PROTECTFP6-036425 Numerical Benchmarks for protecting biota against radiation in the environment Methodology to derive benchmarks, selected methods used

FP6-036425FP6-036425PROTECT PROTECT

Numerical Benchmarks for protecting biota against

radiation in the environment

Methodology to derive benchmarks, selected methods used in PROTECT and Results


Methods to derive benchmarks (reminder)3 main methodologies :

(1) Deterministic : based on application of Assessment Factor to critical ecotoxicity data, or

(2) Probabilistic : based on Species Sensitivity Distribution (SSD) approach associated with an arbitrary cut-off value, usually set at a protection level of 95% of the species, and the use of an additional AF,

(3) a weight of evidence approach using effect data from field exposures

(1) & (2) based on critical ecotoxicity values from tests in labe.g., EC10 (preferred to NOEC) : stressor level in a given medium giving 10% effect in the exposed group in comparison to the control group for chronic exposure.

(1) to (3) usually applied for chemical substances to derive PNEC (e.g., TGD, 2003)

Concerning RAS:(1) and (2) applied and compared within ERICA to derive PNEDR (Garnier-laplace et al., 2006)

(1) used by Environment Canada (2003) for radionuclides(3) used by Thompson et al. (2005) for multipollution context (radionuclides + metals)


Methods used within PROTECT

• Following PROTECT Consultation and availability of various EDR10 values (from ERICA and

follow up), SSD method adopted when data sets were adequate (quality and quantity)(1) allow to make the best use of the available most relevant knowledge in a transparent way,(2) allow to quantify the associated uncertainty,(3) easily allow to revise the outcoming values when new knowledge becomes available

• Need to incorporate documented expert judgement at various stages(1) when selecting ecotoxicity data (EDR10 in our case),

(2) when selecting a cut-off value to estimate the Hazardous Dose Rate of interest (HDR5),(3) when applying an AF to obtain the final benchmark (PNEDR).


FREDERICARadiation Effect Database

STEP 1STEP 1 – Compiling quality assessed exposure-effect data

Data sorted per ecosystem, per exposure condition, per bibliographic reference and per test . Quality of data describing each test was assessed against 3 criteria (dosimetry, exp. Design, stats). Multi-rules applied to accept or reject each data set

STEP 2STEP 2 – Building dose rate-effect relationships to estimate critical ecotoxicity values EDR10

Dose-effect relationship was built for each accepted test.

The quality of the fitted model was judged before accepted EDR10. Two models tested (logistic/hormetic)

Effect (%)

100 %

50 %

10 %

EDR10Dose Rate (µGy/h)

EDR50

The 3-step method used

STEP 3STEP 3 – Deriving benchmarcks

Application of SSD on the estimated toxicity values accepted after step 2.

Application or not of the taxonomic weighted options

Application of an AF to the HDR5 to obtain the final benchmark

020406080

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Dose Rate (µGy/h)

PAF (% of Affected Species)


EDR10 from logistic models

EDR10Dose rate (µGy/h)

Eff

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EDR10

Control(response at dose rate 0)

Logistic model

Data set for one test(a test is defined as a consistent group of (dose or dose rate, effect) couples from a given species and a given effect,

examined under defined exposure conditions (duration, irradiation pathway)

The variation of effect with dose (or dose rate) is monotonous. The pattern is consistent with the state-of-the-art on the tested effect

The data set is made of: at least 3 different couples (dose or dose rate, effect) including one for the control group (no dose(rate)) at least two different couples if the effect is analysed relatively to the control.

The maximum effect value in case it was not reached during the test can be fixed theoretically if knowledge on such effect is sufficient. The difference between the maximum effect value observed y(MaxObs) during the test and the theoretical one y(MaxTheo) are used to calculate the extrapolation percentage needed to model dose-effect relationship as follows : %Extrapol = 100 *(y(MaxObs) – y(MaxTheo))/(y(ControlObs) – y(Maxtheo)) where y(ControlObs) is the effect value observed for the control group.

At least one couple is located within the 10 to 90% of the variation of effect observed. This latter is defined as (y(ControlObs)-y(MaxObs))

The Estimated ED50 or EDR10 are between two experimental couples.

YES NO data set is rejected




The Estimated ED50 or EDR10 can be used within a SSD analysis.



Dose rate (µGy/h)

EDR10

Control(response at dose rate 0)

Eff

ec

t

« Hormetic » model

EDR10 from hormetic models

Curve shape NOEC definition the highest dose with a response ≥ 90% of the control

Inverted U shaped curve Selection criteria

- at least 5 dose-response data points (the minimal number to fit a hormesis model with 4 parameters, requires fixing the lower limit to 0) - 1 control data point - at least 2 doses ≤ NOEC with a response numerically higher than the control - 1 NOEC - at least 1 dose > NOEC with a response ≤ 90% of control

NOEC definition the highest dose with a response ≤ 110% of the control

U shaped curve Selection criteria

- at least 6 dose-response data points (the minimal number to fit a hormesis model with 5 parameters; lower and upper limit are different to 0) - 1 control point - at least 2 doses ≤ NOEC with a response numerically lower than the control - 1 NOEC - at least 1 dose > NOEC with a response ≥ 110 % of control

Exclusion criteria

(1) the absence of a relevant control; (2) the incapacity to achieve responses greater than (or less than, depending on end point) the control response (e.g., studies where the end point was survival and the control response was 100% or where the end point was tumour incidence and the control response was zero); (3) at least two doses below the NOEC; (4) at least one dose showing a priori criteria-based inhibition.

Dose-response exhibiting an initial response stimulation or an effect minimisation


STEP 1 – EDR10 selection (1/4)

•PROTECT : 104 EDR10 obtained for chronic external exposure meeting the previous criteria, covering 23 species (6 plants, 8 invertebrates, 9 vertebrates) and various effect endpoints

•ERICA : 82 EDR10 obtained for chronic external exposure meeting the previous criteria, covering 18 species (5 plants, 9 invertebrates, 10 vertebrates) and various effect endpoints grouped as one geometric mean per effect category (mortality, morbidity, reproduction) and per species (ie. for a given species, a single value per category of effect (the GM)-> this allows to ignore intra-species variation for the same effect category)

SSD on 24 GM -> HDR5=82 µGy/h [24-336]


•Among those 104 EDR10,•One single endpoint per species (the lowest value)•Relevance for population sustainability but expert discussion on ecological relevancy of some of them

•To illustrate the sensitivity of the SSD method and the derived screening level dose rate, two sets of EDR10 selected:« repro » list: most sensitive reproduction endpoint per species (at the population level -i.e. include juvenile survival)« ecol » list: most relevant with regard to « direct » ecological interpretation



ID WildlifeGroup Taxo SpeciesL EffectDescription EDR10

Gilbin Crustaceans Invertebrates Daphnia magnaLarval survival to starvation during 5 days, brood 1 (% survival when food lacks) - stress on stress

test of indirect effect on energy allocation to juveniles production 18537

1065 Crustaceans Invertebrates Daphnia pulex Population birth rate (per day) Data read from graph 277633

247 Crustaceans Invertebrates Porcellio scaber Mean number of offspring per tank per dose rate group 1030

Hertel-Aas Annelid Invertebrates Eisenia foetida Hatchlings per adult during the whole 13 weeks reproduction exposure period (F1/ Adult F0) 3369

296 Molluscs Invertebrates Mercenaria mercenaria Survival of juvenile clams (%) on day 426. Dose = max. cumulative dose 119117

361 Annelid Invertebrates Ophryotrocha diadema The percentage of worms in generation 3 surviving to day 62. 2359

326 Molluscs Invertebrates Physa heterostropha Percentage of eggs that hatched 61229

523 Plants Plants Abies balsamea Summary of mean fir characteristics for seven dose-rate catergories, Number of buds (1975), 2945841 Plants Plants Fagopyrum esculentum Productivity in M3 generation (1979), Yield of seeds (g/sq m) 40151

416 Plants Plants Pinus rigida Effect of long term irradiation on seed development. Dose rate provided as average per day 710998 Plants Plants Triticum sinskjae Productive bush amount, % of the control value. N 13, K-48993 T. sinskjae var. Sinskajae 6434

998 Plants Plants Triticum monococcum Productive bush amount, % of the control value. N 3, K-29602 T. monococcum var. Atriaristatum 8573

616 Mammals Vertebrates Mus musculus Nº of litters per fertile female during 245 days (mean; SE). 26Egami Fish Vertebrates Oryzias latipes Male mean gonad weight (mg) 19730

207 Fish Vertebrates Pleuronectes platessa Mean proportion of plaice testes occupied by different cell types irradiated for 197 days - sperm 53

74 Fish Vertebrates Poecilia reticulata Mean life time fecundity 516593 Mammals Vertebrates Rattus norvegicus A1 Spermatogonia ( % of control) 24621 Mammals Vertebrates Spanish goat Total sperm production (% of control): 730 days irradiation 12629 Mammals Vertebrates Sus crofa Testis weight (g) at 70 days of age (+- SE) 6.7

« repro » list : 19 EDR10µGy/h


X

XX

XX

XXX


ID WildlifeGroup Taxo SpeciesL EffectDescription EDR10

Gilbin Crustaceans Invertebrates Daphnia magnaLarval survival to starvation during 5 days, brood 1 (% survival when food lacks) - stress on stress

test of indirect effect on energy allocation to juveniles production 18537

1065 Crustaceans Invertebrates Daphnia pulex Population birth rate (per day) Data read from graph 277633

247 Crustaceans Invertebrates Porcellio scaber Mean number of offspring per tank per dose rate group 1030

Hertel-Aas Annelid Invertebrates Eisenia foetida Hatchlings per adult during the whole 13 weeks reproduction exposure period (F1/ Adult F0) 3369

296 Molluscs Invertebrates Mercenaria mercenaria Survival of juvenile clams (%) on day 426. Dose = max. cumulative dose 119117

361 Annelid Invertebrates Ophryotrocha diadema The percentage of worms in generation 3 surviving to day 62. 2359

326 Molluscs Invertebrates Physa heterostropha Percentage of eggs that hatched 61229841 Plants Plants Fagopyrum esculentum Productivity in M3 generation (1979), Yield of seeds (g/sq m) 40151

416 Plants Plants Pinus rigida Effect of long term irradiation on seed development. Dose rate provided as average per day 710998 Plants Plants Triticum monococcum Fertility, % of the control value. N 15, K-23653 T. monococcum var. Hornemanni 9819

616 Mammals Vertebrates Mus musculus Nº of litters per fertile female during 245 days (mean; SE). 26

74 Fish Vertebrates Poecilia reticulata Mean life time fecundity 516622 Mammals Vertebrates Spanish goat Number born per female (in 5 consecutive gestation: see comment) 1968204 Fish Vertebrates Oryzias latipes Number of surviving fish 90861

« ecol » list : 14 EDR10Omission of endpoints with difficult ecological interpretation (no direct link with reproductive success) e.g., bud production, gonad weight, sperm cell or spermagonia count µGy/h



STEP 2 - SSD-fit to the selected list (1/3)

SSWD - Log NormalSp = unw eighted; TW: none

0%

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Concentration

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Best-Estimate Centile 5% Centile 95% Vertebrates

Plants Invertebrates

R² = 0.9583KSpvalue = 0.500

w m.lg = 3.24 w sd.lg = 1.41

• Repro-list

HDR5 = 8.4 µGy/hCI 95% = [1.4;99]

Dose rate (µGy/h)


SSWD - Log NormalSp = unw eighted; TW: none

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1 10 100 1000 10000 100000 1000000 10000000

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Best-Estimate Centile 5% Centile 95% Vertebrates

Plants Invertebrates

R² = 0.9625KSpvalue = 0.500

w m.lg = 3.81 w sd.lg = 1.12

• Ecol-list

HDR5 = 91 µGy/hCI 95% = [11;710]


Dose rate (µGy/h)


Influence of the proportion of tested species from different taxonomic groups (trophic levels) on the HDR5 while giving the same weight to each trophic/taxonomic group

Taxonomic weight options to fit SSD

Number of EDR10 Number of species Taxonomic Weight HDR5 min maxRepro 19 19 No (5 P; 7 I; 7 V) 8.4 1.4 99

Yes (1 P; 1 I; 1 V) 11 1.8 159Ecol 14 14 No (3 P; 7 I; 4 V) 91 11 710

Yes (1 P; 1 I; 1 V) 72 8.0 566


For the repro-list, applying the same weight for each TG means to decrease the proportion of the most radiosensitive group from 37% (7/19) to 33% (equiprobability) -> HDR5 increases slightlyEcol-case : V from 28% to 33% -> HDR5 decreases


STEP 3 - Screening generic dose rates(in µGy/h)

Weight option HDR5 min max AF PNEDR Generic Screening Value

Repro No 8.4 1.4 99 1 8.4 10

Yes 11 1.8 159 1 11 10Ecol No 91 11 710 5 18 20

Yes 72 8.0 566 5 14 10

Rounded to the nearest 10

•AF= from 1 to 5 according to the TGD- taxonomic coverage- coverage of the inter-species sensitivity- ecological relevancy of endpoints- line of evidence from field - final value compared to background

e.g., 0.1 to 6 µGy/h for marine organisms (Brown et al., 2004) 0.4 to 4 ________freshwater______________________ 0.07 to 0.6______terrestrial________(Beresford et al. in press)


Repro No 8.4 1.4 99 1 8.4 10

Yes 11 1.8 159 1 11 10Ecol No 91 11 710 5 18 20

Yes 72 8.0 566 5 14 10



STEP 3 - Screening generic dose rates(in µGy/h)


Repro No 8.4 1.4 99 1 8.4 10

Yes 11 1.8 159 1 11 10Ecol No 91 11 710 5 18 20

Yes 72 8.0 566 5 14 10


•For illustration purpose: AF=1 for the repro-list and 5 for the ecol-list

generic screening level recommended by PROTECT : 10 µGy/h


Repro No 8.4 1.4 99 1 8.4 10

Yes 11 1.8 159 1 11 10Ecol No 91 11 710 5 18 20

Yes 72 8.0 566 5 14 10



STEP 3’ – Taxonomic screening dose rates(in µGy/h)

•Data were insufficient to create an SSD for the 3 basic taxonomic groups (plants(5), invertebrates(7), vertebrates(7))

•A limited sensitivity analyses of the overall SSD suggested that the only statistically justifiable separation was to remove vertebrates leaving an SSD for plants and invertebrates (with a total of 12 data points)

Weight option HDR5 min max AF PNEDR Taxonomic Screening Value

Non-Vertebrates No (5 P;2 I-M;2 I-A; 3 I-C) 449 166 1791 1 449 450

Yes (same weight per group) 503 167 1858 1 503 500

•We suggest that a taxonomic screening level of 450 µGy/h be used for plants and 450 µGy/h be used for plants and invertebratesinvertebrates in refined assessments if the results of the screening tier assessment suggest this is required. For vertebrates, a taxonomic screening level of 10 µGy/h. For vertebrates, a taxonomic screening level of 10 µGy/h (i.e. the generic screening level) is recommended.


Regulatory action level

• The statistical extrapolation approach could be used to help in the management decision making process by generating different levels of potential impact (e.g. by taking the 50th percentile of the EDR10 distribution or 20th percentile of the EDR50 distribution etc).

• PROTECT will thus provide a range of tabulated effect levels and percentiles which may prove to be useful to decision-makers

Input data for SSD fit EDR10 EDR25 EDR50Number of data 19 17 14

r2 0.958 0.965 0.953

HDR5 8 30 86HDR10 27 85 217HDR20 114 302 665HDR50 1741 3385 5641

•Different percentiles of an unweighted SSD derived using EDRx values from the repro-list as input and following the same derivation methodology as used for the HDR5 values


Summary of the proposed approachand associated benchmarks (1/2)

The use of the proposed generic screening level should enable assessed sites for which incremental dose rates are estimated as being below 10 µGy/h10 µGy/h to be confidently excluded from further assessment.

•First tier of an ERA-type approach (simple – conservative)

The application of the 10 µGy/h10 µGy/h screening dose rate for vertebratesvertebrates and 450 µGy/h for plants and invertebrates450 µGy/h for plants and invertebrates is likely to identify those sites which pose an insignificant risk when used in such assessments.

•Second tier of an ERA-type approach (less conservative)

•The generic and taxonomic screening values we have derived are within the range of values suggested as being appropriate for population level protection by other organisations using expert judgement.

•Suitable data we have identified are currently insufficient to enable the derivation of more refined levels of taxonomic screening values (if these are required) using the SSD approach.


We have presented a SSD-based scientific analysis of the available data which may help decision makers select a value appropriate to either planned or existing situations in consultation with appropriate stakeholders.

•Regulatory action level

Summary of the proposed approachand associated benchmarks (2/2)

Documents

PROTECTFP6-036425 Numerical Benchmarks for protecting biota against radiation in the environment Methodology to derive benchmarks, selected methods used