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European Risk ModelComparison Study
Lawrence Houlden, Archon Environmental Consultants Ltd
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
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
ObjectivesCompare 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
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
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 2002 CLEA UK
Commercial RAM; RISC ; RBCA Toolkit
Selected Models
Belgium Vlier-HumaanDenmark 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
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
OutputsReceptor 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
Cadmium
Benzo(a)pyrene (BaP)
Benzene
Atrazine
Trichloroethylene
Soil Ingestion
Dermal contact
Vegetable ingestion
Groundwater migration
Indoor air inhalation
Generic Scenario Findings
Compounds Major Pathways
Soil Ingestion (Generic Site)
Cadmium Relative Dose (normalised to Vlier-Humaan)
0
10
20
30
40
50
RISC RBCA Risc-Human
ROME SFT UMS Vlier-Humaan
CLEA
Rel
ativ
e D
ose
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
Dermal Contact (Generic Site)
BaP Relative Dose (normalised to Risc-Human)
0
20
40
60
80
100
RISC RBCA Risc-Human
SFT UMS Vlier-Humaan
CLEA
Rel
ativ
e D
ose
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
Vegetable Ingestion (1)
RISC Risc-Human
SFT UMS Vlier-Humaa
n
CLEA
AtrazineBenzene
0
5
10
15
20
Relative Doses Normalised to RISC
Rel
ativ
e D
ose
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
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
Vegetable Models (2)
0.00E+00
5.00E-03
1.00E-02
1.50E-02
2.00E-02
2.50E-02
3.00E-02
3.50E-02
Do
se (
mg
/kg
/day
)
Risc Risc-Human SFT9906 UMS Vlier-Humaan CLEA
Atrazine
Benzene
Benzo(a)pyrene
Cadmium
Trichloroethene
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
Generic Site – Groundwater Scenario
Plume
Soil Source (mg/kg)
Groundwater Pathway
Receptors
Sand
Sand
GW Source (mg/l)
50m
Groundwater Migration (Generic Case)
TCE Concentrations (mg/l) in well at 50m
RISC JAGG RBCA ROME SFT P20
Soil SourceGW Source
0
1
2
3
4
5
6
7
8
GW
Con
cent
ratio
n (m
g/l)
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
0
0.5
1
1.5
2
2.5
3
RISC JAGG RBCA Risc-Human
SFT Vlier-Humaan
UMS
Soil to Indoor Air
Note: UMS concentration is 650x higher than RBCA
Benzene concentrations in indoor air
46
0.07
Con
cent
rati
ons
(mg/
m3 )
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)
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)
Generic Site ConclusionsSoil 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)
Test Site Cases1. 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.
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
RISC RBCA Risc-Huma
n
SFT UMS Vlier-Huma
an
CLEA
GenericCase Study
0
40
80
120
160
200
Soil Ingestion – Generic vs Test Site
Relative Doses: BaP Soil Ingestion – Generic and Test Site No.2
750
Predicted vs. Actual GW Conc.Test Site 1: TCE concentrations at 57m with biodegradation
Note: Highest model default biodegradation rates used
0
1
2
3
4
5
6
7
8
Actual JAGG RISC RBCA ROME P20
TC
E C
onc
ent
ratio
n (
mg
/l)
Predicted vs. Actual Indoor Air
Test Site 5: Vapour Concentrations in forecourt shop
ActualRISC
RBCARisc-
HumanVlier-
Humaan
Benzene
Toluene0
50
100
150
200
250
Co
nce
ntr
atio
ns
(g
/m3)
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
Overall ConclusionsConsistent 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 assessmodel assumptions & how software applied