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

2. Original Study Team Sponsors Peer Review Team Research Contractor
Akzo Nobel
BNFL BP
Fortum
ICI
JM Bostad
NICOLE
Powergen
SecondSite Property
Shell Global Solutions
Solvay
TotalFinaElf
SKB, Netherlands
Kemakta, Sweden
UK Environment Agency
RIVM, Netherlands
VITO, Belgium
Peer Review Team
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 Austria Assessment Criteria; no model
Belgium (Flanders) Vlier-Humaan
Denmark JAGG update in progress
Finland 3-tier method, no model
France Method; no model
Germany UMS ; SISIM
Greece No model
Ireland No model
Italy Guiditta; ROME

6. Countries and Models (2)
Luxembourg No model
Netherlands HESP; SUS; Risc-Human
Norway SFT 99:06
Portugal No model
Spain LUR (Basque Country)
Sweden Report 4639
Switzerland TransSim (groundwater only)
UK Consim; P20; CLEA CLEA UK
Commercial RAM; RISC ; RBCA Toolkit

7. Selected Models Belgium Vlier-Humaan
Denmark JAGG (no dose calculation; RPCs only)
Germany UMS
Italy ROME
Netherlands Risc-Human
Norway SFT 99:06
UK P20 and CLEA
Commercial RISC 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. Generic Scenario Findings
Compounds
Major Pathways
Cadmium
Benzo(a)pyrene (BaP)
Benzene
Atrazine
Trichloroethylene
Soil Ingestion
Dermal contact
Vegetable ingestion
Groundwater migration
Indoor air inhalation

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 Koc 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. Vegetable Models (2)
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
Receptors
50m
Soil Source (mg/kg)
Sand
GW Source (mg/l)
Plume
Groundwater Pathway
Sand

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 Benzene concentrations in indoor air46
Concentrations (mg/m3)
0.07
Note: UMS concentration is 650x higher than RBCA

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 Lube plant: TCE plume in GW
Will show predicted vs. actual GW conc.
Manufactured gas plant - PAHs
Will show soil ingestion results vs. generic site
Fly ash landfill - heavy metals
Chemical plant with chlorinated solvents & pesticides in soil
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
TCE Concentration (mg/l)
Note: Highest model default biodegradation rates used

31. Predicted vs. Actual Indoor Air
Test Site 5: Vapour Concentrations in forecourt shop
Concentrations (g/m3)

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