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European Risk Model Comparison Study Lawrence Houlden, Archon Environmental Consultants Ltd.

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Presentation on theme: "European Risk Model Comparison Study Lawrence Houlden, Archon Environmental Consultants Ltd."— Presentation transcript:

1 European Risk Model Comparison Study Lawrence Houlden, Archon Environmental Consultants Ltd

2 Original Study Team Sponsors Akzo Nobel BNFL BP Fortum ICI JM Bostad NICOLE Powergen SecondSite Property Shell Global Solutions Solvay TotalFinaElf Peer Review Team SKB, Netherlands Kemakta, Sweden UK Environment Agency RIVM, Netherlands VITO, Belgium Research Contractor Arcadis

3 Reasons for Study Risk-based approach to land management common in Europe, but: Many member states develop own models Differences in model results can be orders of magnitude Poor understanding of differences may undermine credibility of risk assessment Study reported in 2003

4 Objectives Compare human health risk models used in Europe to Increase awareness/understanding of variability Provide confidence in decision making Compare model results to explain output differences - not to show which is better Generic site with standardised inputs Real test cases using model defaults Determine whether fate and transport codes in models are conservative screening tools

5 Countries and Models AustriaAssessment Criteria; no model Belgium (Flanders)Vlier-Humaan DenmarkJAGG update in progress Finland3-tier method, no model FranceMethod; no model GermanyUMS ; SISIM GreeceNo model IrelandNo model ItalyGuiditta; ROME

6 Countries and Models (2) LuxembourgNo model NetherlandsHESP; SUS; Risc-Human NorwaySFT 99:06 PortugalNo model SpainLUR (Basque Country) SwedenReport 4639 SwitzerlandTransSim (groundwater only) UKConsim; P20; CLEA 2002 CLEA UK Commercial RAM; RISC ; RBCA Toolkit

7 Selected Models BelgiumVlier-Humaan DenmarkJAGG (no dose calculation; RPCs only) GermanyUMS ItalyROME NetherlandsRisc-Human NorwaySFT 99:06 UKP20 and CLEA CommercialRISC and RBCA Toolkit

8 Methodology Construct generic site Standardise inputs to extent possible Generate receptor point concentrations, dose levels and human health risk outputs Run sensitivity analyses Run models on 5 real sites for some pathways Accept model defaults (where reasonable) to show likely user-generated outputs

9 Outputs Receptor point concentrations Doses Risk levels Clean-up targets not an output because: Requires assumptions on policy (acceptable risk, additivity) which often have no guidance Some models (e.g. JAGG) compare receptor point concentrations to national quality standards

10 Cadmium Benzo(a)pyrene (BaP) Benzene Atrazine Trichloroethylene Soil Ingestion Dermal contact Vegetable ingestion Groundwater migration Indoor air inhalation Generic Scenario Findings CompoundsMajor Pathways

11 Soil Ingestion (Generic Site) Cadmium Relative Dose (normalised to Vlier-Humaan) Relative Dose

12 Soil Ingestion Models All models have essentially the same soil ingestion algorithms In Vlier-Humaan, exposure time and soil ingestion rate are not independent inputs CLEA uses hard-wired probabilistic exposure at 95% level exposure 4x most models

13 Dermal Contact (Generic Site) BaP Relative Dose (normalised to Risc-Human) Relative Dose

14 Dermal Contact Models CLEA has smaller dose as contaminant is allowed to volatilise as well as absorb Vlier- & Risc-Human limits exposure to 2 hrs/day reflecting skin permeability (generic site has a daily event with no time effect) Risc-Human is very low because its soil-on-skin adherence is hard-wired 10x lower than that in other models

15 Vegetable Ingestion (1) Relative Doses Normalised to RISC Relative Dose

16 Vegetable Models Atrazine (threshold substance) results are similar due to use of similar algorithms For non-threshold substances, doses from SFT:9906, Vlier- and Risc-Human higher due to not averaging doses over a 70-year lifetime RISC is low because it uses a 1% US EPA- derived adjustment factor on Briggs root uptake equation Vlier-Human: hard-wired parameters – fixed total impacted vegetables

17 Vegetable Models UMS hardwires root:leaf ingestion at 85% leaf (vs. 50/50 in generic case). Leaf ingestion has higher uptake for lower K oc substances (e.g. benzene) CLEA is low; six vegetable types and probabilistic dose dissimilar to other models & generic case; second term in Briggs-Ryan equation cannot exceed 1

18 Vegetable Models (2)

19 As (1), atrazine and cadmium results similar due to use of similar algorithms Again, more variability in results of non- threshold substances due to averaging time differences Cadmium relatively high in CLEA due to high BCF factor

20 Generic Site – Groundwater Scenario Plume Soil Source (mg/kg) Groundwater Pathway Receptors Sand GW Source (mg/l) 50m

21 Groundwater Migration (Generic Case) TCE Concentrations (mg/l) in well at 50m GW Concentration (mg/l)

22 Groundwater Models All models for generic site give concentrations within same order of magnitude Most rely on Domenico steady state solution JAGG results may not be comparable because it is limited to transport in one year (steady state may not be reached) SFT:9906 gives lower numbers because it assumes the mixing zone increases with distance

23 Soil to Indoor Air Note: UMS concentration is 650x higher than RBCA Benzene concentrations in indoor air Concentrations (mg/m 3 )

24 Indoor Air – Soil Algorithms RISC and RBCA both use Johnson & Ettinger RISC has infinite source while RBCA has mass balance check (takes lowest value) Both consider diffusion + advection via cracks ROME has indoor air model but does not output air concentrations (only risks) Considers diffusion only via cracks (infinite source)

25 Indoor Air – Soil Algorithms Vlier- and Risc-Human use CSOIL algorithm Diffusion only through pores (not cracks) in concrete foundation UMS is most conservative, assuming indoor air is always 1% of soil gas concentration JAGG uses concrete weathering algorithms for crack density (not straightforward) SFT:9906 requires user to input soil vapour intrusion rate into building (difficult input)

26 Generic Site Conclusions Soil ingestion and groundwater migration models are all similar (one order magnitude) Vegetable ingestion model results surprisingly uniform (one order magnitude) Dermal contact models more variable (two orders magnitude) Indoor air models, particularly UMS code, have highest variability (3 orders magnitude) Differences attributed to identifiable hard- wired parameters or algorithms (indoor air)

27 Test Site Cases 1.Lube plant: TCE plume in GW Will show predicted vs. actual GW conc. 2.Manufactured gas plant - PAHs Will show soil ingestion results vs. generic site 3.Fly ash landfill - heavy metals 4.Chemical plant with chlorinated solvents & pesticides in soil 5.Petrol filling station with BTEX & MTBE Will show predicted vs. actual indoor air conc.

28 Test Site Cases Models unconstrained: Each model run using internal chemical/physical properties data where applicable Model defaults chosen and therefore results should be more typical of those that a user would obtain. Site-specific contaminant suite modelled

29 Soil Ingestion – Generic vs Test Site Relative Doses: BaP Soil Ingestion – Generic and Test Site No.2 750

30 Predicted vs. Actual GW Conc. Test Site 1: TCE concentrations at 57m with biodegradation Note: Highest model default biodegradation rates used TCE Concentration (mg/l)

31 Predicted vs. Actual Indoor Air Test Site 5: Vapour Concentrations in forecourt shop Concentrations ( g/m 3 )

32 Test Site Conclusions Groundwater migration concentrations closely approximated in specific test case, even without biodegradation (e.g. ROME) Using model defaults (vs generic case) can lead to large differences, even for soil ingestion Indoor air models with J&E algorithm closely match real BTEX data for specific test case

33 Overall Conclusions Consistent defensible results possible where fate & transport / chemical parameters well understood Where model defaults are used, significant differences (3 orders magnitude) can occur Limited test sites indicate some models are conservative, but others more predictive Risk managers need to critically assess model assumptions & how software applied

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