European Risk Model Comparison Study
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Transcript of European Risk Model Comparison Study
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 StudyRisk-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 ModelsBelgium Vlier-HumaanDenmark JAGG (no dose calculation; RPCs only)
Germany UMSItaly ROMENetherlands Risc-HumanNorway SFT 99:06UK P20 and CLEACommercial RISC and RBCA Toolkit
MethodologyConstruct ‘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 concentrationsDosesRisk levelsClean-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
CadmiumBenzo(a)pyrene (BaP)
BenzeneAtrazineTrichloroethylene
Soil IngestionDermal contactVegetable ingestionGroundwater migrationIndoor 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 ModelsAll models have essentially the same soil
ingestion algorithmsIn Vlier-Humaan, exposure time and soil
ingestion rate are not independent inputsCLEA 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 ModelsCLEA has smaller dose as contaminant is
allowed to volatilise as well as absorbVlier- & 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 ModelsAtrazine (threshold substance) results are similar
due to use of similar algorithmsFor 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 ModelsUMS 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
Dos
e (m
g/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 algorithmsAgain, 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 ModelsAll 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
ratio
ns (m
g/m
3 )
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 CasesModels 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 Study0
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
TCE
Con
cent
ratio
n (m
g/l)
Predicted vs. Actual Indoor AirTest Site 5: Vapour Concentrations in forecourt shop
Actual RISC RBCA Risc-Human
Vlier-Humaan
BenzeneToluene0
50
100
150
200
250
Con
cent
ratio
ns (
g/m
3 )
Test Site ConclusionsGroundwater 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