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