Development of an integrated database for the management of accidental spills dimas
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VLIZ. Development of an integrated database for the management of accidental spills (DIMAS). Katrien Arijs Bram Versonnen Marnix Vangheluwe Jan Mees Ward Vandenberghe Daphne Cuvelier Bart Vanhoorne Colin Janssen An Ghekiere. Supported by the Federal Science Policy.

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

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Development of an integrated database for the management of accidental spills dimas

VLIZ

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

Katrien Arijs

Bram Versonnen

Marnix Vangheluwe

Jan Mees

Ward Vandenberghe

Daphne Cuvelier

Bart Vanhoorne

Colin Janssen

An Ghekiere

Supported by the Federal Science Policy


Overview dimas project

Overview DIMAS project

  • Background

  • Objectives

  • Phases

    • Selection of substances

    • Data collection

    • Evaluation & interpretation

    • Relational database

  • Data treatment & modelling


Background

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


Objectives

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


Selection substances 1

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


Selection substances 2

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

Selection substances (2)

Lists and databanks

Involvement

in spills

COMMPS

Dump sites

Ecotox

Gesamp bulk-

packaged

Annex I

67-548-EEC

OSPAR

Den Haag

Helcom

Priority EU

UNECE POP

ED North

IMDG marine

pollutants

Selection of compounds

Initial list (5,000 compounds)

Final list (250 compounds)

Website

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


Data gathering

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


Data gathering ecotox

Data gathering: ecotox

NOT ENOUGH DATA!!

  • 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 papers

    E.g. cereals, cocos-oil (no data)

    ↔ anilin:

    • Water: > 60 acute, > 10 chronic

    • Sediment: some

read across

  • micro-organisms

  • other


Phase iii iv

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


Development of an integrated database for the management of accidental spills dimas

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.


Development of an integrated database for the management of accidental spills dimas

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


Development of an integrated database for the management of accidental spills dimas

Exposure modelling (2)

  • Output: partitioning


Development of an integrated database for the management of accidental spills dimas

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


Development of an integrated database for the management of accidental spills dimas

Microcystis

Species Sensitivity Distribution (SSD)

100%

80%

60%

Cumulative probability

40%

20%

0%

10

100

1000

10000

100000

Daphnia

m

Concentration (

g/l)

Pimephales

Effect modelling (2)

Concentration 1 mg/L

PAF 23%


Development of an integrated database for the management of accidental spills dimas

Example: acute effects acetonitrile

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


Development of an integrated database for the management of accidental spills dimas

Conclusion

  • 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


Development of an integrated database for the management of accidental spills dimas

VLIZ

EURAS

VLIZ

LETAE

Rijvisschestraat 118, Box 3,

9052 Gent, Belgium

Pakhuizen 45-52

8400 Oostende, Belgium

J. Plateaustraat 22

9000 Gent, Belgium

Tel.: +32 (9) 257 13 99

Tel.: +32 (59) 34 21 30

Tel.: +32 (9) 264 37 75

(

Fax: +32 (9) 257 13 98

Fax: +32 (59) 34 21 31

Fax: +32 (9) 264 37 66

[email protected]

www.euras.be

[email protected]

www.vliz.be

[email protected]

www.milieutox.ugent.be

:

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


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