Survey of azo-colorants in Denmark

Appendix 4A QSAR

QSAR estimations

In an environmental risk assessment, information of physicochemical properties and ecotoxicity is of basic need. For azo colorants, information of physico-chemical properties and environmental toxicity (ecotoxicity) is very often not exiting or unavailable.

When such data are not available, a possible way to estimate the necessary values is the use of estimation models. These models based on theories of comparable properties between analogous molecular structures are called quantitative structure-activity relationships (denoted QSARs). The models are derived from comparison between experimental values by mathematical variance analysis. The best fitted correlations are then used to develop a mathematical expression to estimate selected end-values of unknown substances.

During the research for the present survey, it was recognised that data on azo dyes necessary for the risk assessment were often unavailable and it was decided to perform estimations based on QSAR methods. The lack of experimental data means that more general QSARs had to be used. It may reduce the accuracy of estimations.

When applying QSAR, it should be taken into account that a QSAR is an estimation method and therefore, there is a certain probability that the estimate is poor even for well evaluated models. QSARs are no better than the data on which they are based. It should be noted that QSAR models, generally, only exist for discrete organic substances and not for more complex substances or reaction mixtures. This should be kept in mind when reading this report. However, this study has found that most literature data were also estimations and the result of experimental studies were so few that it was decided that the use of QSARs was acceptable and necessary for a first estimation. In the survey, QSAR estimations are performed on approximately 140 azo colorants. The estimations are focused on azo colorants used in Denmark.

The methods for deriving QSARs will not be described in this document as other sources exist which review the tremendous amount of literature on the subject (e.g. Lyman et al., 1982; Turner et al., 1987; Karcher & Devillier, 1990; Verhaar et al., 1995; Russom et al., 1997).

The appendix includes a presentation of experimental data, if available and QSAR estimation of:

	Melting point
	Boiling point
	Water solubility
	Vapour pressure
	Octanol-water partition coefficient, log Kow
	Soil adsorption coefficients, correlated to organic carbon content, Koc
	Bioaccumulation factor, log BCF for fish and earthworms
	Acute toxicity on fish, Daphnia and algae.

QSAR and azo dyes

Evaluation of the validity of the latest accepted QSARs is performed by comparing experimental values from handbooks and databases (e.g. NPIRI (1983), HSDB (1993), ECDIN (1993) and the QSAR model estimates where no model input/calculations are changed.

The QSAR estimations are performed by programmes developed by Syracuse Research Corporation: MPBPVP, WSKOW, KOWWIN, HENRY, PCKOCWIN. The programmes are stand-alone programmes but can be run together using the Estimation Programs Interface (EPIWIN) as an interface.

Physico-chemical properties

Melting point

The melting point is an important parameter since it affects the solubility. Solubility controls toxicity by affecting the bioavailability of the substance and the possibility of being transported to the active site within an organism. Melting point tends to increase with molecular size, simply because the molecular surface area available for contact with other molecules increases (Dearden, 1991).

The melting point is estimated by Meylan and Howard (1994) by two different methods. The first is an adaptation of the Joback group contribution method for melting point and the second is a simple Gold and Ogle method suggested by Lyman (1985).

The computer programme MPBPVP by Meylan and Howard (1994) performs minor evaluations. If the values are close to the model averages, the two estimates are averaged, if not, the programme performs and decides which estimate is more likely to be accurate and presents a "suggested" melting point. Although, the suggested MPBPVP estimates are usually adequate for screening purposes, the overall accuracy is not outstanding. The accuracy of the "suggested" value was tested on a 666 compound data set containing a diverse mix of simple, moderately complex compounds and many pesticides and pharmaceutical compounds. MPBPVP estimates yielded a correlation coefficient (r²) of 0.73. However, even if the estimated melting points can only be used for screening purposes, it seems to be the best method currently available (Meylan & Howard, 1994).

With a few exceptions, the estimated values appear to be in agreement with the measured values. However, the origin of the literature values is not always stated and it was often uncertain whether the data were in fact measured or estimated values. Due to the uncertainty of the data origin, no attempt has been made to calculate correlations between the two. The ranges are presented in Table 1. The detailed data on the specific colorants are presented in Appendix 4B.

Målte og estimerede smeltepunkter for azofarver.

*: One value indicates that all substances were estimated to have the same value.

The validity of the estimations on azo dyes could not be evaluated due to the lack of experimental data on melting points for azo dyes. For pigments, where the largest amount of literature values were observed, it was uncertain whether the data were experimental or estimated values. Generally, the method seems to be in agreement with the melting point values, when present and the estimated values acceptable.

Boiling point

The boiling point is defined as the temperature at which the vapour pressure of a liquid is equal to the pressure of the atmosphere on the liquid. For pure compounds, the normal boiling point is defined as the boiling point at one standard atmosphere of pressure on the liquid. Besides being an indicator for the physical state (liquid vs. gas) of a chemical, the boiling point also provides an indication of the volatility.

The boiling point is estimated by using the Stein and Brown (1994) method of group contributions that calculates boiling point (BP) of a compound by adding group increment values according to the relationship:

BP = 198.2 + å n_i g_i

where g_i is a group increment value and n_i is the number of times, the group occurs in the compound.

The resulting BP is then corrected by one of the following equations:

BP(corr.) = BP - 94.84 + 0.5577 BP - 0.0007705 (BP)² [BP£ 700^oK]

BP(corr.) = BP + 282.7 - 0.5209 BP [BP>700^oK]

The Stein and Brown method was derived from a training set of 4,426 organic compounds. Besides the Stein and Brown method, no other estimation method exists that has been validated so extensively or accurately for diverse structures.

Other methods are described in Lyman et al. (1982) but are either not validated or are using a reduced number of chemicals.

A summary of the results of the estimations on azo colorants is presented in Table 2.

Estimerede kogepunkter for azofarver.

The validity of the estimations could not be evaluated due to the lack of experimental data on boiling points for azo colorants. No experimental values on boiling points were found.

Solubility in water

The water solubility is one of the most important physico-chemical properties in ecological hazard assessment and exposure assessment, including environmental fate. The spatial and temporal movement (mobility) of a substance within and between the environmental compartments of soil, water and air depends largely on its solubility in water. The knowledge of the solubility in water is essential when estimating biological degradation, bioaccumulation, hydrolysis, adsorption and the octanol/water partition coefficient, log K_ow. Most of the azo colorants are substances with low water solubility and therefore potentially slowly distributed by the hydrologic cycle, as they tend to have relatively high adsorption coefficients (i.e. high adsorption to soil and sediment).

As the term "insoluble" is frequently met in handbooks and datasheets on azo colorants, it must be emphasised that no organic chemical is completely insoluble in water. All organic chemicals are soluble to some extent. The range observed in azo dyes are usually between µg/l to g/l. In a few instances, it may be lower and some are infinitely soluble, i.e. totally miscible with water.

Several approaches to estimate water solubility have been developed (Lyman et al., 1982; Yalkowsky & Banerjee, 1992). Yalkowsky and Banerjee (1992) have reviewed most of the recent literature where a variety of estimation methods are available. After critical evaluation of the available methods in terms of range of applicability, accuracy, ease of use and strength of underlying theory, Yalkowsky and Banerjee (1992) concluded that only two methods could be considered for universal application:

	group activity coefficient techniques which include group contribution fragment methods.
	correlations based upon log Kow.

The group activity coefficient method is demonstrated with the group contribution approach of Wakita et al. (1986) including the summation of all applicable fragment values. The fragment values are derived from experiments starting with small molecules and increasing the molecular structure with known atoms or functional groups and calculate the contribution from each change in the molecular structure. The Wakita method was fairly accurate for its training set which primarily consisted of hydrocarbons and simple monofunctional compounds.

At present, the most practical method to estimate water solubility involves regression derived correlations using log K_ow. Most of the highly water soluble substances show low log K_ow values. Several correlations have been developed depending on the chemicals used in the calculations. Eighteen different regression equations that correlate water solubility to log K_ow have been found in the literature (Lyman et al. 1982; Isnard & Lambert, 1989).

Meylan and Howard (1994) have developed a QSAR model on water solubility where the water solubility in mol/l is estimated based on log K_ow with and without a melting point. The first equation was developed based on a validation set of 85 substances with an experimental log K_ow and water solubility values but with no available melting point. The second validation set included 817 compounds with measured water solubility and melting points. The Meylan and Howard equations are shown below:

where "MW" is the molecular weight, "MP" is the melting point and "cf" the correction factor. Knowledge of the melting point reduces the standard deviation and improves the correlation coefficient and this model should be used when a measured melting point is available. The melting point is only used for solids. The correction factor is applied to 15 structure types (e.g. alcohols, acids, selected phenols, amines, amino acids, etc). The calculations of the Meyland and Howard QSAR model can be performed on computer (WS-K_OW, Syracuse, Meylan & Howard, 1995).

The water solubility was estimated using the equation:

log S (mol/l) = 0.796 - 0.854 log K_ow - 0.00728 MW + cf (cf. above)

It was decided to use the equation based on log K_ow and not to use the melting point, unless it was clearly stated to be measured.

Målte og estimerede vandopløseligheder for azofarver.

For direct dyes, two measured values were found. For Direct Blue 1, a value of 40,000 mg/l was contradictory to the estimated value 3.5 ´ 10^-6 mg/l. However the estimate was based on the acid and not the salt which would increase the water solubility substantially.

The estimated values are mostly close to the measured values when they were available. However, a few exceptions exist which could not be explained.

Vapour pressure

The vapour pressure is a chemical specific property which is important in evaluating the behaviour and fate of an azo colorant in the environment. Especially, the distribution into the environmental compartments; soil, air and water and its persistence in the compartments.

Numerous equations and correlations for estimating vapour pressure are presented in the literature. They normally require information on:

	the critical temperature
	the critical pressure
	the heat of vaporisation
	the vapour pressure at some reference temperature

The modified Grain method is described in Lyman (1985). The method is a modification of the modified Watson method. It is applicable for solids, liquids and gases. The method converts super-cooled liquid vapour pressure to a solid phase vapour pressure. It is probably the best all-round vapour pressure estimation method currently available (Meylan & Howard, 1994) and is used by the US EPA in the PC-CHEM programme.

The computer estimations made by MPBPVP (Meylan & Howard, 1994) report three methods and a "suggested" value. The suggested vapour pressure for solids is the modified Grain estimate. For liquids and gases, the average of the Antoine and the modified Grain method is suggested. The Mackay method is not used as it is limited to its derivation classes: hydrocarbons and halogenated compounds (both aliphatic and aromatic). Using a data set of 805 compounds, a correlation coefficient (r²) of 0.941 and a standard deviation (sd) of 0.717 were observed.

A summary of the estimated vapour pressures is presented in Table 4.

Målte og estimerede damptryk (VP) for azofarver.

The estimated vapour pressures could not be evaluated due to the few experimental results available. However, as expected the estimated vapour pressures were very low.

Henry’s Law constant

The partitioning between water and air is a physical property that is described by the Henry’s Law Constant, H. The magnitude of H provides an indication of which of the two phases a chemical will tend to partition into at equilibrium. Henry’s Law constant can be estimated from calculation and the bond contribution method.

The calculation method uses the equation:

H = vapour pressure ´ molecular weight / water solubility [Pa m³/mol]

QSARs estimations of H based on group and bond contributions are developed from experimentally measured log K_air-water values, when available. The methods of Hine and Mokerjee (1975) have been further developed and are now available in PC programme (HENRY in EPIWIN, Meylan & Howard, 1992, 1994).

Compounds with large structures which include many different types of bonds and groups may have significant inaccuracies in their estimations.

Two methods were applied: The calculated H and the bond estimation method.

A summary of the estimated Henry’s Law Constants is presented in Table 5.

Beregnet Henrys Lov Konstant,struktur estimeret H_bond for azofarver.

It was possible to use the bond contribution method for 82 of the 140 substances. The bond contribution estimation method generally resulted in lower values than the calculation method.

The estimated Henry’s Law Constant using both methods indicated H to be low for all evaluated substances. This indicate that evaporation from surface water is expected to be insignificant or negligible.

Octanol/water partition coefficient (K_ow)

Hydrophobicity is one of the key parameters in QSARs for environmental endpoints. The property is usually modelled by the n-octanol/water partition coefficient (K_ow) which is an established laboratory method to measure the hydrophobicity of a chemical. K_ow has been found to be a good predictor for relatively non-specific processes. For instance, many distribution processes are found to be related to K_ow, e.g. sorption to soil and sediment, partitioning into air and bioconcentration, and non-specific toxicity. This especially relates to non-polar organic chemicals. When more polar chemicals and more specific processes such as degradation, biodegradation and specific toxic interactions are the subject, other kinds of interactions (stereo-electronic) become more relevant.

The literature contains several methods for estimating log K_ow. The most common method for the estimation of K_ow is based on fragment constants. The fragmental approach is based on simple addition of the lipophilicity of the individual molecular fragments of a given molecule, i.e. atoms or larger functional groups. The most widely used fragment constant method was proposed by Hansch and Leo (1979) and initially computerised for use by Chou and Jurs in the CLOGP programme (Daylight Chemical Information Systems, New Orleans). Other methods have been developed but have, at present, not proven to be acceptable as a general estimation method (Meylan & Howard, 1995). Meylan and Howard (1995) have evaluated 10 different methods and concluded that the CLOGP and the AFC methods (cf. below) are the best comprehensive predictors currently available. A major problem with most fragment constant approaches is their inability to estimate log K_ow for a structure containing a fragment that has not been correlated.

Meylan and Howard (1995) have developed a new fragment constant approach, the atom/fragment contribution (AFC) method which was developed by multiple linear regressions of reliable experimental log K_ow values. The regressions were performed in two stages: The first regression correlated atom/fragment values with log K_ow and the second correlated correction factors. The log K_ow is then estimated by summing up the values from a structure.

In general, each non-hydrogen atom, e.g. carbon, nitrogen, oxygen, sulphur, in a structure is a core for a fragment, and the exact fragment is determined by what is connected to the atom.

The general equation for estimating log K_ow of any organic compound is

log K_ow = å (f_in_i) + å (c_jn_j) + 0.229 (n=2351, r²=0.982, sd=0.216)

where å (f_in_i) is the summation of f_i (the coefficient for each atom or fragment) and times n_i (the number of times, the atom/fragment occurs in the structure). å (c_jn_j) is the summation of c_j (the coefficient for each correction factor) and times n_j (the number of times, the correction factor occurs or is applied in the structure).

The AFC method developed by Meylan and Howard (1995) was applied. A summary of the estimated Log K_ow values is presented in Table 6.

Målte og estimerede log K_ow for azofarver.

Only a few experimental octanol/water partition coefficients were available. However, when present, they were in good agreement with the estimated values. For instance, for disperse azo dyes, 10 experimental values and their estimated values had a correlation coefficient of 0.894. Two solvent dyes, Solvent Yellow 1 and 2, had experimental values of 3.41 and 4.58 and estimated values of 3.19 and 4.29, respectively.

The estimated octanol/water partition coefficients, log K_ow, were in agreement with the experimental values. Therefore, the estimated log K_ow values are used in the estimation of bioaccumulation factors and ecotoxicity. The results indicated that the azo dyes and especially the pigments include several compounds with high log K_ow values.

Sorption

The sorption (adsorption/desorption) to soil and sediments is a determining factor for the mobility of chemicals. This property also contributes to the distribution among soil, sediment and water phases, volatilisation from soil surfaces and bioavailability. The extent of soil sorption and sediment is governed by a variety of physico-chemical properties of both soil and chemical, e.g. organic carbon content, clay content, humidity, pH value, cation exchange capacity, temperature, etc.

The sorption of non-polar substances may be regarded as a distribution process between the polar phase of the soil water and the organic phase of the soil component. The equilibrium constant of this partitioning between solid and solution phase constitutes the adsorption coefficient for soil and sediments. The sorption coefficient is defined, at a steady state, as:

K_d = Concentration sorbed to soil / Mean concentration in aqueous solution.

As the organic fraction is the principal interaction site for hydrophobic compounds, a partition coefficient normalised for the content of organic carbon (OC) is used to reduce the variance of sorption coefficients:

K_oc = (K_d / OC%) ´ 100

The remaining variation may be due to other characteristics of soils (clay content, clay composition, surface area, cation exchange capacity, pH, etc.), the nature of the organic matter present and/or variation in the test methods. Numerous studies of the correlation of adsorption coefficient with these variables found that the organic carbon content usually gave the most significant correlations.

Other factors affect the measured value of K_oc under actual environmental conditions besides the differences in laboratory procedures (Lyman, 1990):

	temperature.
	pH of soil and water.
	particle size distribution and surface area of solids.
	concentration of dissolved organic matter in water.
	non-equilibrium adsorption mechanisms or failure to reach equilibrium conditions.
	solids to solution ratio.
	loss of chemical due to volatilisation, degradation, adsorption to test flask walls etc.
	non-linear isotherm.
	time factor.

The temperature may affect the measured values since adsorption is an exothermic process. Values of K_oc usually decrease with increasing temperature.

Chemicals that tend to ionise are much affected by the pH. Weak acids and weak bases show the greatest sensitivity to pH changes in the range, normally met in soil and surface waters (pH 5 to pH 9).

The fine silt and clay fraction of soil and sediments may have a great tendency to absorb chemicals. The different clay fractions have different adsorptive capacities.

Non-equilibrium adsorption may occur when a chemical moves through an environmental compartment so rapidly that equilibrium conditions cannot be achieved.

Changes in the water content of soil or sediment will change the fraction of the chemical that is adsorbed. As the water content is lowered, the fraction adsorbed will increase as the concentration in solution does.

The chemical may be lost during the test due to volatilisation, degradation, adsorption to test flask walls etc., if this is not considered.

If the adsorption isotherm is non-linear, the reported value of K_oc will depend on the range of chemical concentrations used in the tests.

The time for the chemicals to adsorb/desorb varies depending on conditions.

Several compilations of QSAR models for soil sorption are published in the literature. All of the available methods for estimating K_oc involve empirical relationships with some other property of the chemical:

	water solubility
	octanol/water partition coefficient (Kow)
	bioconcentration factor etc.

Most models are based on K_ow because hydrophobic interactions are the dominant type of interactions between non-polar substances and soil organic carbon. However, chemicals with more polar groups may interact by other types of interaction. It is therefore obvious that not one single model accurately predicts soil sorption coefficients and that different models should be used depending on which class of chemicals that the specific compound belongs to.

The EU Technical Guidance Document (TGD, 1996) for risk assessment presents 19 equations to estimate log K_oc in soil and sediment for different chemical classes. The 19 QSAR models were developed by Sabljic et al. (1995). The soil sorption data used in Sabljic et al. (1995) were determined for non-ionic species of respective chemicals and thus, the QSAR models presented in Table 7 will be applicable only for non-ionised chemicals:

Liste over udledte QSAR modeller til estimering af adsorption med deres kemiske domæner (Sabljic et al., 1995).

n: Number of substances.

r²: Correlation coefficient.

SE: Standard error.

In Table 7, predominantly hydrophobics were in this context defined as compounds that only contain carbon, hydrogen and halogen atoms (i.e. C, H, F, Cl, Br, I). Nonhydrophobics are all the chemicals which are not defined as predominantly hydrophobic. It means that the definition was based on molecular structure and does not imply anything about lipophi- licity.

Of other methods, the first order molecular connectivity index (^1c ) has been used successfully to predict log K_oc for hydrophobic organic compounds (Sabljic, 1987). The calculations are performed by PCKOC, a part of the EPIWIN (Meylan & Howard, 1994).

The structure analysis method developed by Meylan and Howard (1995) and two QSARs from TGD were applied: The QSAR for predominantly hydrophobics and the QSAR for non-hydrophobics (cf. above). However, due to some limitations in their domain (cf. below), not all azo colorants could be estimated.

A summary of the estimated log K_oc values is presented in Table 8.

Estimerede log K_oc for azofarver.

*: Including 3 values above log K_ow 8.

**: Including 17 values estimated to be above log K_ow 8. Their maximum value is included in the brackets.

The estimated adsorption coefficients based on structure analysis were generally above the QSAR estimations. In addition azo pigments with a log K_ow value above the QSAR domain for nonhydrophobics appeared to be in general agreement with the results of the structure analysis.

Generally, substances with a log K_oc below 2.7 may be considered potentially mobile. Except for the solvent dyes, all groups include compounds estimated to be potentially mobile. On the other hand, all groups also include compounds with estimated high adsorption potential.

The estimated adsorption coefficients log K_oc indicate that the azo colorants range from compounds that could be classified as potentially mobile and with a low adsorption to immobile substances with a high adsorption. A case by case evaluation is necessary for evaluation of the adsorption.

Bioaccumulation factor for aquatic organisms

The uptake of chemical substances into living organisms occurs mostly by direct adsorption but also along the trophic food web. The internal concentration, e.g. in fish, may increase by accumulation to a level causing toxic effects, even if the internal concentration remains below the critical limit (OECD, 1993b). The accumulated substance may then be passed on to other organisms higher up in the food web which were not directly exposed themselves.

The bioaccumulation in aquatic organisms is defined by the bioconcentration factor (BCF) which is the ratio between the concentration of the chemical in biota and the concentration in water at equilibrium.

Procedures for estimating the bioconcentration potential have been reviewed by e.g. Lyman et al. (1982), Connell (1988), Nendza (1991b), OECD (1993b). Comparison of non-ionic organic chemicals exhibiting substantial bioconcentration revealed several common characteristics. The bioconcentration potential of a chemical was directly related to its lipophilicity and inversely related to its water solubility, molecular charge and degree of ionisation (Lyman et al., 1982; Connell, 1988). In fish, the lipid tissue is the principal site for bioaccumulation and since n-octanol often is a satisfactory surrogate for lipids, linear correlations are usually observed between log BCF and log K_ow. Most QSAR models on bioconcentration are based on log K_ow. The simplest form of the relationships is based on the partition process of the lipid phase of fish and water:

BCF = a * K_ow

(where a is the lipid fraction actually ranging from 0.02 to 0.20).

It is generally agreed that a linear relationship exists for chemicals, which are not biotransformed with a log K_ow < 6. Veith et al. (1979) developed a linear model based on fathead minnows (Pimephales promelas) valid for log K_ow < 6:

log BCF = 0.85 log K_ow - 0.70 (n = 55, r² = 0.90)

In the log K_ow range above 6, the measured log BCF data tend to decrease with increasing log K_ow.

For azo colorants, where several compounds have log K_ow estimated to be above 6, three QSARs are used in the estimation of BCF. Two are recommended in the TGD (1996) and are used related to their domain, i.e. log K_ow below or above 6. In addition, a model developed by Anliker et al. (1988) specifically for dyes was included (cf. Table 9).

QSAR ligninger til estimering af BCF for fisk.

A summary of the estimated log BCF values is presented in Table 10.

Målte og estimerede log BCF værdier for azofarver.

QSAR _TGD: Model equations from TGD.

QSAR _Anl.: Model equations from Anliker et al. (1988).

The Anliker estimated log BCF values are significantly below the estimated log BCF values made by the other QSARs. None of the used azo colorants have a log BCF above 3 according to the Anliker model. Unfortunately, the test substances used to develop the QSAR could not be identified as any of the substances in this study. The main part, 23 out of 25 dyes, was disperse dyes and the remaining two pigments. The number of dyestuffs that were azo compounds was 20. The test method used to find BCF was a method specified by the Japanese authorities. No details on method are presented.

More detailed information on the test method and data for other azo groups should be included before a final evaluation of the Anliker model relative to the TGD models can be performed.

Aquatic toxicity

QSAR models on aquatic ecotoxicity

Within the aquatic ecotoxicology, QSAR models have been used to estimate biological effects of various chemical substances, and frequently, the octanol-water coefficient (log K_ow) of a substance has been used to estimate the ecotoxicity potential of the substance to organisms.

Most of the literature on developing QSARs for toxicity estimations has assumed that compounds from the same chemical class should behave in a similar toxicological manner.

Base-line toxicity or the "minimum toxicity" is related to the hydrophobicity of the substance and is also referred to as non-polar narcosis. In absence of specific toxic mechanisms, the internal effect concentration is almost constant and a substance will then be as toxic as predicted by its hydrophobicity due to the relation with bioconcentration (McCarthy & MacKay, 1993). Indications of non-polar narcosis are the change of EC₅₀ over time. A ratio EC_{50 (24 hours)}/EC_{50 (96 hours)} of approximately 1.0 is considered indicative of non-polar narcosis. Excess toxicity values, calculated by dividing predicted narcosis Type I EC₅₀ values by the observed values, greater than 10 indicate that the substance does not act by non-polar narcosis (Russom et al., 1997).

The class consists of more polar chemicals such as phenols, esters and anilines. The mode of action of these substances is not very specific, but they are significantly more toxic than predicted by non-polar narcosis.

QSARs for acute and long term effects on fish, daphnia and algae are present for chemicals that act by non-specific mode of action (non-polar narcosis as well as polar narcosis).

The latest evaluation of current models in ecotoxicity resulted in the QSAR models mentioned in Table 11 and Table 12 (Verhaar et al., 1992, 1995).

QSAR for ikke-polær narkotisk virkende stoffer.

Ref.: Verhaar et al. (1995).

TGD (1996).

The models were generated by linear regression analysis. The experimental data were generated according to the OECD test guidelines or comparable methods.

QSAR models for chemicals which act by polar narcosis (esters, phenols and anilines) are also available. The mode of action of these compounds is also not very specific, but they are significantly more toxic than predicted by non-polar narcosis.

QSARs for polær narkotisk virkende stoffer (Verhaar et al., 1995; TGD, 1996).

The models were generated by linear regression analysis. The experimental data were generated according to the OECD test guidelines or comparable methods.

For classification purposes, the Danish Environmental Protection Agency has developed a model to estimate the minimum acute aquatic toxicity: QTOXMIN (Pedersen et al., 1995; Pedersen & Falck, 1997).

The QSAR equations recommended for classification are the same or almost the same as the equations recommended in TGD (1996) which will be used in the estimations of aquatic toxicity of azo colorants.

More recent developments in QSARs for ecotoxicity have been performed by Jay Niemelä in the Danish EPA (Niemelä, pers. comm., 1998). The results are not yet published, but Dr. Niemelä has kindly performed the calculations on fish for this project, and his results are presented in the appendix and the summary table below (Table 13). The Niemelä equations are interesting, because they are bilinear and thus overcome the problems of the wide range of log K_ow. From studying the individual results, it was observed that the results from bilinear QSARs gave comparatively better results, and they are therefore considered to be most representative for the estimations of ecotoxicity to fish.

A summary of the estimated acute LC₅₀-values for fish is presented in Table 13. Both results from non-polar and polar QSARs have been presented in the summary. Generally, the results from non-polar and polar QSARs are close or at least in the same order of magnitude for azo colorants. For detailed results refer to Appendix 4B.

Målte og estimerede EC₅₀ værdier for azofarver.

It should be noted that values above the water solubility indicate low acute toxicity.

Only a few experimental values were available for fish, and the results are mostly in accordance with the estimated. However, exceptions occur, e.g. in the disperse dyes where the estimated acute toxicities to fish are two orders of magnitude lower than the experimental values. Since the experimental data have not been studied, no explanation can be given.

For most of the substances the estimated acute toxicity was above the water solubility and may thus be considered of low acute toxicity to fish.

Many questions were raised by the estimation results and indicate that further investigation was necessary. However, a detailed discussion on the individual results was considered to be outside the scope of this survey.

For Daphnia, the QSARs of the TGD were used. Both non-polar and polar estimations were used and a summary of the acute 48-hour toxicities is presented in Table 14.

Målte og estimerede variationsbredder for azofarvers akutte toksicitet (EC₅₀, mg/l) for dafnier.

It should be noted that values above the water solubility indicate low acute toxicity.

Only one experimental value was available for Daphnia, and the result was in accordance with the estimated value.

For most of the substances, the estimated acute toxicity was above the water solubility and may thus be considered of low acute toxicity to Daphnia.

Many questions were raised by the estimation results and indicate that further investigation was necessary. However, a detailed discussion on the individual results was considered to be outside the scope of this survey.

For algae, the QSAR of the TGD was used. A summary of the acute 72-hour toxicities is presented in Table 15.

Målte og estimerede variationsbredder for azofarvers akutte effekt (EC₅₀, mg/l) for alger.

It should be noted that values above the water solubility indicate low acute toxicity.

Only two experimental values were available for algae, and the results were not in accordance with the estimated values. Since the experimental data have not been studied, no explanation can be given.

For most of the substances, the estimated acute toxicity was above the water solubility and may thus be considered of low acute toxicity to algae.

Many questions were raised by the estimation results and indicate that further investigation was necessary. However, a detailed discussion on the individual results was considered to be outside the scope of this survey.