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Environmental Toxicology and Chemistry, Vol. 19, No. 3, pp. 775–780, 2000q 2000 SETAC

Printed in the USA0730-7268/00 $9.00 1 .00

BIOSENSING THE ACUTE TOXICITY OF METAL INTERACTIONS: ARE THEYADDITIVE, SYNERGISTIC, OR ANTAGONISTIC?

SARA PRESTON,*† NICHOLAS COAD,‡ JOHN TOWNEND,† KEN KILLHAM,† and GRAEME I. PATON††Department of Plant and Soil Science, University of Aberdeen, Cruickshank Building, St. Machar Drive, Aberdeen, AB24 3UU,

United Kingdom‡CORDAH, Environmental Management Consultants, Kettock Lodge, Aberdeen Science and Technology Park, Bridge of Don,

Aberdeen, AB22 8GU, United Kingdom

(Received 22 February 1999; Accepted 29 June 1999)

Abstract—The toxicity of Zn, Cu, and Cd, alone or in combination, was assessed using two luminescence-based microbial biosensorsfrom different ecological niches: Escherichia coli HB101 pUCD607 and Pseudomonas fluorescens 10586 pUCD607. Significantsynergistic interactions occurred between the toxic effects of the Zn and Cu and Zn and Cd combinations and the response of E.coli (i.e., toxicities of combinations of pollutants were greater than predicted from addition of individual toxicities). Significantsynergistic interactions were also observed between the toxicities of Cd and Cu combinations and the response of P. fluorescens.A longer exposure time was shown to significantly increase the sensitivity of E. coli to the metal pollutants. P. fluorescens wasobserved to significantly decrease in its sensitivity toward Zn and Cd with a longer exposure time. It was shown that the toxicityof combinations of metals could not be modeled on the basis that their toxic action was independent of each other. The applicationof different models to describe interactions between combinations of metals is discussed. The importance of considering the testspecies and the exposure period selected for toxicity assessment was highlighted, as was the need to further investigate the toxicityof combinations of pollutants.

Keywords—Heavy metals Pollutant interactions Microbial biosensors Toxicity testing Bioavailability

INTRODUCTION

Contaminated ecosystems, whether aquatic or terrestrial,will often be polluted with a mixture of pollutants rather thana single pollutant. Despite this, and as a result of time andfinancial constraints, toxicity testing has generally been re-stricted to studying the effects of single pollutants on a targetorganism after a specific exposure time and under controlledenvironmental conditions [1]. Various interactions can takeplace when organisms are exposed to mixtures of pollutantsand may be described as additive, synergistic, or antagonistic.Additive effects arise when the toxicity of the mixture is equalto the sum of the toxicities of the individual components.Synergistic or antagonistic interactions arise when the toxicityof the mixture is greater than or less than the sum of thetoxicities of the individual components, respectively. Unlessthere is evidence to the contrary, authorities generally enforceregulations that assume that acceptable concentrations for pol-lutants can be treated independently, even when they are pres-ent in mixtures [2]. However, serious consequences may resultwhen such assumptions are incorrect. For example, the man-ifestation of Pigmented Salmon Syndrome in Atlantic salmonof the River Don in Scotland, which reached epidemic pro-portions in the early 1980s, was a result of the synergisticinteraction of the toxins found in two effluent discharges intothe river [3,4].

Few detailed studies of the toxicity of mixtures of pollutantsand their prediction have been conducted. Those that do existhave concentrated on the toxicity of mixtures of organic pol-lutants, such as chlorinated phenols [5], or organophosphoruspesticides [6]. Much less information is available on the tox-

* To whom correspondence may be addressed(s.preston@abdn.ac.uk).

icity of mixtures of heavy metals, particularly in relation tomicroorganisms. Other studies have used quantitative struc-ture–activity relationships to provide information on the likelyeffects of mixtures of metal ions [7]. The toxicity of mixturesof heavy metals is of particular importance at the present timebecause of the anticipated increase in heavy metal concentra-tions in soil as a result of application of sewage sludge to land[8]. Concern about an increase in the levels of heavy metalsin soil has resulted in legislative bodies detailing maximumpermissible total metal concentrations in the United Kingdomas well as the United States. However, the U.S. EnvironmentalProtection Agency 1993 sewage sludge guidelines have beencriticized for overlooking the importance of heavy metal in-teractions in setting total metal concentration limits in soil [9]and for not being sufficiently protective of plants and micro-organisms [10]. Similar criticisms may also be leveled at Unit-ed Kingdom legislation regulating the application of sewagesludge to land.

Toxicity assessment of multiple combinations of pollutantsrequires application of a sensitive, rapid, inexpensive, and re-liable ecotoxicity test. The Microtoxt assay (Azur Environ-mental, Berkshire, UK) is commonly used for environmentaltoxicity testing and is based on the natural luminescence ofthe marine bacterium Vibrio fischeri. Bacterial luminescenceis linked to electron transport [11] and thus reports on themetabolic activity of the cell. In the Microtox assay, lumi-nescence is therefore negatively correlated with an increase inthe toxicity of a pollutant. However, the Microtox assay isbased on a marine isolate, making it less ecologically relevantto soil or freshwater ecosystems. Furthermore, it requires aneutral pH and the addition of a saline buffer, which may altermetal speciation. The ability to isolate and insert the genes

776 Environ. Toxicol. Chem. 19, 2000 S. Preston et al.

coding for bacterial luminescence into an organism of choicehas enabled wider application of luminescence technology[12]. Luminescence-based microbial biosensors that are rele-vant to soil ecosystems have been developed [13] and havebeen shown to be good predictors of the bioavailable fractionof metals in soils [14].

In this article, we report on the response of two geneticallymodified luminescence-based microbial biosensors to combi-nations of Zn and Cu, Zn and Cd, and Cu and Cd in aqueoussolutions. The use of aqueous solutions provided the simplestexperimental system with which to start investigating the tox-icity of mixtures to microorganisms. The microbial biosensorswere Escherichia coli HB101 pUCD607 [15], a general bac-terial biosensor that can perform across a wide range of en-vironmental conditions, and Pseudomonas fluorescens 10586pUCD607 [16], a soil isolate. The effects of combining metalswere compared with predicted effects using a simple additivemodel. The effect of exposure times and bacterial species onthe determination of toxicity was also highlighted.

MATERIALS AND METHODS

Microbial biosensors

Escherichia coli HB101 and P. fluorescens 10586 weremarked with the lux CDABE genes, originally isolated fromV. fischeri, using a multicopy plasmid, pUCD607. The twostrains were prepared according to standard laboratory pro-tocols [13] and stored as freeze-dried cultures (2208C). Esch-erichia coli and P. fluorescens cultures were resuscitated for1 h in 10 ml of 0.1 M KCl and 10 ml of Luria Bertani broth(10 g/L tryptone, 5 g/L yeast extract, 5 g/L NaCl, and 1 g/Lglucose), respectively. After resuscitation, E. coli cells wereused directly, but P. fluorescens cells were harvested by cen-trifugation and resuspended in 5 ml of 0.1 M KCl because adifferent freeze-drying protocol had been used for this bio-sensor.

Preparation of the test solutions

Stock solutions of Cd, Cu, and Zn were prepared at a con-centration of 1,111.1 mg/L in double-deionized water usingnitrate salts of the respective metal. Stock solutions were acid-ified with 0.1% (v/v) 1 M HNO3 and stored at 3 to 58C. Stan-dard solutions ranging from 0 to 4 mg/L were prepared fromthe stock solutions using double-deionized water and wereused within 24 h. The range of concentrations chosen werebased on the results of preliminary experiments in which thedose–response curves for each metal were obtained for thedifferent biosensors. The pH value of the standards was ad-justed to 5.5 6 0.02 using dilute solutions of NaOH and HCl.Test solutions consisting of two metals were prepared by plac-ing 450-ml aliquots of each heavy metal into 1.5-ml disposablepolystyrene luminometer cuvettes (Clinicon, Petworth, UK).Thermodynamic calculations to determine whether metal pre-cipitation could occur were performed using GEOCHEM-PC,version 2.0 (University of California, Riverside, CA, USA)and confirmed that precipitation of metals under these con-ditions did not occur.

Determination of toxicity of heavy metal combinations

Cell suspensions (100 ml) of either E. coli HB101pUCD607 or P. fluorescens 10586 pUCD607 were pipettedinto cuvettes containing the test solution and mixed for 5 s,with 15-s intervals between each sample. The final cell con-centrations of E. coli and P. fluorescens in the assays were 2

3 107 cfu/ml and 1 3 107 cfu/ml, respectively. Luminescencewas measured after exposure times of 20 and 40 min using a1-s measurement on a Bio-Orbit 1231 luminometer (LabtechInternational, Uckfield, UK). The exposure times chosen wererelevant to investigating the acute response of the biosensorsto the metal toxicants. All assays were performed in triplicate.

Data analysis

The toxicity of heavy metals, alone or in combination, wasdetermined by expressing the luminescence response as a per-centage of the control luminescence (%lum), the control con-sisting of double-deionized water at pH 5.5 6 0.02, after either20 or 40 min of exposure time. To test for interactions betweenmetals, an analysis of variance was initially performed for eachassay on normalized data. The data (%lum/100) were nor-malized using a log10 transformation. A general linear modelwas used to fit the analysis of variance model to the data usingthe statistical package Minitab for Windows, release 12.1(State College, PA, USA). To determine whether any inter-actions observed were synergistic or antagonistic, the follow-ing model was used to predict the response of the biosensorwhen two metals are present:

%lum /100 5 (%lum /100) 3 (%lum /100)M A B (1)

where %lum is the percentage of the control luminescenceremaining due to a mixture of metals (M) or an individualcomponent (A or B). It is useful to note that this model canalso be written as

log(%lum /100) 5 log(%lum /100) 1 log(%lum /100) (2)M A B

It can then be seen that this model is equivalent to that testedin the analysis of variance described above, that is, that thelogarithms of %lum/100 for the individual metals are additive.In the model described by Equation 1, the individual toxicants(metals) are assumed to produce the same proportional de-crease in light output regardless of the concentration of theother, which is equivalent to the model of Stratton [17]. Thismodel could also be applied to mixtures of more than twocomponents if the toxic actions of all of the individual com-ponents were independent of each other. Observations fittingthis model are described here as additive effects. Synergisticinteractions are obtained if the observed levels of %lum aresignificantly lower than the levels predicted (i.e., the toxiceffects of combinations of metals are greater than predictedby this model). Antagonistic interactions are obtained if theobserved levels of %lum are significantly greater than thelevels predicted. Three assays were performed on separatedays; therefore, predicted values for combinations of metalswere obtained from effects of the metals acting alone at therelevant concentrations for each assay. The results from thethree assays were then used to calculate average values andstandard errors for predicted values. Overall tests for inter-actions between metals were performed using analysis of var-iance for each set of assays. Individual comparisons of mod-eled versus observed toxicities, for each combination of metalconcentrations, were performed using a series of paired t tests.Within each assay (16 metal combinations), on average, nomore than one significant difference would be expected tooccur by chance (p # 0.05).

RESULTS

The observed and predicted responses of the biosensors tomixtures of Zn, Cu, and Cd were plotted using the data ob-

Biosensing the acute toxicity of metal interactions Environ. Toxicol. Chem. 19, 2000 777

Fig. 1. Observed and predicted response of Escherichia coli (A) andPseudomonas fluorescens (B) to combinations of Cu and Zn after 20min of exposure. The standard error of the mean is shown (n 5 3).Asterisks represent the level of significance between observed andpredicted values for a given combination: *p # 0.05; **p # 0.01;***p # 0.001.

Fig. 3. Observed and predicted response of Escherichia coli (A) andPseudomonas fluorescens (B) to combinations of Cu and Cd after 20min of exposure. The standard error of the mean is shown (n 5 3).Asterisks represent the level of significance between observed andpredicted values for a given combination: *p # 0.05; **p # 0.01;***p # 0.001.

Table 1. Analysis of variance results obtained for the effect of Cu,Zn, and exposure time on log10-transformed responses of the

biosensors

Factor

p

Escherichiacoli

Pseudomonasfluorescens

Exposure timeZn levelCu levelExposure time·Zn levelExposure time·Cu levelZn level·Cu levelExposure time·Zn level·Cu level

#0.01#0.001#0.001

NS#0.01#0.001

NS

NS#0.001#0.001

NSNSNSNS

Fig. 2. Observed and predicted response of Escherichia coli (A) andPseudomonas fluorescens (B) to combinations of Cd and Zn after 20min of exposure. The standard error of the mean is shown (n 5 3).Asterisks represent the level of significance between observed andpredicted values for a given combination: *p # 0.05; **p # 0.01;***p # 0.001.

tained after 20 min of exposure (Figs. 1 to 3). The data obtainedfor the longer exposure time of 40 min were not shown becausethey followed the same patterns as those observed after 20min of exposure. Figures 1 to 3 illustrate the factorial designof the experiments, the effect of every possible combinationof two metals at different concentrations having been inves-tigated.

Zinc and copper toxicity

Overall, increasing concentrations of Zn or Cu had signif-icant (p # 0.001) inhibitory effects on the luminescence of

E. coli and P. fluorescens (Table 1). The toxicity of Cu andZn varied depending on the biosensor, with equivalent com-binations of Cu and Zn resulting in a greater toxicity to P.fluorescens than to E. coli (Fig. 1A and 1B).

The results indicated that for E. coli, an increase in theexposure time, from 20 to 40 min, resulted in a significant (p# 0.01) overall increase in the toxicity of the Zn and Cutreatment combinations (Table 1). Overall, significant (p #0.01) synergistic interactions were observed between exposuretime and Cu levels on the response of E. coli. Significant (p# 0.001) interactions were observed between Zn and Cu levelson the response of the E. coli biosensor, suggesting that theircombined toxicity was not additive (Table 1). The model fittedto the data clearly illustrated that the interaction between Cuand Zn was, overall, synergistic, their combined toxicity re-sulting in a greater decrease in the luminescence of E. colithan predicted from their individual effects (Fig. 1). Paired ttests showed that significant (p # 0.05) decreases in the ob-served values of luminescence, compared with the predictedvalues, occurred at all combinations with the exception of0.125 mg/L Zn and 0.5 mg/L Cu (Fig. 1A).

Results obtained with P. fluorescens showed that altering

778 Environ. Toxicol. Chem. 19, 2000 S. Preston et al.

Table 2. Analysis of variance results obtained for the effect of Cd,Zn, and exposure time on log10-transformed responses of the

biosensors

Factor

p

Escherichiacoli

Pseudomonasfluorescens

Exposure timeZn levelCd levelExposure time·Zn levelExposure time·Cd levelZn level·Cd levelExposure time·Zn level·Cd level

#0.001#0.001#0.001

NSNS

#0.05NS

#0.001#0.001#0.001

NSNSNSNS

Table 3. Analysis of variance results obtained for the effect of Cd,Cu, and exposure time on log10-transformed responses of the

biosensors

Factor

p

Escherichiacoli

Pseudomonasfluorescens

Exposure timeCd levelCu levelExposure time·Cd levelExposure time·Cu levelCd level·Cu levelExposure time·Cd level·Cu level

#0.001#0.001#0.001

NS#0.01

NSNS

NS#0.001#0.001

NSNS

#0.001NS

exposure time had no significant effect on the toxicity of Znand Cu and that there was no significant interaction in toxicitybetween Zn and Cu combinations (Table 1). However, pairedt tests showed that at combinations of 0.5 mg/L Zn and 0.5or 1.0 mg/L Cu, observed values of luminescence were sig-nificantly (p # 0.05) lower than predicted (Fig. 1B).

Cadmium and zinc toxicity

Overall, increases in the concentrations of Cd or Zn resultedin significant (p # 0.001) increases in their toxicity to E. coliand P. fluorescens (Table 2). The toxicities of Cd and Zn toE. coli and P. fluorescens were similar (Fig. 2).

The results showed that for E. coli, an increase in exposuretime resulted in a significant (p # 0.001) overall increase inthe toxicity of the Cd and Zn treatment combinations (Table2). A significant (p # 0.05) interaction was observed betweenZn and Cd, suggesting that their toxicity to E. coli was notadditive (Table 2). The toxicity of Zn and Cd combinations toE. coli was shown to be, overall, synergistic, because observedvalues of luminescence were lower than predicted values (Fig.2A). However, only at combinations of 0.25 mg/L Zn and 0.375or 1.5 mg/L Cd were significant (p # 0.05) decreases in theobserved values obtained compared with the predicted values(Fig. 2A).

The results obtained with P. fluorescens showed that anincrease in exposure time resulted in a significant (p # 0.001)overall decrease in the toxicity of the Cd and Zn treatmentcombinations (Table 2). No significant interaction in toxicitywas observed between Zn and Cd treatment combinations,although Fig. 2B suggests that there are synergistic interactions(Table 2 and Fig. 2B). In fact, significant (p # 0.05) decreasesin the observed values of luminescence, compared with thepredicted values, were obtained with eight different combi-nations of Zn and Cd (Fig. 2B.)

Cadmium and copper toxicity

Overall, increasing concentrations of Cd and Cu signifi-cantly (p # 0.001) increased their toxicities to E. coli and P.fluorescens (Table 3). Pseudomonas fluorescens was moresensitive than E. coli to Cu and Cd, the former’s luminescencebeing inhibited by more than 50% in general (Fig. 3).

Results with E. coli showed that increasing exposure timesignificantly (p # 0.001) increased the toxicity of Cd and Cutreatment combinations (Table 3). A significant (p # 0.01)synergistic interaction was observed between exposure timeand Cu levels in relation to E. coli (Table 3). No significantinteractions were observed between Cd and Cu levels, sug-gesting that their combined toxicity to E. coli can be ade-

quately described by an additive model (Table 3 and Fig. 3A).The only exception was at a combination of 0.325 mg/L Cdand 2.0 mg/L Cu, when observed values of luminescence weresignificantly (p # 0.05) lower than predicted (Fig. 3A). How-ever, the latter result was probably obtained by chance.

Results with the P. fluorescens biosensor, on the other hand,showed that there was a significant (p # 0.001) interaction intoxicity between Cd and Cu treatment combinations. The mod-el fitted to the data clearly showed that the toxicity of Cd andCu treatment combinations was synergistic to P. fluorescens(Fig. 3B). However, the only combinations where observedvalues of luminescence were significantly (p # 0.05) lowerthan predicted were at combinations of 0.75 or 1.25 mg/L Cdand 0.5 or 1.0 mg/L Cu, respectively (Fig. 3B).

DISCUSSION

A number of models have been proposed to predict thetoxicity of mixtures to an organism, all of which are generallybased on the concept of additivity [5,6,17–19]. However, theemphasis behind the application of the various models varies,with some models having been put forward as alternatives toexperimental data on the toxicity of mixtures [5,6,18] andothers having been used to investigate the nature of the toxicityof mixtures (i.e., were they additive, synergistic, or antago-nistic?) [17,19]. In this study, the latter approach was usedbecause insufficient information was available on the natureof metal interactions and the model proposed by Stratton [17]appeared to be the simplest and most appropriate for the studyin question. Stratton’s method involved preparing two identicaldilution series for one toxicant, A. The response of some or-ganism (e.g., luminescence) was assessed for each of the con-centrations in the first series, which contained only toxicantA (the control series), and similarly for the second series, towhich a fixed concentration of toxicant B was added togetherwith each of the different concentrations of toxicant A (theinteraction series). Stratton [17] argued that if the two toxicitieswere additive, the percentage effect of adding toxicant B, com-pared with the equivalent concentration in the control series,would be the same for all concentrations of toxicant A. In-teraction of the two toxicants was therefore shown if the pro-portional effect of toxicant B changed with changing concen-trations of toxicant A. Using this approach, he demonstratedvarious degrees of interaction between addition of captan anda range of organic solvents on microbial colony growth [19].

Konemann [18] described two ways in which toxic effectson fish might combine if there is no interaction: (1) simplesimilar action, where it is considered that the dose–responsecurve for any chemical is the same when the concentrations

Biosensing the acute toxicity of metal interactions Environ. Toxicol. Chem. 19, 2000 779

are scaled relative to the median lethal concentration for thatchemical; these scaled concentrations can then be added togive an expected toxic effect on a general, scaled dose–re-sponse curve; and (2) independent action, where the proba-bility of survival is some function of the probabilities of sur-vival due to the actions of each of the chemicals in the mixif it was acting alone. In the case where the toxic responsesto each of the individual chemicals are highly correlated (i.e.,individuals in a population that are most susceptible to one ofthe chemicals are also most susceptible to all of the others),the probability of survival due to the mixture is equal to theprobability of survival due to the most toxic component. Amixture toxicity index was developed to determine which ofthese was most likely to be causing the responses observed orwhether synergistic or antagonistic effects were taking place.An important difference between the work of Konemann [18]and this study is that he studied median lethal concentrationvalues, for which a fish can demonstrate toxicity only by beingalive or dead and if it is killed by one pollutant is unaffectedby the others. In luminescence-based microbial biosensors,toxicity is demonstrated by the level of activity of the organ-isms, and if they are severely affected by one pollutant, theymay still be affected further by another. Therefore, the part ofKonemann’s model based on probabilities of survival (inde-pendent action) is probably not appropriate here. For a lu-minescence-based assay, the relevant form of the model ofsimple similar action is

CMEC 5 (3)50M C CA B1 1 . . .EC EC50A 50B

where EC50 represents the effective concentration required tobring about a 50% decrease in light output, C represents theconcentration of a substance, and subscripts A, B, etc., and Mrepresent the component chemicals and the mixture, respec-tively.

Ribo and Rogers [5] found this to be a good model for thecombined effects of a range of phenolic chemicals on theluminescence-based Microtox assay. Furthermore, they sug-gested that if a unimolecular mechanism for the luminescenceinhibition reaction is assumed, Konemann’s model, describedby Equation 3, is equivalent to

G 5 G 1 G 1 . . .M A B (4)

where G represents the ratio of light lost to light remainingwhen the bacteria are exposed to a toxicant and subscripts A,B, etc., and M represent the component chemicals and themixture, respectively. Galli et al. [6] tested the models in Equa-tions 3 and 4 for the effects of mixtures of a range of pesticideson the Microtox assay. They concluded that Equation 3 didnot model the mixtures well. They suggested that better agree-ment between calculated and modeled responses was obtainedusing Equation 4, which is surprising because Ribo and Rogers[5] considered this to be a special case of Equation 3. However,no statistical analyses were performed by Galli et al. [6] toback up their assertion.

In this study, deviation from the behavior described by themodel given in Equation 2 was considered to indicate inter-actions between metals. The toxicity of Zn and Cu combi-nations was shown to be synergistic to E. coli and adequatelydescribed by the model with respect to P. fluorescens. Simi-larly, the toxicity of Cd and Zn combinations was shown tobe synergistic to E. coli and adequately described by the model

in the case of P. fluorescens. The opposite pattern was shownfor the toxicity of Cd and Cu combinations, their toxicity beingsynergistic to P. fluorescens and in the case of E. coli, ade-quately described by the model. The toxicity of Zn to E. coliappears to increase in the presence of either Cu or Cd, sug-gesting that Cu and Cd may damage the cell membrane, re-sulting in an increase in the passage of Zn ions across themembrane. Similarly, in the case of P. fluorescens, the syn-ergistic toxicity of Cd and Cu combinations may be due to anincrease in the passage of Cu or Cd ions across the cell mem-brane as a result of membrane damage caused by either Cu orCd. Cadmium and copper have been previously shown to causedamage to the membranes of microbial cells [20,21]. Clearly,the toxicity of mixtures of metals cannot be modeled assumingthat the toxicity of the individual metals is independent of theother metals in a mixture. Furthermore, because different mi-croorganisms respond differently to different combinations ofmetals, toxicity testing should include more than one test or-ganism to obtain a fuller picture of toxicity and enable saferlimits to be set.

Exposure time had significant effects on the assessment oftoxicity, resulting in different interpretations of toxicity de-pending on the exposure time used. The significant synergisticinteractions observed between exposure time and Cu on theresponse of E. coli, irrespective of the metal combination used,suggested that E. coli may have a mechanism to counteractthe increase in Cu ions, but as the adsorption sites becamesaturated, the mechanism would break down, leading to anincrease in the toxicity of Cu with an increase in exposuretime. Any predictive models of the toxicity of pollutants,whether alone or in combination, should integrate exposuretime to obtain a more accurate assessment of toxicity.

The synergistic effect observed for the toxicity of combi-nations of Cu and Cd to P. fluorescens, a common soil mi-croorganism, suggests that regulatory limits for total heavymetal concentrations will not be sufficiently protective of mi-croorganisms unless a detailed study is conducted on the tox-icity resulting from interactions between various metals. Sim-ilar studies have been performed in plants, and the toxicity ofcombinations of metals was generally shown to be synergisticonce a threshold level of toxicity was surpassed [22,23]. Atpresent, interactions between combinations of metals are notconsidered in regulations on permissible levels of heavy metalsin soil.

More research on the toxicity of mixtures of pollutants isurgently needed, because few studies have been conducted dueto the complexity of such investigations. However, the avail-ability of fast, inexpensive, and sensitive ecotoxicity test sys-tems such as luminescence-based microbial biosensors enablesrapid screening of a large number of pollutants and their in-teractions, which is cost-effective and can be tailor-made to aparticular environment.

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