Report to DEFRA Contract Reference No: PN-0930

Assessment of the Environmental Properties

and Effects of Pesticide Transformation Products

Final Report

December 2002

Cranfield Centre for EcoChemistry

Cranfield University, Silsoe, Beds, MK45 4DT www.cranfield.ac.uk/ecochemistry/

Assessment of the Environmental Properties and Effects of Pesticide

Transformation Products

C J Sinclair & A B A Boxall

Final Report

December 2002

Cranfield Centre for EcoChemistry Contract No. JA3756a

Cranfield Centre for EcoChemistry Cranfield University, Silsoe, Beds MK45 4DT, UK

Tel: 01525 863000 Fax: 01525 863253

E-mail: ecochemistry@cranfield.ac.uk Web: http://www.cranfield.ac.uk/ecochemistry

EXECUTIVE SUMMARY

Once released to the environment, a pesticide may be degraded by abiotic and biotic

processes. Whilst the resulting transformation products are generally less toxic than the

parent compound, there are instances where a transformation product may be more toxic.

Moreover differences in the properties and persistence of the products may mean that

environmental compartments are exposed to the product to a greater extent than to the parent

compound. As a result, an assessment of the risks posed by transformation products to the

environment is required as part of current regulatory schemes and in recent years guidelines

have been developed on how the assessments can be performed. There has however been

significant debate over the guidelines and concerns have also been raised over the increased

testing requirements arising from such guidance.

One possible alternative to experimental testing is to use data on the properties and

ecotoxicity of the parent compound along with modelling approaches to estimate the effects

of a transformation product. A range of possible techniques are available including: 1)

qualitative assessment of the transformation product molecule to assess its likely pesticidal

activity or whether it may have another potent mode of action; 2) the use of quantitative

structure-activity relationships (QSARs) and quantitative structure-property relationships

(QSPRs); and 3) a consideration of the relative uptake of the transformation product

compared to the parent compound. The objectives of this study were therefore to assess the

suitability of these different approaches and to develop a framework for assessing the

potential effects of pesticide transformation products. These approaches could be used in the

future to determine whether a particular transformation product is relevant and if it is relevant,

what the most appropriate testing strategy would be. The study was performed in four

phases: 1) collation of data; 2) data analysis; 3) assessment of predictive models; and 4)

framework development.

During phase 1, information was initially collated on the degradation pathways of 60 active

substances and 485 transformation products were identified from these pathways for further

study. For each of the identified transformation products, a search of the literature, on-line

databases and PSD disclosure documents was performed in order to obtain data on the

environmental properties (log Kow, log Koc, and pKa) and ecotoxicity for both the product and

its parent compound. The final dataset that was obtained contained information for 89

transformation products arising from 37 parent compounds. Log Kow values were available

for 71 transformation products, pKa values for 64 transformation products and Koc values for

33 transformation products. In terms of ecotoxicity data 96h fish LC50 values were available

for 60 transformation products, 48h fish EC50 values were available for 57 products and 72-

96h algae EC50 values were available for 16 products.

A comparison of ecotoxicity values for the parent compounds with values for the

transformation products indicated that the majority of transformation products will have equal

toxicity to or be less toxic than the parent compound. A significant proportion (30%) were

however more toxic than the parent compound. These increases could be explained by a

number of factors, namely: 1) the transformation product contained a pesticidal toxicophore;

2) the transformation product was the active molecule for a pro-pesticide; 3) the product

would be expected to be accumulated to a greater extent by aquatic organisms than the parent

compound; or 4) the degradation process resulted in a molecule with a different mode of

action to the parent compound that is more potent. When substances containing the

toxicophore or arising from pro-pesticides were removed from the dataset it was found that

the greater the toxicity of the parent compound, the lower the toxicity of the transformation

product.

A number of predictive models were evaluated using the experimental data. Predictions of

log Kow and pKa for the transformation products, obtained using QSPRs, were generally

within an order of magnitude of experimental values. Similar results were obtained for

relationships for predicting soil organic sorption coefficients. QSAR methods for predicting

ecotoxicity performed less well. Whilst a large proportion of predictions were within two

orders of magnitude of experimental values, the toxicity of some transformation products was

over or under predicted by up to four orders of magnitude. The suitability of using QSARs in

the assessment process for transformation products is therefore questionable, although in

some circumstances they may provide useful additional data when estimating risks.

On the basis of the results of the data analysis and model assessments, a three-step approach

was developed for estimating the likely effects of pesticide transformation products. In step 1,

the structure of the transformation product is examined to determine whether it contains a

pesticide toxicophore. For those substances containing a toxicophore, an assessment factor of

0.1 is applied to the ecotoxicity data for the parent compound in order to estimate an effects

concentration. Substances that do not contain a toxicophore are assessed at step 2 to

determine whether they are more hydrophobic or less dissociated than the parent compound

and whether they might be expected to have a more potent mode of action to certain species.

QSPRs are used to estimate the log Kow and pKa values required in the assessment. For

substances that exhibit one or more of these characteristics, appropriate assessment factors are

applied to the parent toxicity data to derive an effects estimate. In step 3, effects estimates are

derived for all other substances. In order to perform the assessments, all that is required is the

chemical structure of the transformation product and a dataset for the parent compound.

Consequently, the approach can be applied at a very early stage in the risk assessment process

to identify substances that require further testing.

The dataset used in the current study is limited and only acute effects on aquatic organisms

have been investigated. It would therefore be beneficial if the findings and the proposed

assessment approach could be further evaluated using additional data and endpoints. A

number of initiatives are currently ongoing in the EU to collate information on the effects of

transformation products and these datasets could probably be used for evaluation purposes.

The focus of the project has been on estimating effects. In order to fully establish the risks

posed by a transformation product, exposure will also need to be considered. It is therefore

recommended that in the future, analogous approaches for exposure assessment of

transformation products are developed.

TABLE OF CONTENTS

1 INTRODUCTION .......................................................................................................... 1

2 DATABASE DEVELOPMENT .................................................................................... 4

2.1 DATA COLLECTION AND COLLATION .....................................................................................4 2.1.1 Identification of transformation products.........................................................................4 2.1.2 Data collection .................................................................................................................6

3 COMPARISON OF ECOTOXICITY DATA FOR PARENT COMPOUNDS AND

THEIR TRANSFORMATION PRODUCTS ....................................................................... 8

3.1 POSSIBLE REASONS FOR INCREASES IN TOXICITY .................................................................11 3.1.1 Presence of active moiety (toxicophore).........................................................................11 3.1.2 Pro-pesticides .................................................................................................................13 3.1.3 Increase in uptake...........................................................................................................13 3.1.4 Change in mode of action ...............................................................................................14 3.1.5 Inherent variability in laboratory test results .................................................................14 3.1.6 Evaluation of study compounds ......................................................................................14

4 PREDICTION OF PROPERTIES AND EFFECTS WITH QSPR AND QSAR.... 20

4.1 PREDICTION OF PHYSICO-CHEMICAL PROPERTIES ................................................................20 4.2 PREDICTION OF ECOTOXICITY ..............................................................................................23

5 RISK ASSESSMENT FRAMEWORK ...................................................................... 26

5.1 CASE STUDY 1: CARBARYL..................................................................................................32

6 DISCUSSION................................................................................................................ 36

6.1 COMPARISON OF TOXICITY DATA PARENT AND TRANSFORMATION PRODUCT.......................36 6.2 APPLICATION OF PREDICTIVE MODELS .................................................................................37 6.3 HAZARD ASSESSMENT FRAMEWORK ...................................................................................40 6.4 CONCLUSION........................................................................................................................41

7 RECOMMENDATIONS ............................................................................................. 43

8 REFERENCES ............................................................................................................. 45

APPENDIX A PHYSICOCHEMICAL PROPERTIES AND ECOTOXICITY DATA

FOR PARENT COMPOUNDS AND .................................................................................. 51

FIGURES

Figure 1 Relationship between the ecotoxicity of parent compounds and their transformation products for fish, daphnids and algae............................................................................. 10

Figure 2 Toxicophores for the major classes of pesticide ...................................................... 12 Figure 3 Relationship between the ecotoxicity (to fish, daphnids and algae) of parents and

their transformation product that a) contain a toxicophore (red), b) are pro-pesticides (green), c) are more hydrophobic than the parent (blue), d) are less dissociated than the parent (yellow), e) might be expected to have a more potent mode of action, or f) exhibit none of these characteristics (grey)................................................................................ 19

Figure 4 The relationship between experimentally-derived log Kow values for a range of transformation products and log Kow values predicted using either: a - KOWWIN or b - TOPKAT-VlogP............................................................................................................... 22

Figure 5 The relationship between experimentally-derived pKa values for a range of transformation products and pKa values estimated using either: a - SPARC or b – ASTER ............................................................................................................................ 22

Figure 6 Relationship between experimentally-derived log Koc values for a range of transformation products and log Koc values estimated using: a - PCKOCWIN and b - the relationship of Kenaga and Goring (1980)...................................................................... 22

Figure 7 The relationships between measured acute toxicity to Daphnia (48h EC50) and predicted toxicity estimated by: a - TOPKAT, b - ECOSAR and c - EU recommended relationships.................................................................................................................... 24

Figure 8 The relationship between measured acute toxicity to fish (96h LC50) and predicted toxicity values obtained from: a - TOPKAT, b - EU recommended relationships, c - ECOSAR and d - ASTER. .............................................................................................. 25

Figure 9 Relationship between parent toxicity values (mg/l) and the difference between parent and transformation product toxicity values for fish (stars), daphnids (squares) and algae (triangles) for transformation products containing a pesticide toxicophore .......... 29

Figure 10 Relationship between parent toxicity values (mg/l) and the difference between parent and transformation product toxicity values for fish (diamonds), daphnids (crosses) and algae (circles) for transformation products that are more hydrophobic (red), less dissociated (green) or have a more potent mode of action (black) than the parent.............................................................................................................................. 30

Figure 11 Relationship between parent toxicity values (mg/l) and the difference between parent and transformation product toxicity values for fish (stars), daphnids (squares) and algae (triangles) .............................................................................................................. 31

Figure 12 Degradation of carbaryl by microorganisms and in soil (taken from Roberts and Hutson, 1999) ................................................................................................................. 32

TABLES

Table 1 Classes of active compounds for which transformation products were identified....... 5 Table 2 Test species and endpoints used in the construction of the database ....................... 6 Table 3 Summary of the data available for parent compounds and their transformation

products ............................................................................................................................ 7 Table 4 Transformation products that were more toxic than their parent compound to fish,

daphnids or algae ............................................................................................................. 8 Table 5 Possible explanations for increases observed for the transformation products........ 16 Table 6 Transformation products pertaining to five factors and their ability to indicate those

which may show an increase in toxicity .......................................................................... 18 Table 7 Relationships between properties predicted using a range of available models and

experimentally-derived data............................................................................................ 21 Table 8 The ability of a range of predictive approaches to predict acute ecotoxicity to fish and

daphnids (96h LC50 and 48h EC50 respectively)............................................................. 23 Table 9 Assessment factors for determining LC/EC50 values of transformation products

during the assessment scheme...................................................................................... 28 Table 10 Predicted physico-chemical properties and modes of action for carbaryl and its

transformation products .................................................................................................. 34 Table 11 Estimated LC/EC50 values for the transformation products of carbaryl .................... 34

1 INTRODUCTION

When released to the environment, organic substances may be degraded either by micro-

organisms or chemical processes (Roberts, 1998; Roberts and Hutson, 1999). Generally

pesticide transformation products will have a lower toxicity to biota than the parent

compound (e.g. Stratton, 1981; Day and Maguire, 1990; Day and Hodge, 1996). However, in

some instances a transformation product may be more toxic (e.g. Stratton and Corke, 1982;

Osano et al., 2002a; Osano et al., 2002b) and consequently these substances may pose a

greater risk to the environment than the parent compound. Differences in the environmental

behaviour of many transformation products compared to the parent (e.g. where a

transformation product may have increased mobility compared to the parent) could also mean

that even when a product is less toxic they still have the potential to have an adverse impact

on the environment. Consequently there is a need to consider transformation products during

the environmental risk assessment process and for pesticides, under EU Directive 91/414/EEC

and its subsequent amendments, data must be provided for all relevant transformation

products, degradation and reaction products which account for more than 10% of the amount

of active substance added. However, Directive 91/414/EEC does not provide any guidance

on how to define a relevant transformation product.

Therefore in 1999, guidelines were drafted on the assessment of transformation products

(CTB, 1999). The proposed approach involved an assessment of the persistence,

contamination potential, pesticidal activity and toxicity of all major transformation products

(i.e. those that reach >10% of the amount of parent applied). Persistence and contamination

potential being determined using a combination of experimental testing and modelling

approaches. Pesticidal activity of the potentially relevant transformation products is assessed

by testing the transformation product in the biological screen most pertinent to the active

substance and ecotoxicity is assessed using acute tests on aquatic (fish, daphnids and algae)

and terrestrial (earthworms and soil microbes) organisms. Using this information a decision

is made as to whether the transformation product is deemed relevant or not. For relevant

transformation products, the Uniform Principles are applied as established in Annex VI of

Directive 91/414/EEC, whilst non-relevant transformation products require no further testing.

Testing may also be required for transformation products ‘that give rise to particular concern’.

Since the publication of the draft guidelines there has been considerable debate over the

approaches used. For example, the Scientific Committee on Plants (SCP, 2000) disagreed

with the rigidity of the >10% trigger value and advised that all transformation products have

1

to be considered potentially relevant until further assessment and that the terms major and

minor should be abandoned. The proposed approaches could well result in a large amount of

potentially unnecessary testing which is a drain on resources in terms of both the cost and

time. A more pragmatic approach would therefore offer a number of benefits.

The effect of a compound on an organism will be dependent on the individual chemical and

the interaction between that chemical and the species of interest (Bradbury 1994; Wroath and

Boxall, 1996). For pesticides, a large amount of data will be available on the ecotoxicity of

the parent compound. The ecotoxicity and environmental risk of transformation products will

be dependent on a range of factors including: 1) whether or not the transformation product has

pesticidal activity; 2) differences in the uptake of the transformation product by biota

compared to the parent compound; and 3) differences in the environmental fate of the

transformation product compared to the parent compound. Therefore, by using available data

on the parent compound combined with knowledge of the structure and physico-chemical

properties of transformation product it may be possible to rapidly identify the environmental

risks posed by a transformation product.

This study was therefore performed to determine whether the environmental effects of

pesticide transformation products can be estimated based on data for the parent compound

and information on structure in order to develop a pragmatic approach for the identification

and risk assessment of pesticide transformation products. The specific objectives of the study

were to:

1. collect and collate available data on pesticide transformation products;

2. provide a qualitative means of identifying transformation products which maintain the

specific mode of action of their parental pesticides;

3. investigate the relative ecotoxicity to non-target organisms of pesticide

transformation products compared to their associated parent compound;

4. investigate the application of quantitative structure property/activity relationships in

the assessment of the fate and effects of pesticide transformation products; and

5. derive a framework for estimating the effects of transformation products on the

environment.

2

This report describes the results of the study. In Chapter 2, the approach used to collate data is

described along with the nature of data that was obtained. In Chapter 3, relationships between

the ecotoxicity of parent compounds and their transformation products are explored whilst in

Chapter 4 the use of predictive models for estimating properties and effects is explored. An

assessment scheme, based on the results of the study, is discussed in Chapter 5.

Recommendations have been made on how the work can be taken forward.

3

2 DATABASE DEVELOPMENT

In the first instance transformation products arising from a range of pesticides were identified

and data were obtained on the environmental properties (i.e. octanol-water partition

coefficients (Kow’s), sorption coefficients (Koc’s) and pKa’s) and effects of a number of

pesticides and their transformation products. The data were then used in subsequent analyses.

The approach used to obtain the data and the dataset obtained are described below.

2.1 Data Collection and Collation

2.1.1 Identification of transformation products

Initially, an extensive search was undertaken to identify the environmental degradation

products of a wide range of pesticides. The majority of the degradation products and

pathways were identified using the reviews of Roberts (1998) and Roberts and Hutson (1999)

and disclosure documents produced for individual active substances by the Pesticides Safety

Directorate (PSD). Only those transformation products that are formed by biological,

chemical and/or physical processes in soil, water, sediment and air were selected.

Transformation products formed solely as a product of metabolism by plants and/or animals

were not considered. If a compound was identified as a result of pesticide degradation it was

assessed, no matter what amount, relative to the parent compound, was formed during the

transformation process.

Using this search strategy, information was obtained on the transformation pathways of 60

active compounds and based on these pathways the structures of 485 transformation products

were identified. The active compounds examined covered a range of chemical classes and

included 27 herbicides, 20 insecticides, 12 fungicides and one compound with a mixed mode

of action. All the major classes of pesticides were represented by at least one active

compound ( ). The selected chemicals can therefore be considered as representative as

possible of pesticides in general.

Table 1

4

Table 1 Classes of active compounds for which transformation products were identified

Insecticides Herbicides Fungicides

1 Benzoylureas 1 Anilides 1 Alkylenebis (dithiocarbamates)s3

Carbamates 1 Aryloxyalkanamides 1 Anilinopyrimidines 4 Organochlorine insecticides 2 Aryloxyalkanoic acids 1 Aromatic hydrocarbon derivatives 5 Organophosphorus insecticides 1 Arylphenoxypropionic acids 2 Azoles and analogues 1 Organotin insecticides 2 Benzoic acids 1 Benzimidazoles 2 Oxime carbamates 1 Bipyridilium herbicides 1 Carboxamides 3 Pyrethroids 1 Bis-carbamates 1 Methyl isothiocyanate precursors 1

Miscellaneous insecticides

1 Chloroacetanilides

1 Organophosphorus fungicides 2 Diphenyl ethers 1 Phenylamides

1 Hydroxybenzonitriles 1 Strobilurin analogues 1 Organophosphorus herbicides

1 N-Trihalmethylthio derivatives

1 Pyrazoles1 4-Pyridones1 Quinolinecarboxylic acids

3 Sulfonylureas2 Thiocarbamate2 1,3,5-Triazines

2 Ureas1 PGRs

5

2.1.2 Data collection

Once structures of the transformation products had been identified data were collected on the

physico-chemical properties (pKa, log Kow and log Koc), ecotoxicity and fate and behaviour of

both pesticides and their transformation products. Data were collected from multiple sources

including the open literature, databases such as the USEPA ECOTOX database (EPA, 2002),

the EU IUCLID database (EC, 2000), the Syracuse Research Corporation’s EFDB and

PHYSPROP databases (SRC, 2002a; SRC, 2002b) and PSD disclosure documents.

The ecotoxicity data obtained covered a wide range of test species and endpoints. Moreover,

multiple values were often available from a number of sources for a particular endpoint. Only

a limited amount of information was available on the chronic effects of the transformation

products, effects on aquatic macrophytes and effects on terrestrial organisms. Therefore, for

comparative reasons, only data derived from acute tests using fish, daphnids and algae and

following OECD guidelines were selected for future analysis (Table 2).

Table 2 Test species and endpoints used in the construction of the database

Data set Species

End point Source

Fish

Brachydanio rerio Cyprinus carpio

Lepomis macrochirus Oncorhynchus mykiss

Oryzias latipes Pimephales promelas

Poecilla reticulata

96h LC50

OECD, 1992

Water Flea Daphnia magna Daphnia pulex

48h LC50 and EC50

(intoxication)

OECD, 1984a

Algae Selenastrum capricornutum Scenedesmus subspicatus

Chorella vulgaris

72-96h EC50 (growth) and EC50

(population)*

OECD, 1984b

* This endpoint is not in the OECD guidelines, however was used to increase the number of data points

As many of the data points were obtained from online databases that cite data from the

published literature, it was necessary to assess the accuracy of the citations. As a large

amount of information was obtained it was impractical to assess all data points by obtaining

the original data source that was cited in the database. Therefore the original citation was

therefore only obtained in the following instances:

6

1. when a large number of data points were available on a particular substance from a

number of sources and where the values for one or more of the data points exhibited a

large difference compared to the majority of the data points; and

2. when three or fewer data points were reported for a particular substance.

If appropriate, the data were revised in light of the results of the quality assessment. All

assessed data were then entered into an Accord for Excel 5.0 spreadsheet (Accelrys, 2001a)

which was used for subsequent analyses. Where multiple data points were available for a

particular endpoint, the median value was calculated and used in the analyses.

The final database (Appendix A) comprised property and ecotoxicity values for 89

transformation products arising from 37 parent compounds. Log Kow values were available

for 75 transformation products, pKa values were available for 64 transformation products and

Koc values were available for 33 transformation products (Table 3). In terms of the

ecotoxicity data, 96h fish LC50 values were available for 60 transformation products, 48h

daphnid EC50 values were available for 57 transformation products, whilst only 16

transformation products has EC50 values for algae (Table 3).

Table 3 Summary of the data available for parent compounds and their transformation products

Physico-chemical property/

Taxonomic group

Number of parents Number of

transformation products

log Kow 36 71

pKa 35 64

log Koc* 12 33

Fish 30 60

Daphnids 27 57

Algae 11 16 * This data was analysed independently with different dataset

7

3 COMPARISON OF ECOTOXICITY DATA FOR PARENT COMPOUNDS AND THEIR TRANSFORMATION PRODUCTS

The data described in Chapter 2 were used to explore the relationship between the ecotoxicity

of parent compounds and their transformation products. A comparison of parent and

transformation product ecotoxicity data (Figure 1) demonstrated that the majority (70%) of

transformation products have either a similar toxicity to the parent compound or are less

toxic. However, a significant proportion (30 %; ) of transformation products are more

toxic than their parent compound and 4.2% of transformation products are more than an order

of magnitude more toxic. In terms of ecotoxicity values, in only 20 instances did a

transformation product have an EC50 or LC50 less than 1 mg l-1.

Table 4

Table 4 Transformation products that were more toxic than their parent compound to fish, daphnids or algae

Taxonomic group

Parent compound

Transformation product

Fish

2,4 - D

2,4-dichlorophenol

4-chlorophenol

acephate methamidophos

carbaryl 1,4-dihydroxybenzene

1-naphthol

5-hydroxy-,1,4-naphthoquinone

dazomet hydrogen sulphide

methyl isothiocyanate

diazinon sulfotep

flumeturon 3-trifluoromethyl benzeneamine

fluridone m-(trifluoromethyl) benzaldehyde

glyphosate formaldehyde

napropamide 1-naphthol

parathion paraoxon

quintozene 2,3,4,5-tetrachlorophenol

pentachlorophenol

tecnazene 2,3,4,5-tetrachloroaniline

2,3,5,6-tetrachlorothioanisole

8

Taxonomic group

Parent compound

Transformation product

fish

thiodicarb

methomyl

triclopyr 3,5,6-trichloro-2-pyridinol

trisulfusulforon methyl IN-D8526-2

daphnids 2,4 - D 2,4-dichlorophenol

4-chlorophenol

acephate methamidophos

aldicarb aldicarb sulfone

atrazine deisopropyldeethyl atrazine

azocyclotin cyhexatin

butylate diisobutylamine

ethyl mercaptan

dazomet methyl isothiocyanate

diazinon sulfotep

diuron 3,4-dichloroaniline

fluometuron 3-trifluoromethyl benzenamine

gamma HCH 1,2,3,5-tetrachlorobenzene

alpha-HCH

glyphosate Formaldehyde

methylamine

parathion paraoxon

quintozene 2,3,4,6-tetrachlorophenol

pentachloroanisole

3,4,5-trichlorophenol

rimsulfuron IN-70942

thiodicarb Methomyl

triclopyr 3,5,6-trichloro-2-pyridinol

trisulfusulfuron methyl IN-D8526-2

algae 2,4-D 2,4-dichorophenol

dazomet methyl isothiocyanate

9

0.000001

0.0001

0.01

1

100

10000

1000000

0.000001 0.0001 0.01 1 100 10000 1000000

Parent toxicity (mmol/L)

Tran

sfor

mat

ion

prod

uct t

oxic

ity (m

mol

/L)

x = y

x = y/10

x = y/100

Figure 1 Relationship between the ecotoxicity of parent compounds and their transformation products for fish, daphnids and algae

10

3.1 Possible reasons for increases in toxicity

There are a number of possible explanations for the observed increases in toxicity for the

transformation products, these include:

1. the active moiety of the parent compound is still present in the transformation product

and hence the transformation product has the specific mode of action as the parent;

2. the transformation product is the active component of a pro-pesticide;

3. the bioconcentration factor for the transformation product is greater than the parent

and hence more will reach the site of action;

4. the transformation pathway results in a product with a different and more potent

mode of action than the parent compound; and

5. the inherent variability of ecotoxicity results could indicate that a substance is more

toxic than its parent compound even though it has a similar toxicity or is less toxic.

An evaluation of the test results was therefore performed to determine whether any of these

explanations could explain the observed increases in toxicity. The approaches used in the

evaluation and the results obtained are described below.

3.1.1 Presence of active moiety (toxicophore)

The specific toxic action of a pesticide is due to an interaction between a target site in the

organism and the active moiety of the pesticide (i.e. the toxicophore). If during pesticide

degradation the toxicophore remains intact then the transformation product may maintain the

same specific mode of action of the parental compound. However if the degradation process

removes the toxicophore it is unlikely that the transformation product will have the same

activity as the parent compound.

Toxicophores for each of the major classes of pesticide were therefore identified by looking

for sub-structural similarities within a pesticidal class. The Pesticide Manual (Tomlin, 1997)

was used as a basis for this work. Fifty-four toxicophores associated with a wide range of

pesticide classes were therefore identified and a database of toxicophores was developed

(Figure 2).

11

1,2,4-triazinones carbamate (monomethyl) organophosphorous insecticide precursor pyrimidine

1,3,5-triazine carboxamide organophosphorus insecticide pyrimidinyl carbinol

2-dimethylaminopropane-1,3-dithiol chloroacetanilide oxime carbamate pyrimidinyl carbinol

anilide cyclodiene organochlorine oxyimidothioate carbamate pyrimidinyloxybenzoic.cdx

anilinopyrimidines cyclohexanedione oxime phenylamide quinolinecarboxylic acid

aromatic organotin diacylhydrazine phenylpyrrole strobilurin analogue

aryloxyalkanoic acid dicarboximide phenylurea sulfonylurea

aryloxyphenoxypropionate dinitroaniline pyrazole (herbicide) tetrazine

benzimidazole dinitrophenol pyrethroid (cyclopropane ring) thiocarbamate

benzofuranyl alkanesulfonates diphenyl ether pyrethroid (non-cyclopropane ring) triazolopyrimidine sulfonanilide

benzoic acid hydroxybenzonitrile pyrethroid (non-ester) uracil

benzoylurea imidazolinone pyridazinone urea

bis-carbamate N-trihalomethylthio pyridine

carbamate (dimethyl) organochlorine pyridinecarboxylic acid

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Cl

R1

R2

R3O

O

R2R1

O

ON

O

R3

R2

R4

R1

OO

O

R2R1

NN

OCl

NH

R2 R1

N

F

F

F

F

F

R1

R2

R3NCl

Cl

O

OH

R1

R2

R3

N

N N

O

R2 R1

ON

NOH

R1N

NOH

Cl

R1

R2

N

N OO

O

R1

NCl

O OH

R1

R2

R3

O

O

R1

R3

R4

R5

R6

R2

S N

O

O

O

NN

N

R1

R2

N

N N

NCl

R3R1

R2

N S

O

R1 R2

R3R4

R5

R6

R7

NH SNN

N

O

O

R1

R2

R3N

NH

O

O

R1 R3

R2

N N

O

Figure 2 Toxicophores for the major classes of pesticide

12

It was not possible to identify a toxicophore for all the active compounds considered in the

study. Some classes contained too few members within their pesticidal class for reasonable

toxicophore identification, whilst some compounds had an undefined mode of action and/or

are not a member of a defined pesticidal class.

3.1.2 Pro-pesticides

A number of pesticides are pro-pesticides where the applied substance is designed to be

absorbed by an organism and once absorbed is metabolised to an active substance that elicits

the desired effect. If the same degradation pathway occurs in soil, sediment or water, then the

transformation product will be more toxic than the parent substance. The active components

of pro-pesticides are already considered as part of the registration process so full datasets

should already be available.

3.1.3 Increase in uptake

For substances that act via a similar mode of action, a key factor affecting ecotoxicity is the

amount of substance that reaches the active site. The octanol-water partition coefficient, a

measure of hydrophobicity, can provide an indication of the partitioning behaviour of a

compound between an organism and its aqueous environment (bioaccumulation) and has been

used successfully as the sole descriptor for predicting the acute toxicity of toxicants, acting

through a common mode of action, to numerous species, e.g. Daphnia (Hermens et al., 1984)

and guppy (Könemann, 1981). The uptake of a substance from the aqueous environment into

an organism can also be affected by the degree of dissociation, strongly ionised compounds

do not bioaccumulate significantly (Esser and Moser, 1982). Therefore, compounds which

are less dissociated in environmental media can have greater access across biological

membranes.

A hydrophobic substance will be accumulated to a greater extent than a hydrophilic substance

and an undissociated compound is likely to be accumulated to a greater extent than a

dissociated compound. Therefore, if a transformation process results in a transformation

product that is more hydrophobic or less dissociated than the parent, then it is possible that it

will be more toxic. To determine whether such changes explained the increases in ecotoxicity

observed for many of the transformation products, the octanol-water partition coefficients

(which is a measure of hydrophobicity) and acid dissociation constants (which provide an

indication of the degree of dissociation of a substance at neutral pH values) for parent

compounds and transformation products were compared.

13

3.1.4 Change in mode of action

The ecotoxicity of a substance is primarily determined by its toxic mode of action. If a

transformation process results in a transformation product that has a different but more potent

mode of action than the parent compound, then it is likely that the transformation product will

be more toxic. This is most likely for transformation processes that result in reactive

transformation products that elicit toxicity via a number of mechanisms (including redox

cycling) or products that are polar narcotics or respiratory uncouplers.

A number of rules have been developed over the past 10 years for identifying reactive organic

substances, polar narcotics and respiratory uncouplers (e.g. Verhaar et al., 1992; Lipnick,

1991; Russom et al., 1997) and these were therefore used to determine whether any of the

transformation products identified in this study would be expected to have a reactive mode of

action.

3.1.5 Inherent variability in laboratory test results

There will be inherent variability in the results of any laboratory studies that are performed in

either the same laboratory or in different laboratories. Many of the observed increases in

toxicity of the transformation products could therefore be explained by this variability and the

observation may therefore not be a ‘real’ observation. For many of the substances, a number

of datapoints were available for a particular endpoint. For these substances by looking at the

overlap of the reported ranges of the ecotoxicity values it is possible to begin to determine

which of the observations are real and which are not.

3.1.6 Evaluation of study compounds

When those substances identified as having increased toxicity in relation to their parent

compound were evaluated it was found that over 90% of the observed increases in toxicity

could be explained by the factors described above (Table 5). Four substances still contained

the parent toxicophore, three substances were the active substances resulting from a pro-

pesticide, 15 substances were more hydrophobic than their parent compound, two substances

would be expected to be less dissociated than their parent compound, and four substances

would be expected to have a reactive mode of action ( ). Table 6

14

However, a large proportion (30%) of transformation products that were less toxic than the

parent compound also resulted from a pro-pesticide, contained the toxicophore, has an

increase on uptake or would be expected to have a potent mode of action (Table 6). Many of

these observations could be explained by the following:

1. The presence of a toxicophore in a transformation product does not necessarily mean

that the substance will be more potent than the parent compound. For example, the

product may still have pesticide activity but be accumulated to a lesser extent than the

parent.

2. The presence of a toxicophore in a molecule does not always mean that the molecule

will have pesticidal activity. For example, interactions with other functional groups

in the molecule may mean that the toxicophore cannot interact with the site of action.

3. The mode of action of the toxicophore may not be relevant for certain test species.

For example, a substance containing a herbicidal toxicophore would not be expected

to exhibit an increase in toxicity to fish and daphnids.

4. A transformation product that is more hydrophobic than its parent compound and

does not have pesticidal activity is unlikely to be more toxic than its parent to

sensitive species that have a receptor site relevant to the parent mode of action.

5. The inherent variability in toxicity test results may explain the decrease for

transformation products that are only slightly less toxic than the parent compound.

When assessing the potential impacts of a particular transformation product, ideally as much

information as possible should be used on the mode of action of the parent and the sensitivity

of the different taxa to the parent compound.

Analysis of the relationships between parent toxicity and transformation product toxicity

(Figure 3) indicates that for compounds that do not contain the toxicophore or which are not

pro-pesticides, the more toxic a parent compound is the less toxic the transformation product

will be.

15

Table 5 Possible explanations for increases observed for the transformation products

Taxonomic

group

Parent compound

Transformation product

Toxicophore

present

Pro-pesticide

Increase in

hydrophobicity

Decrease in

dissociation

Change in

mode of action

Fish

2,4 - D

2,4-dichlorophenol

√

4-chlorophenol √

acephate methamidophos √

aldicarb aldicarb sulfone √

atrazine deisopropyldeethyl atrazine √

azocyclotin cyhexatin √

butylate diisobutylamine

ethyl mercaptan

carbaryl 1,4-dihydroxybenzene √

1-naphthol √

5-hydroxy-,1,4-naphthoquinone √

dazomet hydrogen sulphide

methyl isothiocyanate √

diazinon sulfotep √

diuron 3,4-dichloroaniline √

flumeturon 3-trifluoromethyl benzeneamine √

fluridone m-(trifluoromethyl) benzaldehyde √

16

Taxonomic

group

Parent compound

Transformation product

Toxicophore

present

Pro-pesticide

Increase in

hydrophobicity

Decrease in

dissociation

Change in

mode of action

gamma HCH 1,2,3,5-tetrachlorobenzene √

alpha-HCH √

glyphosate formaldehyde √

methylamine √

napropamide 1-naphthol

parathion paraoxon √

quintozene 2,3,4,5-tetrachlorophenol √

pentachloroanisole √

pentachlorophenol √

3,4,5-trichlorophenol

rimsulfuron IN-70942 √

tecnazene 2,3,4,5-tetrachloroaniline √

2,3,5,6-tetrachlorothioanisole √

thiodicarb methomyl √

triclopyr 3,5,6-trichloro-2-pyridinol √

trisulfusulforon

methyl

IN-D8526-2 √

17

Table 6 Transformation products pertaining to five factors and their ability to indicate those which may show an increase in toxicity

Factor Total Increase in toxicity (orders of magnitude)

Decrease in toxicity

<one >one <two >two Pro-pesticide 15 5 8 1 1

Contain toxicophore 13 9 4 - -

Increase in hydrophobicity 35 22 12 1 -

Decrease in dissociation 8 4 3 1 -

Change in mode of action 9 4 3 2 -

Other 61 58 3 - -

18

0.000001

0.0001

0.01

1

100

10000

1000000

0.000001 0.0001 0.01 1 100 10000 1000000

Parent toxicity (mmol/L)

Tran

sfor

mat

ion

prod

uct t

oxic

ity (m

mol

/L)

x = y

x = y/10

x = y/100

Figure 3 Relationship between the ecotoxicity (to fish, daphnids and algae) of parents and their transformation product that a) contain a toxicophore (red), b) are pro-pesticides (green), c) are more hydrophobic than the parent (blue), d) are less dissociated than the parent (yellow), e) might be expected to have a more potent

mode of action, or f) exhibit none of these characteristics (grey)

19

4 PREDICTION OF PROPERTIES AND EFFECTS WITH QSPR AND QSAR

Based on the comparisons of ecotoxicity values for parent compounds and their

transformation products, in order to fully assess the risks posed by transformation products, it

will be necessary to determine the hydrophobicity and dissociation potential of the products.

For exposure assessment it would also be beneficial if information could be obtained on the

sorption of the transformation products in soils and sediments. One approach to obtaining

this information in the absence of experimental data is to predict the properties, based on

chemical structure, using quantitative structure-property relationships (QSPRs). A range of

relationships are available for predicting these parameters, including octanol-water partition

coefficient (Kow) (Hansch and Leo, 1979; Meylan and Howard, 1995), soil organic carbon

coefficient (Koc) (Karickhoff, 1995; Meylan et al., 1992) and acid dissociation constant (pKa)

(Karickhoff et al., 1991).

Models (quantitative structure-activity relationships (QSARs)) are also available for

predicting the ecotoxicity of substances. A wide range of relationships are available covering

a range of species (e.g. daphnid and fish species) endpoints (acute and chronic), chemical

classes and modes of action e.g. (Könemann, 1981; Veith et al., 1983; Hermens et al., 1984;

Deneer et al., 1988; Van Leeuwen et al., 1990; Verhaar et al, 1996). These relationships have

been shown to be adequate for predicting toxicity of substances which do not have a specific

mode of action (Veith et al., 1983; Hermens et al., 1984) and maybe useful for the assessment

of transformation products.

If predictions of ecotoxicity and physico-chemical properties using QSARs and QSPRs can

be shown to be accurate for pesticidal transformation products then it may be possible to use

the predictions in the assessment of the potential risks of transformation products. This

Chapter details the investigations into the potential use of predictive techniques for assessing

ecotoxicity to fish and daphnids and the physico-chemical properties Kow, Koc and pKa.

4.1 Prediction of Physico-chemical Properties

The use of currently available methods for predicting the octanol-water partition coefficient

(Kow), soil organic partition coefficient (Koc), and acid disassociation constant (pKa) of

transformation products was investigated. A number of predictive packages were used,

namely KOWWIN v 1.6 (Meylan and Howard, 1995; Meylan and Howard, 1999) and

20

TOPKAT v 6.0 (Accelrys, 2001b) for Kow; ASTER (Russom et al., 1991) and SPARC

(Karickhoff et al., 1991) for pKa; and PCKOCWIN v 1.6 (Meylan et al., 1992; Meylan and

Howard, 1996) for Koc. Structures were input into these packages using SMILES (Weininger,

1998). In addition, a relationship available in the literature for predicting the Koc of a variety

of pesticides, based on Kow, was also assessed (Kenaga and Goring, 1980). Predictions

obtained using the different packages and the literature QSPRs were compared with

experimentally derived values in order to assess the suitability of the different approaches.

Table 7 Relationships between properties predicted using a range of available models and experimentally-derived data

Table 7

Table 7

Parameter Method Regression equation R2 Koc Kenaga and Goring, 1980 0.833x + 0.632 0.83 PCKOCWIN 0.797x + 0.44 0.51 Kow KOWWIN 0.988x – 0.014 0.97 TOPKAT- VlogP 0.889x + 0.223 0.93 pKa ASTER 0.984x – 0.082 0.91 SPARC 0.97x + 0.086 0.95

KOWWIN and TOPKAT-VlogP provided good estimations of the octanol-water partition

coefficient for the pesticide transformation products. Using both approaches, the majority of

predictions were within one log unit of the experimental values. KOWWIN performed

slightly better then TOPKAT-VlogP with 99.2% of transformation products in the test set

predicted to within one log Kow unit ( , ). SPARC and ASTER predicted the

pKa value of >90% of the transformation products to within one pKa unit (Figure 5; Table 7).

Figure 4

Of the two techniques used to predict Koc, PCKOCWIN did not perform as well as the general

pesticide QSPR, with Koc values for only 85% of transformation products being predicted to

within one log unit of experimental values. Using the pesticide general relationship by

Kenaga and Goring (1980), Koc values for more than 95% of transformation products were

predicted to within one log Koc unit of experimental values ( ; ). Figure 6

21

R2 = 0.97-4

-2

0

2

4

6

8

-4 -2 0 2 4 6 8Experimentally derived Log Kow

a,

R2 = 0.93-4

-2

0

2

4

6

8

-4 -2 0 2 4 6 8Experimentally derived Log Kow

b,

Figure 4 The relationship between experimentally-derived log Kow values for a range of transformation products and log Kow values predicted using either: a - KOWWIN or b - TOPKAT-

VlogP

R2 = 0.950

2

4

6

8

10

12

0 2 4 6 8 10 12

Experimentally derived pKa

a,

R2 = 0.910

2

4

6

8

10

12

0 2 4 6 8 10 12

Experimentally derived pKa

b,

Figure 5 The relationship between experimentally-derived pKa values for a range of transformation products and pKa values estimated using either: a - SPARC or b – ASTER

R2 = 0.510

1

2

3

4

5

6

0 1 2 3 4 5 6

Experimentally derived Koc

a,

R2 = 0.830

1

2

3

4

5

6

0 1 2 3 4 5 6

Experimentally derived Koc

b,

Figure 6 Relationship between experimentally-derived log Koc values for a range of transformation products and log Koc values estimated using: a - PCKOCWIN and b - the

relationship of Kenaga and Goring (1980)

22

4.2 Prediction of Ecotoxicity

The suitability of four models/approaches for predicting the ecotoxicity of transformation

products to fish and daphnids was assessed, these were TOPKAT, ECOSAR, ASTER and EU

recommended relationships. TOPKAT is a commercially available QSAR software package

that can provide predictions of toxicity to fathead minnow (Pimephales promelas) and

Daphnia (Accelrys, 2001b). ECOSAR, developed by the USEPA, has the ability to predict

the toxicity of a range of chemical classes to a range of species and endpoints. ASTER

contains a range of QSAR, based on mode of action, for predicting ecotoxicity to fish

(Bradbury, 1994). The final set of relationships tested were those recommended for the

assessment of new and existing industrial chemicals in Europe (EC, 1996). Chemical

structures were input into ASTER, ECOSAR and TOPKAT using SMILES whereas the

octanol-water partition coefficient was the input parameter for the EU recommended QSAR.

ASTER, ECOSAR and TOPKAT automatically select the most appropriate relationship for

the substance of interest. The EU recommended relationships require the user to assign the

compound to a mode of action. The rules of Verhaar et al. (1992) were used for this purpose.

Predictions were compared with available experimentally-derived ecotoxicity data in order to

assess the suitability of different methods for assessing the toxicity of transformation

products. A number of substances were excluded from the comparisons, namely: those

transformation products acting as the active degradation products of a pro-pesticide; those

transformation products identified as containing the same sub-structural toxicity moiety of the

parent pesticide; those substances that were outside the prediction space of a particular

package; and those substances where the package indicated that the prediction might be

unreliable.

Table 8 The ability of a range of predictive approaches to predict acute ecotoxicity to fish and daphnids (96h LC50 and 48h EC50 respectively)

Taxa QSAR Ability Percentage of predictions to within x orders of magnitude x = one x = two x = three x = four Fish EU recommended 32/48 68.8 87.5 96.9 100 ECOSAR 47/48 74.5 87.2 95.7 97.9 TOPKAT 24/48 75 91.7 95.8 100 ASTER 40/48 72.5 92.5 97.5 97.5 Daphnids

EU recommended 27/41 70.4 85.2 88.9 100

ECOSAR 38/41 68.4 78.9 94.7 100 TOPKAT 28/41 85.7 92.9 100 -

23

The four techniques used varied in their capability of predicting a toxicity value and their

accuracy of prediction (Table 8). The EU recommended relationships were restricted to

predictions for polar and non-polar narcotics and respiratory uncouplers, ASTER to those

modes of actions for which it had relationships, whilst TOPKAT provided an indication of

accuracy and only those predictions where “all validation criteria satisfied” were used.

Consequently TOPKAT was only able to predict the toxicity of 50% and 68% of the

substances to fish and daphnids respectively. The EU recommended QSARs were only able

to predict the toxicity to daphnids of 67% of the substances, this value was 65% for the fish

predictions. ASTER was able to provide predictions for 83% of the substances (fish only).

ECOSAR however had no such constraints and was capable of predicting toxicity values for

>92% of transformation products for both fish and daphnids.

R2 = 0.560.01

1

100

10000

1000000

0.01 1 100 10000

Experimental toxicity (mg/L)

a,

R2 = 0.460.01

1

100

10000

1000000

0.01 1 100 10000

Experimental toxicity (mg/L)

b,

R2 = 0.160.01

1

100

10000

1000000

0.01 1 100 10000

Experimental toxicity (mg/L)

c,

Figure 7 The relationships between measured acute toxicity to Daphnia (48h EC50) and predicted toxicity estimated by: a - TOPKAT, b - ECOSAR and c - EU recommended relationships.

24

The toxicity of >78% of the transformation products to daphnids was predicted to within two

orders of magnitude of experimental values by all three techniques. However, there was a

clear difference in the ability of the methods for predicting acute toxicity to Daphnia with

TOPKAT performing better than ECOSAR and EU recommended relationships (Table 8;

). Figure 7

The four techniques used to predict the toxicity fish all predicted the toxicity of >87% of the

transformation products to within two orders of magnitude of experimental values. However,

there was little difference in the overall performance of the techniques investigated (Figure 8;

). Table 8

R2 = 0.500.01

1

100

10000

1000000

0.01 10 10000

Experimental toxicity (mg/L)

a,

R2 = 0.380.01

1

100

10000

1000000

0.01 10 10000

Experimental toxicity (mg/L)

b,

R2 = 0.360.01

1

100

10000

1000000

0.01 10 10000

Experimental toxicity (mg/L)

c,

R2 = 0.310.01

1

100

10000

1000000

0.01 10 10000

Experimental toxicity (mg/L)

d,

Figure 8 The relationship between measured acute toxicity to fish (96h LC50) and predicted toxicity values obtained from: a - TOPKAT, b - EU recommended relationships, c - ECOSAR and

d - ASTER.

25

5 RISK ASSESSMENT FRAMEWORK

On the basis of the results obtained it is possible to propose a framework for estimating the

effects of pesticide transformation products. This framework is described in more detail

below and illustrated with a case study. It is anticipated that the results of the scheme could

be used in conjunction with simple exposure assessment schemes to determine the likely risk

posed by a particular transformation product.

Step 1 – Toxicophore assessment

In the first instance the structure of the transformation product should be examined to

determine whether it contains the parent toxicophore. If it does contain the toxicophore, then

it is recommended that the toxicity data for the parent compound is used along with an

assessment factor (AF) of 0.1 and Equation 1 to derive an effects concentration. The

assessment factor is derived from the relationship between parent toxicity values and the

difference between parent and transformation product toxicity for substances containing the

toxicophore ( ). Substances that do not contain the parent toxicophore should proceed

to the Step 2 assessment.

Figure 9

If the toxicophore for the parent compound has not been characterised, then the assessment

factor of 0.1 should be used for all identified transformation products.

LC/EC50transformation product = LC/EC50parent x AF Equation 1

Step 2 – Assessment of uptake and mode of action

For transformation products that do not contain the toxicophore, the structures should be

assessed to determine whether: 1) the product is more hydrophobic than the parent compound;

2) the product is less dissociated than the parent compound; or 3) the product has a different

but more potent mode of action than the parent compound.

To determine the hydrophobicity (Kow) of the parent compound and the transformation

product it is recommended that either SRC’s KOWWIN software is used to estimate the

octanol-water partition coefficient. To determine dissociation it is recommended that SPARC

is used. The rule based systems of Verhaar et al. (1992) and Lipnick (1991) should be used to

26

determine whether a transformation product has a reactive mode of action or whether it is a

respiratory uncoupler. Available QSAR packages (i.e. ASTER and ECOSAR) may be useful

at this stage as they can provide information on the mode of action of a substance.

For all compounds that are shown to be more hydrophobic, less dissociated or which have a

more potent mode of action than the parent compound, the assessment factors listed in

should be used along with Equation 1. The assessment factors have been derived from the

relationship between parent toxicity and the difference between parent and transformation

product toxicity for transformation products that are more hydrophobic, less dissociated or

which might be expected to have a more potent mode of action ( ) – this overcomes

the issue of species sensitivity. All compounds that are less hydrophobic than the parent,

equally or more greatly dissociated and which do not have a reactive mode of action or are

not respiratory uncouplers, should move on to Step 3 assessment.

Table

9

Table 9

Figure 10

Step 3 – Assessment of remaining products

The effects of all remaining transformation products should be determined based on the

ecotoxicity data for the parent compound using assessment factors and Equation 1. The

assessment factors ( ) have been derived from the relationship between the toxicity of

the parent compound and the difference between the toxicity of transformation product and

parent for all compounds that are do not contain a toxicophore, which would not be expected

to accumulate to a greater extent than the parent and which would not be expected to have a

more potent mode of action ( ). Figure 11

27

Table 9 Assessment factors for determining LC/EC50 values of transformation products during the assessment scheme

LC/EC50 for parent compound (mg/L)

Assessment factor (AF)

Step 1

Any value 0.1

Step 2

<0.1 1

≥0.1 0.01

Step 3

<0.01 100

≥0.01 - <0.1 10

>0.1 1

28

0.01

0.1

1

10

100

1000

-4 -3 -2 -1 0 1 2 3

Log (Parent toxicity)

Tran

sfor

mat

ion

prod

uct t

oxic

ity /

Pare

nt to

xici

ty

AF = 0.1

Figure 9 Relationship between parent toxicity values (mg/l) and the difference between parent and transformation product toxicity values for fish (stars), daphnids (squares) and algae (triangles) for transformation products containing a pesticide toxicophore

29

0.001

0.01

0.1

1

10

100

1000

10000

-3 -2 -1 0 1 2 3 4Log (Parent toxicity)

Tran

sfor

mat

ion

prod

uct t

oxic

ity /

Pare

nt to

xici

ty

AF = 1

AF = 0.01

Figure 10 Relationship between parent toxicity values (mg/l) and the difference between parent and transformation product toxicity values for fish (diamonds), daphnids (crosses) and algae (circles) for transformation products that are more hydrophobic (red), less dissociated (green) or have a more potent mode of action

(black) than the parent

30

0.01

0.1

1

10

100

1000

10000

100000

1000000

-4 -3 -2 -1 0 1 2 3Log (Parent toxicity)

Tran

sfor

mat

ion

prod

uct t

oxic

ity /

Pare

nt to

xici

ty

AF = 100 AF = 10 AF = 1

Figure 11 Relationship between parent toxicity values (mg/l) and the difference between parent and transformation product toxicity values for fish (stars), daphnids (squares) and algae (triangles)

31

5.1 Case Study 1: Carbaryl

In the following Section the assessment approach is illustrated for the carbamate insecticide,

carbaryl. Once released to soils, carbaryl may be degraded into a number of degradation

products including 1-naphthol, 4- and 5- hydroxycarbaryl, N-hydroxymethylated carbaryl, 2-

hydroxycinnamic acid and salicylic acid (Roberts and Hutson, 1999) ( ). The

assessment approach described above was therefore applied to carbaryl and its transformation

products in order to estimate ‘worst case’ effects concentrations for each substance.

Figure 12

Figure 12 Degradation of carbaryl by microorganisms and in soil (taken from Roberts and Hutson, 1999)

O

HN

O

O

HN

O

OH

O

HN

O

OH

O

HN

O

OH

OH

OH

OH

O

O

O

O

OH

OH

O

OHO

OH

OH

OH

OH

OH

O

1 23

4

5

6

+7

8

9

10

32

Step 1 assessment

Examination of structures of each of the transformation products indicates that two products

(2 and 3) contain the carbamate (monomethyl) toxicophore. Applying the assessment factor

of 0.1 to the ecotoxicity data for carbaryl results in an EC50 value for daphnids of 0.00072 mg

l-1 for transformation products 2 and 3 ( ). All other transformation products moved

on to Step 2 assessment.

Table 11

Table 11

Step 2 assessment

Predictions of octanol-water partition coefficients for the transformation products results

obtained using KOWWIN at shown in Table 10. Only transformation product 10 had a

greater hydrophobicity than carbaryl. Predictions of pKa were obtained for each

transformation product using the SPARC programme. Based on the predictions (Table 10),

none of the transformation products would be expected to be less dissociated than the parent

compound.

Using the ‘rules’ proposed for determining the mode of action of a substance, one

transformation product was identified as potentially having a potent toxic mode of action.

Compound 4 contains the quinone moiety which has shown to take part in redox cycling,

substances eliciting this mode of action have previously been shown to be highly toxic to

aquatic organisms (Mason, 1990).

Using the assessment factors listed in Table 9 and the ecotoxicity data for carbaryl, the lowest

estimated EC50 values for substance 4 and 10 is 0.0072 mg l-1 (Table 11). The remaining

substances are moved on to Step 3 assessment.

Step 3 assessment

A significant proportion of the transformation products (1, 5, 6, 7, 8 and 9) did not contain the

parent toxicophore, they would not be expected to be accumulated to a greater extent than the

parent and, based on current knowledge, they would not be expected to have a reactive mode

of action. The assessment factors listed in Table 9 for Step 3 were therefore applied to the

ecotoxicity data for the parent compound, resulting in a lowest EC50 value for all of these

compounds of 0.72 mg l-1 ( ).

33

Table 10 Predicted physico-chemical properties and modes of action for carbaryl and its transformation products

Substance

Log Kow

pKa

Mode of action

carbaryl 2.35 na weak

acetylcholinesterase

inhibitor

1 1.88 14.03 -

4 1.71 na redox cycling

5 1.16 16.19 -

6 1.59 4.11 / 9.21 -

7 2.24 3.06 / 7.73 -

8 -0.7 15.31 / 14.06 -

9 0.32 7.93 / 3.01 / 9.62 -

10 2.69 9.34 -

Table 11 Estimated LC/EC50 values for the transformation products of carbaryl

Substance Fish 96h LC50 Daphnia 48h EC50 Algae 72-96h EC50

carbaryl 4.6 0.0072 100*

1 4.6 0.72 100

2 0.46 0.00072 10

3 0.46 0.00072 10

4 0.046 0.0072 1

5 4.6 0.72 100

6 4.6 0.72 100

7 4.6 0.72 100

8 4.6 0.72 100

9 4.6 0.72 100

10 0.046 0.0072 1

*Fictional value

34

35

6 DISCUSSION

Over the past few years a number of approaches for assessing the potential impacts of

pesticide transformation products on the environment have been proposed (e.g. CTB, 1999).

There has however been considerable debate over the proposed approaches. This study was

therefore performed to investigate, using data available in the public domain, the relationships

between the ecotoxicity of parent compounds and their transformation products in order to

develop proposals for the assessment of transformation products that are based on sound

science. The use of predictive models (including QSPRs and QSARs) for use in the risk

assessment process for transformation products was also explored. A discussion on the data

analysis, assessment of predictive methods and the development and use of the assessment

scheme is provided below.

6.1 Comparison of toxicity data parent and transformation product

A large quantity of information was obtained on the degradation pathways of pesticides and

on the properties and effects of their transformation products. The ecotoxicity data varied

substantially in the species tested, the methodology used and the endpoint that was assessed.

However, using the available information it was possible to develop a database of effects data

for both parent compounds and transformation products that was obtained using standard

OECD guidelines for assessing acute toxicity to fish, daphnids and algae (OECD 1984a,

1984b, 1992). This database covered a broad range of pesticide classes and modes of toxic

action. Large datasets were obtained for fish and daphnids whereas the dataset for algae was

very limited. Only a limited amount of information was available on the chronic toxicity of

pesticide transformation products or on effects on terrestrial organisms. The data analysis

therefore focused on the acute aquatic endpoints.

A comparison of available effects data for parent compounds and their transformation

products indicated that in general, the transformation products were of equal toxicity or had a

lower toxicity than the parent compound. Similar results have been observed in studies by

ECPA (ECPA, 2002) who investigated the relationships between transformation product and

parent ecotoxicity values using data for 182 transformation products associated with 74 active

ingredients. However, a significant proportion (30%) of transformation products was more

toxic to the aquatic organisms than the parent compound. A number of possible explanations

were identified for the observed increases, including: 1) the transformation product acts as the

36

active component of a pro-pesticide; 2) the transformation product contains the parental

toxicophore; 3) the transformation product exhibits an increase in hydrophobicity; 4) the

transformation product exhibits a decrease in dissociation; or 5) the transformation product

has a more potent mode of action to a test organism than the parent. The observations may

also be due to the inherent variability in laboratory ecotoxicity studies. Factors 1 – 5

explained over 90% of the observed increases in toxicity. However, a large proportion (30%)

of transformation products that were less toxic than the parent compound also had one or

more of these characteristics. Possible explanations for this include: 1) the inherent

variability in toxicity results described above; 2) variability in estimates of dissociation or

hydrophobicity; or 3) the fact that the presence of a 2 – dimensional toxicophore does not

always mean that a substance will have pesticidal activity. The observations do however

indicate that if these factors are included in the assessment process, the assessment will be

precautionary. An analysis of the data for substances that were not pro-pesticides or that did

not contain the toxicophore of the parent compound indicated that the ecotoxicity of the

transformation product is inversely correlated with the ecotoxicity of the parent compound.

This is perhaps not surprising as it is the specific mode of action of a pesticide that makes it

potent to an aquatic organism, therefore once this mode of action is removed, potency will

decline significantly.

The availability of data has meant that it has been only possible to investigate the

relationships between acute aquatic toxicity endpoints (for fish, daphnids and algae) for

parent compounds and their transformation products. Recent studies using chronic data for

aquatic species and data for terrestrial organisms (Grasso et al., 2002) indicate that when

these endpoints are considered generally parents are of equal toxicity to or are more toxic than

their transformation products. However, as in the current study, there were instances where a

transformation product was more toxic than the parent compound. Unfortunately, the studies

are based on confidential data so it is not possible to determine whether the factors that

explain the increases in acute aquatic ecotoxicity values used in the present study also explain

the increases in chronic or terrestrial ecotoxicity.

6.2 Application of predictive models

As hydrophobicity (Kow) and degree of dissociation (pKa) were key factors in determining

whether a transformation product was more toxic than its parent compound and as this

information will be unavailable for untested transformation products, the use of quantitative

structure-property relationships (QSPRs) for obtaining the data based on chemical structure

37

was explored. Hydrophobicity is also used as the input for many of the QSARs for predicting

ecotoxicity values, an accurate estimation of log Kow is necessary before toxicity can be

assessed.

Two QSPRs for predicting log Kow were assessed and both performed very well with

KOWWIN performing slightly better than TOPKAT-VLogP with the log Kow of >99% of

compounds predicted to within one log Kow unit of their experimentally derived values. The

pKa methods investigated estimated pKa of >90% of the transformation products to within

one pKa unit of experimental values. SPARC can operate over a large range of pKa units and

has been rigorously tested for a large range of chemical classes (Hilal et al., 1995). ASTER

was unable to provide pKa predictions for benzenesulfonic acid and p-toluensulfonic acid

whereas SPARC was unable to assess some of the transformation products of glyphosate and

diquat. All of the approaches tested therefore appear to be appropriate for use in the

assessment of transformation products and in instances where a particular method is unable to

assess selected substances, the use of the other techniques should be considered.

A range of quantitative structure-activity relationships are also available for estimating

ecotoxicity values and these may provide useful additional information that could be used in

the assessment of transformation products. Four predictive approaches were assessed for

estimating the toxicity of transformation products to fish, whilst three approaches were used

to assess the suitability of estimating toxicity to Daphnia. TOPKAT automatically performs a

3-stage analysis to determine whether the model used applies to the structure under

consideration. Substances with sub-structures not considered during the model development

or outside the optimum prediction space were excluded from the data analysis. This meant

that predictions for only 28 out of 41 transformation products were obtained for daphnids and

24 out of 48 for fish. EU recommended relationships are available for predicting the

ecotoxicity of substances with the following modes of action: narcosis, polar narcosis and

respiratory uncouplers. The rules of Verhaar et al. (1992) were therefore used to identify

whether a substance was likely to have one of these modes of action and to identify which

relationship to use. Using this approach the toxicity of only 27 out of 41 transformation

products to daphnids and 32 out of 48 transformation products to fish could be assessed.

ECOSAR comprises a range of QSARs covering a range of chemical classes. It automatically

identifies the most appropriate relationship for a particular substance and in this study was

able to provide predictions for almost all of the transformation products studied (38 out of 41

for daphnids and 47 out of 48 for fish). ASTER, which only provides toxicity predictions for

fish was able to predict the ecotoxicity of 40 out of 48 transformation products.

38

There was a clear difference in the predictive capability of the three techniques used to predict

acute toxicity to daphnids. TOPKAT predicted the toxicity of >93% of the transformation

products to within two orders of magnitude of experimental values whereas the EU

recommended relationships (85% to within two orders of magnitude) and ECOSAR (75 % to

within two orders of magnitude) performed less well. Similar results were obtained for the

fish predictions. Here, ASTER and TOPKAT were shown to predict the toxicity of >90% to

within two orders of magnitude of experimental values whereas ECOSAR and the EU

recommended QSARs predicted around 87% of the transformation products to within two

orders of magnitude.

Whilst the QSARs that were tested performed well for the majority of substances, for a

number of substances, ecotoxicity values were either greatly over- or under- predicted. If

QSARs are applied in the assessment process there will therefore be large uncertainties in the

quality of the predictions. The value of their use in any assessment scheme is therefore

questionable. One approach to reducing the uncertainties may be to identify those classes of

transformation products where the QSARs perform well and those classes where the

predictions are poor. Using such an approach, rules can then be developed on when it is

appropriate to use a QSAR and when their use is inappropriate. The developments of such an

approach will probably require significantly more datapoints than have been obtained in the

current study.

When considering the risk of a compound in the environment exposure also has to be taken

into consideration as well as hazard. The fate of compounds is highly dependent on their

sorptive behaviour which determines their distribution between the aqueous and soil/sediment

phase ultimately affecting their transport and degradation (Karickhoff, 1995; Lyman, 1995).

The single relationship of Kenaga and Goring (1980), predicted the log Koc of the majority of

degradation products studied to within one log Koc unit whereas PCKOCWIN performed

poorly. Therefore using simple relationships it is likely that mobility of transformation

products in the environment could be rapidly assessed. In practice an uncertainty factor

would be required to account for the differences between experimental and predicted values.

The variations observed between the experimental and predicted Koc values could be

explained by an inherent variability between tests performed in different laboratories and/or

mechanisms of sorption other than partitioning to organic matter. The behaviour of non-polar

39

compounds in different soils can be accounted for when the soil adsorption coefficient is

normalised for organic carbon content (Lambert et al., 1965). However this is not the case for

polar and ionic compounds were the coefficients for different soils can vary by up to three

orders of magnitude (Watts et al., 1995). Consequently, predictive techniques can

underestimate Koc as they commonly take into account only the adsorption to organic matter

and ignore any sorption to mineral surfaces (ECETOC, 1998). There is therefore the potential

for predictive techniques to provide data indicating that compounds which interact with

mineral surfaces are more mobile than they actually are.

This study has demonstrated that available methods for determining physico-chemical

properties work well for transformation products and hence these approaches could be used to

rapidly assess the mobility and potential for uptake of substances compared to their parent

compound. Whilst ecotoxicity predictions did not perform as well, the toxicity to fish and

daphnids of a significant proportion of substances could be predicted to within two orders of

magnitude of experimental values, after further assessment they may be able to be integrated

into any assessment scheme. By combining these techniques with a knowledge of the

properties, mode of action and the toxicity of the parent compound and by using appropriate

uncertainty factors it may be possible to identify transformation products that do and do not

need experimental testing.

6.3 Hazard Assessment Framework

On the basis of the results of the parent and transformation product ecotoxicity comparisons, a

framework has been proposed that can be used to assess the potential effects of

transformation products on aquatic organisms. The approach has been designed to be

precautionary. The only information required to perform the assessments are the structures of

the transformation products for the substance of interest and experimental ecotoxicity values

for the parent compound. It is envisaged that the methodology could be used at an early stage

in the risk assessment process to identify transformation products that might pose a risk to the

environment, these would then be taken forward for experimental testing. The application of

an approach of this type will result in clear cost and time savings and will minimise the use of

laboratory animals.

The scheme and the assessment factors proposed are based on a limited dataset and whilst the

dataset does cover a range of pesticide classes and modes of action, evaluation against

40

additional data would be beneficial and probably a requirement if the approach is to be

adopted by regulatory authorities across Europe. Other studies into the effects of

transformation products (e.g. ECPA, 2002; Grasso et al., 2002) have had access to

unpublished data produced by industry and these indicate that a large body of data has been

generated that could be used for evaluation purposes. These datasets not only include

information on acute toxicity to fish, daphnids and algae but also include data on aquatic

plants, sediment dwellers, earthworms and chronic endpoints.

The uncertainties in the QSAR predictions of aquatic toxicity that were obtained using

currently available QSAR packages are large, many of the predictions differ from

experimental values by three or more orders of magnitude. Even so, the packages may

provide useful additional data (e.g. ASTER provides a prediction of toxic mode of action) for

use in the assessment of transformation products that can be used to further support estimates

based on parent ecotoxicity and transformation product structure and properties.

The assessment process developed in this study focuses solely on the determination of the

potential effects of a particular transformation product. In order to identify transformation

products that might pose a risk to the environment, it will also be necessary to assess

exposure. The development of approaches to assess exposure was beyond the scope of this

study. In order to perform such assessments, information will be required on the persistence

and mobility of transformation products. Assessment of currently available QSPRs for

determining the sorption of a transformation product in soil or sediment systems, indicate that

these approaches could be used to assess mobility. If this data were supplemented with

information arising from fate studies (e.g. degradation route studies and lysimeter

investigations) and used in exposure models, it may be possible to derive an estimate of

exposure for a transformation product. This could then be used along with the effects

estimate to derive a TER and hence assess the risk of a particular transformation product.

6.4 Conclusion

On the basis of available data on the ecotoxicity of pesticides and their transformation

products, an assessment scheme has been developed for assessing the effects of

transformation products on aquatic organisms. As the scheme is based on experimental data

and information on the structure of the transformation products, this scheme can be applied at

a very early stage in the assessment process to determine which transformation products

require experimental testing. The scheme has been designed to be precautionary. However,

41

before it can be applied in the regulatory assessment of pesticide transformation products, it is

recommended that it is further evaluated against additional experimental data.

42

7 RECOMMENDATIONS

Further work is recommended in three areas: 1) further evaluation of the proposed

methodology using additional acute aquatic data and data for other endpoints; 2) more

detailed evaluation of QSAR approaches; and 3) the development of an exposure assessment

scheme, analogous to the effects scheme that has been proposed, that can be used with effects

estimates to determine the risk posed by an individual transformation product. These areas

are expanded upon below.

Further evaluation of the approach

• The proposed assessment scheme has been derived based on the results of data for a

limited number of pesticides. A large amount of data has however been produced

that is not in the public domain and this has recently be drawn together by industry

and the International Centre for Pesticides and Health Risk Prevention. It is

recommended that, if possible, this data be used to evaluate the proposed assessment

scheme. A key component of the evaluation would be the testing of the suitability of

the proposed assessment factors. Such a validation would increase the acceptability

of the proposed approach by regulatory bodies.

• In this study, due to issues of data availability, the focus of the work has been on

acute aquatic ecotoxicity endpoints. It would be beneficial to apply the findings to

other data types (including chronic endpoints and terrestrial endpoints) to determine

whether the same principals apply for these. Recent reports from other studies on

transformation products indicate that the necessary data do exist to perform these

analyses although most of this is confidential industry data.

Further analysis of QSAR approaches

• The results of the QSAR analyses indicate that many of the packages can predict

ecotoxicity values to within two orders of magnitude. There are however a number of

instances where toxicity values are either greatly over predicted or greatly under

predicted. If further data could be obtained (e.g. from industry) it may be possible to

further evaluate the relationships and propose rules on when and when not to use

QSARs in the assessment scheme. There are a number of ongoing initiatives (e.g. by

ECETOC) relating to the selection and use of QSARs in the risk assessment process

43

and it may be appropriate to adopt relationships and guidelines resulting from these

initiatives.

Exposure assessment

• The routes by which pesticides move to surface and ground waters are well

documented and a range of modelling approaches are becoming accepted as

regulatory tools. Critical model inputs related to the chemical are the compound's

sorption to soil, rate of degradation in soil and vapour pressure. For compounds with

a large number of soil transformation products and/or degradation products generated

at low concentrations relative to the parent, it would be extremely useful if

environmental properties and persistence could be estimated using predictive tools.

This would allow a screening-level estimate of environmental exposure which could

be combined with an estimate of ecotoxicity and an appropriate uncertainty factor to

screen out those transformation products that are very unlikely to pose a risk to the

environment. Attention and experimental work could then be focused on compounds

and issues of potential concern. Generalised information on environmental exposure

to transformation products could also contribute to generic discussions at EU level on

the definition of relevant environmental transformation products. A number of

estimation methods related to environmental fate properties have undergone

preliminary evaluation in this study. The results are promising; for example, sorption

coefficients can be predicted to within an order of magnitude of experimental values

using structure-property relationships (QSPRs). However, the data analysed only

cover a small range of pesticide classes and only methods for estimating

hydrophobicity, sorption and dissociation have been explored. It will therefore be

necessary to perform a more rigorous assessment of the methods as well as to

investigate the suitability of estimation approaches for other parameters such as

degradation and volatility. A number of QSPRs for estimating volatilisation have

been developed and quantitative structure-degradability relationships (QSDRs) are

available for estimating both abiotic and biotic degradation. For degradation it may

also be appropriate to use a read-across approach where predictions are obtained on

the basis of data for a structurally similar compound. By using these relationships it

may be possible to develop an exposure assessment framework that is analogous to

the effects framework that has been developed in this study.

44

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50

APPENDIX A PHYSICOCHEMICAL PROPERTIES AND ECOTOXICITY DATA FOR PARENT COMPOUNDS AND TRANSFORMATION PRODUCTS

Pesticide Transformation product log Kow pKa Fish 96h LC50 (mg/L) Daphnid 48h EC50 (mg/L) Algae 72-96h EC50 (mg/L)

Median Max Min n MaxMedian Min n Median Max Min n

2,3,6-TBA 4.34 1.5 0.009 - - 1 - - - - - - - -

2,4,5-trichlorophenol

3.72 7.4 0.902 3.06 0.0012 11 - - - - - - - -

2,4-D 2.81 2.73 27.7 2779 1.4 28 25 135 1.3 9 41.77 - - 1

4-chlorocatechol 8.67 1.58 - - 1 - - - - - - - -

2,4-dichlorophenol 3.06 7.89 6.7 11.6 112 2.6 0.00265.1 11.67 14 9.2 4

4-chlorophenol 2.39 9.41 5.3 9 1.91 16 4.82 8.9 2.5 9 - - - -

succinic acid -0.59 4.21 - - - - 374.2 - - 1 - - - -

acephate -0.85 non 180 2050 1.34 13 49.35 71.78 1.3 4 - - - -

methamidophos -0.66 non 45.5 100 1.28 10 0.039 0.27 0.026 4 - - - -

aldicarb 1.13 11.7 0.861 10.06 0.05 15 0.497 0.74 0.075 4 - - - -

aldicarb sulfone -0.57 47.5 55 40 4 0.28 - - 1 - - - -

atrazine 2.61 1.7 - - - - 46.5 115 6.9 7 - - - -

deisopropyldeethyl atrazine 1.15 - - - - 19.8 - - 1 - - - -

azocyclotin 5.3 5.36 0.004 - - 1 0.04 - - 1 0.16 - - 1

cyhexatin 5.39 0.003 0.0067 0.0013 6 0.0064 0.013 0.0002 2 - - - -

1,2,4-triazole -0.58 - - - - - - - - 22.5 - - 1

benomyl 2.12 0.41 2.4 0.12 35 0.318 0.64 0.068 6 - - - -

carbendazim 1.52 4.2 0.625 4.5 0.024 22 0.405 0.64 0.11 4 - - - -

n-butylamine 0.97 10.8 268 268 32 3 - - - - - - - -

bromoxynil 2.80 3.86 13.8 23 2.09 7 0.121 74 0.041 31 - - - -

4-hydroxybenzonitrile 1.6 7.97 22.6 - - 1 15 - - 1 - - - -

butylate 4.15 non - - - - 85.3 158.6 11.9 2 - - - -

diisobutylamine 10.9 - - - - 35 - - 1 - - - -

ethyl mercaptan 10.6 - - - - 45.1 90 0.17 2 - - - -

51

Pesticide Transformation product log Kow pKa Fish 96h LC50 (mg/L) Daphnid 48h EC50 (mg/L) Algae 72-96h EC50 (mg/L)

Median Max Min n MaxMedian Min n Median Max Min n

carbaryl 2.36 non 4.6 290 0.76 89 0.0072 16.8 0.0003 20 - - - -

1,2-dihydroxybenzene

0.88 9.45 9.06 9.22 3.5 4 1.66 - - 1 - - - -

1,3-dihydroxybenzene 0.80 9.32 54.95 100 40 6 1.28 - - 1 - - - -

1,4-dihydroxybenzene 0.59 10.9 0.14 0.638 0.044 8 0.21 0.29 0.13 2 - - - -

1-naphthol 2.85 9.34 4.18 4.63 3.57 4 - - - - - - - -

5-hydroxy-1,4-napthoquinone 1.92 0.0432 0.088 0.034 13 - - - - - - - -

chlornitrofen 5.09 non - - - - - - - - 0.0098 - -

4-nitrophenol 1.91 7.15 - - - - - - - - 32 - - 1

chlorpyrifos 4.99 non 0.041 2 0.0013 33 0.0006 0.0017 0.0001 5 - - - -

3,5,6-trichloro-2-pyridinol 3.21 1.5 - - 1 - - - - - - - -

diethyl phosphorothioate non 100 - - 2 100 - - 1 - - - -

oxalic acid 1.25 - - - - 137 - - 1 - - - -

dazomet 1.4 non 1.35 16.2 0.16 6 6.11 11.9 0.31 2 1 - - 1

formaldehyde 0.35 13.3 41.4 149 1.41 33 10.2 29 0.2 4 - - - -

methylamine -0.57 10.6 711.27 - - 1 433 702 163 2 - - - -

hydrogen sulphide 7.04 0.035 0.776 0.007 48 - - - - - - - -

N,N'-dimethylthiourea -0.24 - - - - 16.5 - - 1 - - - -

methyl isothiocyanate 0.94 12.3 0.118 0.142 0.094 4 0.168 0.28 0.055 2 0.25 - - 1

DDT 6.91 non 0.0088 100 0.0012 83 0.002 125 0.0004 29 - - - -

DDE 6.51 non 0.07 4.4 0.042 3 0.035 - - 1 - - - -

DDD 6.02 non - - - - 0.0032 - - 2 - - - -

dicofol 5.02 0.51 2.9 0.124 8 - - - - - - - -

diazinon 3.81 non 0.53 10.3 0.02 52 0.001 0.002 0.0005 20 - - - -

diethyl phosphorothioate 100 - - 2 100 - - 1 - - - -

pyrimidinol 1200 - - 1 - - - - - - - -

sulfotep 3.99 non 0.178 1 0.0016 5 0.0014 0.0025 0.0002 2 - - - -

diclobenil 2.74 non 10.5 18 6 11 3.7 10 3.7 5 - - - -

2,6-dichlorobenzamide 0.77 275 469 235 3 856 - - 1 - - - -

2,6-dichlorobenzoic acid 2.23 1.59 130 140 120 4 - - - - - - - -

1

52

Pesticide Transformation product log Kow pKa Fish 96h LC50 (mg/L) Daphnid 48h EC50 (mg/L) Algae 72-96h EC50 (mg/L)

Median Max Min n MaxMedian Min n Median Max Min n

diclofop-methyl 4.62 3.43 0.335 21.9 0.15 16 - - - - - - - -

diclofop

4.58 3.43 21.9 - - 1 - - - - - - - -

diuron 2.68 non 8.55 300 1.95 18 1.4 12 1.4 5 0.0024 - - 1

3,4-dichloroaniline 2.69 2.97 8.06 13 1.94 18 0.79 13 0.1 19 3.7 4.8 2.2 4

fluquinconazole 3.24 non - - - - - - - - 0.046 - - 1

1,2,4-triazole -0.58 - - - - - - - - 72.5 - - 1

FBC 96912 non - - - - - - - - 0.24 - - 1

fluometuron 2.23 non 37.25 96 2.96 12 9.9 - - 1 - - - -

3-trifluoromethyl benzenamine 2.29 3.49 35 - - 1 2.7 - - 1 - - - -

fluridone 1.87 12.3 8.1 22 4.2 19 - - - - - - - -

benzaldehyde 1.48 non 11.2 12.8 1.07 7 - - - - - - - -

m-(trifluoromethyl) benzaldehyde 2.47 non 0.92 1.13 0.76 3 - - - - - - - -

gamma-HCH 3.72 non 0.068 51 0.016 79 1.19 62 0.25 25 3.2 - - 1

1,2,3,4-tetrachlorobenzene 4.6 non 1.3 1.5 1.1 2 - - - - - - - -

1,2,3,5-tetrachlorobenzene 4.56 non 1.6 - - 1 0.86 9.7 0.86 5 17.7 - - 1

1,2,4-trichlorobenzene 4.02 non 2.9 4.8 1.27 14 2.745 50 0.76 6 25.1 - - 1

1,2-dichlorobenzene 3.43 non 5.4 57 1.52 16 2.35 2.4 0.74 5 76.1 - - 1

1,4-dichlorobenzene 3.44 non 4 34.5 0.88 19 10.5 13.5 0.0007 7 - - - -

alpha-HCH 3.8 non 1.21 8 0.32 8 0.9 1 0.8 2 - - - -

beta-HCH 3.78 1.59non 1.66 1.52 2 - - - - - - - -

delta-HCH 4.14 non 2.21 2.83 1.58 2 - - - - - - - -

glyphosate -4.1 0.8 125 7815.7 10 26 457 930 22 6 - - - -

formaldehyde 0.35 non 41.4 149 1.41 33 10.2 29 0.2 4 - - - -

methylamine -0.57 non 711.27 - - 1 433 702 163 2 - - - -

malathion 2.36 non 0.242 25 0.002 60 0.0018 0.033 0.001 13 - - - -

diethyl fumarate non 4.5 - - 1 - - - - - - -

diethyl maleate non 18 - - 1 - - - - - - - -

dimethyl phosphate 18 - - 1 - - - - - - - -

fumaric acid 0.46 - - - - 208 212 204 2 - - - -

53

Pesticide Transformation product log Kow pKa Fish 96h LC50 (mg/L) Daphnid 48h EC50 (mg/L) Algae 72-96h EC50 (mg/L)

Median Max Min n MaxMedian Min n Median Max Min n

napropamide 3.36 non 12.7 30 9.4 6 - - - - - - - -

1-naphthol

2.85 4.18 4.63 3.57 4 - - - - - - - -

parathion 3.83 non 0.75 10 0.018 41 0.0013 0.0072 0.0006 23 - - - -

4-aminophenol 0.04 10.5 12.6 24 1.2 2 1.1 - - 1 - - - -

4-nitrophenol 1.91 7.15 26.2 78.9 3.8 21 8.4 36 4.7 8 - - - -

paraoxon 1.98 non 0.29 0.33 0.25 2 0.0002 - - 1 - - - -

phenmedipham 3.59 non 3 3.98 1.41 3 - - - - - - - -

3-toluidine 1.4 0.1 168 - - 1 - - - - - - - -

propanil 3.07 non 8.6 14 2.3 7 5.75 11.4 1.2 4 - - - -

propionic acid 0.33 4.88 76.2 115 51 4 36.4 50 22.7 2 - - - -

quinmerac -1.11 3.96 - - - - - - - - 149.25 250 48.5 2

BH-518-5 - - - - - - - - 150 - - 1

BH-518-2 - - - - - - - - 700 - - 1

quintozene 4.64 non 0.435 1.6 0.1 8 0.77 - - 1 - - - -

2,3,4,6-tetrachlorophenol 4.45 5.22 0.682 1.1 0.14 6 0.18 2.66 0.09 5 - - - -

tetrachlorocatechol 4.29 1.27 - - 1 - - - - - - - -

2,3,4,5-tetrachlorophenol 3.88 6.35 0.41 0.441 0.205 3 - - - - - - - -

pentachloroanisole 5.45 4.7 0.65 - - 1 0.027 - - 1 - - - -

2,3,4-trichlorophenol 3.8 - - - - 1.1 - - 1 - - - -

2,3,5-trichlorophenol 3.84 5.8 - - - - 1.1 - - 1 - - - -

2,3,6-trichlorophenol 3.77 5.8 - - - - 3.7 - - 1 - - - -

2,4,5-trichlorophenol 3.72 7.4 0.902 3.06 0.0012 11 1.8 2.7 0.9 2 - - - -

2,4,6-trichlorophenol 3.68 6.23 2.8 9.7 0.32 17 2.2 6 0.27 7 - - - -

3,5-dichlorophenol 3.62 8.18 - - - - 1 - - 1 - - - -

3,4,5-trichlorophenol 4.01 7.84 - - - - 0.57 0.68 0.45 2 - - - -

pentachlorophenol 5.12 4.7 0.233 3 0.018 92 0.88 4.59 0.038 48 - - - -

2,3,5,6-tetrachlorophenol 3.88 5.14 0.17 - - 1 0.86 1.15 0.57 2 - - - -

rimsulfuron -1.47 4.00 - - - - 1000 1000 184 3 - - - -

IN-70942 -- - - 178137 95 2 - - - -

54

55

Pesticide Transformation product log Kow pKa Fish 96h LC50 (mg/L) Daphnid 48h EC50 (mg/L) Algae 72-96h EC50 (mg/L)

Median Max Min n MaxMedian Min n Median Max Min n

tecnazene 4.38 non 0.37 - - 1 - - - - - - - -

2,3,5,6-tetrachloroaniline

4.1 non 0.27 0.272 0.058 3 - - - - - - - -

2,3,5,6-tetrachlorthioanisole non 0.21 - - 1 - - - - - - - -

thiodicarb 1.7 non 2.01 2.65 1.21 4 0.049 0.053 0.027 3 - - - -

methomyl 0.6 non 1.45 32 0.48 44 0.0089 3.2 0.0076 6 - - - -

acetonitrile -0.34 4.3 1020 1850 100 7 3600 - - 1 - - - -

triazamate 2.69 non 0.88 4.4 0.43 9 0.048 1.7 0.0035 9 2.2 240 0.3 5

metabolite II 1.62 10 - - 1 0.35 - - 1 120 - - 1

triclopyr -0.45 3.97 7.5 148 1.1 5 - - - -

3,5,6-trichloro-2-pyridinol 3.21 1.5 - - 1 - - - -

trisulfusulfuron-methyl 7300.96 7604.4 71 3 669.5 1200 139 2 0.0370.2785 0.62 4

IN-D8526-2 2.65 139 - - 1 324 - - 2 177.5 - - 1

zineb non 93.6 180 7.2 2 20.5 40 0.97 2 18 - - 1

ethylenethiourea -0.66 180 7500 7.2 3 26 40 0.97 3 1.8 - - 1

ethyleneurea 13000 - - 1 5600 - - 1 16 - - 1