The aquatic toxicity of anionic surfactants to Daphnia magna—A comparative QSAR study of linear alkylbenzene sulphonates and ester sulphonates

Chemosphere 63 (2006) 1443–1450 www.elsevier.com/locate/chemosphere

The aquatic toxicity of anionic surfactants to Daphnia magna—A comparative QSAR study of linear alkylbenzene sulphonates and ester sulphonates Geoff Hodges

b

a,*

, David W. Roberts b, Stuart J. Marshall a, John C. Dearden b

a SEAC, Unilever Colworth, Sharnbrook, Bedfordshire, MK44 1LQ, United Kingdom School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool, Merseyside L3 3AF, United Kingdom

Received 18 April 2005; received in revised form 19 September 2005; accepted 2 October 2005 Available online 15 November 2005

Abstract This paper develops quantitative structure activity relationships (QSARs) for the acute aquatic toxicity of the anionic surfactants linear alkylbenzene sulphonates (LAS) and ester sulphonates (ES) to Daphnia magna, the aim being to investigate the modes of action by comparing the QSARs for the two types of surfactant. The generated data for ES have been used to develop a QSAR correlating toxicity with calculated log P values: log(1/EC50) = 0.78 log P + 1.37. This equation has an intercept 1.1 log units lower than a QSAR for linear alkylbenzene sulphonates (LAS). The findings suggest that either ES surfactants act by a different mode of action to LAS and other anionic surfactants or the log P calculation method introduces a systematic overestimate when applied to ES. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Ester sulphonate; Anionic surfactant; Aquatic toxicity; QSAR Daphnia magna

1. Introduction Anionic and non-ionic surfactants are widely used in both domestic and industrial applications, particularly in laundry detergents. Since, by the nature of their uses, they are destined to be discharged to waste-water, the major surfactants have been extensively studied in terms of their environmental properties, including biodegradation characteristics and toxicity to aquatic organisms.

* Corresponding author. Tel.: +44 (0)1234 264796; fax: +44 (0)1234 264722. E-mail address: geoff[email protected] (G. Hodges).

Linear alkylbenzene sulphonate (LAS) is the most widely manufactured and applied surfactant other than soap (Roberts, 2003). Its toxicity to a variety of aquatic organisms has been studied extensively (Abel, 1974; Maki and Bishop, 1979; Lal et al., 1983; Lewis and Suprenant, 1983; Roberts, 1989). Aquatic toxicity data for other anionic surfactants exist, and are discussed in a QSAR context by Roberts (2004), but are much more limited than for LAS. In recent years there have been initiatives to increase the usage of surfactants derived from renewable sources, and the ester sulphonates (ES), which have been known at the laboratory and pilot plant scale for many years, have attracted particular attention. The general

0045-6535/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.chemosphere.2005.10.001

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Ester sulphonates (ES)

Linear alkylbenzene sulphonates (LAS)

O

R

1

R

1

R

O

R

SO3Na SO 3Na

Fig. 1. Generic ES and LAS structures.

structure, together with that of LAS, is shown in Fig. 1. Methyl ester sulphonates (MES, R1 = CH3 in Fig. 1) can be made by sulphonation of saturated methyl esters derived form natural oils such as palm, palm kernel and coconut, and have started to be used in consumer products such as laundry powders and hand dishwashing liquids. These recent developments provided an incentive for the present study into the dependence on structure of acute aquatic toxicity of ES. A further incentive was to extend the mechanistic understanding of the aquatic toxicity of surfactants. ES are particularly suited to this latter purpose since they are easily synthesised as pure materials, covering a variety of parent fatty acid and parent alcohol structures. Surfactants used in commercial products are chemically rather inert. In particular, they do not react as electrophiles. In general, chemicals which are of low reactivity exert their toxic effects towards aquatic organisms by narcosis mechanisms. Hydrophobicity is the key chemical feature of narcotic organic compounds in determining their effects in aquatic systems (Donkin, 1994). The most common measure of hydrophobicity is log P (P = octanol/water partition coefficient). The inherent nature of surfactants to aggregate at surfaces makes measurement of log P for these substances difficult. However, for practical purposes log P values calculated using the method described by Hansch and Leo (1979), with some modifications, can be used (Roberts, 2000). There is evidence for two mechanisms of narcosis; general narcosis and polar narcosis, which are distinguished by different QSARs based on log P. The ‘‘classic’’ general narcosis QSAR was developed by Ko¨nemann (1981) for toxicity to Poecilia reticulata (guppy). With EC50 in mol l1 (these units are used throughout this paper) the QSAR is pEC50 ¼ 0:87 log P þ 1:13 n ¼ 50;

R2 ¼ 0:976;

s ¼ 0:24

ð1Þ

where p denotes the negative logarithm. EC50 is the concentration of toxicant which produces the observed toxic effect in 50% of the test organisms: for the fish toxicity data used to produce Eq. (1), the toxic effect is mortality, and often the toxicity is quoted as LC50 (L for lethal); for Daphnia magna studies the toxic effect is immobilisa-

tion. Eq. (1) was developed from a set of acute toxicity data for a range of chemicals including inter alia hydrocarbons, halogenated hydrocarbons, alcohols, ethers and ketones. Subsequently it has been found to be applicable for prediction of toxicity to a range of aquatic species; not only other fish species but other organisms such as D. magna (Sloof et al., 1983). There are very few chemicals that have been found to be less toxic than predicted by this equation (the few that are less toxic can be rationalised in terms of diminished availability due to causes such as low solubility or high volatility) and so it is often referred to as the Ôbaseline toxicity equationÕ. Compounds which fit this equation are usually referred to as general narcotics. Non-ionic surfactants have been shown to act as general narcotics (Roberts, 1991; Roberts and Marshall, 1995). The related polar narcosis mechanism has been proposed to account for polar contributions to binding to membranes (Saarikoski and Viluksela, 1982; Schultz et al., 1986). The distinction between general and polar narcosis has been challenged in view of findings that by using experimentally derived membrane-water partition coefficients, log Kmw, or liposome-water partition coefficients in place of the octanol–water partition coefficient, a single QSAR covering both general narcotics and polar narcotics can be derived (Vaes et al., 1998; Escher et al., 2002). However, Roberts and Costello (2003) have addressed this issue in detail and demonstrated that there is a real mechanistic difference between general and polar narcosis, which manifests itself by: significantly different QSARs, even when based on log Kmw, for general and polar narcotics treated separately; differences in fish acute toxicity syndrome (FATS); non-additivity between general narcotics and polar narcotics in mixture toxicity studies. They propose that the mechanistic difference between general and polar narcosis is based on a difference in the physical chemistry of the water–membrane partitioning process: in polar narcosis partitioning is such that a part of the narcotic molecule associates with the head groups of the membrane lipids whereas in general narcosis the narcotic molecule moves freely in all directions in the membrane. For polar narcotics the ‘‘classic’’ QSAR was developed by Saarikoski and Viluksela (1982) based on phenols and their toxicity to P. reticulata pEC50 ¼ 0:63 log P þ 2:52 n ¼ 17;

R2 ¼ 0:964;

s ¼ 0:16

ð2Þ

This polar narcotic equation is also applicable to other aquatic species. Besides phenols, several other groups of organic compounds act as polar narcotics, including nitroaromatic compounds and aromatic amines (Roberts and Costello, 2003). QSARs similar to Eq. (2) have been derived from data of the aquatic toxicity of LAS to D. magna (Roberts, 1991), and have been shown to pro-

G. Hodges et al. / Chemosphere 63 (2006) 1443–1450

vide good predictions of the aquatic toxicity of other anionic surfactants for which toxicity data are available, suggesting that anionic surfactants generally act by polar narcosis (Roberts, 1991). On this basis, bearing in mind the with structural similarities to LAS (Fig. 1), it would be expected that toxicity of ES substances would also be well predicted by an equation similar to that derived for LAS.

2. Materials and methods Commercial ES surfactants are generally available with a methyl group as R 0 (Fig. 1). For clarity, ES is taken to mean sodium ES and not the acid form unless otherwise stated. Where a carbon number is mentioned it refers to the parent alkanoic acid e.g. for C12 methyl ES, C12 refers to the R chain plus the two carbon atoms leading up to and including the ester carbon and methyl refers to the R 0 chain. ES substances lend themselves to QSAR analysis for several reasons. Firstly, their relative ease of synthesis allows creation of a homologous series of high purity substances with a range of log P values. Secondly, they possess good solubility and can be synthesised with suitable chain length to elicit a measurable and reproducible toxic response below the limit of solubility. Thirdly, being representative of anionic surfactant structures generally, their behaviour and mode of action are of interest in the development of surfactant toxicity predictive capability. Twenty-one ES substances were used in total. The initial assessment included 10 substances obtained at Unilever Research. Another eleven were synthesised for further toxicity assessment. Synthesis was by direct sulphonation of carboxylic fatty acids with sulphur trioxide and esterification of the sulphonated product, as described by Stirton et al. (1954). Six LAS homologues were used of chain length C9–C14; each with known isomer distribution (Table 1), obtained at Unilever Research. Activity of all substances was determined with NMR spectroscopy (Table 2).

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2.1. Toxicity testing All acute toxicity data were determined by exposing less than 24-h-old D. magna neonates to a geometric series of concentrations of each test substance according to the 48 h acute toxicity test procedure defined in the EEC Commission Directive 92/69/EEC No. L383 A/1. Test media were prepared by serial dilution of stocks with Elendt M7 (hard medium 240 mg l1 as CaCO3). Samples were taken from the control, lowest and highest test concentrations from selected tests and preserved with 3% formalin for analysis of test solution concentration by MBAS (methylene blue active substances) analysis. All toxicity data are expressed as EC50 values in mg/l unless otherwise stated. 2.2. Log P calculation The fragment approach of Hansch and Leo (1979) to obtain calculated log P values is a relatively simple summation algorithm. The method uses fragment values (f) which remain constant and allows the application of factors (F) to account for more complex molecular interactions which affect the partitioning equilibrium e.g. proximity between polar groups which reduces the hydrophilicity of each of the polar groups involved (Fp). Having calculated a value for a generic ES structure it is a simple process to calculate values for ES structures with longer or shorter chain lengths. For each additional CH2 fragment (fragment value 0.66) there will always be an increase in log P of fCH2 + Fb(0.66  0.12 = 0.54) to account for the additional fragment and associated bond. Thus referring to Table 2, the calculated log P for C12 methyl ES is 2.22. Insertion of a CH2 group gives C13 methyl ES or C12 ethyl ES, both of which have calculated log P values of 2.76 (=2.22 + 0.54). The modification to log P to account for water sharing between branches of a chain is calculated as a Position Dependent Branching Factor (PDBF) and given by: 1.44 log(CP + 1) where CP is the number of carbon pairings between chains (Roberts, 1991).

Table 1 LAS isomer distribution Chain length

C9 C10 C11 C12 C13 C14 * a

Isomer distribution Isomer

%

2/3/4,5 2/3/4/5 2/3/4,5,6 2/3/4/5,6 2/3/4/5/6 2/3/4,5,6,7

27.9/28.3/42.6 28.5/23.8/23.0/24.6 19.7/20.1/60.2 22.4/21.2/17.5/36.9 16.1/15.7/16.7/19.8/31.8 20.8/16.2/61.2

Value to 3 s.f. Weighted average log P calculated by Hansch and Leo (1979).

MW

Calculated log Pa*

306 320 334 348 362 376

1.63 2.15 2.60 3.17 3.62 4.19

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Table 2 Mean observed 48 h EC50 values and calculated log P values Substance

Activity (%)

MW

Calculated log Pa (3 s.f.)

Observed EC50b (mg l1) (2 s.f.)

Max. variation about EC50 (±% of mean) (2 s.f.)c

Observed EC50.b (mmol l1) (2 s.f.)

Measured CMC (mmol l1) (2 s.f.)

C7 heptyl ES C8 hexyl ES C8 sec hexyl ES C9 amyl ES C9 sec amyl ES C10 butyl ES C10 sec butyl ES C10 iso butyl ES C11 propyl ES C12 methyl ES C12 ethyl ES C12 butyl ES C12 sec butyl ES C12 amyl ES C13 methyl ES C14 methyl ES C14 ethyl ES C14 isopropyl ES C14 butyl ES C14 amyl ES C16 methyl ES C9 LAS C10 LAS C11 LAS C12 LAS C13 LAS C14 LAS

>98 >96 >99 >97 >99 >94 >99 >99 >99 >95 >96 >95 >99 >91 >99 >95 >91 >88 >99 >95 >95 >97 >98 >98 >98 >97 >95

330 330 330 330 330 330 330 330 330 316 330 358 358 372 330 344 358 372 386 400 372 306 320 334 348 362 376

2.76 2.76 2.33 2.76 2.33 2.76 2.33 2.33 2.76 2.22 2.76 3.84 3.41 4.38 2.76 3.30 3.84 3.95 4.92 5.46 4.38 1.63 2.15 2.60 3.17 3.62 4.19

140 180 400 140 270 170 220 150 120 140 150 16 36 7.2 41 8.5 8.0 7.8 3.7 1.3 2.8 53 28 11 4.4 2.7 0.67

2.9 4.6 1.3 8.6 2.1 13 –d –d 9.5 19 5.9 5.5 12 6.3 6.8 33e 1.2 11 1.4 20 14 12 1.8 0.0 28 9.4 5.3

– – – 0.42 – – – – – – 0.45 0.045 – 0.019 – 0.025 0.022 – 0.0096 0.0033 0.0075 – 0.088 0.033 0.012 0.0075 –

– – – 15.6 – – – – – – 7.8 2.8 – 1.7 – 2.8 1.9 – 0.63 0.34 0.4 – 6.5 4.1 2.3 1.5 –

a b c d e

Calculated using Hansch and Leo method (1979) with a PDBF where appropriate. Mean values. Two replicates unless otherwise indicated. No replicate value. Three replicates.

2.3. Methylene blue active substances (MBAS) for analysis of test substance concentrations

3. Results and discussion 3.1. ES and LAS toxicity

The determination of low levels (typically 0– 20 mg l1) of anionic surface active materials by the manual methylene blue technique as described by Abbott (1962) is used in the analysis of a wide range of samples including surface and potable waters. Higher concentrations can be diluted for analysis. The anionic surfactant associates with the methylene blue cation to form a chloroform-extractable ion-association complex, whereas the unassociated cation has very low solubility in chloroform. The ion-association complex in alkaline solution, to avoid proteinaceous interference, is partitioned into a chloroform phase. This is then back extracted with an acidified methylene blue solution in order to remove inorganic anions which form ionassociation complexes with the methylene blue dye but have low chloroform solubility.

The toxicities of LAS homologues are well correlated by equations similar to Eq. (2) although they possess a higher log P coefficient. Toxicity values for the LAS homologous series are presented in Table 2. A plot of log(1/EC50) versus calculated log P is linear (Fig. 2), giving the QSAR equation for LAS toxicity to Daphnia: logð1=EC50Þ ¼ 0:77 log P þ 2:47 ðn ¼ 6;

rsq ¼ 0:991;

se ¼ 0:08Þ

ð3Þ

where EC50 is in moles per litre. Daphnia 48 h EC50 values of other anionic surfactants are also in good agreement with values predicted from the QSAR derived from LAS, suggesting that these anionic surfactants also act as polar narcotics (Roberts, 1991). The structural similarities between LAS and ES,

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6.0

log (1/EC50) (mol/l)

5.0

4.0

3.0 LAS : log (1/EC50) = 0.77 log P + 2.47 (n = 6, rsq = 0.991, se = 0.076)

2.0

ES : log (1/EC50) = 0.78 log P + 1.37 (n = 21, rsq = 0.896, se = 0.262)

1.0

0.0 1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

log P

Fig. 2. Calculated log P versus log(1/EC50) values for LAS and ES substances.

therefore, suggest that ES EC50 values should also be predicted by Eq. (3). However, the toxicities of most of the ES substances were observed to be less than predicted using equations similar to Eq. (3) (Table 2). Regression analysis for the ES substances (Fig. 2) gives the equation: logð1=EC50Þ ¼ 0:78 log P þ 1:37 ðn ¼ 21; rsq ¼ 0:896; se ¼ 0:26Þ

ð4Þ

It has previously been shown that the acute aquatic toxicity of non-ionic surfactants is well predicted by Eq. (1) (Roberts and Marshall, 1995). For D. magna the relationship obtained between observed EC50 values for non-ionic surfactants and those predicted from Eq. (1) was logðobserved EC50Þ ¼ 0:91 logðpredicted EC50Þ  0:005

ð5Þ

Thus the QSAR to describe the acute toxicity of nonionic surfactants to D. magna can be written logð1=EC50Þ ¼ 0:79 log P þ 1:04

ð6Þ

Comparing Eq. (4), for ES, against Eq. (3) (LAS) and (6) (non-ionic surfactants), it is clear that Eq. (4) is more similar to Eq. (6) than to Eq. (3). The slopes of all three equations are very similar, but the intercept of Eq. (4) differs from that of Eq. (3) by 1.10 but differs from that of Eq. (6) by only 0.33. Thus the ES surfactants appear to resemble non-ionic surfactants more than they resemble LAS in their aquatic toxicity. This would appear to suggest that, unlike LAS and other anionic surfactants, ES surfactants act, like nonionic surfactants, as general narcotics rather than as polar narcotics. However, before coming to this unex-

pected and difficult to rationalise conclusion, it is necessary to consider other possibilities. 3.1.1. Quality of toxicity data There are a number of considerations that lead to the conclusion that the quality of the data are high. (a) Repeatability of test results. Good repeatability of tests was found for the majority of both the ES and LAS substances (Table 2). The majority of substances were tested at least twice. Lack of test material was the reason for lack of repetition in the few cases where this occurred. Inherent variability is present in any biological system and reasonably can be used to explain deviation from the mean of most of the substances tested. This has been taken here as ±20% of the mean. For those substances where repeated values are outside this range, the mean values are consistent with other mean values and are not influential in he determination of the slope of regression. (b) Test substance concentrations. MBAS analyses of test solutions from selected tests show that mean measured concentrations over the 48 h test period have <20% difference from nominal concentrations for all except C9 LAS (Table 3). When considering the inherent variability of the toxicity values it was considered necessary to correct observed EC50 values only where >20% difference between nominal and measured test solution concentrations was observed. 3.1.2. Critical micelle concentration (CMC) effects Apparent reduced toxicity would be observed if the EC50s occurred above the Critical Micelle Concentration (CMC) of the test materials. Available CMC values indicate, however, that observed toxicity for each

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Table 3 Measured test solution concentrations for ES and LAS substances as mean percent of nominal Substance

TR

n

Measured concentration (mean % of nominal)

95% Confidence interval

C7 heptyl ES C8 hexyl ES C8 sec hexyl ES

1 1 1 2 1 1 1 1 1 1 2 1 2 1 2 1 2 1 1 2 3 1 2 1 2 1 2 1 2 1 2 1 1

8 8 3 3 8 8 8 8 6 6 4 6 6 7 6 8 8 8 8 8 8 8 8 8 8 8 6 7 8 4 8 8 7

95 96 101 92 95 95 99 96 92 103 106 108 106 108 107 106 102 103 100 101 98 106 105 98 97 98 96 93 89 87 83 90 71

93.2–97.8 93.5–98.6 90.7–110 84.7–98.9 91.5–98.8 92.7–97.3 97.8–100 93.9–99.0 85.3–97.8 101–105 102–109 106–109 102–109 105–111 104–110 103–109 99.2–105 99.7–107 96.4–103 97.5–104 94.6–102 103–109 101–108 84.2–112 83.7–111 90.1–107 91.0–102 79.3–107 78.5–99.3 76.6–97.2 70.1–96.7 86.7–93.3 61.2–80.1

C9 amyl ES C9 sec amyl ES C10 butyl ES C10 sec butyl ES C11 propyl ES C12 methyl ES C12 butyl ES C12 sec butyl ES C12 amyl ES C13 methyl ES C14 methyl ES

C14 ethyl ES C14 isopropyl ES C14 butyl ES C14 amyl ES C16 methyl ES C9 LAS C14 LAS

TR: Test replicate. n: Number of measured samples from test.

substance occurs well below the corresponding CMC for LAS and ES substances (Table 2). 3.1.3. Effects of hydrolysis of the test substances It has been observed for many years that the toxicity of esters can be correlated with their rates of hydrolysis (Overton, 1898). It has, however, always been assumed that whilst esters are non-specific they are also non-polar in action and it is the observed additional toxicity to that predicted by the general narcosis equation which requires the inclusion of a descriptor for hydrolysis rate (Kamlet et al., 1987). However, ES substances are known to be highly resistant to hydrolysis, due probably to the adjacent sulpho group protecting the carboxylate linkage through steric hindrance (Stirton et al., 1954; Weil et al., 1955; Bistline et al., 1956; Stirton et al., 1962a,b, 1965; Stein et al., 1970; Stein and Baumann, 1975). This is particularly the case for acid hydrolysis

although the nature of the R 0 group contributes additional stability to alkaline hydrolysis; sulphonated esters of secondary alcohols are very stable to both acid and alkaline hydrolysis (Stirton et al., 1962b). Consistent with these arguments, our analyses of test solutions showed that the ES substances undergo minimal hydrolysis over the test period. It can, therefore, be assumed that the observed EC50 values accurately describe the inherent toxicity of the ES substances. 3.1.4. Systematic overestimate in the method of calculating log P when applied to ESs Polar fragments possess a negative hydrophobic fragment value due to the reduction of free energy of the water which surrounds them in what is termed the Hydration Sheath. When two polar groups are proximal, if they are both electronegative their dipoles oppose each

G. Hodges et al. / Chemosphere 63 (2006) 1443–1450

other and electron attracting effects reduce the dipole of each fragment, which has the effect of reducing the overall negative contribution of the two fragments to log P. In addition, and perhaps more importantly, particularly when one of the groups is not strongly electronegative, water sharing between overlapping hydration sheaths reduces the overall decrease in free energy. The method for dealing with proximate polar groups conforms to the idea that polar fragments are capable of interaction only when positioned within a molecule by a maximum of three carbons separation. Where four carbons intervene, the two fragments are given their full fragment values. Where 1, 2 or 3 carbons intervene, the proximity effects increase with the total hydrophilicity (expressed as negative fragment values) of the two polar groups in isolation, and decrease with increasing separation between the two polar groups. The proximity factors of Hansch and Leo (1979) are empirically derived values, calculated on the basis of measured partition coefficients. Calculation of the proximity effect is by application of a negative multiplier, whose absolute magnitude decreases with increasing spacing between the two groups, to the simple sum of the two polar fragment values. For single carbon separation, as in ES, the multiplier is 0.42. Thus for ES the proximity factor is given by 0:42ðf CO2 þ f SO 3 Þ ¼ 0:42ð1:49  5:87Þ ¼ 3:09 The absolute value of this calculated proximity factor is substantially larger than that of the CO2 fragment, implying that the CO2 fragment makes an overall hydrophobic rather than hydrophilic contribution to log P. This seems rather implausible and suggests that a modification to the log P calculation may be required in which the proximity factor not only accounts for separation, but also relative hydrophilicities (compared to each other) of polar fragments within a molecule. Thus variability of the toxicity data, CMC effects and hydrolysis can be ruled out as reasons for the low value of the intercept in Eq. (4), but a systematic overestimate in the log P calculation when applied to ES substances seems plausible.

4. Conclusions Data on aquatic toxicity to D. magna of ester sulphonates (ES) have been found to provide a good conventional log P based QSAR. However, compared with a QSAR which was originally developed for LAS and which has provided good predictions of aquatic toxicity to other anionic surfactants, the intercept of the ES QSAR is unexpectedly low, to the extent that the ES QSAR bears similarity with a QSAR originally developed for non-ionic surfactants which behave as general narcotics. This would seem to suggest that, unlike other

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anionic surfactants, which behave as polar narcotics, ES behave with a similar mode of action to non-ionic surfactants i.e. general narcosis. However we cannot exclude the possibility that the difference arises because of a systematic overestimation of log P by the Leo and Hansch calculation method when applied to ES. On the basis of the present information we cannot conclude definitively whether ES surfactants behave, unlike other anionic surfactants, as general narcotics or whether the difference between the ES QSAR and the polar narcosis QSAR for other anionic surfactants arises from a systematic overestimation of log P. In the following paper we present results of mixture toxicity studies designed to address this point and which indicate that the mode of action of ES is polar narcosis. References Abbott, D.C., 1962. The colorimetric determination of anionic surface-active materials in water. Analyst 87, 286–293. Abel, P.D., 1974. Toxicity of synthetic detergents to fish and aquatic invertebrates. J. Fish. Biol. 6, 279–298. Bistline, R., Stirton, A., Weil, J., Port, W., 1956. Synthetic detergents from animal fats. VI. Polymerizable esters of alpha-sulphonated fatty acids. JAOCS 33, 44–45. Donkin, P., 1994. Quantitative structure-activity relationships. In: Calow, P. (Ed.), Handbook of Ecotoxicology, vol. 2. Blackwell Scientific Publications, London. Escher, B.I., Eggen, R., Vye, E., Schreiber, U., Wisner, B., Schwarzenbach, R.P., 2002. Baseline toxicity (narcosis) of organic chemicals determined by membrane potential measurements in energy-transducing membranes. Environ. Sci. Technol. 36, 1971–1979. Hansch, C., Leo, A.J., 1979. Substituent Constants for Correlation Analysis in Chemistry and Biology. Wiley and Sons, New York. Kamlet, M.J., Doherty, R., Taft, R., Abraham, M., Veith, G., Abraham, D., 1987. Solubility properties in polymers and biological media. 8. An analysis of the factors that influence toxicities of organic nonelectrolytes to the golden orfe fish (Leuciscus idus melanotus). Environ. Sci. Technol. 21, 149– 155. Ko¨nemann, H., 1981. Quantitative structure-activity relationships in fish toxicity studies 1. Relationship for 50 industrial pollutants. Toxicology 19, 209–221. Lal, H., Virendra, M., Viswanathan, P.N., Krishna Murti, C.R., 1983. Comparative studies on ecotoxicology of synthetic detergents. Ecotoxicol. Environ. Safety 7, 538– 545. Lewis, M.A., Suprenant, D., 1983. Comparative acute toxicities of surfactants to aquatic invertebrates. Ecotoxicol Environ Safety 7, 313–322. Maki, A.W., Bishop, W.E., 1979. Acute toxicity studies of surfactants to Daphnia magna and Daphnia pulex. Arch Environ. Contam. Toxicol. 8, 599–612. Overton, E., 1898. Osmotic properties of cells in the bearing of toxicity and pharmacology. Z. Physik. Chem. 22, 189–209. Roberts, D.W., 1989. Aquatic toxicity of linear alkyl benzene sulphonates (LAS)—a QSAR analysis. Communicaciones

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