Environmental risk assessment of chemicals

Paul Howe

Centre for Ecology & Hydrology, UK

Foundation in human health risk assessment

• Extrapolation from surrogate species to one species (humans)

• Identification of key endpoint• Application of factors to account for specific types of

uncertainty• Precautionary – all individual humans are valued

For example …..Organotins

Estimates of Tolerable Daily Intakefor use in the risk assessment on the basis of medium-term exposure

TDI (mg/kg body

weight per day, as

chloride)

Toxicity Uncertainty factor

Monomethyltin 0.0012 Neurotoxicitya 500

Dimethyltin 0.0012 Neurotoxicitya 500

Monobutyltin No available data

Dibutyltin 0.003 Immunotoxicity 1000

Monooctyltin Insufficient data to establish a

TDI; indications that MOT less immunotoxic

than DOT

• Different key toxic endpoints for different organotins

• Some have insufficient data to make an estimate

• Uncertainty factors reflect the adequacy of the dataset

Environmental risk assessment for chemicals

• 140,000 chemicals in European consumer products• Starting point is prioritisation of effort• Screening exercise• In theory, we move from consideration of individual

humans to populations of all other organisms• With small datasets, this is difficult or impossible in

practice

Small datasets and the use of uncertainty factors

• Guidance value always based on one study

• Highly dependent on uncertainty factor applied

• Unlikely to reflect true measure of risk at the population level for all organisms

10

1

100

Acute Chronic

Small datasets and the use of uncertainty factors

10

1

100

Acute Chronic

Base set:

Uncertainty factor of 1000 applied to the red organism

PNEC =

0.0008

UF =

1000

Small datasets and the use of uncertainty factors

10

1

100

Acute Chronic

Base set plus chronic test on green organism:

(not most sensitive)

Uncertainty factor of 1000 applied to the red organism

PNEC =

0.0008

UF =

1000

Small datasets and the use of uncertainty factors

10

1

100

Acute Chronic

Base set plus chronic test on green and yellow organisms:

Uncertainty factor of 100 applied to the red organism

PNEC =

0.008

UF =

100

Small datasets and the use of uncertainty factors

10

1

100

Acute Chronic

Base set plus chronic test on green, yellow and red organisms:

Uncertainty factor of 10 applied to the red organism

PNEC =

0.02

UF =

10

Variability in the deterministic approach

• Selection of key study• Distinction between what is ‘acute’ and what is ‘chronic’• Selection of uncertainty factors• Quality criteria against which studies are judged• ‘Flexibility’ in the guidance documentation (not all

aspects of study quality defined)• Inclusion of factors outside the study (for example

consideration of solubility/volatility of the substance)

For nonylphenol …….

• 4 different key studies selected

• 4 different uncertainty factors applied

…. From a dataset with 17 studies by a group of 6 ‘experts’ in risk assessment worldwide

Data rich chemicals

• Probably <0.1% of all chemicals are data rich

• Quality measures of individual studies are highly variable

• Is it sensible to base a guidance value on only one study?

Data rich chemicals – deterministic approach

• Lowest no-observed effect concentration

• Uncertainty factor of 10 applied (even very data rich substances would have a UF applied)

• Guidance value developed• Value below concentration

required by some organisms (Cu is an essential element)

Probabilistic approach – copper

• Uses all of the available data

• Statistically derived value with error estimation

• Transparent methodology with a defined protection target (95% of species)

• In practice, very few chemicals have had guidance values derived this way

• Few have sufficient data points to fit the distribution (often many of the data are acute rather than chronic tests)

• Restrictive criteria for the use of the probabilistic approach (minimum number of species or taxonomic groups)

Guidance values for inorganic ions

10-4.0000

10-3.0000

10-2.0000

10-1.0000

100.0000

101.0000

102.0000

103.0000

Co

nce

ntr

atio

n (

ion

) m

g/li

tre

amphibiaf ishinvertebratesmicroorganisms

Sn Mn As B Ag Zn Cu F CN

95% point

mg/litre

N= T

Ag 0.0005 44 T1

Cu 0.01 177 T1

CN 0.02 18 T2

As 0.03 38 T2

Zn 0.08 109 T2

Sn 0.4 38 T2

B 1 26 T3

Mn 2 79 T3

F 22 51 >T3

• It is only realistic to estimate relative hazard/risk as order of magnitude bands

• Hazard or risk bands determine priorities; they are not accurate or precise risk values

Local PEC/PNEC ratios for the various uses of organotins

Activity MMTC DMTC MBTC DBTC MOTC DOTC

PVC processing sites (using stabilizers)

Large calendering plant (using TGD)

1.3 0.03 0.002 0.04 0.5 0.1

Small spread coating plant (using TGD)

0.8 0.02 0.001 0.03 0.23 0.05

Generic plant (EUSES)

0.003 0.0001 0.00004 0.001 0.002 0.002

Comparison with exposure to estimate risk

Impacts above the guidance value

Total dissolved copper (µg/litre)

Effects with high bioavailability in water

1-10 significant effects are expected for diatoms and sensitive invertebrates, notably cladocerans. Effects on fish could be significant in freshwaters with low pH and hardness

10-100 significant effects are expected on various species of microalgae, some species of macroalgae, and a range of invertebrates, including crustaceans, gastropods and sea urchins. Survival of sensitive fish will be affected and a variety of fish should show sublethal effects

100-1000 most taxonomic groups of macroalgae and invertebrates will be severely affected. Lethal levels for most fish species will be reached

> 1000 lethal concentrations for most tolerant organisms are reached

Conclusions

• In theory, assessments have moved from consideration of individual humans to populations of all other organisms• With small datasets, this is difficult or impossible in practice• For the vast majority of chemicals a deterministic assessment is carried out• The deterministic methodology is a rather precautionary approach with multiple sources of variability• For the few data rich chemicals it is possible to use probabilistic methods and such methods tend to use most of the data• There need to be enough data points (including species & taxonomic groups) to fit the distribution

Conclusions

• Whatever system is used it needs to be transparent• Whether human health or environment, deterministic or probabilistic,

the guidance value is compared with an environmental concentration to develop a risk ratio

• The subsequent ratio can be used to inform risk management • With enough data it may be possible to subdivide the data into

species sensitivity groupings which can be compared with field observations