modelkey (511237-goce) is a research project funded by prioritisation of potential river basin...

MODELKEY (511237-GOCE) is a research project funded by

http://www.modelkey.org

Prioritisation of potential river basin specific pollutants in four European

river basins using MODELKEY and ICPDR databases

Peter C. von der Ohe, Valeria Dulio, Jaroslav Slobodnik and Werner Brack



~ 50 mio

known chemicals

(CAS)

33 + 8

Priority Substances

(Chemical Status)

Problem: too many chemicals

Need to prioritize chemicals

River Basin

Specific Substances

(Ecological Status)

178



233 87 Compounds (>LoQ)

AA-EQS 42 31 Priority Substances 33 + 8

How to assess the others?

AA-EQS 104 13 River Basin Specific

Total 146 44 Quality Standards

Elbe Danube

Total 272 91 Compounds measured

MODELKEY + ICPDR databases



Risk Assessment

Risk Assessment:Hazardous Quotient = PEC / PNEC

Exposure Assessment: Predicted Environmental Concentration (PEC)

Effect Assessment: Predicted No-Effect Concentration (PNEC)

No data, No problem?



micro-algae (Selenastrum capricornutum)

invertebrates (Daphnia magna)

fish (Pimephales promelas)

~550 tests ~ 1100 tests ~ 700 tests

P-PNEC: provisional ′EQS′

predict missing data with read-across models



Example: Phenylacetic acid

ACFs:

5 · CaroH1(~Caro)2

1 · CaroH0(~Caro)2(–Cnon-aro)1 · Cnon-aroH2(~Caro)(–Cnon-aro)1 · Cnon-aroH0(–Onon-aro) (=Onon-aro)(–Cnon-aro)1 · Onon-aroH0(=Cnon-aro)1 · Onon-aroH1(–Cnon-aro)

Example: Phenylacetic acid

ACFs:

5 · CaroH1(~Caro)2

1 · CaroH0(~Caro)2(–Cnon-aro)1 · Cnon-aroH2(~Caro)(–Cnon-aro)1 · Cnon-aroH0(–Onon-aro) (=Onon-aro)(–Cnon-aro)1 · Onon-aroH0(=Cnon-aro)1 · Onon-aroH1(–Cnon-aro)

OH

O

Atom-Centered Fragments (ACF)



ACF SimilarityThreshold

n r2 q2 rms

0.0 692 0.73 0.72 0.730.7 570 0.78 0.78 0.640.8 419 0.78 0.78 0.61

0.85 330 0.79 0.77 0.600.9 230 0.87 0.87 0.43

n = number of valid results rms = root mean squared error r2 = squared correlation coefficient q2 = predictive squared corr. coeff.

Schüürmann et al., EST 2011 DOI:10.1021/es200361r

Read-across: ACF-based

Acute Fathead minnow Toxicity (96-h log LC50)



Step 2: Select lowest effect value (existing or predicted)

Step 3: Select safety-factor (1000 on lowest acute value)

EQS /P-PNEC

Effectthreshold

Safetyfactor

Toxic stress

Eff

ect

AlgaeInvertebrateFish

Species-Sensitivity Distribution

Step 1: Predict missing effect data (acute LC50)

P-PNEC: provisional ′EQS′



Lowest PNEC

Sufficient chronic data?

RASTD

Sufficient acute data?

YesNo

PNEC acuteSTD

Yes

P-PNECPredicted

No

Lowest PNEC

Ecotoxicological database

PNEC acuteRA

PNEC chronicRA

STD = standard test dataRA = data from existing Risk Assessments



Why lowest PNEC?

Chronic data from three trophic levels: AF 10

Acute to Chronic ratio: chronic generally a factor 10

ACR 10 * AF 10 = 100x below the LC50 compared to 1000x

lowest PNEC value to be sure!

This is less protective than acute PNEC, but notnescessarily less uncertain, because:

• Test species not the most sensitive ones• Test durations in chronic tests are still to “short”• Interaction between species are not considered



SP

EA

R [

%]

Eff

ect

AF 1000

log TU Max Daphnia magna- 4 - 3 - 2 - 1 0

0

20

40

60

80

Von der Ohe et al. 2009, IEAM 5, 50-61

LC50

P-PNEC: overly protective?

sublethal concentrations!

P- PNEC

Toxic pressure



LC50

ChlorfenvinphosChlorfenvinphos

Von der Ohe et al. 2011, STOTEN 409, 2064-2077

Mesocosm-based EQS: safe?

- 4 - 3 - 2 - 1 00

20

40

60

80

Chlorpyrifos

P- PNEC

sublethal exposure shapes the community!

SP

EA

R [

%]

log TU Max Daphnia magna



13

Arithmetic mean 1

90th percentile of all measures of all stations for :- A given substance- A given analytical fraction

Raw data for:- A given substance- A given analytical fraction- A given station- At a given time

station 2

station 1

station 3

Arithmetic mean 3

Arithmetic mean 2

Arithmetic means of all measures for :- A given substance- A given analytical fraction- A given station

PECwater

See Fribourg-Blanc (2008)

Selection of manageable list = substances monitored by more than 3 countries

INERIS proposal for calculation of PECs



14

Criticism on the INERIS proposal

• Taking the average per site and compound will underestimate the exposure risk of discontinuous pollutants (e.g. pesticides)

take the maximum in consideration (95th percenile)

• Taking the 90th percentile of all measured concentrations into consideration may overlook the

really bad guys take at least the 95th percentile

considered for new prioritization approach



Cat. Current situation Action needed1 There is already sufficient evidence on exposure

and effects to prioritise them.EQS derivation and inclusion in routine monitoring programmes

2 First evidence of occurrence in hazardous concentrations in the environment is available (e.g. field studies from research projects), but only few observations.

Screening studies to inform about the current exposure situation

3 These compounds were measured in the environment and are suspected to have effects on ecosystems and human health, but hazard assessment is based on predicted toxicity (P-PNEC)

Perform rigorous hazard assessment

4 For these compounds hazard assessment is based on experimental data, but observations in the environment are scarce (analytical capabilities not yet satisfactory).

Improved analytical methods should be developed and validated

5 These compounds have no or few observations in the environment and hazard assessment is based on predicted toxicity (P-PNEC) .

Screening studies and perform a rigorous hazard assessment

6 Toxicity data is sufficient for the derivation of an EQS and there is evidence that the exposure does not pose a hazard to ecosystem and human health.

Monitoring efforts for these compounds could be reduced.

Six Action-categories



No

No

No

No

Sufficient effect data?

YesNo

Monitoring database

Cat. 3: Action ecotox

Risk: MEC95 > lowest PNEC?

Analytical performance sufficient?LOQ < lowest PNEC?

Yes

Cat. 1: Candidates ecological status

Cat. 6: not priority for monitoring

Cat. 4: Action

analytical

Cat. 5: Action monitoring +

ecotox

Sufficient effect data?

Yes

Cat. 2: Action monitoring

YesYes

Evidence of exposure at more than20 water sites with analysis > LOQ

All observations <LOQ Novel endpoints

Classifcation into Action Categ.



1 Frequenzy of exceedance of (P)-PNEC based on maximum concentrations per site (MECsite)

Indicators for Prioritization

2 Maximum exceedance of the 95th percentile concentration of all sites (MEC95 / (P)-PNEC)

Spatial distribution of potential effects

Extent of potential effects

Overall Ranking



Prioritization Results



Conclusions

• Organic chemicals may play an important role in ecological status deterioration

• Pesticides are among the most frequent and problematic compounds

• Prioritized compounds without experimental data should be tested

• Potentially problematic compounds with insufficient monitoring need more attention



Recommendations

• Point source inputs (e.g. WWTP) of some substances should be considered in future River Basin Management Plans

• Measures that reduce run-off and drainage inputs from agriculture (soil erosion, nutrients and especially pesticides) should be favored!

• Monitoring programs should consider the intermittent release and peak concentrations of pesticides presently applied



Thank You! [email protected]



-5

-3

-1

1

3

5

-5 -3 -1 1 3 5

log EQS

log

P-P

NE

C

Verification: P-PNEC vs EQS



-5

-3

-1

1

3

5

-5 -3 -1 1 3 5

log EQS

log

P-P

NE

C

Verification: P-PNEC vs EQS

Second.poisoning

NOEC = LC50 EQS ~ LC50 EQS value based on

Mesocosm studies. PNEC in the range of acute LC50

Acute to chronic ratio:NOEC is similar to the acute LC50

Poisoning of predators:toxicity is difficult to predict



P-PNEC vs PNECchronic

-5

-3

-1

1

3

5

-5 -3 -1 1 3 5

log PNECchronic

log

P-P

NE

C



P-PNEC vs PNECchronic

-5

-3

-1

1

3

5

-5 -3 -1 1 3 5

log

P-P

NE

C

log PNECchronic

NOEC = LC50 Acute to chronic ratio:NOEC is similar to the acute LC50



P-PNEC vs PNECacute

-5

-3

-1

1

3

5

-5 -3 -1 1 3 5

log

P-P

NE

C

log PNECacute



Prioritization: ElbeEnglish name PP AA-EQS P-PNEC Source BQE Use Class # of sites Frequency Exceedance

azoxystrobin 0.1060 L A pesticide 181 87% >1000dioctyltin 0.0001 P F biocide 138 99% >100terbutylazine 0.5 0.0032 L A pesticide 710 81% >100perfluorooctanoate 0.0029 P M industrial baseproduct 14 93% >102,4'-DDT 0.0048 L M pesticide 198 73% >10indeno(1,2,3-c,d)pyrene 28 0.002 0.0013 P F combustion product 539 72% >10diuron 13 0.2 0.0024 L A pesticide 219 69% >104,4'- DDT 34 0.01 0.0048 L M pesticide 400 68% >10irgarol 0.002 0.0014 L A pesticide 260 37% >100terbutryne 0.03 0.0059 L A pesticide 450 65% >10benzo[b]fluoranthene 28 0.03 0.0038 P F combustion product 632 55% >10HHCB (Galaxolide®) 0.0380 L M industrial baseproduct 788 59% >1metolachlor 0.2 0.0284 L A pesticide 301 46% >10alachlor 1 0.3 0.0045 L A pesticide 79 44% >10flufenacet 0.0035 L A pesticide 183 40% >10benzo[k]fluoranthene 28 0.03 0.0047 P F combustion product 585 39% >10ametryne 0.5 0.0042 L A pesticide 25 48% >1hexazinon 0.07 0.0068 L A pesticide 81 37% >10prometryn 0.5 0.0162 L A pesticide 184 46% >1cyanazine 0.0224 L A pesticide 16 44% >1dimethenamid 0.0180 L A pesticide 19 42% >1desethylterbutylazine 0.0154 P A pesticide 271 39% >1malathion 0.02 0.0148 L M pesticide 23 35% >1desphenyl-chloridazon 0.0626 P A pesticide 36 31% >14-nonylphenol (tech) 24 0.3 0.1384 L F industrial baseproduct 627 26% >1benzo[ghi]perylene 28 0.002 0.0071 P M combustion product 521 25% >1chlorphyriphos-ethyl 0.0013 L M pesticide 623 23% >1AHTN (Tonalide®) 0.0303 P M industrial baseproduct 480 22% >1dimetachlor 0.0298 P A pesticide 157 22% >1parathion-ethyl 0.005 0.0020 L M pesticide 19 21% >1pirimicarb 0.09 0.0167 L M pesticide 83 20% >1



Prioritization: Danube

english name PP AA-EQS P-PNEC Source BQE Use Class # of sites Frequency Exceedance

alachlor 1 0.3 0.0045 L A pesticide 23 100% >1004,4'- DDT 34 0.01 0.0048 L M pesticide 24 100% >10metolachlor 0.2 0.0284 L A pesticide 23 100% >104,4'- DDD 0.0373 P M pesticide 23 100% >10simazine 29 1 0.1813 L A pesticide 38 61% >100perfluorononanoate 0.0004 P M industrial baseproduct 61 100% >1terbutylazine 0.5 0.0032 L A pesticide 87 83% >10aldrin 35 0.01 0.0451 P M pesticide 28 82% >104,4'- DDE 0.0301 P M pesticide 23 91% >1b-hexachlorocyclohexane 0.0820 L F pesticide 23 87% >1endosulfan II 14 0.005 0.2432 L M pesticide 23 83% >1perfluorooctanoate 0.0029 P M industrial baseproduct 96 71% >10endrin 37 0.01 0.1876 L M pesticide 28 71% >1diuron 13 0.2 0.0024 L A pesticide 60 70% >1endosulfan I 14 0.005 0.2432 L M pesticide 23 65% >1lindane 18 0.02 0.0820 L F pesticide 28 64% >1methoxychlor 0.0528 L M pesticide 14 50% >1heptachlor 0.1 0.0327 P A pesticide 30 47% >1dieldrin 36 0.01 0.1876 L M pesticide 28 25% >1a-hexachlorocyclohexane 0.0820 L F pesticide 23 22% >1atrazine 3 0.6 0.1427 L A pesticide 128 19% >1desethylterbutylazine 0.0154 P A pesticide 85 14% >1nonylphenol-1-carboxylate 0.1759 P F industrial baseproduct 103 14% >1benzo[k]fluoranthene 28 0.03 0.0047 P F combustion product 15 7% >1fluoranthene 15 0.1 0.0310 L F industrial intermidiate 35 6% >1perfluorooctansulfonate 0.0271 P F industrial baseproduct 98 4% >1desethylatrazine 0.0308 P A pesticide 91 3% >1

modelkey (511237-goce) is a research project funded by prioritisation of potential river basin...

Documents