||72 hr MATC (growth)||2.4 D||4.96||24||7.5||0.30||15||Franklin et al. 2002|
|Raphidocelis subcapitata (Green algae)||algae/plant||72 hr MATC (growth)||5.5 D||10.23||24||7.5||0.30||15||Franklin et al. 2002|
|Salvelinus confluentus (Bull trout)||fish||60 d MATC (survival)||138.57 D||30.56||8.5||7.9||0.48||224||Hansen et al. 2002|
|Salvelinus fontinalis (Brook trout)||fish||30 d EC10 (reproduction)||4.54 T||7.20||10||6.9||0.70||38||Sauter et al. 1976|
|Taylorconcha serpenticola (bliss rapids snail)||invertebrate||28 d LC20 (survival)||13.0 D||1.44||20.35||8.4||1.00||166||Besser et al. 2009, 2016|
|Villosa iris (Rainbow mussel)||invertebrate||28 d EC10 (dry weight)||6.2 D||1.91||20||8.3||0.60||101||Wang et al. 2011|
|Villosa iris (Rainbow mussel)||invertebrate||28 d EC10 (dry weight)||3.8 D||0.24||20||8.3||3.00||98||Wang et al. 2011|
|Villosa iris (Rainbow mussel)||invertebrate||28 d EC10 (dry weight)||21.0 D||0.42||20||8.2||10.00||100||Wang et al. 2011|
ͣ Effect concentrations are denotes as D for dissolved and T for total copper. If studies only reported total copper concentrations, they were converted to dissolved copper using the conversion factor of 0.96 (USEPA 1996). Prior to selecting the individual endpoints from acceptable datasets, reported effect concentrations were normalized for standard temperature (20oC), pH (7.5), DOC (0.5 mg/L) and hardness (50 mg/L as CaCO3). These normalized effect concentrations are plotted in Figure 4. Test water conditions inputted during normalization in BLM are also listed.
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