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Aquatic Toxicology 142– 143 (2013) 114– 122

Contents lists available at ScienceDirect

Aquatic Toxicology

jou rn al hom epage: www.elsev ier .com/ locate /aquatox

etal mixture toxicity to aquatic biota in laboratory experiments:pplication of the WHAM-FTOX model

. Tipping ∗, S. Loftsentre for Ecology and Hydrology, Lancaster Environment Centre, Library Avenue, Bailrigg, Lancaster LA1 4AP, United Kingdom

r t i c l e i n f o

rticle history:eceived 22 May 2013eceived in revised form 2 August 2013ccepted 6 August 2013

eywords:quatic organismshemical speciationetals

oxicityHAM

a b s t r a c t

The WHAM-FTOX model describes the combined toxic effects of protons and metal cations towards aquaticorganisms through the toxicity function (FTOX), a linear combination of the products of organism-boundcation and a toxic potency coefficient (˛i) for each cation. Organism-bound, metabolically-active, cation isquantified by the proxy variable, amount bound by humic acid (HA), as predicted by the WHAM chemicalspeciation model. We compared published measured accumulations of metals by living organisms (bacte-ria, algae, invertebrates) in different solutions, with WHAM predictions of metal binding to humic acid inthe same solutions. After adjustment for differences in binding site density, the predictions were in rea-sonable line with observations (for logarithmic variables, r2 = 0.89, root mean squared deviation = 0.44),supporting the use of HA binding as a proxy. Calculated loadings of H+, Al, Cu, Zn, Cd, Pb and UO2 wereused to fit observed toxic effects in 11 published mixture toxicity experiments involving bacteria, macro-

HAM-FTOX phytes, invertebrates and fish. Overall, WHAM-FTOX gave slightly better fits than a conventional additivemodel based on solution concentrations. From the derived values of ˛i, the toxicity of bound cationscan tentatively be ranked in the order: H < Al < (Zn–Cu–Pb–UO2) < Cd. The WHAM-FTOX analysis indicatesmuch narrower ranges of differences amongst individual organisms in metal toxicity tests than was pre-viously thought. The model potentially provides a means to encapsulate knowledge contained withinlaboratory data, thereby permitting its application to field situations.

. Introduction

The biotic ligand model (BLM, Paquin et al., 2002) was developedo explain how water chemistry (pH, DOC, hardness etc.) affectsoxicity, initially for single metals. The essential idea is to replace

etal concentration in solution as the expression of toxic exposure,y the occupancy of a key (biotic) ligand, the reactions of which areescribed with conventional coordination chemistry. Account canhus be taken of the ever-present competition reactions, in whichoxic and non-toxic cations, including H+, compete for binding toigands, not only the biotic ligand but also those present in solution,n particular dissolved organic matter. The aim of the BLM was to

ake risk assessment more scientific, compared to the use of a sin-le standard concentration, or perhaps hardness-dependent values.his aim has largely been achieved, changing how we think aboutetal toxicity in aquatic and terrestrial ecosystems. Furthermore,

here have been several efforts to use the BLM to account for the

oxic effects of metal mixtures (Playle, 2004; Hatano & Shoji, 2008;amo & Nagai, 2008; Jho et al., 2011), each based on the assumption

hat the different toxic metals share the same biotic ligand. Hatano

∗ Corresponding author. Tel.: +44 1524 595866.E-mail address: et@ceh.ac.uk (E. Tipping).

166-445X/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.aquatox.2013.08.003

© 2013 Elsevier B.V. All rights reserved.

& Shoji (2008) fitted the model to data for the toxicity of Cu andCd to Lemna paucicostata, at different pH values, and obtained farbetter agreement with observations than could be achieved witha conventional model based on LC50 toxic units, and ignoring pHvariations.

The WHAM-FTOX model (Stockdale et al., 2010) provides a differ-ent way of describing metal toxicity, while retaining the idea thatexposure depends on the interactions of metals and protons withthe organism. Instead of postulating a specific biotic ligand throughwhich metal toxicity is mediated, WHAM-FTOX expresses expo-sure of the organism to toxic metals by the overall, non-specific,accumulation of cations at the reversible binding sites presentwithin the organism or on its surface. Such sites exist due to thepresence of weak-acid groups in different biomolecules (e.g. pro-teins, polysaccharides, lipids, nucleic acids, fatty acids), and theiroccupancy depends upon the competitive interactions of toxic andnon-toxic metals and protons, assuming them to be in equilibriumwith the surrounding solution. The binding ligands could, in prin-ciple, include one or more specific biotic ligands but the majorityof them will not be associated directly with the toxic response.

The model then assigns a unique, purely empirical, toxicity coeffi-cient to each cation, which describes the extent to which the boundcation is toxic. Total toxicity is then determined by the sum ofthe products of amounts bound and the toxicity coefficients. The

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E. Tipping, S. Lofts / Aquatic Toxi

hemical interactions and toxic effects are thus formally separated,nlike in the BLM where the equilibrium constants for binding athe biotic ligand reflect not only the chemical strength of binding,ut also toxic strength (Playle, 2004).

An advantage of the WHAM-FTOX approach is that the need tot the model to organism-bound metal data is avoided, by assum-

ng that metal accumulation by living organisms can be estimatedith a pre-existing chemical speciation model, i.e. WHAM, using

ation binding by humic acid (HA) as a proxy. Evidence to jus-ify this assumption comes from field data (Tipping et al., 2008;tockdale et al., 2010), although as yet it must be regarded as incom-lete. But the idea is worth pursuing in order to avoid an inordinatemount of new experimental work and associated modelling touantify cation accumulation by living organisms (cf. Borgmannt al., 2008). If cation accumulation can reliably be estimated a pri-ri, then relatively few parameters are needed to fit toxicity data.he use of cation binding to HA, calculated with WHAM, to expressetal exposure produced a good description of the toxicity of cop-

er towards duckweed in laboratory experiments (Antunes et al.,012).

In previous work with WHAM-FTOX, we focused only on fieldata, firstly using stream macroinvertebrate species richness, at c.00 sites affected by abandoned metal mines and acid deposition,s the toxic response variable for fitting (Stockdale et al., 2010).he same version of the model was used to evaluate acidificationecovery (Stockdale et al., 2013). Analysis of stream mesocosm dataIwasaki et al., 2013) further supports the use of calculated bind-ng to HA as a measure of exposure. However, under circumstancesertaining in the field and in mesocosms, non-chemical factors (e.g.ischarge variation, suspended sediment, competition, predation,ood web structure) also affect the measured variables, hamperinghe precise and unequivocal attribution of toxicity. Therefore therincipal aim of the present work was to test the ability of the modelo fit toxicity data obtained in controlled laboratory experiments.efore addressing toxicity however, we first tested the other aspectf the model, i.e. its appropriateness as a proxy for cation accumu-ation by living organisms. The longer-term goal of this work is toroduce a model that can be parameterised with the abundant lab-ratory data that describe metal toxicity, in order to make use ofhe fundamental knowledge to understand and predict toxic effectsf metals in the field.

. Methods

.1. Modelling chemical speciation with WHAM

In this work we used WHAM (Tipping, 1994) incorporatingumic ion-binding model VII (Tipping et al., 2011). Model VII uses

structured formulation of discrete, chemically-plausible, bindingites for protons in humic and fulvic acids (HA, FA), in order to allowhe creation of regular arrays of bidentate and tridentate bindingites for metals. Metal aquo ions (Al3+, Cu2+, Cd2+ etc.) and their firstydrolysis products (AlOH2+, CuOH+, CdOH+ etc.) compete withach other, and with protons, for binding. The same intrinsic equi-ibrium constant (KMA) for binding to carboxyl or type A groups isssumed to apply to the aquo ion and its first hydrolysis product.he constant (KMB) for binding to weaker acid groups is related toMA, and the contributions of rarer “soft” ligand atoms are factored

n. The intrinsic equilibrium constants are modified by empiricallectrostatic terms that take into account the attractive or repulsiventeractions between ions and the charged macromolecule.

The humic ion-binding model is combined with an inorganicpeciation model, the species list and constants for which wereiven by Tipping (1994). The inorganic reactions in this databasere restricted to monomeric complexes of metals. The effects of

142– 143 (2013) 114– 122 115

ionic strength on the inorganic reactions are taken into accountusing the extended Debye–Hückel equation. Temperature effectson reactions between inorganic species are taken into accountusing published or estimated enthalpy data, but in the absence ofexperimental information, reactions involving humic substancesare assumed to be independent of temperature.

If dissolved organic carbon (DOC) was present in the solutionsconsidered here, we took complexation into account by assumingdissolved organic matter (DOM) to be 50% carbon, with 65% of sitesactive with respect to cation binding, represented by FA (Tippinget al., 2008). For example, a DOC concentration of 5 mg L−1 cor-responds to a FA concentration of 6.5 mg L−1 for modelling. Forwaters from the field, we estimated Fe(III) concentrations with theempirical equation of Lofts et al. (2008), suitably modified for humicbinding model VII.

We calculated the equilibrium binding of the metals to HA byassuming it to be present at a very low concentration, insufficientto affect the bulk speciation, and finding �i values (mol/gHA). Tomatch the values of �i to observed accumulations of metal by livingorganisms, we defined the equivalent HA per gram of organism dryweight, EHA (g g−1). The value of EHA would be 1.0 if the organism’ssite content per gram were equal to that of HA, but is expectedusually to be less than 1.0 because living organisms generally havefewer exposed sites than does HA.

2.2. Fitting toxicity data with WHAM-FTOX

For the toxicity model, it is assumed that each organism pos-sesses binding sites that have the same properties as those of HA,and it is the fractional occupancy of these sites that measures expo-sure to cations, not the absolute amount of metal per unit weightof organism. Different species exposed to the same solution havethe same �i values but differ in absolute body burdens (mol g dryweight−1) if their values of EHA differ. Thus, because only relativebinding is needed, the model simply uses the calculated �i values forHA as the measure of exposure. This means that toxicity parametersfor different organisms are directly comparable.

The toxicity function is defined by the equation;

FTOX = ˙˛i�i (1)

in which ˛i is the toxicity coefficient of cation i. The toxic response(TR), on a scale from zero to unity, depends upon lower and upperthresholds of FTox according to the following definitions;

FTOX ≤ FTOX,LT TR = 0 (2)

FTOX,LT < FTOX < FTOX,UT TR = FTOX − FTOX,LT

FTOX,UT − FTOX,LT(3)

FTOX≥FTOX,UT TR = 1 (4)

For each data set, the object of the fitting was to minimise the sumof the squared differences between observed and calculated toxicresponse (luminescence, survival, growth rate or filtration rate). Tofit the model, the values of ˛i, FTOX,LT and FTOX,UT could in principlebe optimised by fitting the model to the available toxicity data.Since the toxicity coefficients are only relative numbers, the valueof ˛H can be set to the same value in all cases, and unity is chosenfor convenience.

2.3. Conventional toxicity model

For comparison with the outputs of WHAM-FTOX modelling,a conventional toxic unit approach was applied to the datasets,

116 E. Tipping, S. Lofts / Aquatic Toxicology 142– 143 (2013) 114– 122

Table 1Summary of datasets for metal accumulation by living organisms.

Organism Species Metals Conditions References

Bacteria Pseudomonas putida Sr Cd Pb Laboratory 4 hpH 3.5–8.6, ranges of [NaClO4]

Borrock & Fein, 2005

Bacillus subtilisEscherichia coli

Cd Laboratory 2 hpH 2–10, ranges of [NaNO3]

Yee & Fein, 2001

Alga Chlorella kesslerii Zn Laboratory 25–60 minpH 7, ionic strength1.8–3.2 mM

Hassler & Wilkinson, 2003

Amphipod Hyalella azteca Mn Co Ni Cu Zn CdHg Pb

Laboratory 4–10 weeksLake Ontario water and 1:10dilutionspH ∼8, [DOC] 1–2 mg L−1

Borgmann et al., 1993, 2004Norwood, 2007

Be Al Mn Fe Co NiCu Zn Cd Ba Pb U

Mine-affected stream waters,17 days pH 6.2–7.1, [DOC]14–19 mg L−1

Couillard et al., 2008

Mussel Dreissena polymorpha Cu Zn Cd Laboratory 48 h; Kraak et al., 1994

ad

T

wse

T

weitE

FwPPc

ssuming additivity of toxic responses. This entailed fitting theataset to a standard logistic dose–response curve:

R = TR0

1 + TUˇ(5)

here TR is the toxic reponse, TR0 is the control response and ̌ is alope parameter. The term TU quantifies the ‘toxic units’ for a givenxposure:

U = ˙[Xi]EC50(i)

(6)

here [Xi] is the dissolved concentration of toxicant i in the

xposure and EC50(i) is the dissolved concentration of metal i caus-ng a 50% toxic effect. The model, referred to as CTU, was fittedo each entire dataset by optimisation of the parameters ̌ andC50(i).

0

20

40

60

80

100

3 4 5 6 7pH

% s

orbe

d

(a)

0

20

40

60

80

100

0.001 0.01 0.1 1ionic strength

% s

orbe

d

(c)

ig. 1. Sorption of Sr, Cd and Pb by bacteria. The points in panels (a)–(c) are measured daith WHAM/Model VII. Panel (a) results for Cd; open circles, I = 0.01 M, P. putida 10 g L−1

anel (b) results for Pb; open circles, I = 0.01 M, P. putida 1 g L−1; closed circles, I = 0.5 M, P. p. putida 6 g L−1 (open diamonds); Cd, pH 5.9, P. putida 3 g L−1 (closed triangles); Pb, pH 5ircles) and Escherichia coli (closed circles).

Lake water, pH 7.9, [DOC]7 mg L−1

Ivorra et al., 1995De Schamphelaere & Janssen,2004

3. Results

3.1. Accumulation of metals by living organisms

The data summarised in Table 1 were used to compare the accu-mulation of metals by living organisms with binding by HA. Resultsof Yee & Fein (2001) and Borrock & Fein (2005) show how metalbinding to bacteria varied with pH, via “adsorption edges”, andalso as a function of ionic strength. The data were predicted rea-sonably well with WHAM (Fig. 1), after calibration by adjustmentof EHA. Calculated binding closely follows the pH dependence ofobserved binding, approximates the dependence on ionic strength,

and accounts for the relative binding of Cd and Pb. There arequite similar values of EHA of 0.66 and 0.84 for Bacillus subtilis andEscherichia coli respectively, based on bacterial dry weight. A muchlower EHA of 0.060 for Pseudomonas putida arises because Borrock

0

20

40

60

80

100

4 5 6 7 8 9pH

% s

orbe

d

(b)

0

20

40

60

80

100

2 3 4 5 6 7 8 9pH

% s

orbe

d

(d)

ta of Borrock & Fein (2005), those in panel (d) are from Yee & Fein (2001), lines fits; closed circles, I = 0.5 M, P. putida 10 g L−1; open squares, I = 0.1 M, P. putida 3 g L−1.utida 1 g L−1; open squares, I = 0.1 M, P. putida 3 g L−1. Panel (c) results for Sr, pH 6.3,.5, P. putida 1 g L−1 (open triangles). Panel (d) Cd sorption by Bacillus subtilis (open

E. Tipping, S. Lofts / Aquatic Toxicology 142– 143 (2013) 114– 122 117

Table 2Summary of metal mixture toxicity data sets.

Test organism Toxic response pH Ca (mM) DOC (mg L−1) Metals Reference

Escherichia coli Luminescence inhibition 15 min 5.5 0.0 0 Cu Zn Cd Preston et al. 2000Pseudomonas fluorescens Luminescence inhibition 15 min 5.5 0.0 0 Cu Zn Cd Preston et al. 2000Vibrio fischeri Luminescence inhibition 5 min 6.7–7.1a 0.025, 2.5, 25 0 Cd Pb Jho et al., 2011Lemna aequinoctialis Growth rate 96 h 6.5 1 0 Cu UO2 Charles et al., 2006Lemna paucicostata Growth rate 96 h 4.1–7.5 0.49 0 Cu Cd Hatano & Shoji, 2008Ceriodaphnia dubia Survival 96 h 7.4 0.025 0 Zn Cd Shaw et al., 2006Daphnia ambigua Survival 96 h 7.4 0.025 0 Zn Cd Shaw et al., 2006Daphnia magna Survival 96 h 7.4 0.025 0 Zn Cd Shaw et al., 2006Daphnia pulex Survival 96 h 7.4 0.025 0 Zn Cd Shaw et al., 2006Dreissena polymorpha Filtration rate 48 h 7.9 1.5 7 Cu Zn Cd Kraak et al., 1994

0.0625 2 Al Cu Zn Hickie et al., 1993

l.

&na

aalstibZs1

Nmg(ifftBEbteomatfi

FHi

-8

-7

-6

-5

-4

-3

log10 Hyalella mol g-1

log 1

0 HA

mol

g-1 Mn

CoNiCuZnCdHgPb.

-9

-8

-7

-6

-5

-4

-3

-11 -10 -9 -8 -7 -6 -5 -4

log10 Hyalella mol g-1

log 1

0 HA

mol

g-1

BeAlMnFeCoNiCuZnCdBaPbU

(b)

(a)

Fig. 3. Accumulation of metals by Hyalella azteca in (a) the laboratory (Borgmann

Oncorhynchus mykiss Survival 144 h 4.3–5.8

a Calculated from data in the paper, assuming pCO2 to be at the atmospheric leve

Fein (2005) chose to express their results in terms of wet weight,oting that this is about five times the dry weight, which impliesn EHA value of 0.30 on a dry weight basis.

The results of Hassler & Wilkinson (2003) for Zn accumulationt the external surface of the alga Chlorella kesslerii (Fig. 2) cover

wide range of [Zn2+]. As noted by these authors, the slope of theog–log plot is well below unity, implying a high degree of bindingite heterogeneity. This is captured by WHAM (Fig. 2), althoughhe model is unable to predict the complex nature of the bind-ng curve. We could not derive a precise value of EHA in this caseecause the experimental results were expressed in terms of molesn sorbed per unit area, but by assuming a dry weight bulk den-ity of 0.1 g cm−3 for the algae, which are spherical cells of radius.8 �m, we obtain EHA = 0.032.

Borgmann and co-workers (Borgmann et al., 1993, 2004;orwood, 2007) performed laboratory studies on the binding ofetals to Hyalella azteca, and as shown in Fig. 3(a), the model

ives fairly good correlations with their results after calibrationEHA = 0.044). Field measurements on the same organism, exposedn cages to mine-contaminated streamwaters, were best accountedor with EHA = 0.11 (Fig. 3(b)), which is 2.5 times the value derivedrom the laboratory data. Here, there are some systematic devia-ions for individual metals, although the overall prediction is fair.inding to the zebra mussel (Kraak et al., 1994;Fig. 4) yieldedHA = 0.017. Binding to the invertebrates (Figs. 3 and 4) covers theehaviour of many metals, although the results refer only to neu-ral pH values, and shows that the order of binding is essentially asxpected, taking into account the different solution concentrationsf the metals. There do not seem to be any consistently anomalousetals among those that are known to be toxic, although for H.

zteca, the binding of Cd that is predicted tends to be low relative

o the other metals, this being seen in both laboratory (Fig. 3a) andeld (Fig. 3b).

-8

-7

-6

-5

-4

-3

-12 -10 -8 -6 -4 -2

log10 [Zn2+]

log 1

0 Zn

ads

mol

g-1

ig. 2. Binding of zinc to the surface of Chlorella kesslerii. The points are the data ofassler & Wilkinson (2003), converted to mol g−1 as described in the text. The line

s the WHAM/model VII fit with EHA = 0.032.

et al., 1993, 2004; Norwood, 2007) and (b) the field (Couillard et al., 2008), compared

with calculated binding by humic acid in equilibrium with the same solutions. Thelines have slopes of unity, and offsets of 1.36 (a) and 0.96 (b), yielding EHA values of0.044 and 0.11.

Linear regression of the logarithms of all 467 observed andcalculated data pairs, including calibration with EHA, yields an inter-cept insignificantly (p > 0.05) different from zero, and with theintercept fixed at zero the slope is 0.99, with r2 = 0.89, and a root-mean-squared-deviation of 0.44, equivalent to a factor of 2.75.Taking the present results and those for stream bryophytes andmacroinvertebrates, the values of EHA tend to fall as organism sizeincreases or surface area falls, with bacteria and bryophytes havingvalues ∼0.5, stream insects and Hyalella ∼0.1, and the larger mus-sel ∼0.02. The low EHA for the alga may be explained at least partlyby accumulation only at external sites (Hassler & Wilkinson, 2003).

3.2. Fitting toxicity data

If both FTOX,LT and FTOX,UT, together with the ˛i values for metal-lic cations, were allowed to vary for each data set, the average

118 E. Tipping, S. Lofts / Aquatic Toxicology 142– 143 (2013) 114– 122

Table 3Results of fitting WHAM-FTOX to the toxicity data sets of Table 2 The value of ˛H is fixed at 1.0, and the mean of FTOX,LT and FTOX,UT is 4.12 in all cases (see the text).

Test organism FTox,LT FTox,UT ˛H ˛Al ˛Cu ˛Zn ˛Cd ˛Pb ˛UO2 RMSD r2

Escherichia coli 3.57 4.67 1.0 3.2 13.9 18.6 19 0.68Pseudomonas fluorescens 3.29 4.95 1.0 4.0 14.0 23.3 10 0.89Vibrio fischeri 2.45 5.79 1.0 3.8 4.1 15 0.81Lemna aequinoctialis 2.24 6.00 1.0 20.8 16.0 8 0.96Lemna paucicostata 1.73 6.51 1.0 2.7 7.6 18 0.76Ceriodaphnia dubia 1.90 6.34 1.0 5.8 65.3 16 0.77Daphnia ambigua 2.04 6.20 1.0 5.5 133.7 7 0.96Daphnia magna 3.03 5.21 1.0 4.6 27.0 14 0.84Daphnia pulex 1.79 6.45 1.0 6.5 63.8 11 0.88Dreissena polymorpha 1.84 6.40 1.0 30.3 6.0 85.8 11 0.92Oncorhynchus mykiss 2.39 5.85 1.0 2.1 11.9 4.6 5 (−)

-7

-6

-5

-4

-3

-9 -8 -7 -6 -5log10 Dreissena mol g-1

log 1

0 HA

mol

g -1

CuZnCd""

Fig. 4. Accumulation of metals by the mussel Dreissena polymorpha measured byKs

rddaattFa1vaFrFt

ss(piPfiFt

˛ie(

0

20

40

60

80

100

120

0 2 4 6 8

F TOX

% lu

min

esce

nce

0

20

40

60

80

100

120

0 2 4 6 8

F TOX

% lu

min

esce

nce

0

20

40

60

80

100

120

0 2 4 6 8

F TOX

% lu

min

esce

nce

Fig. 5. Toxicity of Cd, Pb and their mixtures to Vibrio fischeri (Jho et al., 2011) fittedwith WHAM-F . Upper panel, Cd only; middle panel Pb only; bottom panel, Cd

raak et al. (1994), compared to predicted binding by humic acid. The line has alope of unity, and an offset of 1.78, yielding EHA = 0.017.

oot-mean-squared deviation (RMSD) in toxic effect over the 11ata sets was 11.5%. However it was found in most cases that manyifferent sets, some physically unrealistic, could fit the data nearlys well. Therefore we sought a means to reduce the number ofdjustable parameters. All 11 data sets were fitted by assuming thathe same values of FTOX,LT and FTOX,UT applied to each, while permit-ing the values of ˛i to be adjusted for each data set. This resulted inTOX,LT and FTOX,UT values of 2.22 and 6.01 respectively, and an aver-ge root-mean-squared deviation (RMSD) in toxic effect over the1 data sets of 12.9%. Then to fit individual data sets, the mid-pointalue of FTOX,LT and FTOX,UT, equivalent to 50% toxic effect, was fixedt the average value of 4.12, but the difference between FTOX,LT andTOX,UT was optimised, along with ˛i. The average RMSD was theneduced to 12.0%, not much greater than the value obtained whenTOX,LT and FTOX,UT were allowed free variation. Table 3 summariseshe fitting results obtained with the fixed mid-point value.

Example results are displayed in Figs. 5–8. Ideally, the modelhould, within experimental error, condense all data points onto aingle line in each case, defined by the upper and lower thresholdsFTOX,LT and FTOX,UT). There should also be minimal bias betweenredictions and observations, and this clearly applies to the plots

n Figs. 7 and 8. The results of Jho et al. (2011) in Fig. 5 show thatb toxicity is predicted to be less than observed at high Ca, andor mixtures the model predicts somewhat higher toxicity thans observed. Also the Charles et al. (2006) data for duckweed inig. 6(a) show that the model expects somewhat more toxicity fromhe mixture than is observed.

Only for three metals, Cu, Zn and Cd, are several values ofi obtained (Table 3), permitting differences to be tested, and it

s found that the mean ˛Cu and ˛Zn are not significantly differ-nt (p > 0.05) whereas both differ significantly from the mean ˛Cdp < 0.05).

TOX

and Pb combined. The symbols indicate low (open), medium (grey) and high (black)concentrations of Ca. Note that the fits refer to the entire data set, not separately tothe data of individual panels.

E. Tipping, S. Lofts / Aquatic Toxicology 142– 143 (2013) 114– 122 119

0

20

40

60

80

100

0 2 4 6 8 10 12F Tox

rela

tive

grow

th ra

te % Cu

UO2Cu+UO2""

0

20

40

60

80

100

0 2 4 6 8 10 12

F TOX

% s

urvi

val

mode lCuCdCu + Cd

Fig. 6. Toxicity towards duckweed species fitted with WHAM-FTOX. Panel (a); toxic-i(2

silftledtt

-20

0

20

40

60

80

100

120

0 2 4 6 8 10 12F Tox

% fi

ltrat

ion

Cu + ZnZn + CdCu + CdCu + Zn + Cdmodel

F(

ty of Cu, UO2 and their mixtures to Lemna aequinoctialis (Charles et al., 2006). Panelb): toxicity of Cu, Cd and their mixtures to Lemna paucicostata (Hatano & Shoji,008).

The average RMSD obtained with the CTU model (Table 4) islightly greater than that with WHAM-FTOX, 13.4% vs. 13.0%, notncluding the Hickie et al. (1993) data, and the average r2 slightlyower (0.81 vs 0.85) indicating that WHAM-FTOX is slightly superioror these data sets. The models can also be compared in terms ofhe ranges of the x-axis (Fig. 9), plotted equivalently in terms of theogarithm of the variable that combines exposure and toxicity for

ach model. Fitting a logistic dose–response curve to the combinedata yields a factor of 2.2 for the range of FTOX covering 5–95% ofhe toxic effect, whereas the factor is ten times greater (21.6) forhe range of CTU toxic units.

0

20

40

60

80

100

0 2 4 6 8 10F Tox

% s

urvi

val

ZnCdZn + Cd

0

20

40

60

80

100

0 2 4 6 8 10F Tox

% s

urvi

val

ZnCdZn + Cd

ig. 7. Toxicity of Zn, Cd and their mixtures to daphnids (Shaw et al., 2006) fitted with Wd) Daphnia pulex.

Fig. 8. Toxicity of mixtures of Cu, Zn and Cd to the zebra mussel Dreissena polymor-pha (Kraak et al., 1994) fitted with WHAM-FTOX.

4. Discussion

4.1. Accumulation of cations by living organisms

The results in Figs. 1–4 show that the observed binding or accu-mulation of metals by living organisms approximately follows theexpectations of competitive binding by an array of binding sitesbased on weak acids, broadly similar to those in humic acid. Thisreinforces previous results for the accumulation behaviours in thefield of stream bryophytes (Tipping et al., 2008) and macroinverte-brates (Stockdale et al., 2010).

Although the WHAM predictions produce strong parallels withobserved accumulation, it would be incorrect to conclude thatthe living organism body burdens are in fact controlled simply byquasi-equilibrium chemical reactions. Interactions at the externalsurfaces of organisms exposed to the bathing solution might rea-sonably be expected to follow chemistry, as long as the organismdoes not affect its immediate environment (for example at fishgills the exchange of potentially-buffering molecules notably CO2

and NH3 will regulate pH; Evans et al., 2005). But metals accu-mulated within an organism must be in contact with solutions ofdifferent composition to the external one. A more mechanistically-correct model would include biodynamic concepts; thus Luoma &

0

20

40

60

80

100

0 2 4 6 8 10F Tox

% s

urvi

val

ZnCdZn + Cd

0

20

40

60

80

100

0 2 4 6 8 10F Tox

% s

urvi

val

ZnCdZn + Cd

HAM-FTOX. Panel (a) Ceriodaphnia dubia, (b) Daphnia ambigua, (c) Daphnia magna,

120 E. Tipping, S. Lofts / Aquatic Toxicology 142– 143 (2013) 114– 122

Table 4Results from application of the Conventional Toxic Units (CTU) model. The EC50 values are in mg L−1.

Test organism ̌ EC50Cu EC50Zn EC50Cd EC50Pb EC50UO2 RMSD r2

Escherichia coli 2.77 2.36 0.33 1.09 16 0.79Pseudomonas fluorescens 2.99 0.75 0.35 0.68 6 0.97Vibrio fischeri 1.37 109 2.50 18 0.72Lemna aequinoctialis 1.60 0.017 0.81 3 0.97Lemna paucicostata 0.89 2.13 1.56 17 0.81Ceriodaphnia dubia 1.55 0.39 0.05 22 0.58Daphnia ambigua 1.96 0.56 0.02 14 0.83

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Daphnia magna 3.20 0.78

Daphnia pulex 1.26 0.32Dreissena polymorpha 2.25 0.044 4.13

ainbow (2005) and Rainbow (2007) highlighted the importancef the dynamic uptake and removal of metals, as well as storage inetal-rich granules and metallothioneins, and internal regulation

f the levels of essential metals such as Cu and Zn. It seems undeni-ble, however, that water chemistry exerts a strong control on theteady-state levels of metals accumulated by living organisms, andhat this phenomenon must have implications for toxicity.

Furthermore, because the modelled cation accumulationnvolves only conventional reversible reactions, it is arguably a bet-er index of toxic exposure than measured body burdens, whichs noted above reflect more complicated processes. In this respecthe modelled values might reasonably be considered proxies forteady-state “metabolically available” cations (cf. Rainbow, 2007).t is worth repeating here that WHAM-FTOX does not use the abso-ute concentrations of reversibly-accumulated metals, but ratherhe fractional occupancy of the binding sites, so that although dif-erent values of EHA are required to explain observed differencesn metal accumulation among species, EHA does not appear in theuantification of toxicity.

.2. Toxicity parameters

The two types of parameter, thresholds and toxicity coefficients,sed in WHAM-FTOX are not uniquely defined by the data sets ana-

ysed here, because (a) the experimental conditions cover relativelymall ranges, and (b) the experimental results tend to be quiteoisy. Thus we were obliged to constrain the threshold parame-er values in order to force unique data fits and parameter sets.his means that the physical meanings of the parameters cannot

e reliably judged. It should also be recognised that the derivedoxicity parameters may well be compensating for errors in thehemical speciation modelling, i.e. incorrect prediction of the actualccumulation of metals by the different organisms.

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0

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40

60

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0.01 0.1 1 10 10 0F TOX

% o

f con

trol

JhoPreston EPreston PCha rlesHatan oKraakSha w CDSha w DASha w DMSha w DPHickie

ig. 9. Normalised log-linear plots of toxic response for the WHAM-FTOX and CTU modight-hand plot.

0.15 13 0.850.05 16 0.690.23 10 0.93

Because the thresholds were constrained, their values cannotbe interpreted at this time. However, we can say that the values of˛i summarised in Table 3 show the toxicities of bound cations tofall approximately into three groups, namely (i) H+ and Al, (ii) Cu,Zn, Pb and UO2, and (iii) Cd on its own. There is a hint that boundCd toxic potency as quantified by ˛Cd increases with taxonomicrank (i.e. from bacteria to plants to invertebrates). Many more datawould be required to discern systematic inter-specific variations inthe toxicity parameters.

4.3. Variations among individual organisms in toxicity tests

The dose in aquatic toxicity tests is conventionally expressedas a concentration, e.g. mg L−1 in aquatic toxicology and mg kg−1

in soil and sediment toxicology, which can be converted to toxicunits by dividing by the LC50 (cf. Fig. 9). Usually the x-axis is log-arithmic, which suits the logistic model, and permits wide rangesof concentration to be displayed. The width of the range is usu-ally at least one order of magnitude; for the data sets analysedwith the CTU model in the present work, the logistic fit of all thedata combined has a range from 5% to 95% of 21.6 fold (Section3.2, Fig. 9). Reasons for the x-axis ranges are contested (Newman &Ungar, 2003). It may be that individuals differ intrinsically, or morestochastic processes may operate such that there is always a rangeof physiological states at the start of the experiment, even amongstcloned cultures (Kooijman, 1996; Newman & Ungar, 2003). The21.6-fold range obtained with the CTU model suggests a very widerange of differences amongst test individuals. However, the picturechanges if the variation in FTOX is considered, for which the presentdata cover a range of only 2.2-fold (Figure 9), implying much less

variation amongst individuals. This reflects the different means ofquantifying exposure.

The smaller range obtained with FTOX arises largely from theway that cation binding varies with solution concentration. If we

-20

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40

60

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0.01 0.1 1 10 100

TU

% o

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els, showing all data. Note that the Hickie et al. (1993) data are absent from the

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E. Tipping, S. Lofts / Aquatic Toxi

rst consider a single (homogeneous) binding site, the Law of Massction demands that binding changes relatively less than the freeetal concentration at high occupancies, i.e. as the Langmuir-type

lot curves towards the upper limiting value. This would be the caseith the BLM approach, so the x-axis range should be narrowesthen the biotic ligand occupancy corresponding to 50% toxic effect

s high. Thus, x-axis ranges of about 5-fold for the 5 to 95% toxicityange can be derived from data presented by De Schamphelaere &anssen (2002), Heijerick et al. (2002) and Jho et al. (2011), for whichhe 50% effect biotic ligand occupancies were between 0.3 and 0.6.owever, a much larger range of c. 100 fold is evident in the resultsf Hatano & Shoji (2008) with 50% effect biotic ligand occupancies ofround 0.01. WHAM-FTOX differs from the BLM in having an arrayf heterogeneous binding sites for cations, the effect of which isllustrated by the results in Fig. 2, where the WHAM predictionhows that, at higher loadings of Zn, a change in dissolved metal ofearly four orders of magnitude is required to change the amountdsorbed by a factor of ten. Thus when exposure is quantified inerms of bound cations, i.e. through FTOX, the toxic response occursver a much narrower range than when solution concentrationsre used (Fig. 9).

.4. Applications of WHAM-FTOX

The model is purely empirical in its description of how metalsnd protons exert their subcellular effects, which are simply rep-esented by the parameter ˛i. However, it is mechanistically moreophisticated than most other models in using detailed chemicalpeciation to quantify exposure, importantly including competitionffects, which appear able to account for mixture effects. And bysing bound metal as the measure of exposure, the results suggesthat individual test organisms are less variable in their responseshan is apparent from solution exposure. Therefore, the WHAM-TOX approach provides new mechanistic insights, and should helpo interpret the results of studies designed to elucidate subcellularoxicity mechanisms, especially those involving metal mixtures orH dependence. The model could also contribute to the planning ofxperiments, for example by forecasting experimental conditionsnder which competition effects are especially noticeable.

As demonstrated here, WHAM-FTOX can readily fit individualoxicity data sets within a coherent and consistent framework. Itherefore has the potential to organise and encapsulate laboratorynowledge, through a meta-analysis of the vast store of availableaboratory toxicity data, for both single metal and mixture toxicity.his activity might reveal relationships and patterns among metalsnd test species, thereby providing new insights into metal toxic-ty. Furthermore, the fully-parameterised version of WHAM-FTOX

ould be valuable in the interpretation and prediction of the toxicffects of metals and protons in the field, either for more realisticisk assessment or understanding ecosystem community response.n this way the model can contribute to the ultimate goal of aquaticoxicological research.

. Conclusions

This study has demonstrated that WHAM-FTOX can be used tot cation mixture toxicity data for aquatic organisms, making theodel an alternative to conventional toxicity parameterisation and

he Biotic Ligand Model. For the data sets considered in the presentork, WHAM-FTOX provides somewhat better fits than the CTUodel overall, but the improvements are not great. This proba-

ly arises firstly because the analysed data refer to relatively smallanges of both pH and metal concentrations, and secondly becausehe measured toxicity responses are quite noisy, so they do notrovide sufficiently stringent tests for proper model comparison.

142– 143 (2013) 114– 122 121

For example, data fitting is reasonably satisfactory if fully additiveeffects are assumed (CTU), or if there is potential antagonism dueto competitive chemical binding reactions (WHAM-FTOX). Furthertesting on more demanding data sets is clearly desirable. There isalso a case for applying the models to large numbers of toxicity datasets, to attempt to rationalise toxicity knowledge within a coher-ent framework, and seek trends and patterns within the parametervalues.

The following specific conclusions can be drawn.

(a) WHAM-predicted binding of metals by humic acid provides agood guide to their accumulation in living organisms, and there-fore a measure of exposure to toxic cations.

b) Laboratory mixture toxicity data can be adequately fitted via thevariable FTOX, which gives slightly better results than obtainedwith a conventional additive logistic toxicity model based ontoxic units.

(c) Toxic potency of bound metal increases in the approximateorder H < Al < (Zn–Cu–Pb–UO2) < Cd.

d) The overall range of FTOX over which the toxic effect changesfrom 5 to 95% is about 10 times smaller than the range of toxicityunits derived with a conventional model. Thus the WHAM-FTOX analysis indicates much narrower ranges of differencesamongst individual organisms in metal toxicity tests than waspreviously thought.

(e) WHAM-FTOX can contribute to the interpretation and design ofmechanistic studies, and potentially provides a means to encap-sulate the knowledge contained within laboratory data, therebypermitting its application to field situations.

Acknowledgements

We are grateful to R. Shoji (Tokyo National College of Technol-ogy) for providing data, and to D. Spurgeon (Centre for Ecology andHydrology, UK) for helpful comments on a draft manuscript. Thispaper arises from research projects sponsored by the InternationalCopper Institute, the Metals Environmental Research Associations,and the UK Natural Environment Research Council.

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