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Integrated Exposure and Dose Modeling and Analysis System. 1. Formulation and Testing of Microenvironmental and Pharmacokinetic Components PANOS G. GEORGOPOULOS,* ASHWIN WALIA, AMIT ROY, AND PAUL J. LIOY Environmental and Occupational Health Sciences Institute, Rutgers University and University of Medicine & Dentistry of New Jersey, 681 Frelinghuysen Road, Piscataway, New Jersey 08855-1179
The conceptual and theoretical framework for a modular integrated Exposure and Dose Modeling and Analysis System (EDMAS) has been formulated, and its stepwise implementation and testing is currently in progress. This system aims to provide state-of-the-art tools for performing integrated assessments of exposure and dose for individuals and populations. The integration of modeling components with each other as well as with available environmental, exposure, and toxicological databases is being accomplished with the use of computational tools that include interactive simulation environments, Geographical Information Systems, and various data retrieval, management, statistical analysis, and visualization methods. This paper overviews the structure and modular nature of this integrated modeling system and focuses specifically on two of its components: (a) a hierarchy of physiologically based pharmacokinetic models (PBPKM), representing various levels of detail and sophistication, and (b) a family of microenvironmental models, that incorporate complex physical and chemical transformations. The deterministic implementation of these components is also presented here in two test applications: (i) a case study of benzene exposure indoors resulting from the volatilization of contaminated tap water and (ii) a case study of photochemical pollution infiltration indoors, in an office building environment.
Introduction The transport and fate of environmental contaminants from their sources or points of formation in the environment to the point of their biological effect can be represented in terms of a continuous exposure sequence (1). Generally, toxic chemicals may be taken up by the human body via one or more of the three common routes of inhalation, dermal absorption, and ingestion. Contaminant uptake by a given route will depend on contaminant concentrations in one or more environmental media (air, water, and soil), and concentration in a given medium may be influenced by concentrations in other media. Exposure systems are dynamic, stochastic entities, resulting from the coupling of environmental (geophysical) systems * Corresponding author e-mail address:
[email protected]; telephone: (908) 445-0159; fax: (908) 445-0116.
S0013-936X(95)00764-4 CCC: $14.00
1996 American Chemical Society
with human population dynamics and the dynamics of microenvironments, i.e., control volumes surrounding the human receptors and characterized by the concentrations relevant to exposure. This dichotomous lineage of exposure systems is apparent in the attributes of the many exposure models that are presently available. Exposure models that are derived from environmental models are usually sophisticated with respect to the description of environmental fate and transport, but relatively simplistic with respect to the description of conditions in specific microenvironments and of population dynamics. The reverse is typically true for exposure models derived from a risk analysis type of approach. A large number of models describing environmental transport that are typically referred to as exposure models have been compiled and are available through the EPA’s Exposure Models Library (EML) (2). Many other models are also readily available as software packages (e.g., refs 3 and 4). Most of the environmental transport models described in EML are simple linear models that consider fate and transport in a single medium, the most notable exception being certain numerical air pollution models, which include complex nonlinear atmospheric photochemistry. Some multimedia environmental transport models are included in EML; the description of contaminant fate and transport in these is generally less detailed than in the single-phase environmental models, but these formulations have the advantage of being able to account for interactions between environmental media. Also, some of the multimedia models provide estimates of exposure (3, 5-7). These exposure estimates are typically obtained through the pathway exposure factor (PEF) approach (8), in which simple algebraic relations are used to calculate exposures from environmental concentrations. A general limitation of the above-mentioned models is that they do not account for dynamic concentration variations in microenvironmental control volumes in which exposures actually occur. Another limitation of environmental transportbased exposure models is that they do not account for the stochastic nature of exposure. Several models have been developed to describe the infiltration of outdoor pollutants indoors (9, 10). These are either single or multi-compartmental ventilation models that account for airchange via infiltration and recirculation (e.g., refs 11-15). Most of these models do not consider interactions between different media, nor do they include complex indoor or outdoor air chemistry mechanisms. Probabilistic exposure models (16-18) account for population dynamics and human activity patterns at various levels of sophistication, by considering time-space distributions and sensitive subpopulation groups, but often these models treat environmental concentration dynamics in a simplified manner. Some approaches (19) combine comprehensive environmental transport-fate models, with population demographics and activity patterns. A major goal of the development of the EDMAS is to provide a platform that can be used to study total exposure and biologically effective dose, by integrating environmental fate and transport models with mechanistic models of physiological pharmacokinetics and models of population dynamics. This system would thus provide a means of checking for consistencies in mass balance, thermodynamic equilibrium, and time-scale considerations between its different components. In EDMAS this can be accomplished in both “prognostic” and “diagnostic” modes, as described by Georgopoulos and Lioy (20) and demonstrated by Georgopoulos and Roy in ref 21, where exposure is calculated from environmental quality and from biomarker information, respectively. This enhances the quality and consistency of the exposure
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FIGURE 1. Conceptual structure of the bi-directional probabilistic EDMAS. assessment, since independent sources of information are used in a complementary manner in the bi-directional assessment (22). So, the EDMAS library of modules aims to synthesize and integrate the processes that affect the estimation of delivered dose (to target tissue) from total exposure to environmental contaminants.
Modeling System Framework EDMAS is an evolving modular system based on the conceptual framework proposed by Georgopoulos and Lioy (20). As shown in Figure 1, the EDMAS framework places human exposure within a sequence that tracks the toxicants of concern from sources to receptors (humans). By adopting a “modular” model-building approach, it allows for “customization” to the specific attributes of the exposure system of concern. The microenvironmental/pharmacokinetic portion of the system, which is the focus of this paper, can further be directly linked with large-scale (spatial and temporal) environmental fate and transport models and with probabilistic models of population dynamics, describing spatial/ temporal patterns associated with different sections of the population; this linking is currently pursued as part of ongoing studies (23, 24). According to the Federal Register (Guidelines for Exposure Assessment) (22), a microenvironment is defined as a spatial region that can be treated as homogeneous (or well characterized) with respect to the concentrations of a contaminant of concern. (In practice, a microenvironment may not be homogeneous, and the local concentration levels may be fluctuating. However, if these fluctuations can be well characterized/modeled, it can still be considered a microenvironment for modeling purposes.) So, a microenvironment could be the interior of a home, an office, an automobile, or even a roomsor a portion of a roomswithin a home. The microenvironmental and pharmacokinetic modules of EDMAS have been designed to estimate multimedia concentrations of contaminants simultaneously in several microenvironments in which human exposure can potentially occur and to estimate biologically effective dose due to a given activity in the microenvironments. Integrated exposure in a
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microenvironment is calculated using:
j(t0,t1) )
∫ c(t) dt t1
t0
where c(t) is the contact concentration experienced by the individual under consideration from the exposed population at time t (20). The integration of microenvironmental and physiologically based pharmacokinetic modules within EDMAS facilitates a combined forward (prognostic) and backward (diagnostic) exposure assessment, as mentioned. EDMAS also utilizes the concept of “nested” models as schematically shown in Figure 2, where regional or subregional grid-based models and databases provide macroenvironmental ambient concentration estimates to the smaller scale microenvironmental modules. Interfacing with multimedia macroscale environmental fate and transport models provides dynamic ambient concentration profiles in the air, soil, groundwater/surface water, sediment, etc. to the microenvironmental modules for the estimation of potential exposures. These potential exposures can iteratively/adaptively be reassessed in order to accommodate additional information obtained from the PBPKM and concentration response studies. Work in progress focuses on incorporating attributes of geographical population distributions (from ref 25) and human activity patterns (from ref 26) into EDMAS applications. The large volumes of data associated with comprehensive exposure assessments require efficient information management and interpretation; this is provided by a set of software tools that includes Geographical Information Systems (27), statistical analysis packages (28, 29), scientific visualization software (30-32), and the relational database management systems (33). The incorporation of space-time activity patterns of population distributions in exposure assessment considerations in EDMAS will be presented in an upcoming paper. In the following, the focus is on the formulation of the PBPK and the microenvironmental modules of the EDMAS. These modules have been developed in SimuSolv (34), which is based on ACSL (35). This programming environment incorporates various efficient equation solvers as well as
FIGURE 2. Nesting concept of environmental/microenvironmental models in the integrated EDMAS. robust statistical parameter estimation/optimization methods. Unknown parameters in the modules, such as surface reactivity, deposition rates, partition coefficients, etc., of toxicants can thus be easily estimated in a statistically optimal manner. The software package can also be used to directly perform sensitivity analysis as well as Monte-Carlo simulations on systems described by algebraic and/or differential equations. An efficient alternative to Monte-Carlo simulations for parametric uncertainty propagation, the stochastic response surface methodology (SRSM), has been implemented in the EDMAS framework for the pharmacokinetic components (36), while application of SRSM to environmental components and incorporation of global methods for sensitivity analysis are in progress. Testing of the microenvironmental and pharmacokinetic components of the EDMAS is presented in this paper for two case studies involving (a) benzene resulting from volatilization from contaminated groundwater and (b) indoor ozone.
Formulation of the Microenvironmental Modules The microenvironmental modules of EDMAS estimate concentrations of the contaminant of concern in the different microenvironments in which an individual can get exposed. Information about microenvironmental (contact) concentrations and actual times spent in each microenvironment are then used to estimate the integrated exposure of an individual. Typically, a microenvironment can be divided into compartments or zones separated from each other either by physical boundaries (e.g., rooms in a home) or by difference in medium (e.g., water, air, soil, etc.). Presently, the microenvironmental component of EDMAS includes the following dynamically coupled compartments: indoor air (including separate shower and bathroom subcompartments), outdoor air, soil, surface water (also consisting of the biota, suspended solids, and sediment subcompartments) and groundwater. The soil and surface water compartments are coupled with the outdoor air compartment. The water compartment is assumed to be locally uniform, whereas the soil and sediment compartments are nonuniform in the vertical direction. Representative equations governing toxicant concentration dynamics in the various uniform (indoor and outdoor)
compartments are given in Table 2. The air microenvironments, both indoor and outdoor, can be treated as either uniform or non-uniform compartments with (optionally) complex atmospheric chemistry described by the Carbon Bond-IV (37) mechanism; the SAPRC (38) atmospheric photochemistry mechanism can be used as an alternative. Outdoor contaminants in the microenvironment affect the indoor contaminant concentration through infiltration, makeup air, and recirculation (10). The chemistry mechanism solver can consider the infiltration and dynamic concentration variation of an arbitrary number of chemical species, the default being the 34 chemical species in the CB-IV mechanism. The concentrations of selected species are calculated by solving simultaneously the transport equations in different media, thus allowing for multimedia interactions. The indoor air microenvironment can incorporate an arbitrary number of subcompartments (e.g., representing different rooms). The air change rates must be obtained for the specific microenvironment type (e.g., home, office, or vehicle) and configuration (e.g., windows open, windows closed, older construction, weatherized, and air conditioned); such information is sometimes available from earlier studies (10) and, when case-specific data are not available, these can be used as default input. Pollutant surface reactivity factors or deposition velocities for all the species are needed as inputs. Deposition velocities for various indoor air species have been compiled by Nazaroff et al. (39), where one can also find an excellent discussion and critique of the underlying concepts. The indoor air compartment includes three special subcompartments representing the bathroom, shower, and kitchen. The governing equations for the bathroom-showerindoor compartments are all similar to the uniform compartment equation given in Table 1, where exposure to chemicals through volatilization is also considered. The indoor source term from volatilization Si(t) for compartment i (and the the transfer efficiency φi of a given species in compartment i ) are calculated as described in Table 1 (40). Default values of the amount, duration, and frequency of daily tap water use in shower, bathroom, and indoors have been obtained from the literature (40). Approximation techniques that can be used in EDMAS to account for incomplete mixing in a compartment include
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TABLE 1. Representative Equations Used in the Exposure and Dose Modeling and Analysis System EDMAS
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AI Az Ci C* ij Di EF H Hij Ii Kij M N QB Qji R Ri Si(t) Γ(t,τ0i ,τ* i) R δ γ φRn i τ* i 0 τi ξs aM aij k rB subscript
symbol
interior surface area (m2) effective cross-sectional area of contaminant flux indoors from soil (m2) concentration of species in compartment i (gmol m-3) concentration in compartment i in equilibrium with that in j (gmol/m3) diffusion/dispersion coefficient in compartment i (m2 s-1) air filter efficiency (F: makeup air; R: recirculation air) Henry’s law constant partition coefficient between compartment i and j total amount of wate rconsumed in compartment i mass transfer coefficient from compartment i to j (m/h) total number of species total number of compartments volumetric flow of air in building volumetric flow rate from j to i compartment (m3/h) universal gas constant rate of formation of species in compartment i (gmol/h) source term of species in compartment i (gmol m-3 h-1) 1 for t [τ* i]; 0 otherwise (compartment i) Attentuation coefficient of soil-gas concentration Csource indoors deposition velocity of a species (m s-1) total porosity of soil a: gas filled porosity rainfall/infiltration rate (m/h) water-to-air transfer efficiency for Radon (Rn) in compartment i time at which water use activity ends in compartment i time at which water use activity starts in compartment i retardation factor in soil/sediment compartment infiltration flowrate (F: makeup air; R: recirculation air) (ACH (h-1) interfacial area between comartment i and j (m2) compartmental mixing factor radius of zone of building’s influence on soil gas (50) a: air, s: soil/sediment, w: water, I: indoors, B: building
Microenvironmental Module Parameters description
Askin Cmedia carterial cj fu,j fGFR Kmi,j Kmedia,skin Pj/k Qalveolar Qcardiac Qj Rj Sj t Vj Vmaxi,j Ds.c. Lc x Aalveolar Aflow Dj JR,aw Lairway r Uaw Y y z
symbol
PharmacoKinetic Module Parameters description surface area of skin concentration of chemical in environmental media concentration of chemical in arterial blood concentration of chemical in compartment j unbound fraction of chemical in tissue j glomerular filteration fraction of kidney blood flow Michaelis-Menten metabolism affinity coefficient for enzyme i in tissue j skin permeability coefficient of chemical with respect to media partition coefficient of chemical between tissues j:k volumetric flow rate of alveolar air volumetric flow rate of blood from heart volumetric flow rate of blood into tissue j rate of metabolism of chemical in compartment j net rate of non-circulatory transport into compartment j time volume of tissue in compartment j maximum rate of metabolism for enzyme i in tissue j effective diffusivity of chemical in stratum corneum characteristic diffusion pathlength in stratum corneum distance into stratum corneum from surface of skin cross-sectional area of alveoli averaged over airway length cross-sectional area of airway lumen effective diffusivity of chemical in compartmen j radial mass flux of chemical from airway lumen to wall total lung airway axial path length radius of airway lumen velocity of air in lung airways radial distance lung tissue wall surface to center of capillary radial distance into lung tissue wall axial distance along lung airways from trachea to alveolar sacs
TABLE 2. Description of Representative Parameters Used in the Exposure and Dose Modeling and Analysis System (EDMAS)
FIGURE 3. Schematic of the basic lumped-parameter perfusion-limited PBPK model (A), the (optional) distributed-parameter lung compartment (B), and the (optional) distributed-parameter skin compartment (C) available as modules of EDMAS. representing a fraction of the volume of a compartment as “dead-volume” and “short-circuiting” of the inlet stream of pollutant (i.e., assuming a fraction of the contaminated air inflow to be vented out of a room directly) (41). These methods represent a posteriori corrections that can be used to potentially explain observed data. Alternatively, non-ideal mixing can be described through solutions of turbulent flow and mass transport equations for the environment of concern. The option of explicit determination of air flow patterns and 3-D contaminant concentrations in EDMAS can be provided via linkage with the EXACT and CONTAM3 models (42, 43). The indoor air compartment is also linked to the groundwater/soil compartments through a module that is based on previous work by Little et al. (44). The attenuation coefficient R of the indoor concentration (Cindoor) of the contaminant with respect to a given uniform soil gas phase concentration (Csource) is calculated as shown in Table 1. An alternative approach, as described by Jury et al., involves incorporating phase equilibria and adsorption phenomena for subsurface contaminant intrusion indoors (45, 46). The sediment and the soil compartments are non-uniform at least in the vertical direction, since contaminant transport processes in these compartments are dominated by diffusive/dispersive processes, which cause significant concentration gradients in these compartments (4). Representative governing equations are shown in Table 1. The surface water compartment includes three subcompartments: biota, suspended solids, and the sediment compartments with the biota and suspended solids being treated as uniform compartments. A reactive term is included in the water compartment representing first-order decay. In the default formulation of the EDMAS microenvironmental components, the groundwater compartment is assumed to be uniform or, alternatively, the concentration patterns are provided by analytical solutions.
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This compartment is coupled with the groundwater models or data from the regional or subregional levels of EDMAS.
Formulation of the Pharmacokinetic Modules The primary goal of the pharmacokinetic modeling component of exposure assessment is the estimation of biologically effective dose (22), which provides the link between exposure assessment and risk assessment. Pharmacokinetics is the study of the absorption, distribution, metabolism, and excretion of chemicals in physiological systems, and pharmacokinetic models are mathematical constructs that are used to calculate amounts or concentrations of chemicals in these systems. The term toxicokinetics is sometimes used to describe pharmacokinetics of toxic substances. Any appropriately coded pharmacokinetic model can be incorporated into EDMAS, provided that it accounts for all relevant routes of uptake and that it provides validated estimates of biologically effective dose. However, physiologically based (mechanistic) pharmacokinetic (PBPK) models are preferred over empirical models as they permit the rational and systematic description of the biological system in terms of transport and kinetic phenomena. PBPK models attempt to represent a biological organism as a set of physiological compartments by lumping together tissues with similar properties and by describing transport between compartments based on actual processes, such as blood circulation. PBPK models were first implemented by Bischoff and Brown (47, 48) and have been extensively applied to describe the pharmacokinetics of environmental contaminants (49-54). PBPK models are now the tool of choice for estimating the concentration-time profiles of toxic environmental chemicals in the body. A schematic diagram of the basic PBPK formulation is shown in Figure 3A, in which the organs of the body that are important to the uptake, metabolism, and
excretion of chemicals (namely, the lung, skin, liver, and kidney) are represented as separate compartments. The remaining tissues are lumped into compartments based on similarity in blood flows and blood/tissue partition coefficients, which are generally the two most important determinants of distribution. Distribution of chemicals is also constrained by the physiologically based interconnections between compartments in the PBPK model. As a first option, the compartments in the basic PBPK formulation shown in Figure 3A are assumed to be homogeneous, and the concentration of a chemical in a compartment is assumed to be in equilibrium with the concentration of the chemical in the blood leaving the compartment. This is the lumped-parameter, perfusion-limited, representation of organ/tissue compartments. Lumped-parameter, perfusion-limited compartments are frequently justified for nonpolar low molecular weight compounds, and in fact the majority of PBPK models for volatile organic compounds (VOCs) are comprised of compartments of this type of PBPK model (50). The equations representing the basic lumped-parameter, perfusion-limited PBPK model are given in Table 1, the parameters of which are described in Table 2. The reaction term Rj accounts for metabolism in compartment j, whereas the source term Sj accounts for all transport into the compartment other than that via the blood. The reaction term is usually most significant in the liver compartment, and the source term is usually zero in all compartments other than in the skin and the lung compartments. A major advantage of PBPK modeling is that its mechanistic basis permits iterative refinement by successively reducing the number of simplifying assumptions used. Therefore, in cases where it is necessary to describe the pharmacokinetics of a contaminant in greater detail than the basic formulation will allow, one or more compartments can be replaced by compartments that are more sophisticated in describing the physicochemical processes involved. Conversely, the basic lumped-parameter perfusion-limited PBPK model can be simplified for some chemicals. The amenability of PBPK modeling to this type of refinement facilitates the modular development of a hierarchy of PBPK models that can be customized to suit a given exposure assessment application. There are several aspects of the basic PBPK formulation that can be refined, for example: (1) the lumping of tissues can be made more detailed by increasing the number of compartments; (2) the perfusion-limited assumption can be relaxed by introducing mass transfer resistances between vascular spaces, intercellular spaces, and intracellular spaces within a compartment; (3) spatial concentration gradients within an organ can be taken into account by replacing the lumped-parameter compartments with distributed-parameter compartments. Any one or more of these three types of refinement can be accommodated in EDMAS. Several examples of the first two types of refinement are available (55-57). Here, we present two examples of the third type of PBPK model refinement, in which lumpedparameter compartments are replaced by distributedparameter compartments. The first example concerns the refinement of the skin compartment and the second the refinement of the lung compartment. It is now generally accepted that the description of dermal uptake provided in the basic PBPK formulation is not appropriate for commonly experienced short dermal exposures (58-60). EDMAS incorporates a refinement of the skin compartment described by Roy et al. (61), which extends the refinements proposed by Chinery and Gleason (62) and by McKone (58). This refinement is analogous to the EPA recommended method of estimating short-term dermal uptake that was proposed by Cleek and Bunge (59) but is more flexible as it can be used for arbitrary temporal variations of microenvironmental concentration. In our approach, the
skin compartment is first subdivided into two compartments representing the viable skin, and the stratum corneum, as shown in Figure 3B. Mass flux into the viable skin is calculated based upon the concentration profile in the stratum corneum, which is obtained by solving numerically the one-dimensional Fickian diffusion equation, for the initial/boundary value problem presented in Table 1. Inhalation uptake is an important route of uptake for gaseous and volatile chemicals as well as for particulate matter. The basic PBPK formulation is not adequate for describing the uptake of highly reactive gases such as ozone since there is significant variation in the amount of ozone that reacts with lung tissue in different regions of the lung, and it is expected that the physiological response to ambient ozone is correlated with regional dose within the lung. EDMAS accounts for the dose of a reactive gas to airway tissue in the lung by incorporating an optional distributed parameter model of the lung based on and expanding the concept of the lung gas transport model of Scherer (63) and the ozone dosimetry model of Miller et al. (64). These models use the morphological model of lung airways described by Weibel (65), in which there are 24 airway generations, each produced by symmetrical branching from the previous generation, beginning with the trachea. Weibel’s morphological data have been adjusted by a correction factor of (2650/4800)1/3 to correspond to the average lung volume in a breathing cycle, as suggested by Paiva (66). A schematic diagram of the EDMAS distributed-parameter lung model is given in Figure 3C, and the main equations in the model are given in Table 1. In this model, the airways of the lung are represented by concentric cylinders, such that the dimensions of the rigid inner cylinder correspond to the airway itself, and the dimensions of the outer cylinder correspond to the alveoli around the airway. Axial transport occurs only through the inner cylinder via advection and dispersion, both of which are computed as functions of the average air flow velocity, which is calculated assuming a sinusoidally varying alveolar volume. Radial transport of the reactive gas is due to diffusion-reaction within the three regions of the airway walls: the mucus or surfactant layer (depending upon whether the airway is in the tracheobronchial or the pulmonary region of the lung), the tissue layer, and the pulmonary capillaries. Radial flux is evaluated by invoking a quasi-steady-state assumption, according to which the concentration of the reactive gas in the various regions of the airway wall is at all times in equilibrium with the concentration in the airway. It should be noted that this assumption is valid when the time scale of diffusion within the airway wall is small as compared to the duration of the breathing rate.
Model Application and Testing The following example applications demonstrate the link between the microenvironmental and pharmacokinetic components of EDMAS. The microenvironmental components are used to predict concentrations (1) of benzene volatilized from tap water and present in several indoor air compartments and (2) of ozone in indoor air. The pharmacokinetic components are used to estimate the internal and biologically effective doses of the toxicants for selected human exposure scenarios. Test Case 1: The Roxboro House Study. Lindstrom et al. (67) estimated total indoor exposure to benzene in a house in Roxboro, NC, following a 20-min shower with gasolinecontaminated water (approximate benzene concentration 300 µg/L). For 3 successive days (beginning on June 11, 1992), benzene air concentrations in the shower stall, bathroom, adjoining bedroom, and living room were measured for several hours following the start of exposure using fixed and personal sampling devices. These data were used to estimate the potential inhaled and dermal benzene dose to residents.
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FIGURE 4. Indoor air benzene concentrations, observations, and estimates for the Roxboro study and corresponding human dose and amount metabolized (data from June 13, 1991).
FIGURE 5. Burbank exposure scenario: Indoor and outdoor ozone concentrations (dv ) indoor ozone deposition velocity; pf ) indoor photolytic activity factor) and cumulative dose of ozone to specific airway generations.
In the exposure scenario for this study, inhalation and dermal exposure occurred in the shower for 20 min, followed by inhalation-only exposure of 5 min in the bathroom, 35 min in the bedroom, and 5 h in the living room. The indoor air was assumed to be comprised of four wellmixed compartments, representing the shower, bathroom, bedroom, and the rest-of-the-house. The volumes of these compartments were estimated from the plan of the house shown in ref 67. Since data on air change rates between the rooms in the Roxboro house were not available, initial estimates were obtained from literature (68). However, the shower air change rates were estimated from the data using statistical optimization (likelihood maximization), since the range of reasonable shower air change reported in ref 68 is very wide (5-300 ACH) and since model predictions were sensitive to these air change rates. The shower water-to-air transfer efficiency was measured to be approximately 0.73 by Lindstrom et al. (67). The basic PBPK model described in the previous sections was parameterized for benzene based on the the benzene PBPK model of Travis et al. (54). In addition, the model implemented in EDMAS incorporates a distributed parameter skin compartment to account for unsteady-state dermal uptake of benzene. The net inhaled and dermal doses for this exposure scenario (calculated using the June 13, 1991, data) were 87 and 41 µg, respectively. In comparison, the inhaled and dermal doses estimated by Lindstrom et al. using an algebraic model were 103 and 168 µg, respectively. The rate of biologically effective dose for this exposure scenario is shown in Figure 4. Test Case 2: The Burbank Study. Weschler et al. conducted continuous simultaneous measurements of indoor and outdoor concentrations of O3, NO, and NOx and temperature, relative humidity, and air change rates over a period of 14 months at a site in southern California (69). A 2-day
subset of this database was used to evaluate the microenvironmental modeling component with indoor air chemistry. The indoor concentration of ozone depends on several factors, such as infiltration rate, extent of heterogeneous removal, and homogeneous chemical transformation. Several models have been developed to model the infiltration of outdoor pollutants indoors (9, 10, 70). These are either single or multi-compartmental ventilation models that account for air change via infiltration and recirculation. These models, however, do not include complex chemistry mechanisms indoors but rather use linear decay-type transformations. The use of a photochemical mechanism (Carbon Bond-IV mechanism (37)) to describe indoor air chemistry follows the concepts introduced in the work of Nazaroff et al. (39). The study of the deposition of reactive gaseous pollutants and their removal on indoor surfaces is an emerging field, and some theoretical models have been proposed recently (71-73). Since heterogeneous removal processes are not very well understood, they have been accounted for by using the simple concept of deposition velocity, which assumes that the flux density of a pollutant to the surface is proportional to its concentration in the core/well-mixed region of a chamber (74). In order to approximately represent the indoor photolytic reactions and to study the effect of their rates on the concentration of ozone indoors, an “indoor photolytic activity factor” (pf) is defined for the indoor air compartment, which when multiplied by the outdoor photolytic reaction rates at a given hour of day would give indoor photolytic reaction rates. A sensitivity analysis of the indoor ozone concentration to this photolytic factor revealed that at the low levels of the photolytic activity factor corresponding to indoor situations ozone concentrations do not vary significantly. Thus, a value of 8%, which represents a reasonable upper bound for most cases, was retained for the rest of the simulations.
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FIGURE 6. Sensitivity of airway ozone dose to activity level and sensitivity of airway tissue ozone to activity level. In the Burbank study, continuous measurements were only available for the indoor and outdoor concentrations of O3, NO, and NO2. So, in order to estimate outdoor concentrations for the other species, the approach of Nazaroff et al. (74) was followed. In order to get concentrations of volatile organic compounds, the species concentrations were derived from the estimates of an Urban Airshed Model-IV simulation incorporating the site of concern (Burbank, CA), obtained from the South Coast Air Quality Management District (SCAQMD). The microenvironmental model simulations suggest that the deposition velocity of ozone indoors (0.012-0.017 cm/s) is close to the lower end of the literature suggested values (0.036 ( 0.021 cm/s) (9). Representative results from the microenvironmental and pharmacokinetic components of EDMAS for this case study are shown in Figure 5. Ozone is a highly reactive gas that reacts rapidly with the tissues of the lung and therefore does not have the opportunity of distributing throughout the body. Therefore, the pharmacokinetic model that was used to estimate ozone dose consists only of the refined lung compartment described earlier. Values for the parameters specifying diffusivity, reactivity, and partitioning of ozone with respect to lung tissue were obtained from Miller et al. (64). In the present study, the sensitivity of ozone dose to reactivity and thickness of the mucus/surfactant lining as well as to activity level was calculated as shown in Figure 6. The sensitivity with respect to mucus reaction rate and mucus thickness are in agreement with the calculations of Miller et al. (64). Data on breathing
rate as a function of activity level were obtained from ref 75. The PBPK model indicates that the transitional region of the lung airways receives the highest dose of ozone (normalized with respect to surface area), which is in agreement with experimental evidence (76). It is of interest to note that the tissue dose to the respiratory zone is predicted to be more sensitive to activity level than the tissue dose to the conducting and transitional zones, and as a result the peak tissue dose shifts from the transitional zone to the respiratory zone as the activity level increases.
Discussion Development of EDMAS represents an attempt to integrate the various steps involved in the source-to-receptor exposure sequence, thus providing consistency and efficient use of information. The need for using explicit time profiles of exposure concentrations in order to calculate meaningful estimates of biologically effective dose rather than average or cumulative exposures has been stated and justified by Smith (77). In the approach presented here, the environmental information required for the proper application of the pharmacokinetic modules is directly provided by the microenvironmental components of the system. Clearly there is a need for additional data in order to demonstrate and thoroughly evaluate the advantages of an integrated microenvironmental/PBPK approach in estimating multipathway/multiroute exposures to toxic chemicals. EDMAS provides a consistent framework for the design and analysis of exposure assessment studies as well as for the optimal
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estimation of parameters in microenvironmental systems. For example, a major problem in the prediction of indoor contaminants is the estimation of microenvironment-specific contaminant removal effects, such as surface reactivity. Future plans for the application and refinement of EDMAS include the use of the system to characterize and quantify surface effects for various types of surfaces using the Controlled Environmental Facility (CEF) at the Environmental and Occupational Health Sciences Institute (EOHSI). The CEF can be adapted to represent various types of indoor environments, and microenvironment-specific removal can be quantified and parameterized using the microenvironmental model via the built-in parameter estimating procedures. Other on-going applications of EDMAS include its use to test assumptions and methods used in the National Human Exposure Assessment Survey (NHEXAS) (24) and other studies involving human exposures to multimedia pollutants (23). Note: A more detailed description of the various EDMAS components and of the two case studies presented in this paper can be obtained from the authors.
Acknowledgments This research has been supported in part by NIEHS (Center Grant ES05022), by the ATSDR Exposure Characterization Program at EOHSI (Grant UGI/ATU298949), by the U.S. EPA National Exposure Research Laboratory (NERL) (Cooperative Agreement CR823717), by the NJDEP-funded Ozone Research Center at EOHSI, and by the DOE Consortium for Risk Evaluation with Stakeholder Participation (CRESP Cooperative Agreement DE-FC01-95EW55084). We would like to express our appreciation to A. B. Lindstrom, V. R. Highsmith, and T. J. Buckley (National Exposure Research Laboratory, U.S. EPA), W. J. Pate (Department of Environment, Health, and Natural Resources, NC), and L. C. Michael (Research Triangle Institute, NC) for providing the data from the Roxboro benzene exposure study (67). We would also like to extend our appreciation to C. J. Weschler, H. C. Shields (Bellcore, NJ), and D. V. Naik (Monmouth College, NJ) for providing the data from the Burbank ozone exposure study (69), and we would like to thank S. Mitsutomi (South Coast Air Quality Management District, CA) for providing the UAM simulation outputs for the Burbank, CA, case study.
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Received for review October 17, 1995. Revised manuscript received August 8, 1996. Accepted August 22, 1996.X ES950764S
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Abstract published in Advance ACS Abstracts, November 15, 1996.
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