Environ. Sci. Technol. 2004, 38, 1799-1806
Estimating Exposure to Chemical Contaminants in Drinking Water EUNYOUNG KIM,† J O H N C . L I T T L E , * ,† A N D N A N C Y C H I U ‡ Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0246, and Office of Water, U.S. Environmental Protection Agency, Washington, D.C. 20460
A model is developed that predicts exposure and absorbed dose for chemical contaminants in household drinking water via three pathways: inhalation, direct and indirect ingestion, and dermal penetration. Extensive probability distributions for building characteristics, activity and water use patterns, operating conditions of water devices, and physiological characteristics of the general population are developed. The impacts of different operating conditions on mass transfer coefficients for the shower, bath, washing machine, dishwasher, and faucet are established. Dichlorobromomethane, inorganic lead, and endosulfan, three compounds associated with adverse birth outcomes that have significantly different chemical properties, are selected for analysis. The primary exposure pathways for dichlorobromomethane are inhalation (62%) and ingestion (27%). Seventy percent of total exposure to endosulfan comes from ingestion, and 18% from dermal sorption with the remaining 12% due to inhalation. Virtually all (99.9%) of the exposure to lead occurs via ingestion. A nested Monte Carlo analysis shows that natural variability contributes significantly more (a factor of 10) toward total uncertainty than knowledge uncertainty (a factor of 1.5). Better identification of certain critical input variables (ventilation rate in the shower and bathroom, ingestion rate, the boiling water mass transfer coefficient, and skin permeability) is required.
Introduction A wide variety of volatile and synthetic organic chemicals, pesticides, inorganic chemicals, radionuclides (1), and disinfection byproducts (2) are present in water supplies. Epidemiological studies have shown that exposure to these chemicals may lead to various cancers (3-5). Elevated trihalomethane (6-12), pesticide (13), and heavy metal (14) concentrations may also be associated with greater risk of stillbirth, spontaneous abortion, preterm delivery, birth defects, and low birth weight. Although exposure to chemicals in drinking water has historically been based on the assumption that ingestion was the primary route, inhalation (15-17) and dermal absorption (18-20) are increasingly taken into account. * Corresponding author phone: (540)231-8737; fax: (540)231-7916; e-mail:
[email protected]. † Virginia Polytechnic Institute and State University. ‡ U.S. Environmental Protection Agency. 10.1021/es026300t CCC: $27.50 Published on Web 02/14/2004
2004 American Chemical Society
Several models evaluating inhalation exposure to volatile contaminants are available. A three-compartment model developed by McKone (21) estimates gas-phase concentrations using measured transfer efficiency (the fraction released during water use) for radon. The Environmental Protection Agency (EPA) model calculates inhalation and ingestion exposure to radon and radon progeny based on the estimated transfer efficiency, but considers only one combined waterusing source in each of three compartments (16). The Model for Analysis of Volatiles and Residential Indoor-Air Quality s MAVRIQ (22) computes volatilization of contaminants during the operation of various water devices using a dimensionless mass transfer coefficient based on residence time (duration of contact between water and air) and reported values for radon volatilization (23). When estimating inhalation exposure to a range of volatile compounds, the transfer efficiency becomes a function of the volatility of the particular compound, especially for those of low volatility. If this is not correctly accounted for, the predicted inhalation exposure will be substantially overestimated (24, 25). Recently, mass transfer coefficients for the faucet (26), bath, washing machine (27), dishwasher (28), and shower (29) have been measured over a wide range of operating conditions (30). This presents an opportunity to more accurately account for mass transfer of the entire range of volatile compounds from the major water using devices in homes. Another important exposure pathway associated with drinking water is the absorption of contaminants through the skin. Brown et al. (18) used a Fickian diffusion model to describe absorption of solutes from dilute aqueous solutions. Estimates of permeability obtained during percutaneous absorption of dilute aqueous chloroform, TCE and PCE in hairless guinea pigs (31) were recommended for use in estimating sorption through human skin. Cleek and Bunge (32) simplified Crank’s equation (33) for chemical uptake through a pseudohomogeneous membrane. Different workers (34-41) have developed equations to estimate the parameters for dermal absorption based on physicochemical properties (octanol-water partition coefficient and molecular weight) and/or the molecular structure of the chemical. The objectives of this study are to develop an integrated household exposure and absorbed dose model that incorporates inhalation, ingestion, and dermal sorption, for the entire range of chemical contaminants found in drinking water. The model is intended to apply to the general U.S. population. A detailed sensitivity and uncertainty analysis is conducted, and the results are used to identify critical input variables requiring further study.
Description of the Model In this model, a residence is divided into three compartments: shower, bathroom, and the main house (Figure 1). Both shower and bathtub are located within the shower stall. The faucet, washing machine, and dishwasher are in the main house, and the toilet is in the bathroom. For simplicity, the initial concentration of chemicals in the water entering the house is assumed to be 1 mg L-1. Because the model equations are linear in concentration, the results can be adjusted to estimate an absorbed dose of contaminants at other concentrations. Between 1 and 6 people live in the house, with one individual identified for exposure analysis. Inhalation Dose. To compute the gas-phase concentration of a chemical in the three compartments, the volatilization of the contaminant from the different water devices into the air and its subsequent distribution throughout the house and outdoors must be predicted. The concentration VOL. 38, NO. 6, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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The source term for each water device (Sik) in compartment i is
(
Sik(t) ) Kv,k C0 -
)
yi(t) H
(5)
where C0 is the initial concentration in the water. Calculation of the volatilization coefficient (Kv,k) depends on the mixing characteristics of the device. The shower is considered to follow a Plug Flow Model (PFM) (24), with volatilization coefficient (Kv,sh) given (43) by
(
Kv,sh ) QL,sh 1 - exp
FIGURE 1. Schematic representation of the residential environment. in the different compartments as a function of time is obtained by solving a set of transient mass balance equations or
Vi
dyi(t) )dt
∑Q ‚y (t) + ∑Q ‚y (t) + ∑S ij
i
ji
j
ik(t)
(1)
where yi(t) is the gas-phase concentration of compartment i at time t, Qij is the ventilation rate from compartment i to j, and Sik(t) is the source due to the volatilization of a chemical from water device k in compartment i at time t. The possibility of compounds being removed from the air by sinks is neglected. The ventilation rate (Qi) between the compartments is calculated from the residence time (Ri) and the compartment volume (Vi) or
Qi )
Vi Ri
(2)
Three cases for ventilation in the bathroom reflect reasonably realistic scenarios; the bathroom door is closed and the fan inside the bathroom is either on or off when a person takes a shower or a bath, and the bathroom door is open and the fan is off when no one is in the bathroom (16). Volatilization is characterized by the overall mass transfer coefficient (KOLA), which is a function of both the nature of the device and the chemical properties of the contaminant (Henry’s law constant and diffusion coefficients). Liquidphase (KLA) and gas-phase (KGA) mass transfer coefficients depend primarily on the degree of fluid turbulence and, to a lesser extent, on the temperature and the diffusion coefficient of the compound being transferred, in each of the respective phases. Nonlinear regression analysis was used to determine the influence of operating condition on both KLA and KGA, where possible. For example, relationships between the experimentally determined mass transfer coefficients for the shower (30, 42) and shower operating conditions were determined as follows
KLA ∝ QLβ1‚β2(T-20)
(3)
KGA ∝ QLβ1‚QGβ2
(4)
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))
- KOLAsh QL,sh
(6)
The bath is considered to be a Completely Mixed Flow Model (CMFM) with volatilization only occurring through the bathwater surface (Kv,bath ) KOLAbath). Further details concerning the various source terms are provided by Kim et al. (25). The toilet, faucet, washing machine, and dishwasher are modeled as either PFM or CMFM depending on the predominant mixing properties. Unlike the other devices, water in the washing machine and dishwasher is in contact with a separate interior headspace, and volatilization from the washing machine and dishwasher to the indoor air does not depend on the gas-phase concentration in the compartment where they are located (30). Chemicals emitted from the headspace of both washing machine and dishwasher contribute directly to the increase in the gas-phase concentration in the main house. To compute the gas-phase concentration in each compartment, it is necessary to determine the duration, frequency of use, and, in most cases, the flow rate and/or volume of water used in different water devices. Based on a detailed study of hourly water use rates by Mayer et al. (44), typical water use patterns for a representative household were determined. The household members are assumed to be from the same population, so they follow the same activity and water use patterns. The overall water use rate is highest in the morning and the evening and slightly lower during the day. Consistent with the activity patterns adopted by EPA (16), the first shower is assumed to start at 7:00 a.m., the second shower starts after the first person leaves the bathroom, and so on. The time spent in the bathroom after each shower ranges between 1 and 30 min. The other devices are operated at random within the established time periods according to the specific water use patterns. Further details of the water use patterns are provided in the Supporting Information. The concentration profile in the three compartments coupled with the human activity pattern is used to calculate dose via inhalation. An Occupancy Factor (OF) is used to quantify the time a person spends in the home (16). Absorbed inhalation dose of a contaminant (Dinh) is estimated from the gas-phase concentrations of a chemical in the three compartments, the location of an individual as a function of time during the day, the breathing rate (BR), and the fraction of the chemical absorbed by the body (AFinh) via the inhalation pathway
Dinh ) AFinh‚BR‚
where QL is the water flow rate, QG is the air flow rate, T is temperature, and β1 and β2 are fitted parameters. A similar approach was applied to the other water using devices. A detailed description of the procedure used to estimate the mass transfer coefficients for each water device is provided in the Supporting Information.
(
∫
time
(yjs‚PFs + yjb‚PFb + yja‚PFa)dt
(7)
where yj is the average concentration between time t and t + dt, PF is a compartmental presence factor, and the subscripts s, b, and a stand for shower, bathroom, and main house, respectively. For example, PFs is 1 if a person is in the shower during the integrating time interval, and 0 if not. The fraction of chemical exhaled at equilibrium varies widely (0.06-0.16 for aromatic compounds and decane; 0.22-0.23
for trichloroethylene and dichloromethane; 0.35 for hexane; and 0.88 for 1,1,1-trichloroethane) (45). In this model, an AFinh value of 1 is assumed for all chemicals (46). Ingestion Dose. Tap water consumed directly and indirect tap water added to food or hot beverages are considered separately. A 1977/1978 survey by USDA revealed that adults consume as much as 40% of total tap water in tea or coffee (47). Water used in the preparation of these hot drinks presumably has a lower concentration of volatile contaminants. The additional contaminant loss has to be accounted for if water is boiled before consumption. Also, the volume ingested may vary considerably for different age groups, climate, and activity patterns (48). Exposure analysis must take these factors into account when establishing the value for the ingestion rate (VI). A log-normal probability distribution was fitted to the data collected by United States Department of Agriculture (USDA) (49) for direct tap water ingestion and total tap water consumption. The difference between total tap water and direct tap water is assigned to indirect ingestion. The current model assumes that directly ingested water is drawn from the faucet and uses the overall mass transfer coefficient for the faucet (KOLAfaucet) to compute the fraction that is volatilized while dispensing the water (Zfaucet) or
(
Zfaucet ) 1 - exp -
)
KOLAfaucet QL,faucet
(8)
The fraction of chemical remaining in the water (F ) 1 Zfaucet) is ingested at the time of drinking. Batterman et al. (50) measured the percentage loss of four trihalomethanes during boiling. Assuming constant volume and neglecting the gas-phase concentration in the compartment, the aqueous concentration at the time of indirect ingestion (Ct,ind) is
(
Ct,ind ) C0 exp -
KOLAind ‚t VL,ind
)
(9)
where KOLAind is the overall mass transfer coefficient for the boiling water. The data of Batterman et al. (50) were used to obtain values for KOLAind for trihalomethanes, with chloroform used as a reference chemical to evaluate the overall mass transfer coefficient for other chemicals. In evaluating KOLAind for boiling water, the Henry’s law constant was corrected to 100 °C. Although the temperature correction is only intended to be used up to about 40 °C, the estimated loss due to boiling with the temperature corrected Henry’s law constant is consistent with the experimental results of Batterman et al. (50) for trihalomethanes. Ingestion includes both direct and indirect pathways. Direct ingestion is evaluated from the daily volume of water ingested directly (VI,dir), the concentration of the contaminant in water (C0), and the fraction of the chemical remaining at the time of ingestion (F). Indirect ingestion is computed from the concentration at the time of indirect ingestion (Ct,ind) and the indirect tap water consumption rate (VI,ind). Complete absorption of the chemical in the stomach (AFing ≈ 1) is assumed (46) when calculating absorbed ingestion dose (Ding):
Ding ) AFing‚
∑ (C ‚F‚V 0
I,dir
+ Ct,ind‚VI,ind)
(10)
event
Dermal Absorption. As described by Bunge and McDougal (39), dermal absorption has been studied primarily in a pharmaceutical connection. Many in vitro studies on both humans and animals have established that skin functions as a membrane. Assuming that the skin behaves as a composite membrane comprising stratum corneum and viable epidermis and that the concentration in the receptor
fluid (the blood) is negligibly low, the total mass entering the skin (Min) can be calculated (32) by
[
G(1 + 3B) + B(1 + 3BG) 1 Min ) AC0Ksc/wLsc‚ τ+ + 1+B 3G(1 + B)
2(1 + B)
∞
sin (λn/xG)sin (λn) exp(-λn2τ)
n)1
λn2σn
∑
-
]
∞
cos(λn/xG)cos(λn) exp(-λn2τ)
n)1
BxGλn2σn
2(1 + B)
∑
(11)
where A is the exposed surface area of skin, C0 is the concentration in water, Ksc/w is the partition coefficient between the stratum corneum and water, Lsc is the thickness of the stratum corneum, G is the ratio of the lag time for stratum corneum to that for viable epidermis, B is the ratio of permeability for stratum corneum (Psc) to that for viable epidermis (Pve), τ is the dimensionless exposure time expressed as τ ) Dsc‚texp/Lsc2, Dsc is the diffusion cofficient in stratum corneum, and texp is exposure time. The thickness of the stratum corneum (Lsc) is assumed to be 10 µm (20). The values for σn are calculated from
σn )
1 [xG(1 + B)cos(λn/xG)cos(λn) BG (1 + BG)sin (λn/xG)sin (λn)]
and λn are the eigenvalues of
BxG‚tan (λn/xG) + tan (λn) ) 0 Assuming that diffusion within the viable epidermis is equivalent to water, B simplifies (32) to
B ) Psc
xMW 2.6
where MW is molecular weight. The mass that enters the blood will actually be somewhat less than the mass that enters the skin, because the skin behaves as a chemical reservoir. Except for highly lipophilic (log Kow > 4) or large (MW > 350) chemicals, the body will continue to absorb some of the chemical that remains in the skin after the external source is removed (51). In this model, the potential epidermal turnover is assumed to be negligible. Unlike organic chemicals, steady state is assumed for dermal exposure to inorganic chemicals (20), because only experimentally measured permeability is available and other variables for dermal penetration cannot be readily evaluated. For most chemicals of environmental concern, experimentally determined values of Psc and Ksc/w are not available (39). Consequently, researchers have developed equations to predict dermal transport properties. The skin uptake is primarily correlated with lipophilicity (the octanol-water partition coefficient) and molecular weight. Bunge and McDougal (39) suggested the following empirical equations
log Psc(cm/h) ) -2.72 + 0.71‚log Kow - 0.006‚MW (12) log Ksc/w ) 0.71‚log Kow
(13)
where Kow is the octanol-water partition coefficient. To account for the relatively smaller molar volume of VOL. 38, NO. 6, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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halogenated hydrocarbons, eq 12 is modified (52) to
Fhc log Psc(cm/h) ) -2.72 + 0.71‚log Kow - 0.006‚MW‚ Fhalo (14) where Fhc is the estimated liquid density of a representative hydrocarbon (0.9 g cm-3) (52) and Fhalo is the liquid density of the halogenated hydrocarbon. The liquid density of dichlorobromomethane is 1.98 g cm-3 (53). Showering, bathing, and washing hands are considered in the dermal exposure analysis. The exposed surface area during the shower or bath is assumed to be 91% of the total body surface area, which excludes the surface area of the head (54). The absorbed dermal dose (Dder) is the sum of dermal absorption during each event or
Dder )
∑M
in
(15)
event
Uncertainty/Sensitivity Analysis. In environmental exposure and risk assessment, there are several sources of uncertainty (16). Due to inherent natural variability, model variables can be defined in terms of a Probability Distribution Function (PDF) that is derived from a limited set of observations. However, the data may not be representative of the entire population, and sample statistics may not be accurate estimates of the true values of the population parameters. This leads to uncertainty in the parameter estimation procedure. A nested Monte Carlo analysis (16) is adopted to explicitly account for variability within the population (natural variability) as well as uncertainty about the true value of a specific parameter (knowledge uncertainty). For example, time spent in the shower is expressed as a log-normal distribution defined by two parameters, the geometric mean (gm) and the geometric standard deviation (gsd). This log-normal distribution quantifies the natural variability of time people spend taking a shower. However, there is some uncertainty about the validity of each parameter (gm and gsd) of the log-normal distribution. This knowledge uncertainty is accounted for by deriving probability distributions for both the gm and the gsd. Thus, the gm of the lognormal distribution is itself represented by an inverse student’s-t distribution and the gsd of the log-normal distribution by an inverse chi-squared distribution. A description of the various distributions for all input variables is provided in the Supporting Information. The twodimensional nested Monte Carlo simulation keeps track of both natural variability and knowledge uncertainty. In an outer loop, the knowledge uncertainty distributions (PDFus) are randomly sampled to obtain values for each of the parameters. With these parameters held constant, the computation passes through an inner loop where the natural variability distributions (PDFvs) are randomly sampled to obtain a value for each model variable. This set of model variables is then used to calculate exposure. The overall uncertainty analysis consists of 250 outer loops and 2000 inner loops. After 2000 inner loops have been completed, the 2000 predicted exposure values are used to derive a Cumulative Distribution Function (CDF) describing the natural variability of exposure for the selected set of PDFu parameters. Over 250 outer loops, the two CDFs that have the lowest and the highest median values are assumed to represent lower and upper bounds (55), while the CDF with the median value of the 250 means is assumed to represent the most likely exposure. The range of exposures associated with a single CDF therefore quantifies natural variability, while the gap between the lower and upper bounds quantifies knowledge uncertainty (55). The PDFs used in this model were developed from data presented in other studies (44, 47, 1802
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56) or obtained directly from the literature (16). PDFs that best represent the sample were chosen, but when no conventional PDF was found to be appropriate, an Empirical Distribution Function (EDF) comprising the actual sample data was used. A sensitivity analysis is conducted to identify the critical input variables for each exposure pathway. The sensitivity of model variables at the point of maximum likelihood is assessed by computing the percent change in exposure from each pathway per unit increase (1%) in an input variable. In this case, the nested Monte Carlo procedure was run for 4000 inner loops and 2000 outer loops to determine the most likely value for each model variable. After a series of 4000 inner loops, a mean value averaged over the 4000 values obtained for each model variable is calculated. After 2000 outer loops the median value of the 2000 mean values is established as the most likely value for each model variable. Although the model equations are linear in concentration, they are not linear with respect to all model variables. Thus, the calculated sensitivity only applies to the specific set of inferred most likely values.
Results and Discussion Three compounds (dichlorobromomethane, endosulfan, and lead) were chosen because of their potential to adversely affect birth outcomes and due to the substantial difference in their chemical properties (Table 1). Exposure to trihalomethanes (THMs) (7, 8, 10), pesticides (13), and heavy metals (14) may be associated with miscarriage, low birth weight, and birth defects. All three chemicals have been found in water supplies (1, 2), and Miles et al. (61) reported a significant increase in THM concentrations in blood after showering. The differential mass balance equations for the gas-phase concentrations in the three compartments were solved simultaneously using the adaptive Runge-Kutta-Fehlberg method. Mass balance equations for certain water devices were included, where appropriate. The numerical solution was checked by comparing the results for a simplified version of the model to an analytical solution. Also, inhalation exposure to the highly volatile compounds is similar to the results of McKone’s model (21, 25), as expected. Wallace (62) measured personal exposures to volatile organic chemicals in residential environments for over 800 participants. To further validate the inhalation component of the integrated exposure model, the predicted personal exposure to chloroform is compared with the measured personal exposure concentration. As shown in Figure 2, the average predicted concentration (4.6 × 10-5 mg L-1air per mg L-1water) obtained from the Monte Carlo simulation is close to the measured personal exposure concentration based on sample size weighted mean and median concentrations of 7.7 × 10-5 and 4.3 × 10-5 mg L-1air per mg L-1water, respectively. The device to measure the personal exposure concentration was left in the bedroom during the shower or bath (62), so the concentration in the main house was used when estimating exposure during these periods. The output from the model is absorbed dose via inhalation, ingestion, and dermal sorption. Contaminant concentrations in different household compartments, as a function of time, are a primary requirement to estimate the absorbed inhalation dose. The concentration profile in the three compartments for a typical day is presented in Figure 3 along with the schedule indicating when each water-using appliance is in use. For simplicity, the frequent use of the faucet and toilet is not shown. Showering has the greatest impact on the gas-phase concentration within the shower and the bathroom as well as that in the main house. In contrast, operating the washing machine or dishwasher hardly affects
TABLE 1. Physicochemical Properties chemical
MW (g mol-1)
Henry’s law constant H (-)
DL (cm2 s-1)
DG (cm2 s-1)
log Kow (-)
Psc (cm h-1)
Ksc/w (-)
dichlorobromomethane endosulfan lead
164 407 207
0.076a 0.0029a 0.0b
9.18 × 10-6 4.72 × 10-6 NAc
0.093 0.046 NAc
2.09d 3.83e NAc
0.021f 0.0036g 0.0001h
30i 524i NAc
a Reference 57. b Henry’s law constant for inorganic lead is assumed to be zero. c NA: not available. from eq 14. g Calculated from eq 12. h Reference 60. i Calculated from eq 13.
d
Reference 58. e Reference 59. f Calculated
FIGURE 2. Predicted average chloroform concentration in three compartments from Monte Carlo simulation as well as comparison between predicted average personal exposure concentration and measured mean and median personal exposure concentrations from Total Exposure Assessment Methodology (TEAM) studies (62, 63). the concentrations within the shower and the bathroom because of the low air exchange rates and the relatively small mass of chemical volatilized. The concentration of endosulfan in the house is about an order of magnitude lower than that of dichlorobromomethane due to the low Henry’s law constant. The relative importance of each of the exposure pathways (Figure 4) is determined primarily by the properties of the contaminant. The most likely absorbed dose obtained from the Monte Carlo analysis is used for comparison. As expected, due to the relatively high Henry’s law constant, the inhalation pathway is a major contributor to total absorption for dichlorobromomethane (62%), with ingestion accounting for 27%. The major exposure pathway for endosulfan is ingestion (70%). The low Henry’s law constant for endosulfan leads to much lower inhalation dose (12%), although the high lipophilicity (Kow) results in a modest contribution to dermal dose (18%). Virtually all (99.9%) of the lead is absorbed via ingestion, because of its negligible volatility and low Psc. It is important to include indirect ingestion for the semi- and nonvolatile compounds. Indirect ingestion accounts for about 60% of the absorbed ingestion dose for endosulfan and lead, although it contributes only 35% for dichlorobromomethane. Assuming 100% absorption during inhalation and ingestion overestimates the contribution of these two pathways to total absorbed dose. Thus, the relative importance of dermal sorption may increase when absorption by the lungs (inhalation) and the gastrointestinal tract (ingestion) is more accurately quantified. A sensitivity analysis is conducted to identify the critical input variables for each exposure pathway and to prioritize the collection of additional data. The results of the sensitivity analysis along with the most likely values of the model variables are presented in Table 2. The effect of the model variables on each exposure pathway depends primarily upon the nature of the contaminant. For a volatile compound, for example, the breathing rate (BR) has a significant impact on
FIGURE 3. Gas-phase concentration profiles for dichlorobromomethane and endosulfan during a typical day.
FIGURE 4. Relative contribution of each exposure pathway to total absorbed dose. inhalation exposure. Henry’s law constant has a greater impact on inhalation exposure to endosulfan than to dichlorobromomethane, which indicates that volatilization increases dramatically with a small increase in Henry’s law constant for a compound with low volatility. The gas-phase VOL. 38, NO. 6, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Results of Sensitivity Analysis exposure pathway/variables
units
time in shower (tsh) breathing rate (BR) shower flow rate (QL,sh) volume of shower stall (Vs) liquid-phase mass transfer coefficient for shower (KLAsh) residence time in shower stall (Rs) temperature of shower water (Tsh) volume of bathroom (Vb) residence time in main house (Ra) volume of main house (Va) temperature of water used in washing machine (Twm) occupancy factor (OF) exhaust fan flow rate (EXFR) Henry’s law constant (H) volume of toilet water (VL,toilet) liquid-phase mass transfer coefficient for toilet (KLAtoilet) gas-phase mass transfer coefficient for shower (KGAsh)
Inhalation min L min-1 L min-1 L L min-1 min °C L min L °C L min-1 L L min-1 L min-1
Ingestion direct ingestion rate (VI,dir) L day-1 overall mass transfer coefficient for indirect ingestion (KOLAind) L min-1 indirect ingestion rate (VI,ind) L day-1 overall mass transfer coefficient for faucet (KOLAfaucet) L min-1 faucet flow rate (QL,faucet) L min-1 log octanol water partition coefficient (logKow) skin surface area - body (Abody) molecular weight (MW) permeability (Psc) skin water partition coefficient (Ksc/w) time in shower (tsh) skin surface area - hands (Ahands) a
Dermal Sorption cm h-1 cm2 g mol-1 cm h-1 min cm2
most likely value
dichlorobromomethane
endosulfan
8.0 9.1 8.4 2000 CDa 3.74 35 16000 81 429000 35 0.74 2757 CD 72 CD CD
1.04 1.00 0.73 -0.51 0.38 0.29 0.25 -0.19 0.19 -0.15 0.12 0.10 -0.07 0.06 0.05 0.03 0.01
0.94 1.00 0.36 -0.07 0.17 0.00 0.58 -0.18 0.08 -0.08 0.01 0.07 -0.05 0.70 0.03 0.03 0.22
0.326 CD 0.764 CD 4.4
0.67 -0.57 0.33 -0.13 0.03
0.30 -0.02 0.70 -0.01 0.01
CD 18000 CD CD CD 8.0 910
3.48 0.84 -0.51 0.50 0.50 0.43 0.16
6.46 0.84 -2.77 0.50 0.50 0.42 0.15
lead
0.30 0.00 0.70 0.00 0.00
0.94 1.00 0.94 0.06
CD: chemical dependent property.
TABLE 3. Uncertainty in Predicted Absorbed Dose via Inhalation, Ingestion, and Dermal Sorption for Dichlorobromomethane, Endosulfan, and Lead (mg yr-1 per mg L-1)a inhalation
ingestion
lower
median
upper
lower
5th median mean 95th
11 147 217 624
19 239 334 976
34 337 474 1446
5th median mean 95th
1 36 50 145
3 51 66 186
5 65 82 233
62 234 285 633
5th median mean 95th
0 0 0 0
0 0 0 0
0 0 0 0
59 215 267 583
median
Dichlorobromomethane 26 44 84 123 94 146 191 326
dermal sorption upper
lower
median
upper
72 183 212 446
3 54 52 122
5 61 59 145
8 69 67 163
99 314 390 976
148 421 504 1231
5 96 93 212
9 104 101 249
13 115 112 294
104 325 403 985
155 461 556 1300
Endosulfan
Lead
a
0 0.05 0.06 0.18
0 0.06 0.07 0.23
0 0.07 0.09 0.32
Each column represents natural variability, while the gap between the lower and upper bounds indicates knowledge uncertainty.
mass transfer coefficient for the shower becomes significant for inhalation exposure to endosulfan, indicating that gasphase resistance is important for a compound of low volatility. The volume of water ingested directly (VI,dir) affects the highly volatile dichlorobromomethane the most, while direct and indirect ingestion rate have equally significant effects on the low- or nonvolatile endosulfan and lead. If the contaminant has a high Henry’s law constant, the overall mass transfer coefficients for the faucet (direct ingestion) and for boiling water (indirect ingestion) have a noticeable effect on ingestion 1804
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exposure. Log Kow and MW have the greatest impact on dermal sorption. The surface area of the exposed body (Abody) is also a significant model variable. Table 3 provides an overview of the Monte Carlo simulation results. Natural variability and knowledge uncertainty for total absorbed dose of dichlorobromomethane, endosulfan, and lead are summarized in Figure 5. The twodimensional analysis reveals that natural variability introduces uncertainty of about a factor of 10, while knowledge uncertainty only accounts for a factor of about 1.5. Natural
log Kow and MW (eqs 12-14) as opposed to the value of 0.14 cm h-1 measured by Nakai et al. (64). At elevated temperatures, a higher Psc (64) and subsequently increased concentration in breath after bathing (65) have been reported. Hence, accurate Psc values and the effect of temperature on dermal sorption need further study. For the ingestion exposure pathway, the probability distribution used to represent ingestion rate did not fit the data well, despite the considerable amount of data, and therefore contributes significantly to uncertainty. This may be explained by changes in personal consumption. A recent survey by USDA indicated that consumption of tap water for drinking has decreased, while bottled water has increased (47, 49). A careful investigation of the impact of such behavioral changes on absorbed dose is necessary. Finally, the overall mass transfer coefficient for boiling water is assumed constant instead of being expressed as a PDF due to the lack of data, and this underestimates the uncertainty about indirect ingestion. In summary, additional data are needed for the following critical input variables: ventilation rate in the shower and bathroom for inhalation, ingestion rate and the boiling water mass transfer coefficient for ingestion, and permeability for dermal absorption. This paper clearly demonstrates the extent to which chemical properties determine the active exposure pathways. When coupled with a physiological model, the integrated exposure model should prove valuable in establishing Maximum Contaminant Levels for the wide range of chemicals found in drinking water. Ultimately, the model could be used to evaluate the potential health effects of drinking water contaminants for the general population as well as vulnerable subpopulations such as pregnant women.
Acknowledgments This research is supported by the Office of Water, U.S. Environmental Protection Agency (EPA) under Cooperative Agreement No. CR 825998-01-0. The opinions and conclusions expressed are those of the authors and do not necessarily reflect those of the EPA. The authors thank Rajesh Khanal and Karpagam Sankaran for their help in the early phases of the research. FIGURE 5. Uncertainty about total inhalation, ingestion, and dermal dose of dichlorobromomethane, endosulfan, and lead showing lower and upper bounds as well as the fitted log-normal CDF for the most likely dose. variability therefore contributes significantly more toward overall uncertainty than knowledge uncertainty. To reduce uncertainty, the model variables possessing both high sensitivity and high uncertainty need to be identified for further investigation. The uncertainty parameters for the inhalation exposure component of the model are better identified than those for ingestion or dermal exposure. Showering and bathing have greater impacts on inhalation exposure than any other water use activities. More than 65% of the inhalation exposure is due to the shower and bathroom combined. Turning the extraction fan on in the bathroom during a shower decreases the inhalation exposure to dichlorobromomethane by about 45%. Thus, the ventilation rate in the shower and bathroom as well as the assumption of a 50% probability in using the extraction fan needs further investigation to reduce the uncertainty about inhalation exposure. In some cases additional information is needed to represent uncertainty more accurately. This is especially true for dermal sorption. Parameter uncertainty for permeability is significantly underestimated due to the lack of reliable data, even though it is one of the most important model variables (Table 2). For example, dermal absorbed dose for chloroform decreases by a factor of 3 when using the Psc value of 0.018 cm h-1 estimated from
Supporting Information Available Further details on the probability distributions for the input model variables, regression analyses for the mass transfer coefficients, model validation, and water use and activity patterns. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review November 4, 2002. Revised manuscript received November 9, 2003. Accepted November 24, 2003. ES026300T
