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Modeling human exposure to indoor contaminants: external source to body tissues Eva M. Webster, Hua Qian, Donald Mackay, Rebecca D Christensen, Britta Tietjen, and Rosemary Zaleski Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00895 • Publication Date (Web): 19 Jul 2016 Downloaded from http://pubs.acs.org on July 19, 2016

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Environmental Science & Technology

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Modeling human exposure to indoor contaminants: external source to body tissues

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Eva M. Webstera*, Hua Qianc, Donald Mackayb, Rebecca D. Christensenb, Britta Tietjenb,d,

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Rosemary Zaleskic

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a

Carrousel Environmental Modelling Research, Peterborough, Ontario K9H 0E2 Canada

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b

Environment and Resource Sciences, Trent University, Peterborough, Ontario K9L 0G2

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Canada

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c

ExxonMobil Biomedical Sciences, Inc., Annandale, New Jersey, USA. 08801

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d

Freie Universität Berlin, Biodiversity and Ecological Modeling, D-14195 Berlin, Germany

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* Corresponding author: Email: [email protected], Tel: 705-748-1011

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Keywords: Indoor, PBPK, fugacity, multimedia model

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ACS Paragon Plus Environment

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Abstract Information on human indoor exposure is necessary to assess the potential risk to

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individuals from many chemicals of interest. Dynamic indoor and human physicologically based

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pharmacokinetic (PBPK) models of the distribution of non-ionizing, organic chemical

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concentrations in indoor environments resulting in delivered tissue doses are developed,

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described and tested. The Indoor model successfully reproduced independently measured,

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reported time-dependent air concentrations of chloroform released during showering and of 2-

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butyoxyethanol following use of a volatile surface cleaner. The Indoor model predictions were

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also comparable to those from a higher tier consumer model (ConsExpo 4.1) for the surface

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cleaner scenario. The PBPK model successful reproduced observed chloroform exhaled air

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concentrations resulting from an inhalation exposure. Fugacity based modeling provided a

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seamless description of the partitioning, fluxes, accumulation and release of the chemical in

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indoor media and tissues of the exposed subject. This has the potential to assist in health risk

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assessments, provided that appropriate physical/chemical property, usage characteristics, and

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toxicological information are available.

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Introduction

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Considerable effort has been devoted to measuring and estimating human exposure to

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chemical substances in the indoor environment 1-13 and enclosed spaces such as automobiles 14-16

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and airplane cabins 17-20, because both indoor environmental characteristics and human behavior

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patterns suggest that in many cases indoor exposures may be more important to human health

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than outdoor exposures. For example, a given mass of chemical released outdoors will result in

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lower exposure than the same release in an indoor environment because of reduced dispersion

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and great proximity in the indoor environment, i.e., enclosed indoor environments have a

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defined, lower air volume with limited ventilation and higher duration of occupancy.

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One widely used single-compartment model is ConsExpo™ 4.1 21. It was developed to

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estimate chemical exposure from non-dietary use of consumer products in an indoor environment

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based on the information on the individual exposed (e.g., body weight, inhalation rate), the

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product use pattern (e.g., product concentration, use amount and frequency), and the exposure

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location characteristics (e.g., room volume, ventilation) for all relevant exposure routes

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(inhalation, dermal and oral). The uptake of the chemical in the model is estimated either by

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applying a fixed uptake fraction and/or by a diffusive uptake based on the chemical skin

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permeability for the dermal route.

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Multimedia or multi-compartment models can describe the fate, transport and

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degradation of chemicals indoors with a view to assessing and reducing exposures. Indoor

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models describe the movement and fate of organic chemicals in domestic spaces such as homes


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and offices, address a variety of scenarios, and have differing levels of sophistication, model

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input requirements and model outputs. Reviews of indoor and exposure models are available

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elsewhere 22-25, only models key to the development of the present models are highlighted here.

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Fugacity-based models express explicitly the relative equilibrium status of the

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compartments and simplify the equations. For example, Mackay and Paterson 3 developed a

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simple, steady state model for assessing exposure in a one-room house with a series of

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resistances to chemical movement from the source to the outdoor air; Matoba and colleagues

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developed and applied a series of models 4, 26, 27 to domestic pesticide use in Japan. A significant

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advance was made by Bennett and Furtaw 1 who developed a fugacity-based dynamic mass

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balance model, for pesticides used in domestic settings, that included a primary treated zone and

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a secondary untreated zone, each with air, six size fractions of airborne particulate (dust), carpet,

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smooth vinyl flooring and wall (painted wallboard) compartments. Building on these works,

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polyurethane foam (PUF) such as in furniture was added to the model to address chemical

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entering the room from treated PUF, carpet or electronic devices 13. This model can be used to

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predict the impact of environmental modifications such as introduction of a new electronic

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devices or human activities that stir household dust, such as walking, sweeping, vacuuming or

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from the mechanical action of compression and release of PUF in the routine use of soft

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furnishings.

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Concentration-based dynamic mass balance models have also been developed and applied to

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indoor environments. A comprehensive and quantitative treatment of phase equilibria and inter-

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phase kinetics of a wide variety of semi-volatile indoor organic contaminants has been described


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by Weschler and Nazaroff 10 based on vapor pressure and octanol-air partition coefficient, KOA to

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describe air-surface sorption and desorption. This modeling demonstrated that, depending on

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these properties, contaminant levels may be under kinetic or equilibrium control and

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characteristic times of air-surface exchange vary greatly. Time-scale estimates were made for

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two scenarios: a crack and crevice pesticide application and three life stages for product

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containing plasticizers or flame retardants. Nazaroff 28 combined models and empirical data to

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show that intake fraction, the pollutant mass inhaled per unit mass released from a source, varies

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with building-related factors such as ventilation, human factors such as breathing rate and

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chemical properties such as sorption to surfaces and reactivity, i.e., degradation half-life. These

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approaches estimate the exposure of humans to substances. However, to assess impacts on

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human health, also pathways into the body and distribution within a body are of importance.

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Physiologically based pharmacokinetic (PBPK) models have long been used to

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investigate drug distribution within a body 29, 30. Inhalation is commonly modeled as the

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exposure route but injection, ingestion and dermal sorption are also considered. Blood flow

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connects tissues or groups of tissues. The grouping of tissues into aggregate compartments has

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been shown to cause minimal loss of predictive power 31. For example, for acidic and basic drugs

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the muscle and fat pair was found to be sufficient 32. Most models include liver, fat, muscle and

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skin; blood may or may not be considered as a compartment. Chemical loss is typically by

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metabolism in the liver and chemical removal by exhalation and urination. A review of PBPK

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models is available elsewhere 33.

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Combining knowledge of chemical fate in indoor environments with chemical uptake and

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kinetics within the body can provide insight into the likely body burdens received by each of the

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exposure routes of inhalation, ingestion and dermal contact. For toxicological assessments,

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linking an Indoor and a PBPK human model has the advantage of being able to demonstrate the

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sources and magnitudes of the chemical emissions, as discussed by Weschler and Nazaroff

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who included body burden data for their target chemicals. This is most useful if the intake

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estimates can be converted into tissue concentrations, since it is tissue concentrations that are

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associated with potential adverse effects. Modeling chemical fate through indoor sources into a

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human receptor spans nearly three decades from a shower model used to estimate inhalation

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exposure to volatile organic compounds (VOCs) from water 34 to multimedia indoor models with

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human intake fractions 35, 36, exposure factors and human activity data 37, and simple PBPK

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models 12.

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,

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Goals and Objectives of the present study

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The goal of the present study was to develop and test a transparent and readily applied

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indoor/human model pair with the indoor model generating a time profile of the air fugacities

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and concentrations associated with domestic exposure scenarios loosely coupled to a human

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PBPK model to provide an improved understanding of chemical distribution within the body and

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internal dose over time. Loose coupling of the models allows the results of the Indoor model to

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be directly imported into the PBPK model but also allows the models to be used independently,

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or alternate models to be substituted for either of the pair. The aim is to determine the


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relationship between individual tissue fugacities and their relative time dependencies as

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influenced by the exposures by inhalation, ingestion and dermal sorption.

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Model Description

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Overall model structure

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Two individual component models were developed, an indoor environment model and a

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physiologically-based pharmacokinetic, PBPK, human model. The fugacity concept, briefly

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described below, was used for both models to provide a seamless description of the chemical

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path from source to receptor to demonstrate and quantify the source-receptor relationships

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between the indoor environment and human tissues and their relative time dependencies as

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influenced by the nature, rates and duration of emissions, chemical degradation, dilution and

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uptake by inhalation, ingestion and dermal routes. This is accomplished by a loose-coupling

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mechanism of the models where the time series of fugacities in room air from the Indoor model

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is stored and becomes an input to the PBPK model. Dermal uptake and ingestion can depend on

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the Indoor model results but these must be provided to the PBPK model independently, there is

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no automatic mechanism provided. The component modeling approach provides flexibility in

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model selection from simple to complex, and further allows the direct substitution of measured

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observations.

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Only one substance may be addressed at a time. To address mixtures of multiple

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substances the models must be applied independently for each substance and the results


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combined; a procedure that is invalid if there are chemical interactions such as may occur in the

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liver. In such cases individual chemical estimates from the Indoor model can be entered into a

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multi-substance PBPK model such as that of Haddad et al.38.

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The quantity of chemical absorbed by the human is not included as a loss from indoor air

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nor does exhaled air contribute to the concentration in indoor air. For chemical originating in the

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occupied space, this is a conservative assumption. The human is restricted to a single simulated

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room for the duration of the simulated time. These are issues for future consideration, as

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discussed later.

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The Fugacity Concept

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Briefly, fugacity is defined as the escaping tendency or partial pressure of a chemical

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from the phase in which it currently resides. Fugacity, f (Pa), is related to concentration, C

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(mol/m3), by a fugacity capacity, Z (mol/m3·Pa), such that C equals Z·f. The Z value

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characterizes the ability of a phase to absorb and retain a chemical and depends on the chemical,

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the nature of “solvent” phase and temperature. D-values or fugacity rate coefficients are used to

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express rates of chemical uptake, loss and reaction. These D-values (mol/Pa·h) are essentially

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fugacity-based transport and transformation rate constants. The chemical fluxes, N (mol/h), by

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degrading reaction or by transport between phases or compartments are expressed as D·f. D

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values for advective transport in air are defined as GZA where G is the flow rate (m3/h) and ZA

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applies to the air phase. Large D-values correspond to fast processes. More detail is available in

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the textbook by Mackay 39. Conventional concentration or rate constant models are algebraically


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equivalent to fugacity models but fugacity is convenient for comparing the relative equilibrium

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status of the chemical in various media.

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Model compartments and processes

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In the Indoor model, a chemical is distributed between the air, wallboard, and hard and

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soft surfaces in multiple rooms connected by air circulation and with outdoor air exchange, as

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shown in Figure 1. Details of the fugacity formulation of the Indoor model are given in the

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Supporting Information (Section S-1). Surfaces are modeled as quasi-liquid layers, QLLs,

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(Section S-1c-iii). Air-borne and settled particles are included in each modeled as a fraction of

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the volume of each compartment; a mass balance of particles is not calculated by this model.

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Time-dependent concentrations of organic chemicals in indoor compartments are distributed by

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diffusive and particle-associated movement and lost through degradation and ventilation.

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Chemical in indoor air becomes available for respiratory uptake by a human.

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Human exposure is treated using the multi-compartment PBPK model (Figure 2, Section

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S-2a). The PBPK model calculates the time-dependent fate and distribution of the chemical

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between various tissue concentrations to simulate conditions of periodic or continuous exposure.

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Chemical may enter the body in inhaled air, by dermal absorption, and in ingested food and

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water. It distributes through the body with blood flow between tissue groups, accumulating or

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degrading in the fat tissue, richly and poorly perfused tissue, liver, skin and venous and arterial

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blood, and is removed by excretion, exhalation, dermal desorption, and metabolism. The model

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allows for the direct entry of measured blood-air partition coefficients or, because these and


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blood-tissue partition coefficients are available for only a limited number of chemicals, they can

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be calculated in the model from the more widely available KOW values. Determination of blood-

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tissue, and hence blood-air, partition coefficients from routinely collected physical chemical

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properties, such as KOW, has been demonstrated to be a valid approach 40, 41. Metabolism or

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biotransformation of the chemical is treated as first order degradation in each compartment

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except in the liver where the Michaelis-Menten method is applied, as described in the Supporting

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Information (Section S-2a-v). PBPK model results are not scalable because of this non-linear

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treatment of metabolism in the liver. An overall chemical residence time in the human body can

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deduced as the ratio between the sum of the amounts in all compartments and the sum of intake

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rates by all routes. This model represents intermediate complexity between the steady state

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model of Paterson and Mackay 42 and the highly complex, multi-tissue, multi-process, dynamic

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model of Cahill et al 43.

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Parameter values are not integral to the models; model design allows all of the defining

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values to be entered by the user. For real systems, when known, actual system-specific

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experimental values can and should be used. Some parameter values are routinely measured and

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reported (e.g., room dimensions, the octanol-water partition coefficient, human subject mass),

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some may need to be estimated from measurements not specific to the space or subject (e.g.,

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room air exchange rate constant, human subject tissue lipid content or breathing rate), others may

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be more challenging (e.g., mass transfer coefficients) but, for order of magnitude estimates,

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generic or default values such as those suggested in the present work may often suffice.

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Model Application Scenarios

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Test of model mass balance

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An important requirement of models of this type is that a complete mass balance can be

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obtained. This is most readily accomplished by running the model to a steady state condition in

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which total chemical influx must equal total losses of chemical, i.e., there is no net accumulation.

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This test lends confidence to the reliability of the dynamic model results. For this test a constant

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600 µg/h emission of an illustrative chemical is considered as released in a small room with an

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‘Average Man’ over a 120 hour period (5 days). All system defining values are given in Tables

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S-3a-c. Details of the value selections defining the Average Man are given in the Supporting

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Information (Table S-2e). An ‘Average Woman’ or “Child” could be defined by the same

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method but most experiments use adult male subjects.

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Under conditions of normal human activity is such that steady state conditions are unlikely

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to be achieved; chemical residence time within the body would need to be less than the

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approximately 8-hour duration of a sleep period, for example, before steady state could

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reasonably be expected. To ensure that a mass balance is achieved and that all mass fluxes and

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accumulations are consistent, the PBPK model was tested with the ‘Average Man’ showering

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naked, described in Table S-3c of the Supporting Information, to the hypothetical, illustrative

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chemical, described in Table S-3a of the Supporting Information, with constant exposure to, the

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steady state concentration in air in the test of the Indoor model (9280 ng/m3), an arbitrary

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concentration in food (3 × 106 ng/kg), and in the dermal contact medium (water; 106 ng/m3). A


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mass balance check was performed on the chemical fluxes after apparent steady state conditions

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had been achieved.

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Test by comparison: Showering experiments

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The experimental studies of Jo et al. 44 were simulated to test the Indoor and PBPK

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models. The air in a shower stall was monitored for chloroform concentrations resulting from

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water use and exhaled air concentrations of chloroform were reported for two scenarios: the

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participant wearing a rubber suit to prevent dermal sorption, and a normal shower where both

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dermal sorption and inhalation would occur 44. The properties of chloroform used in the

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simulations are given in Table S-3a of the Supporting Information. A chemical transfer

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efficiency (TE = 61%) from the shower water to the air is calculated by the method provided in

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the Supporting Information (Section S-1c-vi). The Indoor model was parameterized to simulate

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the experimental laboratory shower stall (Table S-3b in the Supporting Information) and

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showering scenario (Table S-3d in the Supporting Information). The air exchange was

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minimized in the experiment by the use of a shower curtain and the absence of an exhaust fan but

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no value was reported 44. In later experiments using a similar shower chamber under similar test

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conditions, air exchange was estimated to be between 6.0 and 19.2 air changes per hour 45; a

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value of 15/h was selected as the model input. Indoor model results were compared to reported

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air concentrations. The PBPK model was parameterized for the Average Man (Table S-3c in the

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Supporting Information) because no information was provided on the experiment participants.

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The area of exposed skin was set to a negligibly small value (10-11 m2) for the ‘inhalation only’

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experimental scenario and to 1 m2 for the ‘normal showering’ scenario where both inhalation and


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dermal sorption are considered (Table S-3c in the Supporting Information). The Average Man

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was exposed to the air concentrations predicted by the Indoor model for reported water

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concentrations for the ‘inhalation only’ and to both air and water concentrations for the ‘normal

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showering’ scenario (Table S-3d in the Supporting Information). For convenience in modeling

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‘normal showering’, during the five minutes of drying after showering the water definition of the

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dermal contact medium was retained but with a negligible concentration. To be more correct the

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contact medium should have been redefined as air and the air concentrations applied. PBPK

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model results were used to generate exhaled air concentrations for comparison to reported

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observations. The effect of redefining the dermal contact medium for the final five minutes was

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tested as described in the Supporting Information.

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For simulation of the Xu and Weisel experiments, the Indoor model was parameterized

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for the experimental shower stall and showering scenario 45, 46 as defined in Tables S-3b and S-

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3d with chloroform as defined in Table S-3a all of the Supporting Information. Subject C was

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selected as most similar to the ‘Average Man’ defined in Table S-3c of the Supporting

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Information. In addition, the reported total blood volume of subject C of 5.5 L (sub-divided

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0.83:0.17 between venous and arterial, as for ‘Average Man’) 45 was used. The lighter weight of

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subject C was arbitrarily assigned as a lower fat volume of 0.0150 m3. The area of exposed skin

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was set to a negligibly small value (10-11) is simulate the experimental condition of inhalation

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only exposure. All other model inputs for the human subject were as defined for the Average

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Man in Table S-3c of the Supporting Information.

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Test by comparison: Surface application of consumer product

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To further test the Indoor model, the experimental study of Singer et al. 7 was simulated

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for a surface application of consumer product in an experimental chamber. A cleaning agent

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containing 2-butyoxyethanol (2-BE) was sprayed onto a 0.56 m2 section of laminate tabletop in

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the chamber. After 1 minute, the surface was wiped with paper towels that remained in the room.

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Total application time was estimated to be 2 minutes; total observation time was 24 hours. The

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chemical and chamber properties used in the Indoor model to simulate this experiment are given

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in Tables S-3a and S-3b in the Supporting Information, respectively, and emission data as input

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to the model are given in Table S-3e of the Supporting Information. For comparison, the

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ConsExpo model was also used to simulate this cleaning scenario with model inputs as given in

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Table S-3e of the Supporting Information.

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The human is simulated as remaining in a room for a full hour after using a cleaning

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product on a hard surface with a 2-minute application period. The Indoor model was used to

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generate the fugacities in air over the entire time-course.

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Results and Discussion

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Results of model mass balance tests

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Mass balance of the Indoor and PBPK models are confirmed in the results shown in Figures 1 and 2, respectively. The time-dependent increase in tissue concentrations and


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fugacities shows the achievement of apparent steady state after approximately 15 days of

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constant exposure (Figures S-4b and S-4c in the Supporting Information). It is concluded that the

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models are mathematically correct and give a complete and consistent mass balance for the

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chemical in question. The accuracy of the results depend, of course, on the accuracy of the

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parameter values and on the applicability of the model assumptions. A comparison of model

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results against experimental observation provides a further test of model accuracy.

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Results of comparison test: Showering

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The volatile organic contaminant, chloroform, entering a shower stall through a domestic

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water supply was modeled and compared to observations from two sets of experiments 44, 45, 47. A

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shower water – air transfer efficiency, TE, of 61% (Table S-3a) was calculated for chloroform by

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the method described in the Supporting Information (Section S-1c-vi). Previous estimates

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include 56% 45, 61% 48, and 75% of the TE of radon (63%) 34 or 47.25%. To avoid unnecessary

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repetition, only a small selection of the scenarios from each experiment are modeled.

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In the simulations of the Jo et al experimental showering scenarios 44 (Table 1), the

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concentration and fugacity of chloroform in the shower stall air increased with time from an

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assumed pristine state to a maximum when the shower was turned off (Figure S-5a in the

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Supporting Information). The body burden of chloroform also increased through-out the shower

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and then declined after water was turned off (Figure S-5a in the Supporting Information). The

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Indoor model predicted the observed average air concentrations (Table 1); no time dependent air

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concentrations were available for comparison. At five minutes after showering, the ratio of


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predicted to measured exhaled air concentration ranged from 7 to 10 (Table 1). The model

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predictions for exhaled air concentration are dependent upon the breathing rate and the efficiency

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of uptake through breathing. For example, for the ‘normal’ showering scenario with a water

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concentration of 0.021 mg/L, decreasing the breathing rate from 1.5 m3/h for the Average Man

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(Table S-3c in the Supporting Information) to 0.5 m3/h to reflect a lower activity level (a lying

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man or standing woman, Table S-2e in the Supporting Information) decreased the predicted body

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burden and hence the concentration in the exhaled air by half (from 40.5 to 19.4 µg/m3). (More

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detail is given in Figure S-5c in the Supporting Information.) Similarly, decreasing the efficiency

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of uptake through breathing (50% instead of 70%) decreased the body burden and hence the

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exhaled air concentration (from 40.5 to 33.4 µg/m3). The combined effect of inhalation and

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dermal exposure cause an over-prediction of the concentration in exhaled air of less than a factor

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of four (Table 1); this was because of the mitigating effect of an under-prediction of the effect of

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dermal exposure (Table 1). Model estimation of the effect of dermal exposure depends upon the

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selected skin permeability mass transfer coefficient, here assumed to be a constant value of 0.001

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m/h for all test chemicals. Note that this mass transfer coefficient describes chemical transport

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into the skin whereas the skin permeability coefficient is routinely measured for transport

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through the skin 49. Increasing the mass transfer coefficient into skin from the dermal contact

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medium, and hence the rate of transfer, by a factor of 5 caused the concentration in predicted

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exhaled air due to dermal exposure to increase from 3.1 to 11.6 µg/m3 which compares favorably

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with the experimental result of 8.9 µg/m3 (Table 1). Redefining the dermal contact medium as air

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for the five minutes of drying time had little effect on the overall predicted fate of chloroform for

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the scenario tested, as discussed in the Supporting Information.

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Observed room air concentrations 47(from Table B2 of Xu 47 and as shown in Figure 1 of

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Xu and Weisel 45) are well predicted by the Indoor model (Figure 3) for the modeled scenario

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with subject C. The anomalous reduced room air concentration at 15 minutes was observed by

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Xu 47 with four of the six subjects. Including this data point in the comparison, the model

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predictions are within better than a factor of two of observations, i.e., excellent for models of this

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type. It was assumed that there was no chloroform the air external to the shower. This with the

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air exchange rate constant may have contributed to the difference between modeled and observed

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concentrations. The PBPK model shows good agreement with the lowest observed (extracted

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from Figure 2a of Xu and Weisel 45) exhaled air concentrations (Figure 4). Subject specific

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exhaled air concentrations were not reported47. This and the absence of a measured breathing rate

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and efficiency may be the cause of the difference between the predicted and observed values. As

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shown above in the simulation of the Jo et al 44 experiments and detailed in the Supporting

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Information (Section S-5), if subject C was breathing more rapidly than estimated, the model

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would predict higher exhaled air concentrations in closer agreement with observation.

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Results of comparison test: Consumer product applied to a hard surface

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Figure 5 shows the measured and calculated concentrations of 2-BE in the chamber air

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from both Indoor and ConsExpo models over a 24-hour period. There was an initial rise in

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concentration of 2-BE followed by a gradual decline. The paper towels used to wipe the surface

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were an ongoing source after the initial spray and wiping of the tabletop. The Indoor model does

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not capture the extremely rapid rise in concentration during the first hour but shortly thereafter

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the simulation is in agreement with the experimental observations 7. This initial under-prediction


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is approximately a factor of two at the first measured time interval (30 minutes after application).

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For this scenario, ConsExpo also under-predicts the initial concentrations in the air but over-

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predicts concentrations thereafter. The Indoor model seemed to capture the exposure better than

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ConsExpo did especially 2 hours after the application, which may be related to the consideration

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of chemical partition in different compartments through the fugacity concept in the Indoor

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model.

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Future considerations

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It has been suggested that the presence of a human occupant may influence the overall

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fate of chemicals indoors 12. It is expected that the presence of a human occupant will have

385

greatest effect on the overall indoor fate of chemicals with low vapor pressures, consistent with

386

the importance of any sorbent material for increasing chemical residence time in indoor

387

environments, as suggested by Neretnieks et al 50. For the Jo et al 44 experiments, the PBPK

388

model predicted a maximum body burden at 4% of the maximum amount of chloroform in the

389

shower air. In the showering experiments the presence of an occupant appeared to slightly

390

increase the air concentrations of chloroform, although authors considered the difference to be

391

not significant. Additional experimental evidence is needed to clarify the effect of occupancy on

392

the indoor fate of less volatile organic contaminants. It is expected that sorption by humans will

393

be limited by the shorter duration of occupancy relative to stationary sorbing materials. For

394

longer term studies, it may be necessary to consider the effect of the human moving between

395

spaces.

396


18

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397


Advantages

398 399

The Indoor model has the potential, when properly parameterized, to simulate dynamic

400

concentrations indoors. This can be combined with a dynamic model of uptake and distribution

401

within a human to deduce the likely time course of chemical concentrations in tissues and the

402

whole body. This can form the basis of evaluation of both exposure and risk if toxicological data

403

are available. The fugacity formulation allows this to be done seamlessly.

404 405

These models can inform experimental design and protocol. The collection of system

406

dimensions, for example, is often driven by common practice and standard design. Modeling

407

shows the importance of data collection on indoor system dimensions and properties including

408

air exchange rates. Clearly there is a need for further testing of the model against empirical data.

409

With properties specific to the test system model testing can become more rigorous and lead to

410

an improved understanding not possible with assumed, estimated and average values.

411 412 413

Acknowledgement We gratefully acknowledge financial support provided by the Natural Science and

414

Engineering Research Council (NSERC) of Canada as a Collaborative Research and

415

Development (CRD) Grant No 335163-05 to the University of Montreal and Trent University

416

(2008) in collaboration with ExxonMobil Biomedical Sciences Inc (EMBSI) and with a

417

subsequent direct grant from EMBSI to Trent University (2010). The University of Montreal

418

received additional funding from Health Canada, the Institut national de santé publique and


19


Page 20 of 30

419

Réseau de rech en santé environnementale under the CRD grant. The funds from EMBSI for the

420

CRD grant were provided though its Canadian counterpart, Imperial Oil..

421 422 423

Supporting Information The Indoor and PBPK models are freely available online as stand-alone software from

424

www.trentu.ca/cprg/models. Additional information and results are available free of charge via

425

the Internet at http://pubs.acs.org.

426


20

Page 21 of 30

427 428 429 430 431


Table 1. The reported average water, shower air and exhaled air concentrations of chloroform from experimental ‘inhalation only’ and ‘normal’ (with both inhalation and dermal sorption) scenarios of Jo et al. 44 selected to test the Indoor and PBPK models and the concentrations predicted by the models.

Water,

Shower stall air, µg/m3

mg/L

Exhaled air, µg/m3, 5 minutes after showering

Reported

Indoor model

average

Reported

PBPK

Predicted/

predicted

model

Reported

average and

predicted

maximum Occupied

0.022

125.9

167; 256

n/r

-

shower stall

0.0356

313.4

270; 415

n/r

-

‘Inhalation only’

0.010

n/r

-

2.4

19.4

8.1

0.021

n/r

-

4.1

40.5

9.9

0.035

n/r

-

8.9

67.4

7.6

0.0053

n/r

-

6.0

11.1

1.8

0.021

n/r

-

13

43.6

3.4

0.036

n/r

-

21

74.7

3.6

13 - 4.1

43.6 - 40.5

0.3

= 8.9

= 3.1

‘Normal’

Implied dermal only 432

0.021

n/r = not reported

433


21


Page 22 of 30

2284 µg/m3 0.23 mPa

Wallboard

Outdoors 0 µg/m3 0 mPa

17.63 µg/h 371.21 µg/h 17.76 µg/h

0 µg/h

Air 9.3 µg/m3 0.23 mPa

showering flux

8.18 µg/h

Hard Surfaces

8.18 µg/h

2299 µg/m3 0.23 mPa

372.00 µg/h

0.12 µg/h

Room 2

0 nmol/h

4 ×10-3 µg/h 0.64 µg/h 0 nmol/h 0.64 µ/h

Air Circulator

0.64 µg/h

Soft Surfaces 2064 µg/m3 0.23 mPa

6 ×10-4 µg/h

434 435 436 437 438 439

Room 3

Figure 1. Apparent steady state fluxes (µg/h), concentrations (µg/m3), and fugacities (mPa) of the illustrative chemical in the illustrative room after 3 hours of the 5-day showering scenario showing mass balance is achieved. The water fugacity was 10 mPa for a concentration of 1 mg/m3. Degrading reaction losses are shown as dashed arrows.

440


22

Page 23 of 30


441 ROOM AIR 9.28 µg/m3; 230 µPa

13.920

12.977

4.176 9.744

8.801 ALVEOLAR AIR 208 µPa 8.103

7.159 10-4

10-5

VENOUS BLOOD 18.4 µg/m3; 184 µPa

ARTERIAL BLOOD 20.8 µg/m3; 208 µPa

FAT 1505 µg/m3; 188 µPa

0.367

0.405

0.038 RICHLY PERFUSED 104 µg/m3; 208 µPa

3.483

3.484

0.001 SLOWLY PERFUSED 41.5 µg/m3; 207 µPa

1.739

1.742

0.003

0.297

LIVER 20.9 µg/m3; 29.8 µPa

2.066 0.0027

GIT 2752 µPa

1.772

1.274

0.001

442

SKIN 65.3 µg/m3; 653 µPa 0.130

1.000

DERMAL EXPOSURE MEDIUM 1000 µg/m3; 5025 µPa

0.405

0.0030

0.0003

FOOD 1000 µg/m3; 5025 µPa

443 444 445 446 447 448

Figure 2. Mass balance is shown for the illustrative chemical in the Average Man after 15 days of constant exposure (when steady state conditions appeared to have been achieved) Chemical fluxes (µg/h), concentrations (µg/m3) and fugacities (µPa) shown. Biotransformation reactions shown as dashed arrows. GIT = gastrointestinal tract


23


Page 24 of 30

350

Air concentration, µg/m3

300 250 200 150 100 50 0 0

5

10

15 20 Time, minutes

25

30

449 Figure 3. The reported (dots) (from Table B2 in Xu 47 ) and Indoor model predicted (solid line) air concentrations of chloroform for subject C during showering.

Exhaled breath concentration, µg/m3

450 451 452

90 80 70 60 50 40 30 20 10 0 0

5

10

15 20 Time, minutes

25

30

453 454 455 456 457

Figure 4. The reported exhaled air concentrations of chloroform for all experimental participants with inhalation-only exposure during showering (extracted from Figure 2a in Xu and Weisel 45) and the exhaled breath concentrations of subject C predicted by PBPK (solid line).


24

Page 25 of 30


1.6 indoor model ConsExpo measurement

Air Concentration (mg/m3)

1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0

5

10

15

20

25

458

Duration (hrs)

459 460 461 462 463 464 465

Figure 5. Air concentrations in an experimental chamber due to the surface application of 2BE*. Dashed line represents measured, thick solid the Indoor model predictions, and thin solid the ConsExpo predictions. * The measurements are estimations based on Figure 2 in the original study and represent average values for the time periods (0-30 min, 30-60 min, 1-2 hr, 2-4 hr, and 4-24 hr)7


25


Page 26 of 30

466

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(47) Xu, X. Dermal and inhalation exposure to disinfection by-products in “drinking water”. Ph.D. Dissertation, Rutgers University New Brunswick, NJ. 2002.

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(50) Neretnieks, I.; Christiansson, J.; Romero, L.; Dagerholt, L.; Yu, J.-W. Modelling of emission and re-emission of volatile organic compounds from building materials with indoor air applications. Indoor Air 1993, 3 (1), 2-11; 10.1111/j.1600-0668.1993.t01-3-00002.x

601

602

For Table of Contents Only

603 Indoor Model Wallboard Outdoors

Air

Hard Surfaces

Room 2

Air Circulator

Soft Surfaces

PBPK Model

Room 3

604 605 606


30

Modeling Human Exposure to Indoor ... - ACS Publications

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