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
VLIZ
Supported by the Federal
Science Policy
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Overview DIMAS project
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Background
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Objectives
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Phases
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–
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Selection of substances
Data collection
Evaluation & interpretation
–
Relational database
Data treatment & modelling
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Background
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
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
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Objective DIMAS: development of an easy to interpret, reliable, upto-date database with data specifically for the marine environment
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Involvement of different stakeholders → users committee
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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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Selection substances (2)
Lists and databanks
Involvement
in spills
COMMPS
Dump sites
Gesamp bulkAnnex I
packaged
67-548-EEC
Ecotox
Den Haag
Helcom
ED North
Priority EU
OSPAR
UNECE POP
IMDG marine
pollutants
Selection of compounds
Initial list (5,000 compounds)
Properties, expert judgement, transport, OSPAR
dynamec, …
Website
(www.vliz.be/projects/dimas/)
Final list (250 compounds)
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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
NOT ENOUGH DATA!!
read
across
− micro-organisms
− other
 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
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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 loglogistic 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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Effect modelling (2)
Species Sensitivity Distribution
(SSD)
Microcystis
Cumulative probability
100%
80%
60%
40%
20%
0%
10
100
1000
10000
100000
Concentration (mg/l)
Concentration 1 mg/L
PAF 23%
Pimephales
Daphnia
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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
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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
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
bram.versonnen@euras.be
www.euras.be
info@vliz.be
www.vliz.be
:
Colin.janssen@ugent.be
www.milieutox.ugent.be
http://www.vliz.be/projects/dimas
VLIZ