Final Version Faridi (12okt)

An overview of QSAR-studies for sorption and
accumulation of anionic and non-ionic
surfactants: Limitations and
new perspectives
Utrecht University
Faridi Purperhart
October 2016

An overview of QSAR-studies for sorption and accumulation of anionic and non-ionic surfactants October 14, 2016
2 Title page | Utrecht University, IRAS
Title page
An overview of QSAR-studies for sorption and
accumulation of anionic and non-ionic surfactants:
Limitations and new perspectives
Colophon
Institute
Utrecht University
Course
Writing assignment
Author
Faridi Purperhart
Student number
3429032
Supervisor
dr. J.L.M. (Joop) Hermens
Date
October 2016

3 Content | Utrecht University, IRAS
Content
1. Introduction
2. A short introduction into QSAR
a. Hydrophobicity and the octanol-water partition coefficient
3. Objectives of this study
4. Surfactants
a. Structure
b. Critical micelle concentration (CMC)
5. Effects of chemical structure of surfactants on environmental properties or parameters and
examples of QSARs
a. Environmental behaviour: biodegradation, sorption and bioaccumulation
b. Surfactant toxicity
6. A critical evaluation and discussion of Quantitative structure-activity relationship
a. Limitations of log P based QSARs for anionic and non-ionic surfactants
b. Alternatives for log P or log P based QSARs for anionic and non-ionic surfactants
c. Recent developments
7. Conclusion
8. References

4 Introduction | Utrecht University, IRAS
Figure 2: Surfactants tend to lower the surface tension at
low concentration and form micelles at high
concentration. (x=surfactant concentration; y= surface
tension) (Illustration: self-constructed).
Introduction
1. Introduction
Surfactant, which is a blend of surface-active agent, is a compound that lowers the surface tension at
different interfaces, for example between two liquids or between a liquid and a solid (Ivancovic and
Hrenovic, 2009). A lower surface tension of a liquid leads to an increase in the surface area. Because
of this property, surfactants are widely used as detergents and cleaning products (Ivancovic and
Hrenovic, 2009; Burlatsky et al., 2013), e.g. in household cleaning detergents, and industrial
products, such as soap, personal care products, oil recovery (Burlatsky et al., 2013), pharmaceuticals
and pesticides (Rosen, 2004). Surfactant molecules, are amphiphilic, meaning that they exert
hydrophobic and hydrophilic behaviour. They consist of a polar head group (hydrophilic surface-
active portion) and a non-polar hydrocarbon tail (Rosen,
2004; Ivancovic and Hrenovic, 2009) (figure 1). The
presence of a hydrophobic and a hydrophilic group
influences the behaviour of surfactants. At low
concentrations surfactants tend to adsorb onto the surfaces
or interfaces of liquids, meaning that they diffuse to the
surface, thereby lowering surface tension (figure 2). This
results in a shift of free energies between surfaces or at
interfaces. At high concentrations surfactants tend to form
micelles (figure 2). A micelle is a colloidal sized cluster of
surfactant molecules in solution (Rosen, 2004; Ying, 2006).
Here, the hydrophobic tails cluster together while the
hydrophilic heads come in contact with the environment.
However, depending on their environment, it can be the
heads that cluster together while the tails come in contact
with the environment. The formation of micelles takes
place at a specific concentration, called the critical micelle
concentration (CMC). The CMC is the concentration of
surfactant molecules in a liquid at which micelles start to
form (Ying, 2006) and it establishes the detergency and the solubility of the surfactant (Jensen,
1999).
There are different classes of surfactants, depending on the charge of their head group. The head
group can either be charged or free of charge (table 1). The nature of the non-polar hydrocarbon
tail of a surfactant, which can consist of eight to twenty hydrocarbons, affects surfactant properties
in five distinct ways. These will be discussed in chapter 4.
The use of surfactants experienced two shifts during the seventies. Industries shifted from the use of
soap-based detergents to synthetic surfactants and solid domestic detergents (powder) made room
Figure 1: Sodium dodecyl sulphate (SDS), an example
of a surfactant molecule with a hydrophobic tail and a
hydrophilic head. Source: Learn Biochemistry, 2011.

5 Introduction | Utrecht University, IRAS
for the use of liquids. These shifts resulted in an excess of non-biodegradable surfactants in, for
example, wastewater (Scott and Jones, 2000), sewage systems (van Compernolle et al., 2006) and
surface water (Tolls et al., 1997). Since the use of synthetic surfactants is increasing worldwide and
chemical waste products can have devastating effects on the environment, it is important to
determine the aquatic eco-toxicity of surfactants (Rosen, 1987). This has resulted in a change of
legislation in many European countries, calling into existence the European law in 1973. The
European Commission has directed that all new chemical products must be tested for their
biodegradability. Compounds used as detergents must be degraded at least 80% within 28 days after
disposal (Hallmann et al., 2013). This, in order to minimize the eco-toxic potential of chemical
substances in the environment.
There are different ways to determine the eco-toxicity of chemical substances. For surfactants,
researchers have adopted the quantitative structure-activity relationship (QSAR) method. This is a
computational method used to relate the physicochemical properties or descriptors (such as
solubility, stability, form definition, partition coefficient and ionization constant) of chemicals to
predict certain environmental parameters, such as their toxicity/bioactivity (Muller et al., 19991
;
Roberts, 2000). Such a relation can then be applied to predict the environmental parameter for
untested compounds and this is the strength of the QSAR approach.

6 A short introduction into QSAR | Utrecht University, IRAS
A short introduction into QSAR
2. A short introduction into QSAR
As previously mentioned, there are many ecological reasons to determine the toxicity of waste
surfactants in the environment. For many risk assessment studies on the effects of surfactants on the
environment and animals, QSAR modelling is often applied.
QSAR modelling is based on the assumption that the molecular structure of molecules present
characteristics which may explain their physical, chemical and biological properties. With such
models, the biological activity of a chemical can be predicted when compared to similarly structured
substances whose activities have already been experimentally determined (Gramatica, 2011).
There are two approaches to QSAR modelling. Firstly, the toxicity is related to the structural
parameters of the substance, and secondly, the physicochemical properties (descriptors) can be
related to toxicity. It has been shown that the latter is the most relevant (Roberts, 2000).
The most widely used physicochemical property is the log P (KOW), which indicates the
hydrophobicity of a molecule (Versteeg et al., 1997; Muller et al., 19991
; Uppgard et al., 2000;
Roberts, 2000; Roberts and Castello, 2003; Haftka et al., 2015). Hydrophobicity plays a role in the
uptake of compounds by organisms and in the sorption of the substance to dissolved and natural
organic matter (NOM) (Muller et al., 19991
). Therefore, methods focused on determining the
hydrophobicity of chemicals are highly valuable in risk assessment studies.
There are different methods to ascertain the degree of hydrophobicity/hydrophobic potential of
chemicals. They can be determined in vivo, in vitro or using computational methods, such as the
QSAR method. In QSARs the most widely used descriptor is the log P, or the octanol-water partition
coefficient (Versteeg et al., 1997; Muller et al., 19991
; Uppgard et al., 2000; Roberts, 2000; Roberts
and Castello, 2003; Haftka et al., 2015).
Hydrophobicity and the octanol-water partition coefficient
The most frequently used descriptor is the octanol-water partition coefficient (KOW) of organic
contaminants. This is an important parameter which describes the hydrophobicity of a substance. It
is an indication of the solubility and predicts the bioaccumulation, toxicity and sorption to soil
(natural organic matter; NOM) (van Compernolle et al., 2006). Even though KOW is a key descriptor,
many scientists disapprove of its use in QSARs when predicting the eco-toxicity. Some scientists
argue that using the KOW is a misleading or unreliable way to determine the eco-toxicity of
surfactants. They argue that it is difficult to determine the KOW for surfactants, because of their
tendency to adsorb to the surface and to accumulate at interfaces, which serves as a major limitation
for QSAR modelling (Roberts, 2000; Haftka et al., 2015).
The KOW parameter used in QSARs is often determined according to the Leo and Hansch method (or
fragment method). According to this method, the components of a molecule contribute additively to
its total KOW value. Thereby, KOW of the total molecule can be deduced from summing up the partial

7 A short introduction into QSAR | Utrecht University, IRAS
KOW values of each component (called f). This method also takes into account, the variation in how
the different components are connected (called F) (Roberts, 2000).
Experimentally, KOW is determined by the shake-flask method (Haftka et al., 2015, Muller et al.,
19991
) or HPLC on octadecane-coated silica particles (Muller et al., 19991
; EOSCA, 2000).

8 Objectives of this study | Utrecht University, IRAS
Objectives of this study
3. Objectives of this study
In this study I will examine QSARs with the focus on Alcohol Ethoxylates (AE), representing non-
ionic surfactants, and Linear Alkyl benzene Sulphates (LAS), representing anionic surfactants. The
fate and adverse effects of residual AE and LAS in sewage are becoming a great interest for industry
and regulators for many reasons, based on their use and production. For instance, the largest bulk of
non-ionic surfactants produced are AE and they are especially applied in household detergents (Tolls
et al., 1997). They are widely used in different fields of research and technology to strengthen the
efficiency of the active ingredient in different formulations, ranging from pharmaceutical to
biotechnological to cosmetics (Cserháti et al., 2002). While, LAS accounts for more than 40% of
surfactants used with applications in industrial as well as household laundry detergents (Scott and
Jones, 2000).
Thus, the application of QSAR modelling has become a frequently used method to determine the eco-
toxicity of such surfactants. However, there are many discussions on its validity for surfactants, due
to its limitations. Therefore, in this study I will address the limitations of QSARs for non-ionic
(neutral) and anionic surfactants and analyse how different scientists coped with these limitations. I
will also discuss alternatives for this method.

9 Surfactants | Utrecht University, IRAS
Surfactants
4. Surfactants
Surfactants generally consists of a polar head group and a non-polar hydrocarbon tail. The head
region of a surfactant molecule is hydrophilic and can either be charged or uncharged. Consisting of
both a hydrophilic and hydrophobic region, makes these chemicals unique amphiphilic compounds.
Because of their amphiphilic nature they have a unique characteristic to alter surface and interfacial
tension, they also possess the ability to self-associate into aggregates called micelles (Rosen, 2004;
Hallmann et al., 2013). The most commonly used synthetic surfactants are LAS and AE and because
they are largely discarded as waste after use, their behaviour and fate once they reach the
environment has long been researched.
Structure:
The actions of surfactant molecules are highly influenced by their structures. A major factor in
surfactant behaviour is the charge of their head group (Rosen, 2004). Surfactants can be divided in
different classes based on the charge of their head group. The head group of surfactants can either
be charged or free of charge. Table 1 illustrates the different classifications.
Table 1: The classification of surfactants based on the charge of their head group.
Anionics, the most common type of surfactants, have a negatively charged head and are historically
the oldest surfactants. Examples of anionics are linear alkylbenzene sulphonic acid (LAS), sodium
dodecyl sulphate (SDS), alkyl sulphate (AS) and alkyl ethoxysulphate (AES).
Cationic surfactants consist of a positive charged head and contain at least one hydrophobic
hydrocarbon chain which is linked to a positively charged nitrogen atom. Examples of cationics are
quaternary ammonium compound (QAC), Benzalkonium chloride (BAC) and
hexadecyltrimethylammonium bromide (HDTMA).
Amphoteric surfactants, the newest form of surfactants, consist of a both a positive and a negative
charged head, making them capable of changing between charges depending on the pH. Examples of
amphotheric surfactants are amide oxides (AOs) and sulfobetaine.
Charge of the head group Classification Example
Anionic Linear Alkyl benzene
Sulphonate
Sodium Dodecyl Sulphate
Alkyl Sulphate
Alcohol Ethoxy Sulphates
Cationic Quarternary Ammonium
Chloride
Benzalkonium Chloride
Hexadecyltrimethylammonium
Amphoteric
Amine Oxide
Sulfobetaine
Non-ionic Alcohol Ethoxylate
Alkyl Phenol Ethoxylate
Fatty Alcohol Ethoxylate

10 Surfactants | Utrecht University, IRAS
Nonionic (neutral) surfactants lack a charged head preventing them from dissociating into ions in a
water solution which makes them useful as emulsifiers, wetting agents and various biotechnological
processes. Examples of nonionic surfactants are alkylphenol etoxylate (APE), alcohol ethoxilate (AE)
and fatty acid ethoxilate (FAE) (Rosen, 1987).
The head group does not only determine the class of surfactant, but the charge of the head also
affects the sorption efficiency between surface and surfactant head-group and the adsorption of the
surfactant on hydrophilic surfaces. Differently charged head and surface will attract one another,
while similarly charged head and surface will act repellent (Yana et al., 2005)
Critical micelle concentration (CMC):
The critical micelle concentration, as mentioned before, is the concentration at which surfactants
cluster to form micelles (Rosen, 2000). After reaching an aqueous system, surfactants will initially
partition at the interface, where they lower the interfacial tension, thereby protecting the
hydrophobic component of the surfactant from the aqueous environment. As the surfactant
concentration increases, the surface tension decreases further and aggregation into micelles
commences (Ying, 2006). When surfactant
concentrations are above the CMC, the effectiveness of
solubilising organic compounds is at its highest.
Compounds are dissolved readily, more so then would
be dissolved in water (Ying, 2006). This is known as the
hydrophobic effect and leads to an increase of entropy
in the encompassing water molecules (Rangel-Yagui et
al., 2005). The efficiency with which surfactants
solubilise water insoluble or poorly soluble substances is
dependent on the sorbed compound, the environmental
milieu in which it persists and the chemical nature of the
surfactant itself (Ying, 2006).
Figure 3: The amount of material solubilized increases linearly
with increasing surfactant concentration after CMC.
Source: Ying, 2006.

11 Effects of chemical structure of surfactants | Utrecht University, IRAS
Effects of chemical structure of surfactants
5. Effects of chemical structure of surfactants on environmental properties or parameters
and examples of QSARs.
A structural factor which influences surfactant behaviour is the hydrocarbon tail. The nature of the
non-polar hydrocarbon tail of a surfactant, which can consist of eight to twenty hydrocarbons,
affects surfactant properties in five distinct ways (Rosen, 2004):
1. A longer hydrophobic tail decreases the solubility of a surfactant in water, while it
increases the solubility in organic materials; it may also result in closer packing of the
molecules at the interface; increases the tendency of surfactants to form micelles; it
increases the melting point of the surfactant and the absorbed film; it can also increase
the sensitivity of an ionic surfactant to precipitate from water by counter ions.
2. Branching or unsaturated hydrophobic tails can decrease the solubility of surfactants in
water or in organic materials; it decreases the melting points of the surfactant, and of the
absorbed film as well; unsaturated fatty acids also causes looser packing of the surfactant
molecules at the interface; a tail of this nature may also cause oxidation and colour
formation in unsaturated compounds; it decreases the biodegradability in branched-
chain compounds; and increases thermal instability.
3. Having an aromatic nucleus in the tail can increase the adsorption of surfactants onto
polar surfaces; decrease its biodegradability; and cause looser packing of the molecules
at the interface.
4. Polyoxypropylene units may increase the hydrophobic character of the surfactant, while
polyoxyethylene decreases its hydrophobic properties.
5. Having either a perfluoroalkyl or polysiloxane group in the tail region, allows the
surfactant to reduce the water surface tension to lower values than those obtained from
hydrocarbon-based hydrophobic tails. Interestingly, perfluoroalkyl surfaces are both
water- and hydrocarbon repellent.
Environmental behaviour: biodegradation, sorption and bioaccumulation
After being discarded as waste, surfactants often end up in sewage treatment plants (WWTP: waste
water treatment plants), where biodegradation plays an important role in removal of surfactants
from the environment thus reducing their deleterious effects on biota (Jensen, 1999). For this reason,
legislation has pushed for laws that compel manufacturers to determine the biodegradation rate of
these chemicals, and to accept only those that are at least 80% degraded after 28 days (Hallmann et
al., 2013).
The biodegradation rate of surfactants can be influenced by many factors, such as chemical structure
and the physical and chemical composition of the environmental media (Ivancovic and Hrenovic,
2009). Furthermore, surfactant class also affects its biodegradation rate. Table 2 shows the
biodegradation rate of anionic surfactants LAS, and non-ionic surfactants AE (Ying, 2006).

Table 2: The biodegradation rate of LAS and AE
Degradation of LAS is highly determined on its structure. LAS are composed of n-(p-sulfophenyl)
alkanes (n-p-SPA), with the carbon chain lengths ranging from 10-13 carbons (Tolls et al., 1997;
Hallmann et al., 2013). The position of the benzenesulfonate moiety could be attached to any
internal carbon unit in the alkyl chain, creating homologues with 5-7 positional isomers (Tolls et al.,
1997; Jensen, 1999; Hallmann et al., 2013). The N-oxidation of the alkyl chain and cleavage of the
benzene ring are processes that require oxygen, thus, LAS can only be fully degraded under aerobic
conditions with a half-life of 7-33 days (Jensen, 1999). In reality, however, LAS is not always
completely degraded in treatment plants, and through sewage discharge they can reach the outside
environment. In river water it can be completely degraded by the occupational natural microbial
flora, but in marine environment such flora is absent, resulting in a slower degradation of LAS and
its degradation products sulfophenyl carboxylates (SPC) (Jensen, 1999). Once on land, LAS are
readily metabolised by residential aerobic bacteria in the soil and will not bioaccumulate further
(Jensen, 1999).
For AE, however, it is quite the opposite. They can undergo anaerobic as well as aerobic degradation
(Ying, 2006), with the main difference being in the cleavage site of the molecule. The degradation
rate can also differ according to the treatment surfactant-containing sludge in WWTPs receives. A
continuous flow, for example, resulted in better degradation than a static state (Ying, 2006).
Another process surfactants undergo after reaching the outside environment is sorption onto
sediment or soil. This characteristic also determines the bioavailability in the environment and is
different for every class of surfactant (van Compernolle et al., 2006). Research has shown that
surfactants adsorb well to sludge and sediment and that non-ionic surfactants have a higher
sorption to sediment than anionic surfactants
(EOSCA, 2000). The sorption efficiency of ionic
organic compounds depends on their molecular
structure, the characteristics of the sediment and the
specific ionic composition, such as the organic
carbon content, temperature and pH, of the aqueous
phase (EOSCA, 2000; Rico-Rico et al., 2010). Figure 4
shows the sorption isotherms for anionic and non-
ionic surfactants (Ying, 2006). If concentrations are
lower than 90 µg/mL, LAS show a linear isotherm,
however, the amount of sorbed LAS increases exponentially at higher concentrations of LAS in
Surfactant Aerobic conditions Anaerobic conditions
LAS Degradable Persistent
AE Readily degradable degradable
Figure 4: A. Sorption isotherm for LAS. The amount of sorbed LAS
increases exponentially at concentrations higher than 90 µg/mL. B.
When the CMC is reached, there is a maximum of sorbed AE.
Source: Ying, 2006.

solution (figure 4A). For AE, however, there is a maximum of sorbed AE on solid surface when the
CMC of the surfactant is reached (figure 4B) (Ying, 2006).
Besides the bioavailability, sorption may also affect the bioconcentration of surfactants in biota.
Bioconcentration, noted as the bioconcentration factor or BCF, is the ratio between the concentration
in biota (CB) and the concentration in water (CW). Thus, BCF= CB/ CW (Ying, 2006).
Compounds with a higher bioaccumulation potential (high BCF) are mostly lipophilic, or
hydrophobic, compounds (Ying, 2006). The hydrophobicity of a substance can be determined by the
octanol-water partition coefficient (KOW) and has been considered the driving force for
bioconcentration. Research has shown that the bioconcentration increases with the KOW (Versteeg et
al., 1997; Ying, 2006).
Surfactant toxicity
Toxicity can be derived from the sorption potential, the bioaccumulation potential, the
bioconcentration of surfactants and their degradation rate, among other characteristics (Haftka et al.,
2015).
According to the CHARM model (chemical hazard risk assessment management system), the KOW is
an essential input parameter for risk assessment and from here other factors are derived (figure 5)
(EOSCA, 2000).
HPLC or shake flask
KOW
KSWCPW
BCF
PECsediment
PECwater
PECbiota
PECsediment
PNECbenthic
PECbiota
PNECfoodchain
Figure 5: KOW as an essential input parameter for risk assessment.
Pow = octanol/water partition coefficient
Cpw = concentration in produced water
Psw = sediment/water partition coefficient
BCF = bioconcentration factor
PEC = predicted environmental concentration
PNEC = predicted no observed effect concentration

In general there are two ways chemicals can exert their toxicity; there are the general narcotics and
the polar narcotics. Toxicity of general narcotics can be determined with the Könemann equation
(Uppgard et al., 2000; Roberts and Castello, 2003).
Chemicals that adhere well to the general narcosis equation developed by Könemann are considered
“unreactive” compounds. Such compounds do not interact specifically with receptors in organisms
(Roberts, 1991; Uppgard et al., 2000; Roberts and Castello, 2003). Hydrocarbons, alcohols, ethers,
ketones and non-ionic surfactants are considered such compounds and it was generally viewed that
these are as toxic as their hydrophobic parts (Roberts and Castello, 2003). Chemicals that do not
adhere to this equation, meaning that their predicted toxicity is often lower than their actual
observed toxicity, are the phenols, aromatic amines and anionic surfactants, for example. These are
referred to as the polar narcotics and their toxicity can be predicted by the polar QSAR developed by
Saarikoski and Viluksela (Roberts and Castello, 2003):
The main difference between both modes of action, general narcosis (Narcosis I) and polar narcosis
(Narcosis II), is based on their interactions with membranes (Hodges et al., 2006). In polar narcosis
the water-membrane partitioning takes place due to the interaction between the narcotic molecule
and the head groups of the membrane lipids. For general narcosis, however, the narcotic molecule
can move easily in all directions of the membrane (Hodges et al., 2006). The hydrophobicity of the
chemical in this case plays an important role in membrane interactions. Hydrophobicity is an
important characteristic of narcotic organic compounds; it influences their effects in aquatic systems
(Hodges et al., 2006). For this reason the KOW has become an essential parameter in risk assessment
for organic chemicals and can be determined according to the Leo and Hansch method (EOSCA,
2000; Hodges et al., 2006).
The hydrophilic and hydrophobic parts of anionic surfactants have been shown to interact with the
polar and non-polar components of, e.g. proteins and cellulose (Cserháti et al., 2002). Anionic
The general narcosis equation:
pLC50=0,87 log P + 1,13
(n=50, R2
=0,976, s=0,24)
(LC50 in mg/kg)
pLC50=0,63 log P + 2,25
(n=17, R2
=0,964, s=0,16)

surfactants have also been shown to be more toxic than non-ionic surfactants. Longer alkyl chains of
LAS result in increased acute toxicity in D. magna probably due to higher interactions (Ying, 2006).
Most AE have been shown to possess a high bioaccumulation potential (log KOW >) and are known to
interact with biological membranes (Muller et al., 19992
). These surfactants are considered general
narcotics (Muller et al., 19992
; Roberts, 2000), and their toxicity towards aquatic organisms
increases as the length of the alkyl chain increases paired with decreased branching (Muller et al.,
19992
, Muller et al., 19991
). Once more confirming the link between surfactant behaviour and their
chemical structure.
Because of the aquatic and terrestrial toxicity surfactants may have, and their increased use and
disposal into sewage, determining the toxicity of these chemicals beforehand is a major priority for
manufacturers (Hallmann et al., 2013). For this purpose, researchers have developed a mathematical
method, the QSAR, to predict the toxicity of chemical compounds.

16 A critical evaluation and discussion | Utrecht University, IRAS
A critical evaluation and discussion
6. A critical evaluation and discussion of Quantitative structure-activity relationship
Even though the log P is the most dominant parameter in QSARs, there is some speculation on its
predictive value for surfactant toxicity (Versteeg et al., 1997; Muller et al., 19991
; EOSCA, 2000;
Roberts and Castello, 2003; Haftka et al., 2015).
KOW based QSARs have been predominantly applied to chemicals that are described as ‘unreactive
compounds’. These chemicals do not interact specifically with receptors in organisms, and adhere
well to the general narcosis equation developed by Könemann, when determining their toxicity
(Roberts and Castello, 2003). Such chemicals are often non-polar organic compounds, which are
devoid of specific interactions, such as hydrogen bonding. Which all results in a good correlation
between KOW and their general toxicity (Haftka et al., 2015).
This raises a problem for applying the KOW as a predictive parameter for surfactants, due to their
amphiphilic nature and long carbon-chains (EOSCA, 2000; Haftka et al., 2015).
Furthermore, it has been shown that for surface-active substances the shake-flask method creates
emulsions, which can lead to experimental problems (Muller et al., 19991
; EOSCA, 2000; Haftka et
al., 2015) that cannot be avoided. Additionally, capacity factors used in the HPLC method when
determining the KOW for AEs, are erroneously influenced by the length of the ethoxy chains (Muller
et al., 19991
).
The difficulties with surface-active compounds, such as anionic and non-ionic surfactants, arise
from their tendency to aggregate at interfaces, form micelles, and act as solubilising and emulsifying
agents (Roberts, 2000). Furthermore, when determining the octanol-water partition coefficient for
surfactants, they interact with each other and octanol, even at concentrations below their CMC
(Muller et al., 19991
). Because of their amphiphilic nature, they are distributed easily to both the
octanol as well as the water phase (Versteeg et al., 1997).
The main idea behind using the octanol-water partition coefficient to determine the hydrophobicity
of chemicals, is to determine how well they react with biological compounds. Octanol serves as a
surrogate for such biological compounds, e.g. NOM or biomembranes. However, no in vitro analysis
can correctly mimic the exact interactions/reactions of complex compounds, such as surfactants,
with NOM or biomembranes (Muller et al., 19991
; Haftka et al., 2015).
Limitations of log P based QSARs for anionic and non-ionic surfactants
Since the physical and chemical properties of surfactants greatly influence their biological activity,
log P based QSARs are often inadequate to predict their toxicity (Boeije et al., 2006; Ivancovic and
Hrenovic, 2009).
For AEs, the hydrophobicity is greatly influenced by alkyl chain length and EO number. Longer alkyl
chains in combination with low EO numbers, show greater hydrophobic potential compared to
higher EO numbers (Muller et al., 19991;
Dyer et al., 2000; van Compernolle et al., 2006; Ivancovic

and Hrenovic, 2009). The most common AEs used have alkyl chain lengths ranging from 12 to 18
carbons, with as many as 20 ethoxylate units ester-linked to each chain (Wong et al.,1997; Muller et
al., 19991
; Boeije et al., 2006; Eadsforth et al., 2006; van Compernolle et al., 2006). Many authors
agree that, specifically for AEs, the assumption of additivity, meaning that every component adds to
the overall toxicity of the molecule, applies (Boeije et al., 2006).
Furthermore, for LAS it has been shown that the sorption potential increases two- to three-fold with
every added carbon in the tail region (Jensen, 1999; Scott and Jones, 2000). Commercial LAS is built
up of alkyl chain lengths ranging from 10 to 13 carbons and isomers that have different positions of
the benzenesulfonate moiety on the alkyl chain (Tolls et al., 1997; Rico-Rico et al., 2010). Moreover,
it has been shown that the isomers whose phenyl groups are on the outmost carbon atom have a
higher sorption affinity, than those where the moiety is located more internally (Rico-Rico et al.,
2010).
This effect of the structure on the hydrophobicity is problematic, because commercial LAS or AE are
available in mixtures. Such complex mixtures could contain multiple structurally similar molecules
and eco-toxicity data are readily available for the mixtures as a whole, but not for the individual
molecules (Boeije et al., 2006; Rico-Rico et al., 2010).
Since the toxicity is highly dependent on the distribution of multiple components of a chemical
structure, the toxicity is not always linearly related to molecular descriptors. According to the theory
of additivity, the eco-toxicity for surfactants shall increase logarithmically with increasing alkyl
chain length (Boeije et al., 2006). An important fact to keep in mind, is that studies are often
performed with commercially available complex mixtures of surfactants, and individual testing of
the molecular components would be time consuming and expensive (EOSCA, 2000; Boeije et al.,
2006). Consequently, in experimental data the more highly toxic components in a complex mixture
will have a greater impact on the toxicity, which is not proportionate to their actual concentration.
Complex mixtures are often represented by an average structure. So when using QSAR to derive the
toxicity of a substance, it is derived from an average structure in a mixture. This may result in an
overestimation of toxicity of multiple individual components in the mixture (Boeije et al., 2006).
Alternatives for log P or log P based QSARs for anionic and non-ionic surfactants
Over the years authors have proposed many alternatives to either the log P as a descriptor, or the
traditional QSAR as a whole.
Roberts (Roberts, 1991), for instance, proposed adjusting the Leo and Hansch method to also account
for the branching positions of certain components in anionic as well as non-ionic surfactants, by
extending the fragment method with a position-dependent branching factor (PDBF). Roberts was
convinced that the Leo and Hansch method fit adequately for AE/non-ionic surfactants with just a
slight adjustment, and that these compounds behaved as general narcotics (Roberts, 1991). The log P
data that were collected by Roberts were compared to log KOW determined by the Syracuse Research

Corps. This comparison showed similar results between the two methods, but also significant
differences for some part of the data (EOSCA, 2000).
Since the main problem with QSARs has been the use of KOW as a descriptor for surfactants, Müller
(Müller et al., 19991
) suggested using the liposome-water partition coefficient (Klipw) as descriptor in
QSARs for AEs. They argued that, first of all, the Klipw is readily determined experimentally, as long
as concentrations are below CMC. Second of all, they find this value more superior to the KOW
because its values are determined according to the fragment method tested on commonly available
mixtures, while those of KOW are deduced from nonsurface-active compounds. And thirdly, they find
that membrane-partitioning truly affirms bioaccumulation (Müller et al., 19991
). Because of the
distinctive mechanistic difference between polar and non-polar narcotics, this descriptor can,
unfortunately only be applied for non-ionic surfactants (Roberts and Costello, 2003; Hodges et al.,
2006).
Boeije, on the other hand, suggested adjusting the traditional QSAR in such a way that the new QSAR
would be suitable for complex substances, such as surfactant mixtures (Boeije et al., 2006). The new
QSAR would determine toxicity with respect to the “ethoxymer” distribution, instead of the usual
average structure. However, it is important to keep in mind that as the complexity of a molecule
increases, the accuracy of certain methods decreases (EOSCA, 2000).
With respect to this, instead of focusing on adjusting the QSAR method, which is still dependent on
an existing incomplete and possibly mostly inaccurate database, it would seem logical to seek new
analytical methods to determine parameters such as sorption or hydrophobicity of complex
compounds as surfactants (EOSCA, 2000), as an alternative for the traditional octanol-water
partitioning.
Recent developments
Because of the complex nature of surfactants, Haftka (Haftka et al., 2015) suggested that alternative
methods, which focus on sorption to a hydrophobic phase, could best indicate the hydrophobicity.
Besides the stationary phases used in liquid chromatography, solid-phase extraction devices and
polymers used in passive sampling show a lot of promise (Haftka et al., 2015).
Recently the focus has been on the latter option in determining certain surfactant characteristics.
Using polyacrylate polymers, the SPME (solid-phase micro-extraction) method was developed. This
method is based on the partition of a chemical between a certain matrix and a stationary phase
coated on silica fibres (Aulakh and Malik, 2005). The SPME method is advantageous for surfactants,
because no phase separation and purification steps are required (Rico-Rico et al., 2010; Haftka et al.,
2015). The main idea is to place a fiber with a specific coat into a solution, and removing it after a
certain amount of exposure time (Haftka et al., 2015). When the concentration of the sample
chemical and the amount of sorbed sample chemical reaches an equilibrium (Aulakh and Malik,
2005), a polyacrylate-water partition coefficient (or fibre-water partition coefficient; Kfw) can be
determined.

The SPME method has been mostly applied to analyse freely dissolved concentrations of organic
compounds, however, there is only limited application known for ionic organic compounds (Rico-
Rico et al., 2010). Research has shown, for anionic and non-ionic surfactants, that the measured Kfw
correlates well with marine sediment sorption tests and BCF values (found in literature), suggesting
that this may be used as an alternative parameter for the KOW (Rico-Rico et al., 2010; Haftka et al.,
2015). It is, however, important to consider that the sorption mechanism of surfactants to sediment
compared to the sorption mechanism of surfactants to fibers can differ. Different interactions take
place to ensure the sorption distribution on a certain surface or other (Rico-Rico et al., 2010; Haftka
et al., 2015).

20 Conclusion | Utrecht University, IRAS
Conclusion
7. Conclusion
In conclusion, although many scientists would agree that the octanol-water partition coefficient as a
parameter to determine the hydrophobicity of surfactants with QSAR modelling is inefficient and
inaccurate, there is still no universally accepted alternative method to determine the eco-toxicity of
these compounds. A major obstruction in QSAR modelling is the scarce and often inaccurate data on
the hydrophobicity of surfactants. Improvement and expansion of the main QSAR database could
eventually lead to complete abandonment of animal testing, and accurate computational
determination of the eco-toxicity, among other endpoints, of surfactants. Therefore, research
focussed on new analytical methods to determine hydrophobicity and replace the octanol-water
partition coefficient, is highly beneficial for surfactant risk assessment.

21 Conclusion | Utrecht University, IRAS

22 References | Utrecht University, IRAS
References
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