Federal environmental quality guidelines - Lead

Official title: Federal environmental quality guidelines - lead

Environment and Climate Change Canada

July 2020


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 1999) (Government of Canada (GC) 1999). The intent is to develop FEQGs as an adjunct to 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 (e.g., to support the federal Risk Management Strategy for lead or other monitoring activities) 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.    

This factsheet describes the Federal Water Quality Guideline (FWQG) for the protection of aquatic life from adverse effects of lead (Table 1). The guideline is based on toxicity data identified up to June 2019. No FEQGs have been developed for the biological tissue, sediment or soil compartments at this time.

Table 1. Federal freshwater quality guideline for lead (dissolved)

Freshwater aquatic life

Guideline value (µg/L)a

FWQG equation

FWQG  =  exp(0.514[ln(DOC)] + 0.214[ln(Hardness)] + 0.4152)

Example FWQG


a FWQG is for dissolved lead for waters and is calculated using the equation above. As an example, the FWQG at a DOC concentration of 0.5 mg/L and hardness of 50 mg/L is 2.5 mg/L. The FWQG equation is valid for DOC 0.5- 31.5 mg/L and hardness 4.7-511 mg/L.

Substance identity

Lead (Pb) is a highly toxic naturally occurring element (CAS Number 7439-92-1) found in bedrock, soils, tills, sediments, surface waters, groundwaters and seawater (Reimann and de Caritat 1998; Health Canada 2013). Lead is one of the most abundant metals in the earth’s crust (Adriano 2001), is highly resistant to chemical corrosion, and has no known biological function (Mager 2012). It is a class B, post-transition metal with a density of 11.34 g/cm3 and a molecular weight of 207.2 g/mol. Lead has several oxidation states, but in nature the plumbous form predominates (ATSDR 2007). Lead can occur as stable organic compounds, such as tetraethyl lead, but this FWQG is for inorganic lead. Lead was one of the first substances added to the List of Toxic Substances (Schedule 1) of the Canadian Environmental Protection Act (CEPA), 1988 (GC 1988). It was determined that the scientific evidence of the adverse effects of lead on human health and the environment had been sufficiently demonstrated, and that determination of its adverse effects was consistent with the criteria set out in section 11 of CEPA 1988 (Health Canada 2013).

Canada is a major global producer and supplier of refined lead, ranked eighth in the world, producing  14,000 tonnes (t) of lead in concentrate and  277,000 t of refined lead production in 2017 (NRCan 2019). Canada operates two primary lead and four recycled lead smelters. Because of the significant recycling of lead-acid batteries, recycled lead accounted for 55% of Canada's total refined lead production. Canada exported 261,480 t of unwrought lead metal in 2017 and the majority of these exports were to the United States (NRCan 2019)


The primary use of lead is in the manufacturing of lead-acid batteries, but it is also used extensively in the manufacture of cable sheathing, circuit boards, lining for chemical baths and storage vessels, chemical transmission pipes, wheel weights, electrical components, polyvinyl chloride (as a chemical stabilizer) and radiation shielding (Health Canada 2013). Other uses include: lead used in ammunition and fishing sinkers and jigs. Historically, leaded gasoline and lead-based paints were important sources of lead, but these products have been phased out (Health Canada 2013).

Total lead emissions to air in Canada in 2014 were 136 t, representing an increase of 5% (about 7 t) from 2013, and 89% (1153 t) lower than in 1990 (ECCC 2016). Emissions to air decreased between 1990 and 2014 as a result of reduced emissions from many industrial sectors, most notably the non-ferrous smelting and refining industry and the mining industry. The national lead releases to water were 146 t of lead in 2014, more than 10 times the total releases reported in 2013, mainly due to 134.1 t released from the Mount Polley mine dam failure (ECCC 2016). Other categories contributing to most releases of lead to water were waste, accounting for approximately 5.1 t, followed by the pulp, paper and paperboard industry, representing 1.9 t of the national total. Further 500 t per year is released into aquatic environments from the unintentional loss of lead sinkers and jigs (Health Canada 2013). Historically, use of lead shot was a significant source of lead to aquatic environments. However, lead inputs from this source have declined in Canada since the 1997 national regulation prohibiting the use of lead shot for hunting migratory game birds (exempting American woodcock, mourning doves and band-tailed pigeons) within 200 m of any watercourse and the 1999 extension of ban to include all areas of land and water nationwide (Stevenson et al. 2005). Not accounted by this regulation, lead ammunitions annually release about 5,200 t of lead into environment and this source represents the single most significant source of lead releases in Canada (GC 2018).

Fate, behaviour and partitioning in the environment

Atmospheric deposition, urban runoff and industrial discharge are the major sources of lead in surface waters (Health Canada 2013; USEPA 2006). In surface waters lead is predominantly found as PbO or PbCO3. Fate, transport and the subsequent bioavailability of lead in both aquatic and terrestrial systems are primarily controlled by the solubility (USEPA 2013). In aquatic environments at a pH of ≥7, lead is readily complexed and, with the exception of nitrate, chlorate and chloride salts, most inorganic lead salts are poorly soluble, however, the solubility of lead salts greatly increases under acidic conditions (Mager 2012). Lead speciation in freshwater environments is largely driven by pH, alkalinity and dissolved organic matter (DOM) (Mager 2012). Lead concentrations in surface water are largely controlled by exchange with sediments, and the cycling of lead between water and sediments is governed by chemical, biological and mechanical processes that are affected by many factors, including salinity, organic complexation, oxidation-reduction potential, and pH (USEPA 2006).

The majority of lead in surface waters occurs in undissolved forms, as colloidal particles or undissolved particles of lead carbonate, lead hydroxide, or other lead compounds, which precipitate to the sediment bed (Getz et al. 1977; Eisler 1988). Concentrations of dissolved lead are generally small in surface and groundwaters because lead forms complexes with sulphates, hydroxides, phosphates, carbonates and other anions in the water. Lead can occur as surface coatings on sediment mineral particles, sorbed ions or within suspended organic matter, living or non-living (LDAI 2008). The ratio of lead in suspended solids to dissolved lead varies between 4:1 in rural streams and 27:1 in urban streams (Getz et al. 1977).

The bioavailability of lead to freshwater aquatic organisms is influenced by a variety of water chemistry parameters (Van Sprang et al. 2016). It is generally assumed that the free Pb ion (i.e., Pb2+) is the most toxic form of lead, although it still needs to be determined whether other chemical species (e.g., PbOH+) are also important contributors to lead toxicity (Mager 2012). As low pH favours a greater proportion of Pb2+, lead toxicity tends to be greater at low pH (although the higher concentration of protons [H+] at low pH can also compete with Pb2+ for uptake by aquatic organisms) (Mager 2012). Conversely, as pH increases, an increasing proportion of lead binds to carbonate and hydroxide ions, which are less bioavailable forms of lead. Very high hardness may also reduce lead solubility, but pH appears to be an important factor in determining lead solubility for the range of water chemistries typically used in toxicity testing (Mager 2012). In addition to pH, DOM is another water quality variable that strongly influences the bioavailability of lead to aquatic organisms (Mager et al. 2010). DOM can complex the majority of lead under most environmentally-relevant conditions, although the quality of the DOM also has an important influence on its binding capacity (Richards et al. 2001; Mager 2012). Finally, several water chemistry parameters co-vary, which can make it difficult to elucidate the relative influence of each water chemistry parameter on lead bioavailability. For example, hardness and pH often co-vary with alkalinity and it can be difficult to determine the relative influence of each (Mager 2012).

In general lead is relatively stable in sediments, with long residence times and limited mobility (Das et al. 2008). However, lead-containing sediment particles can be remobilized into the water column. As a result, trends in sediment concentration tend to follow those in overlying waters (LDAI 2008). Desorption, dissolution, precipitation, sorption and complexation processes can all occur concurrently and continuously, leading to transformations and redistribution of lead (USEPA 2013).

Lead is typically bound to organic matter and soil in terrestrial systems and the most important factors determining its solubility in soils are pH and cation exchange capacity (Smolders et al. 2009). Iron and manganese oxides are also known to play an important role in lead sequestration in soils (USEPA 2013). Because lead is strongly adsorbed to soil, it generally is retained in the upper layers of soil and does not leach appreciably into the subsoil and groundwater (ATSDR 2007). Organic matter decreases bioavailability of lead, but over time as it is broken down, pedogenic minerals become more important in sequestration of lead (Schroth et al. 2008). The binding of lead to organic matter is relatively weak and as the organic matter is broken down the lead may be released into soil solution (USEPA 2013). However, as lead ages in soils, through its incorporation into particulate solid-phase of the soil, the bioavailability of lead is reduced to plants and soil organisms.

Ambient concentrations

Environment and Climate Change Canada (ECCC) monitoring data (unpublished), along with data from Alberta’s Regional Aquatics Monitoring Program (RAMP) and British Columbia Ministry of Environment for lead concentrations in surface waters are summarized in Table 2. These data collected between 1997 and 2019 ranged from 0.002 µg/L to 309 µg/L for total lead and from 0.003 µg/L to 121 µg/L for dissolved lead.

Table 2. Concentrations of total and dissolved lead in Canadian surface waters.
Location Sampling years Total or dissolved Mean (µg/L) Min (µg/L) Max (µg/L)
Great Lakes 2004-2014 Total




Great Lakes Connecting Channels 2003-2014 Total




St. Lawrence River 2007-2014









Newfoundland 2003-2006 Total




New Brunswick 2010-2013 Total




Nova Scotia 2013 Total




Saskatchewan 2003-2014









Manitoba 2003-2014









Athabasca Region 1997-2015









Alberta 2003-2015









British Columbia 1998-2019









Northwest Territories 2003-2014 Total 2.2 0.005 222

Mode of action

Mechanisms of acute lead toxicity in fish include a smothering effect at very high lead concentrations (e.g., 20 to >100 mg/L) and an ionoregulatory effect at lower lead concentrations that may be more typically observed in the environment (Mager 2012). As demonstrated in a series of studies with rainbow trout (Oncorhynchus mykiss), acute lead exposures can result in the disruption of Ca2+, Na+, and Cl- homeostasis, with hypocalcemia being the primary cause of lead toxicity in fish (Rogers et al. 2003, 2005; Rogers and Wood 2004). In chronic lead exposures to fish, lead can adversely affect the growth and development of fish, including spinal deformity (Davies et al. 1976; Holcombe et al. 1976; Hodson et al. 1978). Although the mechanisms of how lead may affect growth in fish are not fully understood, it could be related to reduced feeding ability or appetite due to neurological effects of lead (Mager 2012).

The mechanisms of lead toxicity in invertebrates are less understood than in fish. However, where Pb2+ competes with Ca2+ at a common uptake site, it appears, at least for the sensitive cladoceran C. dubia at low aqueous lead concentrations, that Pb2+ is taken up via a channel or transporter that has a low affinity for Ca2+ (Mager et al. 2011a,b). For the snail L. stagnalis, also a chronically-sensitive species to lead, Ca2+ homeostasis can be affected by lead exposure, but this appears to be a secondary effect and does not explain the sensitivity of snail to lead (Brix et al. 2012). In plants, excess lead can lead to reduced mitosis, photosynthesis and water absorption, as well as growth inhibition (Eisler 1988).

Aquatic toxicity

A detailed review of studies was performed following the CCME (2007) guidance for data quality. Determinants of test acceptability included, but were not limited to, exposure duration, analytical determination of lead 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.

The chronic freshwater toxicity studies for lead were identified from existing data compilations (ECCC 2020). It is now generally accepted that the dissolved rather than the total recoverable fraction of most metals, including lead, reflects the fraction that is potentially bioavailable to aquatic organisms (Reiley et al. 2003). As summarized in Diamond et al. (1997), the relationship between total recoverable and dissolved lead is variable because it is heavily influenced by the amount of carbonate and hence alkalinity (Davies et al. 1976; Sprague 1995). Van Sprang et al. (2016) assessed dissolved versus total lead measurements in toxicity studies and concluded that in all studies they examined estimated inorganic lead concentration was lower than the inorganic solubility limit, and therefore precipitation was considered unlikely to have occurred in studies where only total lead concentrations were reported. Therefore, the dissolved lead concentration was assumed to be equal to the total lead concentration. 

Acceptable chronic toxicity data were available for 28 species (11 fish, 13 invertebrates and 4 plants/algae) (ECCC 2020) and the dataset met the CCME’s (2007) minimum data requirements for developing the SSD-based guideline (i.e., Type A guideline). Within the acceptable dataset, several studies evaluated the influence of varying water chemistry on the bioavailability of lead and related chronic toxicity to aquatic species. The toxicity data for these species were used to evaluate toxicity modifying factors (TMFs) to develop FWQGs for lead that could be adjusted for the site-specific water chemistry (ECCC 2020). 

The CCME (2007) protocol states that, where possible, it is important to account for exposure and toxicity modifying factors in guideline derivation. This may be done through single or multi-factor equations, matrices or models (CCME 2007). Multiple linear regression (MLR) analysis was explored as an approach to account for the simultaneous effect of multiple water chemistry variables on lead toxicity. In conducting forward step-wise MLR (e.g., Neter et al. 1990), the independent variable (in this case water hardness, DOC or pH) that explains the greatest amount of the variability in the dependent variable (in this case lead toxicity) is entered first. If the relationship between this independent variable and the dependent variable is not significant, the modelling process would be considered complete (that no MLR model could be developed). If the relationship is significant, the variable is retained and the independent variable that explains the greatest proportion of the remaining variability is entered next. If this second variable does not explain a significant additional percentage of the variability, the second variable is removed and the final model contains only the first independent variable that was entered. If the relationship is significant, the second variable is retained, and the independent variable explaining the next highest proportion of the remaining variability is entered, and so on.

The increased understanding of how various factors influence the bioavailability of metals has led to the development of the biotic ligand model (BLM) (Di Toro et al. 2001; Santore et al. 2001; Paquin et al. 2002). The BLM is a mechanistic model that predicts metal bioavailability to aquatic organisms by considering competition for metal ions at the "biotic ligand" of the organism (e.g., fish gills) and other ligands in the water (e.g., DOC), as well as competition between uptake of metals and other cations in the water (e.g., calcium) (Di Toro et al. 2001). The BLM has been used to develop water quality criteria/guidelines (USEPA 2007; EU 2008a,b,c, 2010; DeForest et al 2017). The BLM for lead and other metals served as an important basis for identifying the key variables considered in the MLR-based approach considered in the current evaluation. 

Multiple linear regression analysis was evaluated as a potential tool for deriving the FWQG for two primary reasons. First, MLR models are reliable tool for predicting metal toxicity (DeForest et al. 2018), including lead toxicity (Esbaugh et al. 2011, 2012) over a range of water chemistries. Second, it is a linear model that is conceptually similar to the already accepted hardness-based model. As such, the procedure for developing potential MLR-based WQGs would follow the same basic steps that have already been accepted for developing hardness-based WQGs (CCME 2007) and the resulting equation would have the same basic structure as a hardness-based WQG, but instead of just a hardness slope it may, for example, contain slopes for hardness, DOC and pH. For several metals (e.g., aluminium, lead, nickel, zinc), it has been observed that MLR models are able to predict toxicity as well as BLMs (Esbaugh et al. 2012; Brix et al. 2017; DeForest et al. 2018; USEPA 2018).

Forward stepwise multiple linear regression (MLR) analyses were conducted using SYSTAT (Version 13) statistical software. The MLR analyses determines whether a significant portion of variability in toxicity could be explained by hardness, pH and/or DOC. MLR analysis was conducted for a given species if toxicity data were available from tests in which the range of hardness exceeded 100 mg CaCO3/L (with the highest hardness being three times the lowest), the range of DOC exceeded 5 mg/L (with the highest DOC being three times the lowest) and the range of pH spanned at least 1.5 units. The acceptable toxicity data could include data combined from multiple studies for the same species. MLR analyses were conducted on a species-by-species basis, whereby toxicity values for a given species were the dependent variables and the water chemistry values were the independent variables. Individual species MLRs were conducted for the following species: Brachionus calyciflorus, Ceriodaphnia dubia, Lymnaea stagnalis, Pimephales promelas, Pseudokirchneriella subcapitata (Grosell et al. 2006; Nys et al. 2016; Cooper et al. 2009; Mager et al. 2011b; Esbaugh et al. 2012; Parametrix 2010a,b; De Schamphelaere et al. 2014). A pooled MLR analyses was also conducted including the invertebrates and fish stated previously with the addition of Philodina rapida. Philodina rapida was not included in the individual species MLR analyses because the range of pH did not meet the minimum requirement but was included in the pooled MLR since it has been tested over a wide range of DOC and hardness conditions. The green algae P. subcapitata could not be included in the pooled MLR since the MLR analyses indicated the TMF relationships were significantly different compared with the fish and invertebrates. All MLR models were derived using EC10 effect concentrations. 

The results of MLR analysis are presented in Table 3. None of the variables were significant for B. calyciflorus. The P. subcapitata MLR was the only MLR to retain pH. Both DOC and hardness were significant in the MLR analysis for the pooled invertebrate and fish model, whereas pH was not significant. These MLR relationships are referred to as MLR models hereon in this factsheet. 

Table 3. Summary results of MLR analysis.
Speciesnr2Ln DOC*pH*Ln Hardness*Intercept*
Brachionus calyciflorus180----
Ceriodaphnia dubia280.20.682--2.649
Pimephales promelas80.68--0.9841.989
Lymnaea stagnalis60.691.259---0.229
Pooled (inverbrates and fish)a660.720.514-0.2142.156

* Model coefficents
Includes data from 5 species (B. calyciflorus, C. dubia, P. promelas, L. stagnalis and P .rapida). P. rapida was not included in the individual species MLR analyses because the range of pH did not meet the minimum requirement but was included in the pooled MLR because it has been tested over a wide range of DOC and hardness conditions.

The pooled MLR model incorporates 66 data points from 5 different species, retained both DOC and hardness variables with r2 value of 0.72 and wide water chemistry ranges (DOC range 0.5- 31.5 mg/L and hardness range 4.7-511 mg/L). The pooled (invertebrate and fish) MLR model was therefore chosen for deriving the guideline. 

Federal water quality guideline derivation

Federal Water Quality Guidelines (FWQGs) are preferably developed using the CCME (2007) protocol. In the case of lead, there were sufficient acceptable chronic toxicity data to meet the minimum data requirements for the preferred CCME Type A approach. A Type A guideline is a statistical approach that uses species sensitivity distributions (SSD) to calculate a hazard concentration of 5% of species (HC5), which in turn is the final guideline value (CCME 2007).  

The first step in developing the FWQG for lead was to normalize the toxicity values to a common water chemistry using the pooled MLR. The chronic toxicity data for all 28 species were normalized to water with a DOC concentration of 0.5 mg/L and hardness of 50 mg/L. All data in the toxicity dataset were within the acceptable ranges of the pooled MLR (DOC range 0.5- 31.5 mg/L and hardness range 4.7-511 mg/L) and therefore were able to be considered in guideline derivation. Where multiple comparable endpoints were available for the same species, effect, life stage and exposure duration, a geometric mean was calculated. In an effort to include data for preferred endpoints, if studies did not report an EC10, but reported sufficient information to develop a concentration-response curve, EC10 values were calculated using the USEPA Toxicity Relationship Analysis Program (TRAP v. 1.3) (USEPA 2015).

The most sensitive and preferred endpoint (or geometric mean) was then selected for each species following CCME (2007). A total of 82 endpoints (79 EC10s, 1 EC20, 1 NOEC and 1 MATC) for 28 species were included in the SSD dataset and summarized in Table 4. Lymnaea stagnalis was the most sensitive species with a normalized species mean toxicity value of 1.6 µg/L and smallmouth bass (Micropterus dolomieui) was the least sensitive species with a normalized effect concentration of 348 µg/L.

Table 4. Pooled multiple linear regression based normalized chronic lead toxicity endpoints for toxicity data used in deriving the FWQG for lead.
Species Group Endpoint Effect Concentration (µg/L)Normalized Effect Concentration (µg/L)aReference 
Lymnaea stagnalis (great pond snail)Invertebrate14-d EC10 (growth)Geomean (n=6)1.6Esbaugh et al. 2012; personal communication
Ceriodaphnia dubia (cladoceran)Invertebrate6-d EC10 (survival and reproduction)Geomean (n=3)3.3AquaTox 2012
Philodina rapida (rotifer)Invertebrate4-d EC10 (population growth)Geomean (n=6)3.5Esbaugh et al. 2012
Hyalella azteca (amphipod)Invertebrate42-d EC10 (survival, growth and reproduction)55.2Besser et al. 2016
Pseudokirchneriella subcapitata (alga)Plant72-h EC10 (Mean growth rate)Geomean (n=15)6.1De Schamphelaere et al. 2014
Oncorhynchus mykiss (rainbow trout)Fish62-d EC10 (weight)76.1Mebane et al. 2008; personal communication
Lampsilis siliquoidea (fatmucket)Invertebrate28-d EC10 (survival and growth)66.3Wang et al. 2010
Chironomus dilutus (midge)Invertebrate21-d EC10 (emergence)1513.0Mebane et al. 2008; personal communication
Diaphanosoma birgei (cladoceran)Invertebrate25-d EC10 (net reproductive rate)1314.1Garcia-Garcia et al. 2006; Van Sprang et al. 2016
Lymnaea palustris (marsh snail)Invertebrate120-d EC20 (survival and growth)2314.2Borgmann et al. 1978; Van Sprang et al. 2016 
Daphnia magna (cladoceran)Invertebrate21-d EC10 (survival and reproduction)Geomean (n=3)18.8Chapman et al. 1980
Pimephales promelas(fathead minnow)Fish30-d EC10 (survival and growth)Geomean (n=3)28.8Mager et al. 2011b
Baetis tricaudatus (mayfly)Invertebrate10-d EC10 (molting)3730.5Mebane et al. 2008; personal communication
Acipenser transmontanus (white sturgeon)Fish53-d EC10 (survival)2631.5Ingersoll and Mebane 2014
Chlaydomonas reinhardtii (green alga)Plant48-h EC10 (growth rate)8236.5De Schamphelaere et al. 2014
Alona rectangula (cladoceran)Invertebrate25-d EC10 (gross reproductive rate)4042.6Garcia-Garcia et al. 2006; Van Sprang et al. 2016
Salvelinus namaycush (lake trout)Fish60-d EC10 (weight)5048.3Sauter et al. 1976
Brachionus calyciflorus (rotifer)Invertebrate2-d EC10 (population size)Geomean (n=18)51.5Nys et al. 2016
Chlorella kesslerii (green alga)Plant48-h EC10 (growth rate)12053.2De Schamphelaere et al. 2014
Lepomis macrochirus (bluegill)Fish60-d EC10 (survival)5755.4Sauter et al. 1976
Salvelinus fontinalis (brook trout)Fish12-wk EC10 (growth)8857.9Holcombe et al. 1976
Lemna minor (duckweed)Plant7-d EC10 (root growth)Geomean (n=7)65.3Antunes and Kreager 2014
Ictalurus punctatus (channel catfish)Fish60-d EC10 (survival)7673.1Sauter et al. 1976
Catostomus commersoni (white sucker)Fish60-d EC10 (weight)10198.0Sauter et al. 1976
Chironomus riparius (midge)Invertebrate14-d EC10 (survival and growth)Geomean (n=2)112.5Nguyen et al. 2012
Stizostedion vitreum (walley)Fish30-d EC10 (survival)148.62144.0Sauter et al. 1976
Esox lucius (Northern pike)Fish20- d MATC (survival)349.5697355336.5Sauter et al. 1976
Micropterus dolomieui (smallmouth bass)Fish90-d NOEC (growth)308348.3Coughlan et al. 1986

aNormalized to a hardness of 50 mg/L and DOC of 0.5 mg/L using the pooled invertebrate and fish MLR

The R package (R version 3.4.2) ‘ssdtools’ (version 0.0.3) (Thorley and Schwarz 2018) as well as the corresponding user friendly “Shiny App” (Dalgarno 2018) were used to create SSDs from the dataset. The package fit several CDFs (log-normal, log-logistic, log-Gumbel, gamma and Weibull) to the data using maximum likelihood estimation (MLE) as the regression method. Akaike information criterion (AIC), which is a measure of the relative quality of fit to the data set, was calculated for each distribution (Burnham and Anderson 2002). Using AIC, AIC corrected for small sample size, a model averaged HC5 can be established. The smaller the AIC the better the distribution fits the data set. Each model was then weighted, models with high value weight fit the data well compared to the others. The SSD and accompanying summary statistics at water hardness 50 mg/L and DOC 0.5 mg/L are presented in Figure 1 and Table 5, respectively.

Table 5. SSD summary statistics at 50 mg/L hardness and 0.5 mg/L DOC
DistributionAICPredicted HC5 (µg/L)95% LCL and  UCL (µg/L)WeightWeighted HC5 (µg/L)Weighted 95% LCL and  UCL (µg/L)
Log-Normal 2862.93(1.43-6.64)0.441.29(0.63-2.91)
Guideline =2.5(1.09-6.25)
Figure 1. Species sensitivity distribution (SSD) for the chronic toxicity of lead at DOC of 0.5 mg/L and hardness of 50 mg/L. The HC5 is 2.5 µg/L.
Long description

The normalized chronic toxicity data for 28 fish, invertebrate and plant species are plotted in a species sensitivity distribution along with the confidence intervals. The 5th percentile value of the plot is 2.5 µg/L. This value is the site-specific federal water quality guideline for the site water that has the DOC concentration of 0.5 mg/L and hardness of 50 mg/L. The guideline value represents the concentration below which one would expect either no, or only a low likelihood of, adverse effects on aquatic life.

Because DOC and hardness were identified as significant toxicity modifying factors, the FWQG is expressed as an equation where the local water DOC and hardness are entered in order to calculate a site-specific FWQG. The equation is based on the pooled MLR model slopes of 0.514 and 0.214, respectively, and the 5th percentile value of 2.5 μg/L derived from the SSD at DOC of 0.5 mg/L and hardness of 50 mg/L. 

Based on the pooled MLR model and the HC5 from the SSD, the equation to derive FWQG for lead is:

y-intercept  = ln(5th percentile) – [DOC slope × ln(DOC)] – [hardness slope × ln(hardness)]

                     = ln(2.45) – [0.514 × ln(0.5)] – [0.214 × ln(50)]

                     = 0.4152

FWQG (μg/L) = exp( 0.514 [ln(DOC)] + 0.214[ln(hardness)] + 0.4152)

The FWQG equation was derived for dissolved lead and is found by using the equation above or by using the FWQG calculator (Appendix). Users can input site-specific DOC and hardness measurements to calculate a FWQG for the specific water chemistry. The FWQG equation is valid between DOC 0.5 and 31.5 mg/L and hardness 4.7 and 511 mg/L, which are the ranges of data used to derive the MLR slopes. Only values within these DOC and hardness ranges should be entered into the guideline equation to ensure the equation is accurate and the FWQG is protective. Users should be extremely cautious if extrapolating beyond the recommended ranges of DOC and hardness and should contact their local authority for advice. Although the hardness is regularly measured during monitoring, DOC may not always be routinely measured. In the absence of site-specific data, a DOC concentration of 0.5 mg/L and a hardness concentration of 4.7 mg/L may be assumed, which are the lower limits of the FWQG equation. For water bodies where lead concentrations are potentially of concern, it is recommended that both DOC and hardness be measured. It is to be also noted that the FWQG for lead is for dissolved concentration of lead. When guideline users only have total lead concentrations for their site, it is recommended that they first compare their total lead concentration to dissolved lead guideline, and where there is an excedance, re-sample the waterbody for the dissolved lead. Examples of FWQGs for lead for selected DOC and hardness values are given in Table 6.

Table 6. FWQGs (μg/L) for lead for the protection of aquatic life for selected DOC and hardness values.
DOC (mg/L)50 *
100 *
200 *
300 *
400 *
500 *

*Hardness (mg/L)

To assess whether the FWQG for lead is sufficiently protective, a protectiveness assessment was conducted following CCME (2007). FWQGs were calculated for each of the 134 acceptable endpoints in the toxicity dataset. The FWQGs were then compared to measured toxicity values at their associated water chemistry (Figure 2). Values that plot above the 1 to 1 line indicate that the FWQG is protective of the toxicity value in that particular test, while values below the 1 to 1 line indicate that the FWQG is lower than the observed toxicity, and hence may require further evaluation. This protectiveness assessment resulted in 11 out of 134 (92%) acceptable toxicity data points being above the site-specific guideline. These 11 endpoints include 8  endpoints for invertebrates (3 for C. dubia, 4 for L. stagnalis, 1 for Philodina rapida) and  3 endpoints for plant species P. subcapitata. It is important to note that an equal or greater number of endpoints for these 4 species were above the 1 to 1 line (31 for C. dubia;  4 for L. stagnalis, 5 for Philodina rapida and  12 for P. subcapitata. None of the endpoints below the guideline were for a species at risk, or for lethal effects equal to or above a level of 15% (CCME 2007). Overall examination of the available data suggests the lead FWQG is protective. 

Figure 2. Ratio of chronic effect concentration for lead to FWQG calculated using the 5 species pooled MLR model containing hardness and DOC.
Long description

Ratios of effect concentrations to federal water quality guidelines of 134 acceptable endpoints are plotted against the measured effect concentrations. Data points that plot above the dashed line (1 to 1 line) indicate that the federal water quality guideline is protective of the toxicity value in that particular test, while values below the line indicate that the guideline is lower than the measured toxicity, and hence may require further evaluation. The figure shows that the water quality guideline for lead is protective.


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List of acronyms and abbreviations


Akaike information criterion


Agency for Toxic Substances and Disease Registry


biotic ligand model


Chemical Abstracts Service


Canadian Council of Ministers of Environment


Canadian Environmental Protection Act


Chemicals Management Plan


dissolved organic carbon


dissolved organic matter


effect concentration


Environment and Climate Change Canada


European Union


Federal Environmental Quality Guideline


Federal Water Quality Guideline


Government of Canada


maximum acceptable toxicant concentration


maximum likelihood estimation


multiple linear regression


no observed effect concentration


Natural Resources Canada


species sensitivity distribution


toxicity modifying factor


United States Environmental Protection Agency

Annex. Federal Water Quality Guidelines: Lead (Pb) calculator

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