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ARTICLE IN PRESSG ModelTOX-51110; No. of Pages 14Toxicology xxx (2012) xxx xxx

Contents lists available at SciVerse ScienceDirect

Toxicology

jou rn al hom epage: www.elsev ier .com

Application of data fusion in human health risk amixtures on contaminated sites

Roberta Dycka,, M. Shaqul Islama, Amin Zargara, Asish Mohaa School of Eng Kelowb Contaminated uite #

a r t i c l

Article history:Received 23 AReceived in reAccepted 24 NAvailable onlin

Keywords:Data fusionHuman healthContaminated sitesDempsterShafer theoryPetroleum hydrocarbons

sed inures fl of then pth issple

Data ST) w

response analysis, neurotoxic metabolites toxicological data were fused with predictive toxicologicaldata and then probability-boxes (p-boxes) were developed to represent the toxicity of each compound.The neurotoxic response was given a rating of low, medium or high. These responses were thenweighted by the percent composition in the illustrative F1 hydrocarbon mixture. The resulting p-boxeswere fused according to DSTs mixture rule of combination. The fused p-boxes were fused again with

1. Introdu

Human ponent of thowever, thobtained frtainty arisevariability iin a populatdifferent exorgan, systecal end poinis also inevi

Disclaimer:tract to HealthProgram. Howreport does nCanada guidan

CorresponE-mail add

(M.S. Islam), a(A. Mohapatra

1 Tel.: +1 40

0300-483X/$ http://dx.doi.o this article in press as: Dyck, R., et al., Application of data fusion in human health risk assessment for hydrocarbon mixtures onted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

toxicity data for n-hexane.The case study for F1 hydrocarbons illustrates how data fusion can help in the assessment of the health

effects for complex mixtures with limited available data. 2012 Elsevier Ireland Ltd. All rights reserved.

ction

health risk assessment (HHRA) is an important com-he management of risk related to contaminated sites;e exposure and toxicological data used in HHRA are

om diverse and often heterogeneous sources. Uncer-s from incomplete, vague or ambiguous data, whiles found in the inherent differences between individualsion. Toxicity data may be obtained for different species,posure routes, different organization levels (gene, cell,m, individual, and population) and different toxicologi-ts. Exposure data are variable by nature, but uncertaintytable it is impossible to completely characterize a site

This paper uses material from a report that was prepared under con- Canada (Prairies Region), Contaminated Sites, Environmental Healthever, the views and opinions, if any, expressed in this paper and theot necessarily reect the opinion of Health Canada nor is it Healthce.ding author. Tel.: +1 250 486 2936.resses: [email protected] (R. Dyck), [email protected]@ubc.ca (A. Zargar), [email protected]), [email protected] (R. Sadiq).3 221 3284.

for all chemical and physical parameters given limited and incom-plete data. Complex mixtures found on contaminated sites can posea signicant challenge to effectively assess the toxicity potentialof the combined chemical exposure and to manage the associatedrisks. Because of the variable nature of complex mixtures, thereare not always sufcient or consistent data about the environmen-tal fate, persistence, bioavailability and toxicity across the range ofconstituents in the mixture.

The toxicity of chemical mixtures is generally assessed withrespect to the mixture as a whole, or by somehow combining thetoxicities of the individual components of the mixture. Often, thereis incomplete information for mixtures or their components, orconicting information about the toxicity of the components. Whenconsidering exposure to chemical mixtures, there are a number ofcomplicating factors to be considered

variability in the number of constituents and percent weight ofeach constituent compound in the mixture,

variability in contaminant transport due to different chemicalproperties (e.g., solubility and volatility),

variability in the ability of a receptor to take up the compounds(kinetic parameters including absorption, metabolism, distribu-tion, and excretion),

see front matter 2012 Elsevier Ireland Ltd. All rights reserved.rg/10.1016/j.tox.2012.11.010ineering, Okanagan Campus, The University of British Columbia, 3333 University Way, Sites, Environmental Health Program Regions and Programs Branch Health Canada, S

e i n f o

pril 2012vised form 9 November 2012ovember 2012e xxx

risk assessment

a b s t r a c t

The exposure and toxicological data uheterogeneous sources. Complex mixteffectively assess the toxicity potentiarisks. A data fusion framework has bepotential risk for various public healfusion framework, an illustrative exam

The Joint Directors of Laboratories ture and DempsterShafer Theory (D/ locate / tox ico l

ssessment for hydrocarbon

patrab,1, Rehan Sadiqa

na, BC V1V 1V7, Canada 674, 220-4 Avenue SE, Calgary, AB T2G 4X3, Canada

human health risk assessment are obtained from diverse andound on contaminated sites can pose a signicant challenge toe combined chemical exposure and to manage the associated

roposed to integrate data from disparate sources to estimateues. To demonstrate the effectiveness of the proposed datafor a hydrocarbon mixture is presented.Fusion architecture was selected as the data fusion architec-as chosen as the technique for data fusion. For neurotoxicity


ARTICLE IN PRESSG ModelTOX-51110; No. of Pages 142 R. Dyck et al. / Toxicology xxx (2012) xxx xxx

Fig. 1. Proposlow level fusio

variabilityget organand toxic

interactioachieved tivity is ucan be eslevels (doindividua

In orderized data fuHHRA framences (NAS

Data fusfrom data omate shoulsource datathe human ferent inforoffer signalto an enhanto make desystems whwhich at tim this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

ed data fusion based human health risk assessment framework (Sadiq et al., 2011). The n, feature level fusion, and decision level fusion.

in the toxic response to the compounds (including tar- or tissues, mode of action, temporal variation in effects,odynamic properties),ns which cause effects either higher or lower than thoseby either dose or response additivity. The term addi-sed when the effect of the combination of chemicalstimated directly from the sum of the scaled exposurese addition) or the responses (response addition) of thel chemicals (US EPA, 2000).

to integrate datasets from numerous sources, special-sion techniques (Fig. 1) can be incorporated into theework earlier suggested by National Academy of Sci-, 2009).ion is a process used to generate a combined estimateriginating from multiple sources. The resultant esti-

d be more accurate and informative than the original. Dasarathy (1997) compares multi-sensor systems tobrain. Data coming in from our ve senses give us dif-mation about our environment. Each of these sensorss in a different format, but when combined they leadced understanding of our environment that allows uscisions. This same analogy can be extended to otherich have information coming from different sourceses do not fully agree. In the case of HHRA, this would

specicallyand whole allowing usdeterministstudy resulmay also neResults of incorporateweight in omore reliab

A data ffrom disparhealth issueto integrateorganizatiointegrates dtiveness ofexample fo

2. Data fus

Data fusconict (diinclude thrfusions appan health risk assessment for hydrocarbon mixtures on

11 steps within the framework are grouped as problem formulation,

include the consideration of data from in vitro, animal,body studies. Accuracy is improved by data fusion by

to include more information about the system. In aic approach, we may ignore information about somets in favour of one particular study and in doing so weglect to propagate any uncertainty in the study results.data fusion can be more informative because we can

weighting of each dataset which allows us to give moreur nal result to studies that we consider more credible,le, or more relevant.usion framework has been proposed to integrate dataate sources to estimate potential risk for various publics (Sadiq et al., 2011). The proposed framework attempts

various toxicological datasets from different biologicalnal criteria (gene, cellular, tissue, and organ level) andata from multiple sources. To demonstrate the effec-

the proposed data fusion framework, an illustrativer a hydrocarbon mixture is presented.

ion

ion has been used in a number of settings to resolvesagreement) between datasets. Military applicationseat assessment and surveillance. More recently, datalication has been expanded in commercial applications


ARTICLE IN PRESSG ModelTOX-51110; No. of Pages 14R. Dyck et al. / Toxicology xxx (2012) xxx xxx 3

Table 1Recent examples of data fusion techniques in HHRA and public health (Zargar et al., 2012).

Data fusion technique Application areas Sources

Statistical Genomic data fusion Lanckriet et al. (2004)GIS overlay ( UXO)Bayesian inf

DempsterS

netw

Articial neuFuzzy sets Not specied

such as rodiagnosis, Ga valuable provides soand public hof data fusio

rening d creating a

and improvin

The diffesets dependa possibilistinferencinging the conthe presencincomplete

2.1. Data fu

Accordinfusion task,tion of the parchitecturealgorithms models useture providand integrable scenariopossible to et al., 2005)cic methodifferently as threat asfeatures andlevels can ccell, tissue,

The Joinwork (LlinaBowman, 2for military2005):

Level 1: Oattributes

l 2: level 3: Tdvan

sevel 4: Perfonsors

exathy

r usember

leveata rta,

level pat

ext.sion ntial omes

ta fu

gar ematidata.e of ms aual scturl leveheuristic approach) Assessment of UneXploded Ordnance (erence Multi-study and multi-endpoint BMD

UXO detection Assessment of UXO contamination Syndrome surveillance Disease surveillance

hafer theory Risk assessment of water treatmentDrinking water qualityMicrobial water quality in distributionAssessment of UXO contamination

ral networks Air pollution level monitoring Risk assessment of ambient air qualityPublic health biosurveillanceGene prioritization

botics, manufacturing, sensor technology, medicalIS and remote sensing. Data fusion is also becoming

tool for handling the data involved in HHRA. Table 1me examples of data fusion techniques used in HHRAealth applications (Zargar et al., 2012). The main goalsn include (Bostrm et al., 2007; Zargar et al., 2012)

ata and improving data quality,dditional inferences and increasing benet from data,

g understanding and decision.

rent formulae available for dealing with conicting data on the method that was used to model the data, e.g., inic (fuzzy sets theory) or in a probabilistic (e.g., Bayesian) setting. The framework or technique chosen for resolv-ict depends on the nature of the problem, includinge of additional types of uncertainty in the data (e.g.,ness).

sion architecture

g to Esteban et al. (2005), before undertaking a data a strategy needs to be established to facilitate the solu-roblem in a robust and organized manner. A data fusion

is a platform that connects databases with data fusionand techniques. The techniques are the mathematicald to combine several sources of datasets. The architec-es a strategy to gather the data from different sourceste data at various levels. Due to the numerous possi-s and the variability between them, it would not beuse a specic architecture for all situations (Esteban. The architecture is only the framework in which spe-ds can be used. The levels of fusion are interpreted

Levefrom

Levethe afrom

Levethe pof se

TheSatp

ture fothe nu

Low and dof da

Highusefucont

Decipoteoutc

2.2. Da

Zarmathesingle a framproblethe actarchitesevera this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

in different architectures. For some applications, suchsessment from surveillance, the levels could be pixels,

images. In a similar analogy for HHRA applications, theorrespond to different organization levels such as gene,organ and individual.t Directors of Laboratories (JDL) Data Fusion (DF) frame-s et al., 2004; Steinberg et al., 1999; Steinberg and004) is a popular model that was originally developed

applications. It includes four main levels (Esteban et al.,

bject Renement locates and identies objects using of the object from multiple sources.

Statistica Bayesian Dempster Articial Fuzzy fus

DempsteOne advantuncertaintyuncertaintylations. In spatio-temppresent in e contamination Johnson et al. (2009)Schmitt (2006)Zhang et al. (2003)Johnson et al. (2009)Banks et al. (2012)Burkom et al. (2011)Dmotier et al. (2006)Sadiq and Rodriguez (2005)

ork Sadiq et al. (2006)Johnson et al. (2009)Barron-Adame et al. (2009)Ping et al. (2010)Khan et al. (2010)Aerts et al. (2006)

Situation Assessment uses incomplete informationl one to create a picture related to observed events.hreat assessment uses results from level 2 to analyzetages and disadvantages to taking a course of actionral possible opportunities.rocess renement provides a feedback loop to monitorrmance of the rst three levels and optimize allocation.

ibility of this architecture is one of its key strengths. and Mohapatra (2009) modied the JDL DF architec-

in cyber security applications. The modication reduced of levels to three as shown in Fig. 2:

l fusion includes data cleaning, data transformationeduction to improve the quality and reduce the quantity

l fusion uses classication and clustering to extractterns in the data for the interpretation of activities and

level fusion pertinent patterns are identied andactions are evaluated to draw inferences about future.

sion models (techniques)

t al. (2012) denes data fusion models (or techniques) ascal models that combine multiple data for a feature into

While data fusion architectures are designed to providereference for discussing fusion, recognizing applicablend categorizing solutions, the data fusion models areolutions used (Llinas et al., 2004). In most data fusiones, it is possible to incorporate data fusion models atls. Some of the popular models includean health risk assessment for hydrocarbon mixtures on

l data fusion (classical inference)inferenceShafer Theory (DST)Neural Networksion.

rShafer Theory (DST) was used in this case study.age of DST is its ability to express both variability and

simultaneously. Variability, also known as aleatory, refers to natural variations present in sampled popu-HHRA this type of uncertainty can be present in theoral distribution of concentration of contaminantsnvironmental samples as well as exposure factors (e.g.,



Fig. 2. Modie Mohalevel and decis

exposure dtion rates atoxico-kineto uncertaintem (Sentz models whunclear. In represented

When daa series of structed to bound on ththe upper a(distance) rcal distancedata are avnumber of dnation) undSome commYagers ruleA more dettheir use wInformation

3. Methods

The existiproblem formity) assessmenBased on reco2009), Scienceand managemposed framewsources to repsteps that are gand decision leoverview of thin Section 4.

blem f

NAS (tion sssors

f concent (N

Planning a

nput fadth

Problore d Joint Directors of Laboratories modelling framework (adapted from Satpathy andion level fusion.

uration, exposure frequency, receptor weight, inhala-nd ingestion rates). Natural variation can also exist intics and toxico-dynamics. Epistemic uncertainty refersty arising from an imperfect understanding of the sys-

and Ferson, 2002). This type of uncertainty can occur inich attempt to describe processes that are unknown orDST, variability and uncertainty can simultaneously be

as a p-box (Ferson et al., 2003).ta are expressed as a probability density function (PDF),cumulative distribution functions (CDF) can be con-form a p-box. The CDFs represent an upper and lowere true CDF which lies at an unknown location between

3.1. Pro

The formularisk asseissues oassessm

Step 1:Plann

more iand breStep 2:

The m this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

nd lower bounds. Therefore, the horizontal variationepresents the variability in the data, while the verti-

represents the uncertainty. When multiple sources ofailable which are likely to be conicting, there are aifferent rules that can be used for data fusion (combi-er DST (Ferson et al., 2003), depending on the situation.only used rules are Dempsters rule (Dempster, 1967),

(Yager, 1987), and mixture rule (Ferson et al., 2003).ailed explanation of the construction of p-boxes andith mixture rule in DST is provided in Supplementary.

ng HHRA protocol consists of four stages illustrated in Fig. 3. Theulation (hazard identication) is followed by dose response (toxic-t, exposure assessment and risk characterization (Sadiq et al., 2011).mmendations and guidelines by National Academy of Sciences (NAS,

and Decisions: Advancing Risk Assessment, a general risk assessmentent framework has been proposed (Fig. 1; Sadiq et al., 2011). The pro-ork incorporates data fusion techniques to process data from multipleresent more relevant information. The basic framework includes 11rouped as problem formulation, low level fusion, feature level fusion,vel fusion. Each of the steps is described in this section to provide ane method. The detailed analysis done in each step is presented later

ing sources, shealth effectstion can be pStep 3: Suitab

The complenot a detailedpreliminary anot warrant f

3.2. Low level

Step 4: Data Data are co

variability is tfor the exposof contaminaStep 5: Data

Data that atransformatiexpressed inalso allows thStep 6: Asses

At this stagtitative risk adetermine w

3.3. Feature le

Steps 7 andone simultanpatra, 2009). Data fusion is completed in three levels: low level, high

ormulation

2009) report places emphasis on the planning, scoping and problemteps of the risk assessment process. Stakeholders are involved with

in a dynamic discussion about the extent of the problem and theern, while risk assessors establish the breadth, depth and focus of theAS, 2009).

ing and scopingnd scoping for HHRA has been expanded by NAS (2009) to includerom stakeholders early in the process. This step outlines the depthof the study.em formulationtechnical aspects of the problem are evaluated at this stage includ-an health risk assessment for hydrocarbon mixtures on

tressors, receptors, exposure pathways and potential adverse human. As the problems are introduced, options for remediation or mitiga-roposed so that the HHRA answers the right questions.ility of risk-based assessmentxity of the problem and availability of the data will govern whether or

risk assessment should be done. At this stage, a type of screening orssessment can rule out further detailed analysis if the problem doesurther study.

fusion

collectionllected from a variety of sources in this step. Spatial and temporalaken into consideration for collection of data on site. The data requiredure assessment may include detailed analysis of samples, modellingnt transport, and human exposure factors.estimationre collected requires further preparation through data reduction, dataon and data clean-up. This allows data from diverse sources to be

similar ways for further fusion at a later step in the process. Thise combination of large quantities of data.sment method selectione the data are further evaluated to determine the suitability of quan-ssessment. Complex mixtures should be evaluated at this stage to

hich method of handling data regarding mixtures is most appropriate.

vel fusion

d 8 are not necessarily conducted sequentially; rather they may beeously.



otoco

Step 7: DoseDose respo

different orgamixtures).Step 8: Expos

Exposure aget organs todata regardinabsorption by

3.4. Decision l

Step 9: Risk cDuring risk

is evaluated.Step 10: Rela

The cost-becosts. Risk mStep 11: Repo

Reporting osions as wellaspect of repoods and varia

These stepF1 hydrocarbo

3.5. Current a(RfC)

In the dosecan be used to 2006).2 The R(NOAEL) or thehighest concenobserved. The

interspeciesfold),

intraspecieslation, typic

decienciestive studies,

considering Canada, 201

The BMD mmark Responsmodels are usmethod also inferences, consdeciencies inused in the caextrapolation

2 Health Cainstead of refe

erivin on on

studysis fo

studys detertaintycurreniple hies cois giveudy. Tt, studDL) i

le uncle exphrougelectirporatL is nthods

a mod thro

se s

ckgr

pres sitesies (H

is sprocarctionrocamew

simirm se froscripFig. 3. Existing human health risk assessment pr

response assessmentnse data collected Step 4 are fused here to include different end points,nization levels and/or different compounds (in the case of complex

ure assessmentssessment is conducted to estimate the dose delivered to the tar-

produce a deleterious effect. Data fusion can be used to integrateg different sources, stressors, exposure pathways, exposure routes,

the body, and receptor sensitivities.

evel fusion

haracterization characterization, the probability of harm in receptors or populations

tive cost benet analysis and selection of management optionsnet analysis can include monetary costs, as well as social and culturalanagement options can be evaluated for their utility.rting, guidelines and implementationf the assessment is summarized to implement the management deci-

as identifying the requirements for further research. One importantrting is careful explanation of assumptions, uncertainty in the meth-bility in the population and data.

s are examined and explained further in the following case study ofn contamination.

nd proposed approaches for development of reference concentration

response step of HHRA, the current weight of evidence approachcalculate an RfC for a given chemical or mixture of chemicals (Schmitt,fC is calculated using either the no observed adverse effects level

Benchmark Dose (BMD) approaches. The NOAEL method uses thetration in animal or human studies for which no adverse effect wasNOAEL is divided by uncertainty factors for

differences (when only animal studies are available, typically 10-

differences (consideration of sensitive individuals within the popu-ally 10-fold),

in the toxicological dataset (lack of chronic studies, lack of reproduc- lack of NOAEL, etc., typically 3- to 10-fold), andthe nature and severity of the potential effects (3- to 10-fold; Health

For ding dataand oneas the basuperiorNOAEL iby unce

The are multent studstudies evant stendpoinlimit (BMapplicab

Whian RfC tby this sthe incoone BMDBMD meprovidesis carriethe RfC.

4. A ca

4.1. Ba

Theinatedliabilitnationof hydA colleon hydthe fration oflong tegasolinthe de this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

0a).

ethod uses the dose associated with a given response rate (Bench-e, BMR) in a given toxicology data set (usually 110%). Mathematicaled to model the response rate for a particular toxic effect. The BMDcorporates uncertainty factors for interspecies and intraspecies dif-ideration of the nature and severity of the effects, and also some

the dataset (e.g., lack of chronic studies). No uncertainty factor isse that no NOAEL is found because uncertainty related to low-doseis already considered within the BMD method (Health Canada, 2010a).

nada uses the terminology toxicological reference value (TRV)rence dose or reference concentration.

described.

4.1.1. ProblStep 1: Pla

The sitewere presecollected frgasoline stotanks at ea2530 yearremoval astaminated l (Sadiq et al., 2011).

g the RfC, either the NOAEL or BMD can be used for situations involv-e study, species or health endpoint. When there are multiple studies

is identied as being superior to others, then one study can be usedr the RfC (Schmitt, 2006). [An illustration of the use of NOAEL and one

is given in Supplementary Information.] The one study is chosen, thermined for that study and the RfC is calculated by dividing the NOAEL

factors.t weight of evidence approach can also be used in cases where thereealth endpoints, relevant animal studies or where results from differ-nict (Schmitt, 2006). [An illustration of the use of BMD for multiplen in Supplementary Information.] A BMD is calculated for each rel-he toxicologist uses their judgement to choose the most sensitivey or species based on the lowest BMD. The benchmark dose lowers calculated from the 95th percentile lower limit and divided by theertainty factors to obtain an RfC.ert judgement is built into the NOAEL and BMD methods of derivingh the selection of studies to be included, some information is loston (Schmitt, 2006). The data fusion framework proposed here allowsion of multiple studies, species or endpoints, however one study orot chosen from the multiple alternatives (as done in the NOAEL and). Information about each study is included in the RfC and thereforere robust RfC (Schmitt, 2006). Additionally, uncertainty informationugh the data fusion so that we can better represent the uncertainty in

tudy for F1 hydrocarbon contaminated site

ound

ence of various potentially toxic chemicals on contam- can lead to environmental, human health and nancialealth Canada, 2010b). One common cause of contami-illing and leakage during the storage, transfer and usebons on industrial, commercial and government sites.

of sites, which reect many of the features commonrbon contaminated sites, has been used to demonstrateork. A risk assessment has been carried out for a collec-lar commercial sites which have been contaminated bypilling of gasoline on the ground surface and leakage ofm underground storage tanks in the subsurface. Duringtion of the case study, details of each step have beenan health risk assessment for hydrocarbon mixtures on

em formulationnning and scoping

-specic concentration data used for this case studynted by Sevigny et al. (2003). The study data wereom 5 sites in western Canada that had undergroundrage tanks with similar tank size and fuel types. Thech of the sites were in active use for a period ofs, followed by decommissioning (which included tank

well as removal of some of the hydrocarbon con-soils). In the study, soil vapours were collected and



Table 2Summary of carbon fractions recommended by CCME (2008) and Edwards et al. (1997).

Fraction EC # CorrespondingTPHCWG subfractions

TDI (mg/kg d) RfC (mg/m3) Critical effect used by TPHCWGto derive criteria

F1

F2

F3

F4

EC equivalena Aromatics hey arb Subfractio

analyzed frcentrationsJohnsonEtcommonly

At the sand spillinand grounddetected inzene, xylenBTEX and voan immediarounding arA risk asseslong-term hdermal exp

Dependinumber of oon the rece

further re vapour ex

ment, passive ba

sion, additiona positive p

Step 2: Pro(a) Contam

Gasoincludibons. Aof hydrC1C5carbonhydroctime; tchangechemicmicrooter. Forwith thtion 1 (Counci

on wdroc

the ctionily inr sepopose impis stuceptIn or

estie grorne p

assentacFor te releoundd grolatilee unrposater y anould C6C10 Aromatics C>7C8 a

C>8C10 0.04 Aliphatics C6C8 5.0

C>8C10 0.1

C>10C16 Aromatics C>10C12 0.04 C>12C16 0.04

Aliphatics C>10C12 0.1 C>12C16 0.1

C>16C34 Aromatics C>16C21 0.03 C>21C34 0.03

Aliphatics C>16C21 0.1 C>21C34 2.0

C>34C50 Aromatics C>34 0.03 Aliphatics C>34 20.0

t carbon. C>6C8 are not considered, as benzene and toluene are the only components and tn not considered volatile, therefore RfC not determined.

om monitoring wells on site. The indoor vapour con- of hydrocarbon components were estimated using thetinger model (Johnson and Ettinger, 1991), which is aused vapour intrusion pathway model.ites presented by Sevigny et al. (2003), the leakageg of gasoline resulted in contamination of the soilwater underlying the site. The gasoline components

the subsurface included benzene, toluene, ethylben-es (BTEX) and volatile hydrocarbons. The presence oflatile hydrocarbons in the environment may constitutete and long term health risk to people living in the sur-ea with respect to both cancer and non-cancer effects.sment will be performed to evaluate potential short anduman health impacts due to inhalation, ingestion andosures.ng on the results of the risk assessment, there are aptions available for reducing the impact of the exposureptors (Hers, 2010)

moval of the contaminated soil and groundwater,traction fan system, with or without groundwater treat-

rriers for the receptor buildings to reduce vapour intru-

l ventilation in the receptor buildings, andressure ventilation within the buildings.

upHyinfradafoprthth

(b) Re

tothbotoco

thgranvothpuwcitw this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

blem formulationinants of concern

line contains a number of hazardous compounds,ng BTEX and a complex mixture of other hydrocar-

gasoline contaminated site may have different typesocarbons: C6C10 aliphatic and aromatic hydrocarbons,aliphatic hydrocarbons, hydrocarbons with equivalent

numbers greater than C10, and polycyclic aromaticarbons. The gasoline in the subsurface weathers overhe components and concentrations of hydrocarbons

as they migrate across and off the site, react with otherals in the groundwater and soil, and are metabolized byrganisms naturally occurring in the soil and groundwa-

the purposes of this case study, we are only concernede volatile hydrocarbons that we will refer to as Frac-F1) aliphatic hydrocarbons (as dened by the Canadianl of Ministers of the Environment (CCME, 2008), based

undergis assuTherefointo thinhalat

(c) ReceptThe r

the occcent toto be m5 daysassumeday 7 d

Impaent subrespiracan incbody wa a

0.2 Hepatotoxicity, neurotoxicity18.4 Neurotoxicity1.0 Hepatic and haematological changes

0.2 Decreased body weight0.2 Decreased body weight1.0 Hepatic and haematological changes1.0 Hepatic and haematological changes

NAb NephrotoxicityNAb Nephrotoxicity1.0 Hepatic granulomaNAb Hepatic granuloma

NAb NephrotoxicityNAb Hepatic granuloma

e considered separately.

ork by Edwards et al. (1997) for the Total Petroleumarbons Criteria Working Group (TPHCWG) which eluteC6 to C10 range during laboratory analysis. The carbons are summarized in Table 2 along with the tolerabletake (TDI) and RfC for each subfraction. Other strategiesarating hydrocarbon mixtures into fractions have beened (Park and Park, 2010); however, the consideration ofacts of changes in the fractions is outside the scope ofdy.or pathwaysder to assess the risk to the receptors, it is necessarymate the concentrations of hydrocarbons present inundwater, soil gas, indoor air, outdoor air, dust or air-articulate, surface water, sediments, and food in orderss exposure through ingestion, inhalation and dermalt routes.he purposes of this case study, it has been assumed thatase of gasoline has been predominantly from the under-

storage tanks; therefore, the release would occur in soilundwater. The F1 hydrocarbons are both soluble and

and can be present in the groundwater, as well as insaturated zone of soil above the groundwater. For thees of this case study, we are not considering ground-use as we are assuming that the site is located in thed the use of groundwater for drinking within the citybe unlikely. Because the contamination originated froman health risk assessment for hydrocarbon mixtures on

round storage tanks and remains in the subsurface, itmed that dermal contact with impacted soil is limited.re, the most important pathway is intrusion of vapours

e building and the most important route of exposure ision of vapours.or characteristicseceptors for the hydrocarbons in the case study site areupants of commercial and residential properties adja-

the site. The people on commercial sites are expectedostly adults with exposure durations of 10 h per day,

a week, and 48 weeks per year. The residences ared to house people of all ages, who may be home 24 h aays a week.cts of exposure to F1 hydrocarbons may vary in differ-

groups of population. Acute exposures may result intory and neurological health effects. Longer term effectslude hepatic and haematological changes, decreasedeight and respiratory irritation and inammation

Please cite humcontamina 0


(Massachusetts Department of Environmental Protection(MADEP), 2003). In the case of inhalation exposures, the mostexposed receptors are considered to have a reasonable max-imum of 70 years continuous exposure at the identied site,unless spent aof the c

Step 3: Sui

The impchronic. Theicity for hydfor Toxic Suways and rexplained. effects and is possible a

There arCanada (Hequalitative tal concentinclude siteAssessmentcomplex. Wdetailed quthere is sudetailed ris

(a) MixtureMany

line is a 150 indisuch comicity of enot avai

(b) Whole mWher

ture or deriveddata regthe toxiknown m

(c) ComponIf ther

a wholetion for tevaluateture whproperticommonequivalecient tquantitabe helpfAdditioncould le

The usethat is comhydrocarbohexane hasrange of thconcluded tof other hydrelative pre

overlook interactions leading to effects that are either greater orless than additive. This consideration is also based mainly on neu-rotoxic properties of hexane, while newer reports are questioningwhether or not that would be protective of irritancy, especially

s whting

ultitureogy f thefcie

therir tox

Low l: Da

this ourcal cabilitlikelyion

intrn antrati

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as athereand mr comle than effic ef

be adse th

relan prerce oeringl mae wempo

toxictotal icoloards

etrolDEP trolertingnce Vtandman ilablf F1. sent

Bahimter eation this article in press as: Dyck, R., et al., Application of data fusion inted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.01

actual exposure averaging times are known (i.e., yearst a location where receptors have potential for intakeontaminants).

tability of risk-based assessment

acts of gasoline constituent compounds can be acute orre are numerous studies addressing exposure and tox-rocarbon exposure in animals and in humans (Agencybstances and Disease Registry (ATSDR), 1995). The path-outes of exposure are generally well understood andBased on the availability of data, the severity of thethe nature of the compounds, a risk-based assessmentnd suitable.e several levels of HHRA that are recommended inalth Canada, 2010a). A screening assessment may beor semi-quantitative (e.g., comparison of environmen-rations to established criteria), but does not generally-specic risk assessment. Preliminary Quantitative Risk

and Detailed Quantitative Risk Assessment are morehere there are data available and the risks warrant,

antitative risk assessment should be done. In this case,fcient evidence and potential for harm to allow ak assessment.

s contaminated sites involve chemical mixtures. Gaso-complex mixture of hydrocarbons which contains overvidual compounds present in a range of quantities. Forplex mixtures, it can be prohibitive to consider the tox-

ach compound individually if sufcient toxicity data arelable for each compound.ixture toxicity

e sufcient data are available related to the whole mix-a sufciently similar mixture, one BMDL or RfC can be

for the entire mixture. Alternatively, when there arearding a group of similar mixtures, we must evaluatecity of our mixture of interest against the toxicity of the

ixture for which more data are available.ents toxicitye are insufcient data to consider the entire mixture as, there are a number of ways that the toxicity informa-he individual components of the mixture can be used to

the toxicity of the mixture. For constituents of the mix-ich are toxicologically similar to each other and to thees of the mixture as a whole, dose addition methods arely used. These include relative potency factor, toxicitynce factor and hazard index methods. If there are insuf-oxicity data for numerous components of the mixture,tive structure activity relationships (QSAR) could alsoul in determining the potential toxicity of a compound.ally, interactions between chemicals in the mixturead to effects that are greater or less than additive.

of surrogate chemical toxicity represents a methodmonly used for chemical mixtures. In the case of

ns in the C6C10 range, the toxicity of commercial been used to represent the toxicity of the C6C8e hydrocarbon fraction (Edwards et al., 1997). It washat consideration of hexanes toxicity will be protectiverocarbons in that range. This method may neglect thevalence of the compounds in the F1 fraction, and also

in caseConsul

Thecal mixtoxicolnents ois insuwhole,on the

4.1.2. Step 4

In sible schemicvulnermost migratvapourJohnsoconcen2003),(http:/

ThecarbonEdwargest thEquilibhexanein weasmall, of othepossibresult iilar toxit maythen fu

Thefractional souweathTypicagasolineach coto the of the

Toxby EdwTotal Pby MAfor PeSuppoRefereWide Sthe hu

Avanent oare pre1983; CarpenOccupan health risk assessment for hydrocarbon mixtures on

ere the hexane concentration is low (Wilson ScienticInc. and Meridian Environmental Inc., 2007).mate choice of method for risk assessment of chemi-s can only be made following collection and analysis ofdata for the mixture as a whole, as well as the compo-

mixture. For F1 hydrocarbons in this case study, therent evidence for the consideration of the mixture as aefore individual components will be considered basedicity.

evel data fusionta collection

step, data were collected for all identied pos-es, stressors, transport pathways, exposure routes,ombinations, end points receptors, and populationies. As discussed above in problem formulation, the

exposure pathway is inhalation following vapourto the indoor environment. Exposure models forusion are available from Health Canada (2008) andd Ettinger (1991). Background and typical vapourons are available from literature (Sevigny et al.,well as the American Petroleum Institute websitew.api.org/ehs/groundwater/bioattenuation.cfm).E Canada-Wide Standards (CWS) for Petroleum Hydro-HC) were derived using the TPHCWG analysis by

al. (1997). Recent studies (Park and Park, 2010) sug-se of other hydrocarbon fractionation systems, and

Environmental Inc. (2005) suggested that the use of n- surrogate is over-conservative. The percent of n-hexaned gasoline and in the weathered F1 fraction is relativelyay be insignicant in comparison to the concentrationspounds in the fraction (e.g., octane or heptane). It is alsot while not evidently toxic individually, mixtures couldects that are greater than additive which may elicit sim-fects as n-hexane (MADEP, 2003). Due to these factors,visable to consider the toxicity of each component andem to form a single toxicity for the entire F1 fraction.tive amounts of the components of the F1 hydrocarbonsent on a contaminated site will vary based on the origi-f contamination, the subsurface contaminant transport,, and the degradation of the hydrocarbons over time.ss fractions and percent by weight for components ofre provided by ATSDR (1999). The relative percent ofund in the F1 mixture will be used to provide weightingity of each compound in the mixture for determinationtoxicity for the mixture, as explained in Step 5.gy data for F1 hydrocarbons have been summarized

et al. (1997), by the ATSDR Toxicological Proles foreum Hydrocarbons (1999) and Gasoline (ATSDR, 1995),(2003) as well as the CCME Canada-Wide Standard

um Hydrocarbons (PHC) in Soil: Scientic Rationale Technical Document (2008), and the CCME Toxicityalue (TRV) Advisory Sub Group 2005 Review of Canada-ards for Petroleum Hydrocarbons in Soil. Data regardinghealth endpoints can also be obtained from literature.e toxicological data were reviewed for each compo-The compounds and summaries of the relevant studiesed in tables in Supplementary Information (API, 1982,a et al., 1984; Biodynamics Inc., 1978; Bus et al., 1979;t al., 1978; Cavender et al., 1984; Criteria Group foral Standards, 1983; Dunnick et al., 1989; Egan et al.,



1980; Equilibrium, 2005; Frontali et al., 1981; Galvin and Bond,1999a,b; Howd et al., 1982; Huang et al., 1989, 1992; Ichihara et al.,1998; IRDC, 1992; Litton Bionetics, 1979; Lungarella et al., 1984;MacEwen and Vernot, 1985; Malley et al., 2000; Mast et al., 1987,1988; NTP, et al., 1988;1986; Sanz1993, 1994,et al., 1996)

Step 5: Da

Before pwhat wouldenes an impairmenthe whole oan additionidentify advies that demhistopathol

After comedia and carried out.

were con considere considere used neur

As a mospecies forcould also advised by

(a) Constru

The rstthe toxicityPDF of a datthe ability tThis is espeon our datastudies as bverted into the expertsthe probabiaxis on p-bis providedprovided he

Three tyrotoxic resstructureasummarizeplementaryand Bond (made in thewas allocatnoted, and of the respoto the compa study by signicant distal latencdifference iweeks and

be highly subjective and depend strongly on the judgement of theassessor and would therefore be an excellent opportunity for toxi-cologists to improve the assessment. The production of metaboliteswas determined by reporting in the studies (yes or no, depending on

wereoxic ary brotoxan thm ore fu

inpu in Fi

e typSAR.espoity in-box

resp res

the ydiumicate%) anh resed b-boxis) in-box

comch les thaual toaterch gvalueideretudispone typure re tecsentrent

at ( onlye resp-bopounox re3).

p-bure wpositresulpounated

s was mixe to tn thebox . This allo this article in press as: Dyck, R., et al., Application of data fusion inted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.01

1991; Olson et al., 1986; Ono et al., 1981, 1982; Parnell Perbellini et al., 1986; Pryor et al., 1982; Rabovsky et al.,

et al., 1995; Sayre et al., 1986; Serve et al., 1991, 1992, 1995; Takeuchi et al., 1981; Valentini et al., 1994; Yuasa.

ta estimation

erforming toxicity assessment, it is essential to dened be considered an adverse effect. The US EPA (2012)adverse effect as A biochemical change, functionalt, or pathologic lesion that affects the performance ofrganism, or reduces an organisms ability to respond toal environmental challenge. As such, it is possible toerse effects of chemical exposures from toxicity stud-onstrate clinical responses, physiological effects and

ogical effects.llection of data from sources listed above, for differentdifferent exposure routes, lower level data fusion was

The studies included were limited to studies which

ducted on rats,d sub-chronic exposure (less than 6 months),d inhalation, andotoxicity as an endpoint.

re generalized approach, studies conducted on other other exposure durations, pathways and endpointsbe fused using conversion factors and weighting astoxicologists.

ction of p-boxes and data fusion

step in the data fusion was the derivation of p-boxes for of each substance. Each p-box was constructed for thea source. One of the benets of data fusion using DST iso convert words (qualitative terms) into probabilities.cially useful where we want to capture expert opinion. Our experts can dene certain signals or responses ineing low, medium or high. These words are con-p-boxes which represent, not only the categories which

have designated (Fig. 4, x-axis on p-box plots, but alsolity or condence they have in that assessment (Fig. 4, y-ox plots). Details regarding the construction of p-boxes

in Supplementary Information. General information isre.pes of evidence were summarized including neu-ponse, formation of neurotoxic metabolites, andctivity relationships. These types of evidence wered from the studies themselves (Tables S-V and S-VI Sup-

Information) or from the summary provided by Galvin1999a) and Equilibrium (2005). Based on comments

studies or in the summaries, the neurotoxic responseed a rating of No when no neurotoxic response wasLow, Medium or High dependant on the strengthnse, and how much the response could be attributedound of interest. For example, for 2-methylpentane inOno et al. (1981), the response was described as nodifference in motor nerve conduction velocity, motory; mixed nerve conduction velocity (distal); signicantn mixed nerve conduction velocity (proximal) after 8

was allocated a rating of medium. This allocation would

if theyneurotsummto neuless thmediucould b

Theshown

Threand

The rtoxic

The p no low

by me

ind66

higcat

The p(x-ax

The picity mu les eq gre mu

The cons

The scorredencmixton threprediffeFig. 4werein th

The coma p-bber (

Eachmixtcom

The comindic

Thicarbonrelativmass iThe p-is toxicity waan health risk assessment for hydrocarbon mixtures on

reported as present), as was the structural potential formetabolites to be formed (which was described in they Equilibrium (2005) as having a structure favourableicity of much greater, greater, equal, less than or muchat of n-hexane). The denition of the response as high,

low and the structural considerations are areas thatrther rened by expert opinion of toxicologists.t used for data fusion is shown in Table 3. The p-boxesg. 4 were generated as follows:

es of evidence are considered, Response, Metabolites

nses of no, low, medium or high were allocateddex values in %.es for response (labelled (1) in Fig. 4) representonse (0% on the x-axis which represents toxicity),ponse (033% on the x-axis, with probabilities indicated-axis for each value of x between 0% and 33%),

response (3366% on the x-axis, with probabilitiesd by the y-axis for each value of x between 33% anddponse (66100% on the x-axis, with probabilities indi-

y the y-axis for each value of x between 66% and 100%).es for metabolite show 0 or 100% on the toxicity index

Fig. 4.es for SAR represent structure favourable to neurotox-

pared to n-hexaness than (05% on the x-axis),n (535% on the x-axis),

(3565% on the x-axis) than (6595% on the x-axis) orreater than (95100% on the x-axis).s for each evidence type for each of the compoundsd are listed in Table 3.es for each compound were given a Study number whichds to number (2) in Fig. 4. The p-boxes from the evi-e for each study (labelled (1)) were fused (using DSTule see Supplementary Information for more detailhnique) to form the p-boxes labelled (2) in Fig. 4 which

the toxicity indicated by each individual study for thecompounds considered. The compounds are grouped in2) because in fusing the different evidence types, there

5 different p-boxes that resulted due to the similarityponse in the studies for the different compounds.xes labelled (2) represented various studies for eachd. The studies for each compound were fused to providepresenting toxicity for each compound shown at num-

ox at number (3) representing a compound in the F1as allocated a weight based on its percent of the mass

ion in the mixture (labelled (4)).tant p-box (labelled (5)) is the fused p-box for all theds shown at (3), weighted according to the percent mass

at (4).

done including all of the components of the F1 hydro-ture for which there were studies indicating the toxicityhe types of evidence used here and where the percent

mixture was known. This did not include n-hexane.for n-hexane is constructed to indicate that n-hexanes is represented by the vertical line at 50%. This toxic-cated to n-hexane because it was the compound that

Please cite

this

article in

press

as: D

yck, R

., et

al., A

pplication

of d

ata fu

sion in

hum

an h

ealth risk

assessmen

t for

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rocarbon m

ixtures

oncon

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ated sites.

Toxicology (2012),

http

://dx.d

oi.org/10.1016/j.tox.2012.11.010

AR

TICLE IN PRESSG

Mod

el

TOX-51110;

No.

of Pages

14

R.

Dyck

et al.

/ Toxicology

xxx (2012) xxx xxx9

Table 3Data fusion input for F1 in weathered gasoline, inhalation, rats only, sub-chronic, up to 6 months.

C Compound Author Duration Resp. 1 Resp. 2 Metabolite SAR SAR Study Mass % ingasoline

Recalcd %in F1

6

2-Methylpentane(summarized by Galvinand Bond (1999a))

Frontali et al. (1981) 14 wk, 9 h/d, 5 d/wk No 0% 0 05% C6-1 8.12 28.14

API (1982, 1983) 22 h/d, 7 d/wk Low 033.3% 0 05% C6-2Up to 6 mo

API (1982, 1983) 22 h/d, 7 d/wk No 0% 0 05% C6-3Up to 6 mo

Egan et al. (1980) 22 h/d, 7 d/wk No 0% 0 05% C6-4Up to 6 mo

3-Methylpentane(summarized by Galvinand Bond (1999b))

Frontali et al. (1981) 14 wk, 9 h/d, 5 d/wk No 0% 0 05% C6-5 5.18 18.0

API (1982, 1983) 22 h/d, 7 d/wk Low 033.3% 0 05% C6-6Up to 6 mo

API (1982, 1983) 22 h/d, 7 d/wk No 0% 0 05% C6-7Up to 6 mo

Egan et al. (1980) 22 h/d, 7 d/wk No 0% 0 05% C6-8Up to 6 mo

Cyclohexane (fromsummary inEquilibrium (2005))

Frontali et al. (1981) 9 h/d, 5 d/wk 1500 ppm No 0% 0 05% C6-9 1.75 6.07

10 h/d, 6 d/wk 2500 ppmMalley et al. (2000) 6 h/d, 5 d/wk, 14 w Med 33.366.6% 0 05% C6-10

n-Hexane See studies in Table S-VI 6.32 21.9

7

Heptane Takeuchi et al. (1981) 12 h/d, 7 d/wk, 16 wk No 0% 1 < 535% C7-1 2.76 9.58Frontali et al. (1981) 9 h/d, 5 d/wk, 30 wk No 0% 1 < 535% C7-2Bahima andMenendez-Gallego (1984)

6 h/d, 5 d/wk, 12 wk No 0% 1 < 535% C7-3

2-Methylhexane Perbellini et al. (1986) andSayre et al. (1986)

No 0% 0 05% C7-4 3.49 12.1

82,5-Dimethylhexane Serve et al. (1991) No 0% Not reported < 535% C8-1 Inconclusive3,4-Dimethylhexane Sanz et al. (1995) In vitro 3,4-dimethylhexane No 0% Not reported 95100% C8-2

9 n-Nonane Carpenter et al. (1978) 6 h/d, 5 d/wk, 12 wk No 0% 1 < 535% C9-1 0.60 2.04

10 n-Decane Criteria Group forOccupational Standards(1983)

18 h/d, 7 d/wk, 123 d No 0% 0 < 535% C10-1 0.63 2.18

100



Fig. 4. Multi-srepresent the tweights accor

each other ies, and alsothis hydrocthe F1 mixt

The resuof the entirthe x-axis. Tthe mixture

Step 6: Ass

In the caare commocomponent this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

tudy and multi-compound inference for F1 neuropathic toxicity in rats using Dempstoxic response, generation of toxic metabolites and the structure activity relationship for cding to the percent composition in the F1 range.

compound was compared to in the summaries of stud- because it is used for some derivations of toxicity for

arbon fraction. Following this, we apply the toxicity ofures to the dose response curve for n-hexane.lting p-box in Fig. 4 at number (5) represents the toxicitye mixture with probabilities for each possible value onhis provides a more complete picture of the toxicity of

than choosing one reference compound or one study.

essment method selection

se of a petroleum hydrocarbon site, F1 hydrocarbonsnly detected in the subsurface, along with BTEX. Thes of the F1 hydrocarbon fraction can be present in

varying coamount of was a mixtur

4.1.3. FeatuStep 7: Do

High leva case adasources ofempirical model. Ththe case ofsidered wastudies onan health risk assessment for hydrocarbon mixtures on

erShafer mixture fusion (averaging). p-Boxes were constructed toompounds in the F1 range. p-Boxes for six compounds were assigned

ncentrations based on the source chemical and theeathering, therefore it is recommended to consider F1

e.

re level fusionseresponse assessmentel or feature level data fusion can be illustrated usingpted from Schmitt (2006). Schmitt (2006) uses two

data in the derivation of BMDL: ndings from multiplestudies along with data calculated from a mechanistice proposed framework can be applied to this data. In

F1 hydrocarbons, the toxicity of each compound con-s applied to the PDF of the NOAEL concentrations from

n-hexane, for which there were much more toxicity



hydro

data. The plower CDFsCarlo simu2010). Theshould repvalues (Lowthese two.of the NOAgenerated Step 8: Exp

In this casure pathwfor dermalgroundwator irrigatiogroundwatresidential

Hydrocasoil. Sevignsion modelProbability tors are useconducted residential

Health Cindoor inha

Dose(

mg/kd

where Cia(mg/m3); IRative absorpterm (unitle

PDFs forsite indoor In this case1. Because texposed 24the exposur

4.1.4. DecisStep 9: Ris

NOA-chr

obseuncelied i

r intr intdec

unce, as a

RfC . Thes sofnal ser b

fC oof illuw Rfrderion recicFig. 5. p-Box of neurotoxicity NOAEL for F1

-boxes for the toxicities were separated into upper and and multiplied by the distribution for NOAEL by Montelations using the software @Risk (Palisade Corporation,

separation of the p-box into upper and lower CDFsresent the possible values (upper CDF) and probableer CDF) because the true CDF lies somewhere within

The combined p-box for the upper and lower limitsEL for F1 mixture is shown in Fig. 5. This p-box wasby placing the upper and lower CDFs on one plot.osure assessmentse only vapour inhalation was considered as an expo-ay because the sub-surface soil would not be available

contact or accidental ingestion on a paved site. Also,er in the city is not usually used for consumptionn. As a result, the relevant pathways are soil ander contamination to soil vapours through cracks in the

foundation to the residents inside.

rbon vapour concentrations have been measured in they et al. (2003) used the Johnson Ettinger vapour intru-

to estimate indoor air concentrations in the buildings.distributions for the exposure factors for the recep-d to estimate exposures. The exposure assessment wasusing exposure factors provided by Health Canada forand occupational exposures.anada (2010a) recommends the following equation for

Thein a sublowestby an be app

10 fo 10 fo 3 or

No effectsfor F1.

Thefactorsthere iputatioand lowin an Rposes as a ne

In oinhalatage-sp this article in press as: Dyck, R., et al., Application of data fusion in humted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.010

lation of contaminant vapours

g)

= Cia IRA AFinh ETRW

is the concentration of contaminant in indoor airA is the air inhalation rate (m3/day); AFinh is the rel-tion factor for inhalation (unitless); ET is the exposuress); and BW is the body weight (kg).

each of the exposure factors were combined with thevapour concentrations using Monte Carlo simulations., the absorption factor for inhalation is assumed to behe receptors are residents that have the potential to be

h a day for a lifetime, it is conservative to assume thate term is also 1.

ion level fusionk characterization

Step 10: Rment optio

A cost bepotential mof the scopStep 11: R

The Healquantitativpresents aform the bof this caseHHRA, not

3 These resuHealth Canadacarbon mixture.

EL from the doseresponse assessment applies for ratsonic study. Where NOAEL values were not available, therved adverse effect level (LOAEL) values were dividedrtainty factor of 10. Other uncertainty factors that cannclude

erspecies differencesraspecies differences andiencies in the data set.

rtainty factor is being used for the severity of toxic factor was included in calculating the combined NOAEL

is calculated by dividing the NOAEL by the uncertainty NOAEL in this case was presented as a p-box. Whiletware available to multiply p-boxes together, for com-implicity in this case we used the average of the upperound 95th percentiles which is 639 mg/m3. This resultsf 2.13 mg/m3. This value is calculated only for the pur-strating the approach. This should not be interpreted

C or TDI.3

to compare this to the exposure doses, we used theate and body weight of each age group to generate an

TDI.an health risk assessment for hydrocarbon mixtures on

elative cost-benet analysis and selection of manage-nsnet analysis should always be conducted to assess theanagement options. The cost benet analysis is outsidee of work of this case study.eporting, guideline and implementationth Canada Guidance on Complex human health detailede risk assessment for chemicals (Health Canada, 2010a)

suggested report outline. This outline could be used toasis for reporting for this case study, however, the intent

study is the demonstration of the use of data fusion in the actual risk assessment for this site.

lts do not necessarily reect the opinion of Health Canada nor is it guidance.



5. Discussion

The case study considered only those compounds for whichsufcient data were available on toxicity as well as percent compo-sition in theconsidered lent carbonneurotoxicisider

contamin other frac other hea

tive effect results th chronic o toxicity st toxicity st

Three ty

neurotoxlogical res

formation chemical

metabolit

The neucated valuecould be excase study,the neurotoother appliered. Wheraddress theweighted acexperts.

Toxicityfor the othetoxicity datnents. The picture of ththey incorprelative amillustrative RfC of 18.4and Edward1.0 mg/m3. ducted for twere low; hobjective ofdata fusionis used hertions and toassessmentRfC for petfusion in th

For the arate characmixture wothis assessmstudies or sbe includedderived forchronic or a

Data fusion as a tool for toxicologists conducting or evaluatingHHRA does have some limitations. These mathematical constructsare somewhat unfamiliar in the eld of toxicology. This unfamil-iarity might make it difcult for the analyst to choose data fusion

cturantle eaore, ncesa fusle. A

orderone eing fions.mplt. W

mod, the st. Comre prilitiether

osurenot ncesn spining ation

ma

erta can emincesress oposfor crs o

as td as side

cascallyarbompleeviewtypes

rotoxencecture

icitywithrmatmix

EdwaC10.ure derati

aminstrat this article in press as: Dyck, R., et al., Application of data fusion inted sites. Toxicology (2012), http://dx.doi.org/10.1016/j.tox.2012.11.01

gasoline on site. The CCME F1 hydrocarbon mixture wasto consist only of aliphatic hydrocarbons with equiva-

numbers C6C10. The health end point considered wasty for sub-chronic exposure. The case study did not con-

ants other than CCME F1 hydrocarbons,tion strategies for classifying hydrocarbon mixtures,lth effects including respiratory irritation and reproduc-s,at are greater or less than additive,r acute exposure,udies on animals other than rats, orudies for oral or dermal exposure.

pes of evidence were included in the data fusion

ic responses, including clinical, physiological and histo-ponses

of neurotoxic metabolitesstructure favouring the formation of neurotoxic

es (structureactivity relationship).

rotoxic responses reported in the studies were allo-s of no, low, medium or high. These responsespressed in numerical values where appropriate. In this

the risk assessor assigned a value to the strength ofxic response shown in each animal study; however, incations, the input of several experts could be consid-e these opinions differed, data fusion could be used to

conict between the opinions. These opinions could becording to the perceived reliability or credibility of the

data were more readily available for n-hexane thanr components of the mixture, therefore the n-hexanea were considered separately from the other compo-results of the data fusion represent a more completee toxicity than just consideration of n-hexane, becauseorate data from many compounds weighted by theirounts in the mixture. The resulting RfC (presented forpurposes only) is 2.13 mg/m3. This is smaller than the

mg/m3 for C6C8 fraction provided by CCME (2008)s et al. (1997), but larger than that given for C8C10 ofThis result was expected because the fusion was con-he entire range of C6C10. The calculated Hazard Indicesowever, the actual risk assessment was not the main

this case study, rather the application of the proposed framework to HHRA for complex mixtures. Data fusione in the context of conducting doseresponse evalua-xicity assessments in the context of human health risk; the purpose of this exercise was not to present a newroleum hydrocarbons, but to present the use of datae derivation of RfCs.pplication of this type of data fusion to other sites, accu-terization of components and relative amounts in theuld be required. Although data fusion was conducted inent for sub-chronic inhalation studies, data from oraltudies with other durations (chronic or acute) could

in the fusion if adequate conversion factors could be comparing oral to inhalation data and sub chronic tocute data.

architesignicbecausTherefcumsta

Datavailabilar in point, weightconditther copresenods forfuzzy)datasedata apossib

Anoin expsite is Differehere. Icombiinform

6. Sum

Uncwhichplex chdiffereTo addwas prtypes Directochosenmentefor con

Thehistorihydrocas a cowere rThree

1. neu2. pres3. stru

Toxbined of infoC6C102008; for C8

Futconsid

conttion an health risk assessment for hydrocarbon mixtures on

e and methods. The results of the data fusion may bey affected by the choice of fusion method. This is partlych of the fusion methods handle conict differently.a solid understand of the fusion methods and the cir-

which make one preferable is important but attainable.ion methods are somewhat limited by the data that isnalysts require sources of data that are sufciently sim-

to conduct data fusion. In this case study, only one endxposure route and one species were considered: otheractors would need to be added for extrapolation to those

The selection and use of the data fusion methods are fur-icated by the different types of uncertainty that can behile data fusion allows the selection of different meth-elling uncertainty (e.g., possibilistic, probabilistic, andelection of those methods often depends heavily on theputational complexity can increase when some of the

esented in probabilities while others are presented ins.

limitation specic to this application is the uncertainty. Generally, a hydrocarbon mixture on a contaminateduniform. The composition will also change with time.

in spatial and temporal variation were not consideredte of these limitations, data fusion is a useful tool fordata from numerous sources in a way that preserves

about uncertainty.

ry and conclusions

inty occurs in HHRA due to data used in the assessmentbe incomplete, conicting, vague or ambiguous. Com-cal mixtures can present further uncertainty due to the

in toxicity between the components in the mixture.these uncertainties, a data fusion framework for HHRAed to integrate toxicity information from three evidenceomponents of the F1 hydrocarbon mixture. The Jointf Laboratories (JDL) Data Fusion (DF) framework washe data fusion architecture, in which DST was imple-the data fusion technique. The use of p-boxes allowedration of probabilities and uncertainty.e study considered was a contaminated site which

contained underground gasoline tanks. The CCMEn fraction F1, corresponding to C6C10, was consideredx mixture for which risk assessment was required. Dataed for the toxicity of the components of the mixture.

of evidence were considered for neurotoxicity

ic response, of neurotoxic metabolites, andactivity relationships.

data for the components of the mixture were com- toxicity data for n-hexane due to the greater amountion available for n-hexane. The resulting RfC for theture was lower than RfCs given by other sources (CCME,rds et al., 1997) for C6C8, and higher than that given

ata fusion studies are recommended and should includeon of

ants other than CCME F1 hydrocarbons, other frac-egies for classifying hydrocarbon mixtures, other health



effects including respiratory irritation and reproductive effects,emerging system biology datasets integration and application ofdata fusion

Human Health/Public Health Data Fusion interactio

effects thexposurestudies fo

other data

The inclan opportuof informatdata fusioncomponentuse the ava

Conict of

The auth

Appendix A

Supplemthe online v

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Application of data fusion in human health risk assessment for hydrocarbon mixtures on contaminated sites1 Introduction2 Data fusion2.1 Data fusion architecture2.2 Data fusion models (techniques)

3 Methods3.1 Problem formulation3.2 Low level fusion3.3 Feature level fusion3.4 Decision level fusion3.5 Current and proposed approaches for development of reference concentration (RfC)

4 A case study for F1 hydrocarbon contaminated site4.1 Background4.1.1 Problem formulation4.1.2 Low level data fusion4.1.3 Feature level fusion4.1.4 Decision level fusion

5 Discussion6 Summary and conclusionsConflict of interest statementAppendix A Supplementary dataAppendix A Supplementary data

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