Ecotoxicology and Environmental Safety 86 (2012) 93–100

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Ecotoxicology and Environmental Safety

0147-65

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n Corr

E-m

journal homepage: www.elsevier.com/locate/ecoenv

Toxicity on the luminescent bacterium Vibrio fischeri (Beijerinck). II:Response to complex mixtures of heterogeneous chemicals at lowlevels of individual components

Sara Villa a,n, Sonia Migliorati b, Gianna Serafina Monti b, Marco Vighi a

a Department of Environmental Sciences, University of Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italyb Department of Statistics, University of Milano Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy

a r t i c l e i n f o

Article history:

Received 4 May 2012

Received in revised form

6 August 2012

Accepted 28 August 2012Available online 22 October 2012

Keywords:

Mixtures

Microtoxs test

Narcotics

Pesticides

Best-fit regression

Non-parametric test

13/$ - see front matter & 2012 Elsevier Inc. A

x.doi.org/10.1016/j.ecoenv.2012.08.030

esponding author. Fax: þ39 02 64482795.

ail address: sara.villa@unimib.it (S. Villa).

a b s t r a c t

The toxicity of eight complex mixtures of chemicals with different chemical structures and toxicolo-

gical modes of action (narcotics, polar narcotics, herbicides, insecticides, fungicides) was tested on the

luminescent bacterium Vibrio fischeri. There were maximum 84 individual chemicals in the mixtures.

Suitable statistical approaches were applied for the comparison between experimental results and

theoretical predictions. The results demonstrated that the two models of Concentration Addition (CA)

and Independent Action (IA) are suitable to explain the effect of the mixtures.Even extremely lower

concentrations of individual chemicals contributed to the effect of the mixtures. Synergistic effects

were not observed in any of the tested mixtures.

In particular, the CA approach well predicted the effects of six out of eight mixtures and slightly

overestimated the effects of the remaining two mixtures. Therefore, the CA model can be proposed as a

pragmatic and adequately protective approach for regulatory purposes.

& 2012 Elsevier Inc. All rights reserved.

1. Introduction

It is well recognized that aquatic and terrestrial ecosystems areexposed to complex mixtures of chemicals having variable composi-tion. Therefore, the assessment of toxic effects and ecological riskbased on individual chemicals suffers from a lack of environmentalrealism.

The number of potential mixtures likely to be present in theenvironment is virtually infinite, so experimental testing is notfeasible and there is the need for predictive approaches capable ofestimating the effect of mixtures with known composition.

Generally, two basic models are accepted by the scientific com-munity: Concentration Addition (CA) and Independent Action (IA).

The CA approach applies to substances expected to act withthe same mode of action. It is based on the Loewe additivitymodel (Loewe and Muischnek, 1926), described for a binary mix-ture by the following equation:

Ca

ECx,aþ

Cb

ECx,b¼ 1 ð1Þ

where Ca and Cb are the actual concentrations of two toxicants ina mixture producing X% effect (for example 50 percent mortality),ECxa and ECxb are the concentrations of each toxicant alone, which

ll rights reserved.

would produce the same X% effect (for example EC50). For a multi-component mixture, the equation can be written as follows(Berenbaum, 1985)

Xn

i ¼ 1

Ci

ECxi¼ 1 ð2Þ

The quotients Ci/ECxi have been termed Toxic Units (TUs)(Sprague, 1970). They represent the concentrations of mixturecomponents as fractions of equi-effective individual concentrations.If the sum of TUs equals 1, mixture components acts concentrationadditive, like being dilutions of the same compound.

The concept of Independent Action (or Response Addition) wasintroduced by Bliss (1939), assuming that different substancescause a common integral biological effect (e.g. death) throughprimary interaction with different molecular target sites. Thereference equation of the Bliss independence model for a binarymixture, is as follows:

f ab ¼ f aþ f b�f af b ð3Þ

where fa, fb and fab are the fractions of total possible effectproduced by the individual toxicants ‘‘a’’, ‘‘b’’ and their combina-tion, respectively. For a multiple mixture, the model can bewritten as follows (Faust et al., 2003)

F 1,2,:::nð Þ ¼ 1� 1�F1ð Þ* 1�F2ð Þ*:::::::: 1�Fnð Þ� �

ð4Þ

S. Villa et al. / Ecotoxicology and Environmental Safety 86 (2012) 93–10094

The two models have some significant advantages that makethem very useful for assessing mixture risks. They are relativelysimple and the general concepts are equally valid for ecotoxicol-ogy and human toxicology. Their reliability has been experimen-tally checked. In ecotoxicology, several examples exist in theliterature to support their predictive capability (Backhaus et al.,2000, 2004; Faust et al., 2001, 2003: Gonc-alves et al., 2008;Knauert et al., 2009, 2010). However, some relevant drawbacksmust be also taken into account. First of all, they need the knowledgeof modes of action which is frequently unavailable. Moreover, theycannot be applied in case of chemical interactions producing syner-gism or antagonism; chemical interactions need to be consideredcase by case. Finally, the problem of mixture effect at extremelylower levels (below a NOEC) of individual components may becontroversial. Theoretically, such lower concentrations should beadditive in a CA model, while they should not produce any effect inthe IA model, based on the combination of effects of individualchemicals.

In this work, the responses to complex mixtures of a high numberof individual components with different chemical and toxicologicalcharacteristics, were tested on the bacterium Vibrio fisheri. Experi-mental responses were compared with IA and CA predictions, inorder to assess the validity of the models for mixtures, consisting of ahigh number of components with extremely lower individual con-centrations, far below NOEC.

Table 1List of tested chemicals and main physico–chemical (MW: molecular weight; WS: wate

toxicological properties ordered according to increasing EC50. Data are referred to 20 1

calculated using the EPI model (U.S. EPA, 2012). EC50 represent the 15 min toxicity on

Non-polar narcotics CAS MW

1 nonane 111-84-2 128.3

2 heptane 142-82-5 100.2

3 cyclohexane 110-82-7 84.2

4 octanol 111-87-5 130.2

5 toluene 108-88-3 92.1

6 hexane_n 110-54-3 86.2

7 benzene 071-43-2 78.1

8 butylacetate 123-86-4 116.2

9 acetaldehyde 075-07-0 44.1

10 butanol 071-36-3 74.1

11 diisopropylether 108-20-3 102.2

12 chlorophorm 067-66-3 119.4

13 ethyl_acetate 141-78-6 88.1

14 dichloromethane 075-09-2 84.9

15 mercaptoethanol_2 060-24-2 78.1

16 diethylether 060-29-7 74.1

17 acetone 067-64-1 58.1

18 ethanol 064-17-5 46.1

19 methanol 067-56-1 32.0

20 isopropanol 067-63-0 60.1

21 dimethylsulfoxide 067-68-5 78.1

22 acetonitrile 075-05-8 41.1

Polar narcotics1 phenylphenol-o 090-43-7 170.2

2 diethylaniline_2.6 579-66-8 149.2

3 diphenilammine 122-39-4 169.2

4 bromoaniline_4 624-19-1 172.0

5 chloroaniline_4 106-47-8 127.6

6 phenol 108-95-2 94.1

7 benzylacetic acid 140-11-4 150.2

8 methylaniline_4 106-49-0 107.2

9 trichloroacetic_acid 076-03-9 163.4

10 formaldehyde 050-00-0 30.0

11 chlorophenol_2 095-57-8 128.6

12 phenylhidrazine 100-63-0 108.1

13 nitroaniline_4 100-01-6 138.1

14 metoxyaniline_4 104-94-9 123.2

15 fluoroaniline_3 372-19-0 111.1

16 triethanolamine 102-71-6 149.1

2. Materials and methods

2.1. Chemicals

Three different sets of chemicals were selected for this study: non-polar narcotics

(aliphatic and aromatic hydrocarbons, alcohols, ethers, etc.), polar narcotics (phenols,

amines, etc.) according to the definition of Verhaar et al. (1992) and pesticides with

different modes of action and chemical classes (herbicides, insecticides, fungicides).

All chemicals were individually tested for the assessment of EC50 on V. fisheri.

Complete and detailed results are reported elsewhere (Vighi et al., 2009).

The list of chemicals, their main physicochemical properties and individual

15 min EC50 are reported in Table 1. All chemicals used in this study were of

analytical or technical grade (purity499.5 percent).

2.2. Tested mixtures

Several mixtures were prepared by mixing individual chemicals at equitoxic

concentration. Chemicals were mixed in a ratio corresponding to their individual

EC50. The tested mixtures were

�

r so

C (a

Vib

Mixture1: non-polar narcotics, 22 chemicals.

�

Mixture 2: polar narcotics, 21 chemicals.

�

Mixture 3: polar and non-polar narcotics, 43 chemicals.

�

Mixture 4: herbicides, 24 chemicals.

�

Mixture 5: fungicides, 9 chemicals.

�

Mixture 6: insecticides, 8 chemicals.

�

Mixture 7: total pesticides, 41 chemicals.

�

Mixture 8: narcotics and pesticides, 84 chemicals.

lubility; VP: vapor pressure; log Kow: n-octanol/water partition coefficient) and

fter Vershueren, 1996; Mackay et al., 1995; Tomlin, 2003). Log Kow values are

rio fisheri (Vighi et al., 2009).

WS (mg/L) VP (Pa) Log Kow EC50 (mg/L)

0.40 428.00 4.8 0.150

10.00 4655 4.7 1.11

55.00 10,241 3.4 29.7

300.00 10.56 3.0 46.2

515.00 292.6 2.7 73.2

9.50 15,960 3.9 335

1780 10,108 2.1 364

14,000 1330 1.8 498

miscible 6251 �0.34 910

77,000 257.5 0.88 2227

12,000 17,290 1.5 2655

8000 21,280 2.0 2767

79,000 9682 0.73 3504

20,000 46,417 1.3 4607

miscible 76 �0.03 5848

69,000 5879 0.89 8633

miscible 11,837 �0.24 23,365

miscible 5838 �0.31 59,481

miscible 12,236 �0.77 77,072

miscible 4256 0.05 91,893

miscible �1.4 180,677

miscible 9842 �0.34 527,457

700 0.266 3.1 0.400

14,400 18.62 2.8 0.511

300 0.03 3.5 0.628

707 3.046 2.08 2.37

3900 3 1.8 5.86

82,000 46 1.5 7.18

5046 0.355 2.0 8.50

7400 130 1.4 11.5

miscible 7.98 1.3 13.8

miscible 2500 0.35 14.0

28,500 337 2.4 14.8

miscible 3.46 1.3 15.8

800 15 1.4 15.9

21,000 4 1.0 16.5

8370 0.019 1.3 26.8

miscible o1.33 �1.0 34.8

Table 1 (continued )

Non-polar narcotics CAS MW WS (mg/L) VP (Pa) Log Kow EC50 (mg/L)

17 acetic_acid 064-19-7 60.1 50,000 �0.17 38.8

18 benzylammine 100-46-9 107.2 miscible 86 1.1 42.9

19 dinitrophenol 051-28-5 184.1 5600 0.05 1.7 45.0

20 ethanolammine_2 141-43-5 61.1 miscible 53.2 �1.3 48.5

21 citric_acid 077-92-9 192.1 miscible 0.000015 �1.72 515

Herbicides1 chlorbromuron 13,360-45-7 293.5 35 0.000053 3.1 15.4

2 atrazine 01912-24-9 215.7 33 0.00004 2.6 22.8

3 fluometuron 02164-17-2 232.2 110 0.000125 2.4 23.6

4 neburon 00555-37-3 275.0 5 1.00E-06 4.1 24.9

5 linuron 00330-55-2 249.0 64 0.0005 3.2 25.6

6 buturon 03766-60-7 236.0 30 0.006889 3.0 38.0

7 terbuthylazine 05915-41-3 229.7 8.5 0.00015 3.2 41.7

8 methoprotryn 00841-06-5 271.4 320 4.E-05 2.8 45.5

9 chloridazon 01698-60-8 221.6 340 0.0001 1.1 47.8

10 simazine 00122-34-9 202.0 6.2 0.000003 2.2 50.4

11 diuron 00330-54-1 233.0 36.4 0.0001 2.7 54.3

12 alachlor 15972-60-8 269.8 170.3 0.002 3.5 63.2

13 monuron 00150-68-5 199.0 230 6.65E-05 1.9 67.1

14 terbumeton 33693-04-8 225.0 130 0.0003 3.1 89.4

15 chlortoluron 15,545-48-9 212.7 74 0.000005 2.4 94.9

16 metobromuron 03060-89-7 259.0 330 0.0004 2.4 96.0

17 metribuzin 21,087-64-9 214.3 1050 0.00006 1.7 109

18 metolachlor 51,218-45-2 283.8 448 0.0042 2.9 124

19 cyanazine 21,725-46-2 240.7 171 4E-07 2.2 144

20 gliphosate 01071-83-6 169.1 10,500 0.00001 �1.7 159

21 metoxuron 19,937-59-8 229.0 678 0.0043 1.6 250

22 propanil 00709-98-8 218.1 130 0.00002 3.1 335

23 metamitron 41,394-05-2 202.2 1700 9.0E-07 0.83 336

24 fenuron 00101-42-8 164.0 3.85 o0.01 1.0 757

Fungicides1 dithianon 03347-22-6 296.3 0.14 3.0E-06 2.8 0.116

2 captan 00133-06-2 300.6 3.3 0.001 2.8 0.152

3 dichlofluanide 01085-98-9 333.2 1.3 0.000014 3.7 0.335

4 ziram 00137-30-4 305.8 1.00 1.00E-06 1.23 2.37

5 pencolazole 66,246-88-6 284.2 73 0.00017 4.7 16.9

6 cymoxanil 57,966-95-7 198.2 898 0.00015 4.2 30.1

7 procimidone 32,809-16-8 284.1 4.5 0.018 3.1 33.9

8 oxadixyl 77,732-09-3 278.3 3400 3.0E-06 0.80 213

9 metalaxyl 57,837-19-1 279.3 8400 7.5E-04 1.7 315

Insecticides1 coumaphos 00056-72-4 362.8 1.5 1.3E-05 4.1 3.52

2 fenitrothion 00122-14-5 277.2 14 0.018 3.3 14.4

3 azinphos_methyl 00086-50-0 317.3 28 5E-07 2.8 17.0

4 parathion_ethyl 00056-38-2 291.3 11 8.9E-04 3.8 33.9

5 paraoxon 00311-45-5 267.0 24 0.0006 2.0 40.3

6 chlorpyriphos_methyl 05598-13-0 322.5 2.6 0.003 4.3 54.2

7 acephate 30,560-19-1 183.2 790,000 2.3E-04 �0.85 534

8 vamidothion 02275-23-2 287.3 400,000 1.0E-09 0.16 613

S. Villa et al. / Ecotoxicology and Environmental Safety 86 (2012) 93–100 95

Stock solutions were prepared in the saline solution (2 percent NaCl) used for

the toxicity test.

2.3. Theoretical calculation of mixture response

To predict the effect of a mixture (ECxmix) of chemicals with same mode of

action, the CA model may be written as follows (Faust et al., 2003)

ECxmix ¼Xn

i ¼ 1

Pi

ECxi

!�1

ð5Þ

where Pi¼Ci/Cmix is the concentration Ci expressed as relative proportion of the

total concentration Cmix¼SCi.

The individual effect concentrations ECxi may be calculated from individual

concentration–response functions Fi. For this purpose, inverse functions Fi�1 were

used which provided the concentration c of substances i that caused an individual

effect Ei

c Eið Þ ¼ F�1i Eið Þ ð6Þ

For Ei¼/x, c(Ei) becomes ECxi, and hence

ECxi ¼ F�1i xið Þ ð7Þ

Thus, we can write

ECxmix ¼Xn

i ¼ 1

Pi

F�1i xið Þ

!�1

ð8Þ

This equation calculates the effect concentrations of a mixture provided that

the individual concentration–response functions are known for each chemical.

If the total effect E(cmix)¼x caused by a mixture with given concentrations of

constituents ci is needed, Eq. (2) may be written as follows:

Xn

i ¼ 1

ci

F�1i ðEðcmixÞÞ

¼ 1 ð9Þ

The value of E(cmix) satisfying this equation has to be computed by an iterative

procedure.

Independent Action (IA) may be calculated from the equation

F Cmixð Þ ¼ 1�Yn

i ¼ 11�F Cið ÞÞð ð10Þ

where F(Cmix) and F(Ci) are the predicted joint effect caused by the total con-

centration of the mixture and the effect of the substance if applied singly at concen-

tration Ci, respectively (Cmix¼SCi).

S. Villa et al. / Ecotoxicology and Environmental Safety 86 (2012) 93–10096

In a mixture producing a given effect level (e.g. Fx¼50 percent of mortality),

the total concentration of the mixture (Cmix) can be written as follows:

Cmix ¼ ECxmix ð11Þ

If individual concentrations are expressed as relative proportions of the total

concentration of the mixture (Pi¼Ci/Cmix), the prediction of the ‘‘x’’ effect can be

calculated by the following equation (Faust et al., 2001)

X%¼ 1�Pni ¼ 1 1�Fi Pin ECxmixð ÞÞÞðð ð12Þ

2.4. Testing procedure

Toxicity tests were performed using luminescent bacterium V. fischeri (Beijer-

inck) according to the Microtoxs standard procedure (Microbics Corporation,

1982), based on the reduction of luminescence after short term exposure to toxic

chemicals. As some of the chemicals used in these studies were volatile, all the

tests were performed in closed vials.

Frozen dry bacteria were obtained for this study from SDI Newark and

luminescence was measured using a Microtox Model 500 analyzer. Bacteria were

exposed to the test chemicals for the following time periods: 5, 15 and 30 min. As

negligible difference in toxicity was observed between the three different exposure

durations, only the 15 min data has been reported and used for all elaborations. The

minor time dependence of Microtox data within the considered temporal range, was

in agreement with the literature data (Schultz and Cronin, 1997).

The described testing protocol was performed in duplicate on a control and nine

different concentrations of the toxicant. To increase the statistical robustness of the

corresponding data set, all tests were repeated five to eight times, depending on the

inherent scatter observed in each data set. Thus, concentration–response curves of all

the mixtures were based on a minimum of 90 experimental data, upto a maximum of

140, with 10–16 controls.

2.5. Statistical procedure

For all individual chemicals as well as mixtures, concentration–response curves

which give the intensity of an effect as a function of a chemical concentration, were

estimated by adapting the best-fit method (Scholze et al., 2001, Vighi et al., 2009).

Thus, 8 different non-linear regression models (Collett, 2003) were independently

fitted for each chemical/mixture, with the best fitting model selected on the basis of

strict criteria. In particular, residual based indices (i.e. sum of absolute residuals and

sum of absolute deviations) and goodness-of-fit criteria associated with the likelihood

function (Akaike Information Criterion and Bayesian Information Criterion) were

adopted. A list of the models is reported in Table 2, where f(c; b, l) denotes the

probability of a toxicity event (effect) as a function of the chemical concentration c

and of a vector of parameters (b, l)¼(b1, b2, b3, l) that have to be estimated.

From the estimated best-fit concentration–response functions, the effective

concentrations ECx (with x¼50 percent and x¼10 percent) together with the

corresponding 95 percent confidence intervals, were derived. The calculation of the

latter quantities was based on asymptotic normality of the maximum likelihood

estimators of the model parameters and on the delta method to evaluate their

standard errors.

Once the predictions of mixture toxicity according to CA and IA models were

calculated from the single substance concentration–response functions by using

Eqs. (9) and (12), a formal evaluation of the agreement of such predictions with

the experimental ones was performed via non-parametric hypothesis testing. In

particular, a test of fit was applied in order to decide whether a theoretical model

(CA or IA in our framework) cannot be rejected. The Kolmogorov–Smirnov test

allowed to tackle such issues (see for example Stuart et al., 1999). Such test is

based on the maximum absolute difference between the model specified under

Table 2Regression models used to calculate the concentration–response curve

Regression model (acronym)

Logit (L)

Box-Cox-Logit (BCL)

Generalized Logit (GL)

Box-Cox-Generalized Logit (BCGL)

Weibull (W)

Box-Cox-Weibull (BCW)

Probit (P)

Box-Cox-Probit (BCP)

the null Hypothesis (say F(c)) and the empirical distribution function Sn(c), i.e. the

proportion of experimental observations not exceeding c.

More precisely, the Kolmogorov–Smirnov test statistic is defined as

Dn ¼ supc SnðcÞ�FðcÞ�� �� ð13Þ

and the p-value can be approximated as follows:

p�value¼ 2exp �2nd2n

n oð14Þ

where n represents the number of experimental observations, dn represents the

observed value of the test statistic on the experimental data.

A final statistical question concerns one of the conditions under which CA and

IA become equivalent, i.e. comparison among the slopes of the concentration–

response curves of individual chemicals. Such comparison can be easily made

provided that a Weibull model (see Table 2) can be adopted to describe all the

curves, and in this case a single parameter (bz) can then be interpreted as the slope

(Backhaus et al., 2004). Clearly, the a-priori choice of a single model is a renounce

to the best-fit approach thus, it requires an evaluation of the goodness of

approximation, for example, in terms of distances between ECx estimated from

the best and from the Weibull models.

All statistical analyses were performed using the Rs software package (R

Development Core Team, 2011).

3. Results and discussion

The complete results of tests are reported in the SupplementaryInformation (Table SI1–SI8) where the toxicity results (best-fitmodel, parameter estimates, EC50 and EC10 with the corresponding95 percent confidence intervals) for all 84 chemicals are given alongwith their graphical representation (Table SI9, Figure SI1–SI4).

Toxicity data for all the tested mixtures are reported in Table 3,where the concentrations of the mixtures are expressed as fractionsof EC50 for individual chemicals (e.g. a mixture concentration of0.031 indicates that all chemicals are present in the mixture at aconcentration corresponding to 0.031 times their individual EC50).

In Fig. 1, the experimental curves (fitted models of Table 3)and the calculated CA and IA curves are compared. Furthermore,for each experimental curve, the 95 percent confidence limits ofthe ECx’s (0oxo100) are reported in order to graphicallyevaluate the goodness of fit of the two theoretical curves.

In order to formally evaluate the agreement of CA and IA withexperimental data, the Kolmogorov–Smirnov test was applied toboth the models. Table 4 reports the values of the test statisticstogether with their corresponding p-values.

It is remarkable that both the models could not be rejected (p-value greater than 0.05) for the 4 mixtures involving narcotics andpolar narcotics, alone as well as in combination with pesticides.Moreover, the CA model could not be rejected also for herbicidesand insecticides, considering a 0.01 level or a 0.05 level test,fungicides and total pesticides being the only mixtures for whichthe model was unsuitable. For these last mixtures, both the modelsseemed unsuitable and the experimental data was found between

s.

Function (f)

f c;b� �

¼ 1

1þexp �b2 lnðcÞ�ln b1ð Þð Þ� �

f c;b,l� �

¼ 1

1þexp �b2cl�1l �ln b1ð Þ

� �f c;b� �

¼ 11þexp�b2 lnðcð Þ�ln b1ð ÞÞÞ

b3ð

f c;b,l� �

¼ 1

1þexp �b2cl�1l �ln b1ð Þ

� � b3

f c;b� �

¼ 1�exp �exp b2 lnðcð Þ�ln b1

Þ

� �� �f c;b,l� �

¼ 1�exp �exp b2cl�1l �ln b1

� �n on of c;b� �

¼F b2 lnðcð Þ�ln b1

Þ

� �f c;b,l� �

¼F b2cl�1l �ln b1

� �n o

Table 3Toxicity data of tested mixtures. The acronym of the model (see Table 2) used for describing the concentration–response relationship (column 2), the estimated parameters

(columns 3–6) and the EC50 and EC10 (expressed as fractions of the EC50 of the individual chemicals) together with the corresponding 95% confidence intervals (columns

7–10) are reported.

Model_b1

_b2

_b3

_l EC10

_CI f or EC10 EC50

_CI f or EC50

Narcotics BCL 0.03 1.61 0.75 0.008 [0.007; 0.009] 0.031 [0.029; 0.032]

Polar Narcotics BCGL 0.05 1.10 0.5 0.006 [0.005; 0.008] 0.047 [0.040; 0.053]

Narcotics and polar narcotics BCL 0.08 6.27 0.11 0.75 0.003 [0.002; 0.003] 0.028 [0.026; 0.029]

Herbicides BCL 0.04 1.81 0.5 0.012 [0.011 0.013] 0.040 [0.038; 0.043]

Insecticides BCGL 0.22 6.37 0.16 0.75 0.024 [0.021; 0.026] 0.112 [0.107; 0.117]

Fungicides BCGL 0.25 3.20 0.46 0.75 0.053 [0.046; 0.060] 0.169 [0.159; 0.179]

Total pesticides BCGL 0.08 2.94 0.30 0.75 0.006 [0.004; 0.008] 0.039 [0.035; 0.043]

Total narcotics and pesticides BCL 0.01 1.30 0.75 0.0017 [0.0015;0.0019] 0.009 [0.009; 0.010]

Fig. 1. Comparison between the theoretical CA (light gray solid lines) and IA curves (dark gray solid lines) and experimental data (black circles) for the tested mixtures. The

best-fit curves (thick black solid lines) and 95 percent confidence intervals (dashed lines) are also reported.

Table 4Kolmogorov–Smirnov test statistics and p-values for CA and IA models.

CA model IA model n

Mixture of EC50 dn P�value dn P�value

Narcotics 0.2109 0.0972 0.1994 0.1339 34

Polar narcotics 0.2182 0.1682 0.2106 0.1993 26

Narcotics and polar narcotics 0.1408 0.5405 0.2285 0.0637 33

Herbicides 0.1110 1.0000 0.5108 0.0000 26

Insecticides 0.1909 0.1450 0.3476 0.0003 36

Fungicides 0.3273 0.0026 0.4650 0.0000 31

Total pesticides 0.4124 0.0003 0.3133 0.0121 26

Pesticides and narcotics and polar narcotics 0.1490 0.5049 0.1140 0.8935 31

S. Villa et al. / Ecotoxicology and Environmental Safety 86 (2012) 93–100 97

the theoretical CA and IA curves, although a 0.01 level test led to theacceptance of IA model for the mixture of total pesticides.

From the described results, some relevant comments on thebehavior of the tested mixtures can be made.

For all narcotic and polar-narcotic mixtures, the differencebetween the curves calculated with the CA and IA was negligible,even in case of high number of chemicals (narcoticsþpolar-narcotics¼45 chemicals). Experimental results appeared equallypredictable by both CA and IA models. This result is not com-pletely unusual. Examples for coincident predictions were reportedin the literature (Backhaus et al., 2004).

For pesticides, the two predictions gave different results. However,the difference was relatively small, far below one order of magnitude.For the total mixture of 84 chemicals, the difference was again negli-gible, probably due to the prevailing effect of polar and non-polarnarcotics.

According to Dresher and Bodeker (1995), the ratio betweenCA and IA predictions depends on four parameters:

1.

The number of mixture components. 2. The concentration ratio of mixture components. 3. The response level under consideration.

S. Villa et al. / Ecotoxicology and Environmental Safety 86 (2012) 93–10098

4.

The slopes of the concentration–response curves of individualtoxicants.

For the mixtures tested in this work, the number of chemicalswas generally high, particularly for polar and non-polar narcotics,and all chemicals were mixed at equitoxic concentrations.

For mixtures with comparable CA and IA curves, the smalldifferences between the two curves are independent from theresponse level. On the contrary, for pesticide mixtures, differentresponses may be observed. For fungicides, the CA and IAconcentration–response curves were almost parallel in a widerange of response level (from 10 to 90 percent of inhibition). Inthis range, predictions with the two models differ by a factorlower than 3. For insecticides, herbicides and total pesticides, thedifference between CA and IA predictions increased from the lowlevels of response, reaching a maximum at about 90 percent ofinhibition. However, also in this case, the difference between thetwo predictions was relatively low, reaching a maximum of afactor lower than 4 in the total pesticide mixture.

It follows that, for the tested mixtures, a key parameter forexplaining the small (or negligible) difference between CA and IApredictions is represented by the slopes of the concentration–response curves of individual toxicants.

As mentioned by Backhaus et al. (2004), no universal measure forcharacterizing the slope of a curve as a whole currently exists,however, the bz parameter of the Weibull model may be assumed asa reasonable slope parameter, at least for comparative purposes.

As mentioned in section 2.5, the a-priori choice of a single modelis a renounce to the best-fit approach. However, the results obtainedby applying the Weibull model to all chemicals (reported in the SITable SI10 and in Figures SI5–SI8) confirmed that such model can beconsidered as a good approximation. For example, the differencebetween EC50 values derived from the best-fit and the Weibullmodel exceeded by 20 percent for only 9 of the total 84 chemicals(11 percent of the cases), while for 22 chemicals (26 percent of thecases), it exceeded by 10 percent. Considering that EC50 valuesrange over six orders of magnitude, their differences may beconsidered as negligible. Moreover, the majority of such cases

Table 5

Estimated slope parameters (b2) for the Weibull model and their mixture averages.

Narcotics _b2

Polar narcotics _b2

nonane 1.334 phenylphenol-o 0.529

heptane 1.418 diethylaniline_2.6 0.623

hexane_n 1.107 diphenilammine 0.525

cyclohexane 0.798 bromoaniline_4 0.655

octanol 0.809 chloroaniline_4 0.540

toluene 0.643 phenol 0.732

benzene 1.166 benzilacetate 0.675

butylacetate 1.032 methylaniline_4 0.668

acetaldehyde 1.102 tricloroacetic_acid 1.315

butanol 0.982 phormaldehyde 0.785

diisopropylether 1.069 chlorophenol_2 0.98

chlorophorm 1.081 phenylhidrazine 0.696

ethyl_acetate 0.810 nitroaniline_4 0.656

dichloromethane 0.983 metoxyaniline4 0.608

mercaptoethanol_2 1.183 fluoroaniline_3 0.771

diethylether 1.165 acetic_acid 0.913

acetone 1.412 dinitrophenol 1.305

ethanol 2.164 trietanolamine 0.501

methanol 0.900 ethanolammine_2 1.400

isopropanol 1.461 benzylammine 0.999

dimethylsulfoxide 1.212 citric_acid 1.209

acetonitrile 1.025 mean 0.813

mean 1.130

concerned exclusively polar narcotics, all the other chemicals beingonly slightly affected by the different models. Table 5 reports theestimated slope parameters (bz) for all 84 chemicals and theirarithmetic means, once the substances were grouped according toeach mixture composition.

According to Dresher and Bodeker (1995), CA and IA curves fora binary mixture are equivalent if two conditions are fulfilled: theconcentration–response curve of the individual chemicals can bedescribed by the Weibull model and the curves are strictlyparallel with a slope parameter (bz) of 2.3. The hypothesis wasconfirmed for multi-component mixtures by Faust (1999).

The first condition was fulfilled for all 84 individual chemicals.The values reported in Table 5 indicate that, even if the curveswere not strictly parallel, the slope parameter (bz) was in any casequite low, particularly for narcotics and polar-narcotics, thoughslightly higher for pesticides.

This justifies the practical coincidence of the CA and IA curvesfor the mixtures of narcotics and polar-narcotics and the moder-ate difference observed for all the other mixtures.

The second relevant point is the capability of the two modelsfor a reliable prediction of toxic effects of mixtures and theconfirmation of the theoretical hypotheses.

For narcotics and polar narcotics, the absence of a specificmechanism of toxicity may be assumed as a common toxicologi-cal mode of action. Indeed, both CA and IA models adequatelypredicted the experimental results.

All tested insecticides were neurotoxic (inhibitors of acetylcho-linesterase). However, bacteria are not target organisms for thesechemicals and the toxic effect of insecticides may be assumed asnarcotic-type. Therefore, in this case too, the theoretically applicablemodel was the CA, as perfectly confirmed by the experimentalresults. On the other hand, IA model appeared to be inadequate.

Similar comments can be made for herbicides. Most of themare photosynthesis inhibitors (triazines, urea derivatives), even if,in some cases with different specific mechanisms. A few othershave different modes of action specific for plants (e.g. inhibition ofroot growth by the chloroacetamides alachlor and metolachlor).Thus, also for herbicides, the effect on bacteria may be assumed as

Herbicides _b2

Fungicides _b2

chlorbromuron 0.962 dithianon 1.319

atrazine 1.567 captan 0.812

fluometuron 2.249 dichlofluanide 0.967

propanil 0.998 ziram 1.9197

neburon 1.464 pencolazole 1.9722

linuron 1.417 cymoxanil 1.1257

buturon 1.440 procimidone 1.2618

terbuthylazine 2.123 oxadixyl 1.7197

methoprotryn 1.124 metalaxyl 1.2498

chloridazon 0.921 mean 1.372

simazine 2.239

diuron 3.612 Insecticidesalachlor 2.162 coumaphos 1.967

monuron 2.694 fenitrothion 0.737

terbumeton 1.133 azinphos_methyl 0.765

chlortoluron 2.397 parathion_ethyl 1.417

metobromuron 0.923 paraoxon 1.473

metribuzin 1.314 chlorpyriphos_methyl 2.138

metolachlor 1.499 acephate 1.307

cyanazine 1.469 vamidothion 1.339

gliphosate 2.105 mean 1.393

metoxuron 1.384

metamitron 1.000

fenuron 2.489

mean 1.695

S. Villa et al. / Ecotoxicology and Environmental Safety 86 (2012) 93–100 99

narcotic type and the suitable model being CA, as confirmed byexperimental data.

The case of fungicides was quite more complex. The testedchemicals behaved to very different chemical groups determiningvarious modes of action. In many cases, the toxic activity affectedmechanisms that are common to fungi and bacteria, such as inhibi-tion of cellular respiration (dithianon, captan, dichlofluanide), inhibi-tion of cell membrane development (penconazole), inhibition ofprotein and DNA synthesis (metalaxyl, oxadixyl). Therefore, testedfungicides can be considered as a heterogeneous group, representedby sub-groups with different modes of action. This was confirmed bythe experimental results that remained in between the two theore-tical curves and both the models could be rejected according to theKolmogorov–Smirnov test (p-value lower than 0.01). The suitablesolution in this case should be the application of the Two StagePrediction (TSP) (Junghans, 2004), capable to combine both CA and IAapproaches in an unique theoretical curve. However, the soundapplication of the TSP requires a deeper and more precise knowledgeof the specific modes of action on bacteria. Similar comments can bemade for the mixture of total pesticides.

A third point to be highlighted is the relevance in the mixtureresponse of low or very low concentrations, close to the NOEC. Itmust be mentioned that the NOEC is a quite controversial conceptthat may be interpreted in different ways as a function of the testingprocedures. In toxicity tests performed on animals, using a limitedand well defined number of test individuals, NOEC is the concentra-tion that would not lead to the observed end point (e.g. death) inany of the individuals tested. This definition is not feasible for testswith micro-organisms (algae, bacteria) where a functional para-meter of the population (growth, luminescence, etc.) is measured.Therefore, a NOEC may be defined as the concentration that wouldnot produce a response, significantly different from the control.Considering that the variability of the response may be very high,particularly at the lower levels of effect, due to pragmatic reasons,an arbitrary value of 10 percent is often accepted and EC10 isassumed as a surrogate of NOEC.

To assess the effects of chemicals in mixtures at low level ofindividual concentrations, it is important to use statistical meth-ods for the development of concentration–response curves, cap-able of calculating low or very low effect levels of individualchemicals (EC10, EC1) with sufficient reliability, such as thoseused in the present paper.

The results obtained with the tested mixtures, particularly thoseobtained with the higher number of individual components clearlydemonstrated that, low levels of individual chemicals, far belowconcentrations traditionally assumed as NOECs, are suitable tocontribute to the toxicity of the mixture. In the mixture composedof 84 chemicals an effect of 50 percent has been obtained withconcentrations of individual chemicals orders of magnitude lowerthan the traditional NOEC: for about 50 percent of the individualsubstances the concentration was below EC0.1 and for about 90percent below EC1. One must be aware that, even if the concentra-tion ratio remains constant, the effect ratio changes, especially atlow concentrations, being different the slope of individual concen-tration/response curves.

This result is justified by the theoretical calculation if the CAmodel was assumed, where the concentrations of the chemicals(however low they are) were added. Nevertheless, in most of thetested mixtures, including all the 84 chemical mixtures, also the IAmodel appeared equally suitable for the prediction. It follows thatthe real meaning of NOECs would require a careful consideration.For practical reasons, the traditional approaches may be suitable toindicate a level at which a potentially toxic chemical is consideredmore or less ‘‘safe’’ for a given organism. However, this may not be areal zero effect concentration, if the chemical is combined in acomplex mixture.

4. Conclusion

The discussed results allowed to confirm and support someimportant statements to explain the effects of mixtures in ecotox-icology and to develop proposals relevant for regulatory purposes.

1.

The suitability of the two theoretical models, CA and IA wasconfirmed. The CA approach allowed predicting the effect ofmixtures of chemicals with non-specific (narcotic-type) modeof action while, for mixtures of chemicals with variable andunknown mode of action on bacteria (fungicides and mixedpesticides), the effect was found in between of two models.Synergistic effects seemed to be excluded for all the testedmixtures.

2.

Even extremely lower concentrations, far below EC10 ofindividual chemicals, produced a significant effect in mixtures,coherent with the theoretical approaches.

3.

For four out of the eight tested mixtures, the difference betweenCA and IA prediction was very low or negligible. In all the othercases, the difference was never higher than a factor of four.

In conclusion, the results support the hypothesis that the CAmodel may be considered as a pragmatic and realistically acceptableworst case, capable to ensure an adequate level of protection.

This conclusion is in agreement with a recent opinion approvedby the Scientific Committees of the European Commission (EC,2012) on the toxicity of chemical mixtures.

Appendix A. Supporting information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.ecoenv.2012.08.030.

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