Development of an integrated database for the management of accidental spills (DIMAS) Katrien Arijs...

Development of an integrated database for the management of

accidental spills (DIMAS)

Katrien Arijs Bram Versonnen Marnix Vangheluwe

Jan MeesWard VandenbergheDaphne CuvelierBart Vanhoorne

Colin JanssenAn Ghekiere

VLIZ

Supported by the Federal Science Policy

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Overview DIMAS project

Background

Objectives

Phases– Selection of substances

– Data collection

– Evaluation & interpretation

– Relational database

Data treatment & modelling

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Background

Accidents on sea

– prompt reaction: importance of immediate and accurate information on environmental partitioning, bioavailability and (eco)toxicity

– need for impact analysis tools

Currently: GESAMP, IMDG → limited use

– data not specifically marine

– long term effects?

=> expert judgement currently, slow reaction

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Objectives Objective DIMAS: development of an easy to interpret, reliable, up-

to-date database with data specifically for the marine environment

Involvement of different stakeholders → users committee

4 phases:

– Phase I: identification of compounds lists, transport data, criteria, 100 000 → 5 000 → 250

– Phase II: data collection phys-chem, ecotox (freshwater + marine), human

– Phase III: evaluation and interpretation data quality, freshwater → marine

– Phase IV: relational database, GUI and modelling reliable, simple, expandable, pictograms

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Selection substances (1)

Tiered approach

– Started with NSDB/IMDG/ESIS → IMDG, structure NSDB: 15,000 to 100,000 compounds

– Selection 2,000-3,000 substances:• IMDG: P, PP, ●

• COMMPS

• Ecotox

• Gesamp

• Priority substances EU (ESIS)

• …

– Further selection: intrinsic properties, expert judgement, input users committee, TRANSPORT DATA (RAMA)

– Validated against transport data from harbours

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Website(www.vliz.be/projects/dimas/)

Selection substances (2)

Selection of compounds

COMMPS Dump sites

EcotoxGesamp bulk-

packagedAnnex I

67-548-EECOSPAR

Den Haag Helcom Priority EU UNECE POP

ED NorthIMDG marine

pollutants

Involvementin spills

Lists and databanks

Initial list (5,000 compounds)

Final list (250 compounds)

Properties, expert judge-ment, transport, OSPAR dynamec, …

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Data gathering Physico-chemical data

– ECB-ESIS: • RAR European Commission

• IUCLID Chemical Data sheet

– NSDB

– peer reviewed literature

Ecotoxicological data– ECB-ESIS (RAR)

– US-EPA ECOTOX database (only peer reviewed data)

– ED-North database & UGent ECOTOX database

– peer reviewed literature

Human toxicological data– UGent ECOTOX database

– ECB-ESIS

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Data gathering: ecotox Water / sediment

Saltwater / freshwater

Acute / chronic toxicity

Different trophic levels:– fish– plants– algae– invertebrates

Different endpoints:– mortality– growth– reproduction– other

Data: few or none up to tens of papersE.g. cereals, cocos-oil (no data)

↔ anilin:• Water: > 60 acute, > 10 chronic• Sediment: some

− micro-organisms − other

NOT ENOUGH DATA!!

read across

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Phase III-IV

Data evaluation: quality data ecotox: ‘data reliability & relevance’

– Detailed quality screening of marine data (high relevance)

– Rough quality screening of freshwater data (lower relevance)

→ quality score depending on data source

e.g. RAR: reliable, EPA: not fully verifiable

Database– Input/storage data

– Lay-out database + output

– ‘modelling’: environmental concentrations, effect concentrations

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Data treatment, ‘modelling’ After data are entered in the database, exposure & effect

modelling is carried out

Exposure: environmental partitioning modelling (Mackay)– estimate of compound concentration in different compartments after an

accidental spill;

– based on amount of compound spilled & physico-chemical properties;

– can be automated (advantage when database is updated).

Effect: expressed as Potentially Affected Fraction (PAF)– estimate of % species that will be affected at a certain environmental

concentration;

– based on SSD (Species Sensitivity Distribution) approach with a log-logistic model fitted to the data;

– can be calculated for acute and chronic data;

– can be automated (advantage when database is updated);

– easy to interpret.

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Exposure modelling (1) Mackay level I: estimates the equilibrium partitioning of a quantity of

organic chemical between the different compartments (marine-specific environment was used → no soil compartment)

Input: amount of compound spilled & physico-chemical parameters of the compound

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Exposure modelling (2) Output: partitioning

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Effect modelling (1)

Gather + input all toxicity data

Assess quality (reliability and relevance)

Bring data to same level / units (e.g. LC50, NOEC)

Order data (LC50, NOEC)

Plot cumulative number of species (%) against endpoint (LC50, NOEC)

Fit curve (log-logistic)

Read % of species affected at given (estimated) water concentration after spill

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PAF 23%

Daphnia

Microcystis

Pimephales

10000 100000

g/l)

Species Sensitivity Distribution (SSD)

0%

20%

40%

60%

80%

100%

10 100 1000

Concentration (

Cum

ulat

ive

prob

abili

ty

Concentration 1 mg/L

Effect modelling (2)

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Low risk (< 5% PAF): < 1,500 mg/L

Attention (5-25% PAF): 1,500-3,000 mg/L

Major risk (> 25% PAF): > 3,000 mg/L

Example: acute effects acetonitrile

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Integrated and multi-disciplinary database embedded in a fully web-enabled searching graphical user interface:

http://www.vliz.be/projects/dimas/

This tool will increase transparency and allow for rapid communication in case of an accidental spill

First beneficiaries: people directly involved in the first phase of a contingency plan

Final indirect beneficiaries: general public, who will be better informed and ultimately better protected

VLIZ

EURAS

Rijvisschestraat 118, Box 3,9052 Gent, Belgium

Tel.: +32 (9) 257 13 99

Fax: +32 (9) 257 13 98

[email protected]

www.euras.be

LETAE

J. Plateaustraat 229000 Gent, Belgium

Tel.: +32 (9) 264 37 75

Fax: +32 (9) 264 37 66

[email protected]

www.milieutox.ugent.be

VLIZ

Pakhuizen 45-528400 Oostende, Belgium

Tel.: +32 (59) 34 21 30

Fax: +32 (59) 34 21 31

[email protected]

www.vliz.be

http://www.vliz.be/projects/dimas

Development of an integrated database for the management of accidental spills (DIMAS) Katrien Arijs...

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