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

Overview DIMAS project  Background  Objectives  Phases –Selection of substances –Data collection –Evaluation & interpretation –Relational database  Data treatment & modelling

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  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, → → 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) 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

Website ( Selection substances (2) Selection of compounds COMMPSDump sites Ecotox Gesamp bulk- packaged Annex I EEC OSPAR Den HaagHelcomPriority EUUNECE POP ED North IMDG marine pollutants Involvement in spills Lists and databanks Initial list (5,000 compounds) Final list (250 compounds) Properties, expert judge- ment, transport, OSPAR dynamec, …

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  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 − micro-organisms − other NOT ENOUGH DATA!! read across

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

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.

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

Exposure modelling (2)  Output: partitioning

Effect modelling (1)  Gather + input all toxicity data  Assess quality (reliability and relevance)  Bring data to same level / units (e.g. LC 50, NOEC)  Order data (LC 50, NOEC)  Plot cumulative number of species (%) against endpoint (LC 50, NOEC)  Fit curve (log-logistic)  Read % of species affected at given (estimated) water concentration after spill

PAF 23% Daphnia Microcystis Pimephales  g/l) Species Sensitivity Distribution (SSD) 0% 20% 40% 60% 80% 100% Concentration ( Cumulative probability Concentration 1 mg/L Effect modelling (2)

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

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

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