Cation bioconcentration

Roy et al. 2009 Sacan et al. 2009 Mendes et al. 2010 Su et al. 2010). Analysis of these studies produced 14 references associated with 183 QSARs for predicting cation bioconcentration potential, biosorption capacity, binding strength, and toxicity (Table 5.4). These QSARs are discussed in more detail in Sections 5.2, 5.3, 5.5, 5.6, and 5.7. The acronyms used in Chapter 5 and associated tables are defined in the Chapter 5 appendix. 

PHYSICOCHEMICAL PROPERTIES USED TO PREDICT CATION BIOCONCENTRATION... 

Experiments with various species simultaneously carried out in ponds, streams or mesocosms have been reported for non-ionic, anionic and cationic surfactants. NP bioconcentration has been studied in the shrimp Crangon crangon, the mussel M. edulis and the fish... 

If released into air, phenylmercuric acetate is expected to be bound to particulates. If released into soil, its mobility may be high based on a Koc of 60 of the undissociated form, but is likely to be much lower because it will dissociate and the cation will sorb to organic matter or clay. Water releases would result in quick dissociation of the salt and sorption of the cation to particulates or humics, with little bioconcentration in aquatic species. Photolysis of phenylmercuric acetate and subsequent loss through volatilization of inorganic mercury is expected in superficial soils and water. 

The toxicity of surfactants to marine organisms and their concentration in them depends upon their tendency to adsorb onto them and their ability to penetrate their cell membranes (Rosen, 1999). The parameter AG0ad/a, where AG°a(j is the standard free energy of adsorption of the surfactant at the aqueous solution-air interface (Chapter 2, Section IIIF) and am is the minimum cross-sectional area of the surfactant at that interface (Chapter 2, Section IIIB), was found to correlate well for several anionic and nonionic surfactants with rotifer toxicity. The same parameter was found to correlate well for a series of cationic surfactants with rotifer and green algae toxicity and, for a series of linear alkylbenzenesulfonates, with bioconcentration in fish (Rosen, 2001). 

Relationships have been found between the adsorption properties described above of surfactants and their environmental effects (toxicity, bioconcentration) on aquatic organisms (algae, fish, rotifers). The log of the EC 50 (the surfactant molar concentration in the water at which the organism population is reduced by 50% relative to a no-dose control) and the log of the BCF (the ratio of surfactant concentration in the fish relative to that in the water) have both been shown (Rosen, 1999, 2001c) to be linearly related to the parameter AG°d/u for a series of anionic, cationic, and nonionic surfactants. The values of asm and AG°d were obtained by the methods described above in Sections IIIB and IIIF, respectively. 

With regard to bioconcentration, it is important that surfactants are characterized by combining a lipophilic and a hydrophilic moiety in the same molecule. This is true for all four classes, namely anionic, nonionic, cationic and amphoteric surfactants. Although these classes possess quite different hydrophilic groups, the lipophilic part usually consists of an alkyl chain or alkyl chains of different lengths. There is some evidence that the lipophilic groups of surfactants are metabolized after uptake by aquatic invertebrate species (Daphnia and Chironomus) and fish. 

QICARs use the metal-Iigand bonding characteristics to predict metal ion toxicity (Newman et al., 1998). In general, the models developed for metals with the same valence were better than those combining mono-, di-, and trivalent metals. The metal ion characteristics included a softness parameter and the absolute value of the log of the first hydrolysis constant. The first stable reduced state also contributed to several two-variable models. Since most metals can interact in biological systems as cations and because toxicity of metals depends on cationic activity, the term (quantitative) cationic-activity relationships or (Q)CARs also describes the qualitative and quantitative relationships for predicting the bioconcentration, biosorption, or toxicity of metals, from their physicochemical properties and natural occurrence levels. 

This chapter discusses quantitative structure-activity relationships (QSARs) for predicting cation toxicity, bioconcentration, biosorption, and binding strength. Several approaches were used to identify these QSARs. First, the test systems, test substances, QSARs, and statistical analyses of each QSAR were extracted from the references cited by Walker et al. (2003). These efforts produced 21 references associated with 97 QSARs for predicting cation toxicities (Table 5.1). These QSARs are discussed in more detail in chapter Sections 5.2,5.3, and 5.5. 

QSARs for Predicting Cation Toxicity, Bioconcentration, Biosorption... 

QCARs for Predicting Cation Toxicity and Bioconcentration Factors for the Mussels, Mytilis edulis and Perna viridis Using the Covalent Radius, Logarithm of the First Hydrolysis Constant, Pearson and Mawby Softness Parameter, and the Ionic Index... 

Van Kolck et al. (2008) developed 4 QSARs to predict the bioconcentration factors (BCF) of cations to the mussel Mytilus edulis, and 4 QSARs to predict the BCFs of cations to the mussel Perna viridis (Table 5.4). The BCFs for Mytilus edulis were developed for 8 cations and the QSARs with highest values were obtained using the ionic index (ZVr) and the covalent index (x i) (Table 5.18). The BCFs for Perna viridis were developed for 7 cations and the QSARs with highest values were obtained using the Pearson and Mawby softness parameter (Op) and the covalent index xit) (Table 5.18). 

Vails, M., P. Fernandez, J. M. Bayona, Fate of cationic surfactants in the marine environment. I Bioconcentration of long-chain alkylnitriles and trialkylamines, Chemosphere, 1989, 19, 1819-1827. 

---