Environ. Sci. Technol. 1989, 23, 1154-1 163
Rowland, R. L.; Giles, J. A. Tob. Sci. 1960, 4, 29. Carruthers, W.; Johnstone, R. A. W. Chem. Ind. 1960,4, 867.
Woolen, B. H.; Jones, D. H. J. Chromatogr. 1971,61,180. Severson, R. F.; Ellington, J. J.; Schlotzhauer,P. F.; Arrendale, R. F.; Schepartz, A. I. J . Chromatogr. 1977,139,
269.
Ellington, J. J.; Schlotzhauer, P. F.; Schepartz, A. I. J . Agric. Food Chem. 1978,26, 270.
Ellington, J. J.; Schlotzhauer, P. F.; Schepartz,A. I. J . Agric. Food Chem. 1978,26, 407. Sheen, S. J.; Davis, D. L.; DeJong, D. W.; Chaplin, J. F. J. Agric. Food Chem. 1978,26, 259. Severson, R. F.; Ellington, J. J.; Arrendale, R. F.; Snook, M. E. J . Chromatogr. 1978,160, 155. Severson, R. F.; Arrendale, R. F.; Chaplin, J. F.; Williamson, R. E. J . Agric. Food Chem. 1979,27, 896. Schepartz, A. I.; Mottola, A. C.; Schlotzhauer,W. S.; DeJong, D. W.; Lam, J. J. Tob. Sci. 1981, 25, 120. Wooten, J. B. J . Agric. Food Chem. 1985, 33, 419. Rodgman, A.; Cook, L. C. Tob. Sci. 1959, 3,86. Rodgman, A.; Cook, L. C.; Latimer, P. H. Tob. Sci. 1959, 3, 125.
(30) Rodgman, A.; Cook, L. C.; Chappell, C. K. Tob. Sci. 1961, 5, 1. (31) Mold, J. D.; Booth, J. S. Tob. Sci. 1957, 1, 38. (32) Schepartz,A. I.; Ellington, J. J.; Schlotzhauer, W. S. Tob. Sci. 1980, 24, 56. (33) Wellburn, A. R.; Hemming, F. W. J . Chromatogr. 1966,23, 51. (34) Ellington, J. J.; Schlotzhauer, P. F.; Schepartz, A. I. J . Chromatogr. Sci. 1977,15, 295. (35) Heavner, D. L.; Thome, F. A.; Eudy, L. W.; Ingebrethsen, B. J.; Green, C. R. In Proceedings of the 79th Annual
Meeting of the Air Pollution Control Association; June 22-27, 1986, Minneapolis, MN, Paper 86-37.9. (36) Tobacco and Health Research Institute The Reference and Research Cigarette Series; The University of Kentucky Printing Services: Lexington, KY, 1984. Received for review January 6,1989. Accepted M a y 15, 1989. This work was presented, in part, at the Indoor and Ambient Air Quality Conference, 13-15 J u n e 1988 in London, England, and at the 42nd Tobacco Chemists’ Research Conference, 2-5 October 1988 i n Lexington, KY.
Human Exposures to Chemicals through Food Chains: An Uncertainty Analysis Thomas E. McKone”
University of California, Lawrence Livermore National Laboratory, P. 0. Box 5507, L-453, Livermore, California 94550 P. Barry Ryan
Harvard School of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115 Using models that link human ingestion exposures to chemical concentrations in air and soil, we assess the amount and source of uncertainties in model predictions. We use pathway exposure factors (PEFs) to convert environmental concentrations to human exposures for the air/plant/food- and soil/ plant/food-ingestion pathways. Input data are presented as probability distributions, which are used to construct output probability distributions for child and adult exposures associated with arsenic and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Our analysis reveals that, without information on the distribution and variance of input parameters, one can underestimate the mean value of exposure distributions by using only mean or median values of the input parameters. We also find that much of the overall uncertainty in exposure is attributable to uncertainty in biotransfer factors and that uncertainties in the input data limit the precision of exposure predictions to a 90% confidence range of roughly 2 orders of magnitude. Introduction
The EPA ( 1 ) defines exposure as “the contact with a chemical or physical agent”. This implies that, when we perform an assessment of human exposure to environmental contaminants, we translate environmental concentrations into quantitative estimates of the amount of contaminant that passes through the lungs, across the gut wall, and through the skin surfaces of individuals within a specified population. The quantity of chemical daily crossing these boundaries provides the basis for assessing health detriment within the population. This process of 1154
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1989
estimating exposure with limited data and extrapolating to large and diverse populations requires many assumptions, inferences, and simplifications. How well these exposure estimates reflect actual exposures is still largely an unanswered question. There are many uncertainties and some defy quantification. Nonetheless, only by examining exposure data and proposing exposure models can we gain the insight needed to manage the potential hazards of environmental contaminants. An exposure assessment can be most valuable when it provides a comprehensive view of exposure routes and identifies major sources of uncertainty and what impact this will have on the decisionmaking process. Yet, the common practice in exposure evaluations has often been to use single exposure routes and mean or point estimates for most parameters. Human exposures to ambient airborne pollutants is an area where uncertainty is particularly important. Much of the uncertainty is attributable to ignorance about the values of particular process parameters. Inhalation is typically the pathway given the most attention even though food-chain pathways, which are less well understood, could be the dominant contributor to total exposure. Consider for example the chemical 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and the trace metal arsenic, which are found in ambient air attached to suspended dust or combustion particles. These contaminants can be found in the air as a result of hazardous waste incineration or as a result of land disposal of municipal or industrial wastes. In order to assess the contribution to lifetime risk of ingestion exposure relative to inhalation exposures, it is useful to construct steady-state models that allow comparison of lifetime average exposures. A risk assessment
0013-936X/89/0923-1154$01.50/0
0 1989 American Chemical Society
Table I. Matrix of Environmental Concentrations and Pathway Exposure Factors air (gas phase)
air (particles)
Environmental Concentrations ingestion pathways ca CP Pathway Exposure Factors" fruits and vegetables FV , FPV grains Fa8 FP, meat Fat FPt dairy products Fak Fpk
soil
c, FBV Fsg Fat Fsk
" See text or Appendix A for definitions. Atmosphere (gas and partlcles)
B a c k g r o u n d D a t a on H u m a n s a n d A n i m a l s
Inhslatlon
1a
Considered here are transfers of particles onto the surfaces of vegetables, fruits, and grains and the transfer of airborne contaminants to meat and dairy products as a result of inhalation by cattle and ingestion by cattle of contaminants deposited onto pasture. Following this, we consider PEFs for the uptake of contaminants from soil to fruits and vegetables, grains, milk, and meat as a result of transfer from soil to vegetation. In the next section the P E F expressions are applied to two pollutants, arsenic and 2,3,7,8-TCDD. Parameter uncertainty is propagated through the expressions in order to estimate the variance in the PEF terms. The data and models used to construct the PEFs are examined in order to determine significant contributors to overall uncertainty. Finally, we conclude with a summary and discussion of the results.
vegetables, and grains
SOH
Figure 1. Schematic diagram of the transfer of contaminants from air and soil to meat, dairy products, fruits, vegetables, and grains.
by its very nature involves uncertain consequences, and thus one useful output is a probability distribution characterized by expectation (mean) and spread (variance). In this paper we derive simple steady-state air/plant/ food- and soil/plant/food-ingestionmodels and use these models to examine uncertainties in the daily human exposure associated with transfer of chemicals from air and soil to humans. These models link environmental concentrations to human exposure with the use of pathway exposure factors (PEFs). The P E F incorporates information on human physiology, human behavior patterns, and environmental transport and partitioning into a term that translates a unit concentration (in mg/m3 of air or mg/kg of soil) into a daily exposure in mg/kg.day through four ingestion routes-(1) fruits and vegetables, (2) grains, (3) meat, and (4) dairy products. These exposure pathways are linked to environmental concentrations in ambient-air particles and soil. For the purposes of this paper, we limit ourselves to these four pathways and two environmental media. This should not be interpreted as implying that transfers from other environmental media through alternate pathways (i.e., inhalation, dermal absorption) are unimportant or more precisely understood. McKone (2) described a more comprehensive matrix of environmental media and exposure pathways and the PEFs that link them. Table I lists the matrix of PEFs that we use to link the four exposure pathways with the two environmental media. Figure 1provides a schematic diagram of the exposure pathways that link contaminant concentrations in air and soil with food intakes. Our analysis of food-chain exposure uncertainty is divided into five steps. First, we review data on human anatomical and dietary parameters and data on cattle relevant to calculations of meat and dairy exposures. Next, we describe PEFs that deal with the transfer of contaminants from ambient air particles to agricultural products.
This section provides background material on the anatomical and dietary properties of humans and cattle. Particular attention is given to the variance in these data. Human Body Mass and Food Uptake. Data on the age distribution of and variance of body mass in human populations have been reviewed and published by the ICRP (3). On the basis of these data, we have determined that the distribution of body mass among adult males and females (ages 15 and up) has an arithmetic mean of 66 kg and standard deviation of 14 kg and the distribution of body mass among male and female children (ages 0-15) has an arithmetic mean of 27 kg and standard deviation of 14 kg. Data on food intakes in the United States have been compiled in a number of studies (3-6). Yang and Nelson (6)analyzed statistically data from the 1977-1978 USDA Food Consumption Survey to estimate the daily intake by food group for various age categories and regions within the United States. This survey provides a stratified sample of households in the 48 conterminous states and the District of Columbia for 3 days in each of four seasons from April 1977 to March 1978. Samples were classified according to the geographic region of the country, geographic divisions within these regions, and central city, suburban, and nonmetropolitan populations. Data were collected for 30 770 individuals within 114 primary sampling unitsapproximately 270 individuals per sampling unit. Table I1 summarizes the nationwide results of Yang and Nelson's analysis for five food classes and two age groups, children (ages 0-15) and adults (ages 15 and up) averaged over the four seasons. The mean value for each food intake is the mean of the means for the 114 sample units. The standard error is the standard deviation of the sample means among the 114 sample units. For the two age categories, the average intake is obtained by combining data from the age categories used by Yang and Nelson. We are interested in the distribution of intake among individuals in a population and not the distribution of intake for the whole population; that is, we want the upper bound to correspond to the upper bound individual intake and not the upper bound population average intake. For this reason we convert the standard error into a standard deviation by multiplying the standard error by the square root of N , the number of people in each sample unit. We assume 20% of the population is in the child category and 80% in the adult category. Because the USDA survey lasted only 3 days per individual and because variation in consumption for an individual over a short period can be greater than over the longer term used in a risk assessment, the Yang and Nelson results, which apparently do not adjust the short-term data to long-term distributions, could overestimate variability Environ. Sci. Technol., Vol. 23, No. 9, 1989
1155
Table 11. Food Intakes (kg/day) for Children and Adults"
SEMb
comp ranged
SD'
intake per unit body weight, kg/kg.day~ mean SD
food type
intake
dairy products milk meat fruits and vegetables grains
0.48 0.39 0.12 0.22 0.20
Child (0-15 years) 0.2 0.027 0.027 0.2 0.067 0.05 0.021 0.2 0.013 0.1
0.47-0.90 0.10-0.11 0.42-1.1 0.13-0.34
0.018 0.015 0.0044 0.0086 0.0075
0.0014 0.0022 0.00036 0.0031 0.0011
dairy products milk meat fruits and vegetables grains
0.26 0.22 0.19 0.30 0.20
Adult (15-70 years) 0.09 0.0062 0.0054 0.08 0.0062 0.09 0.012 0.2 0.0068 0.1
0.30-0.85 0.21-0.30 0.40-1.1 0.12-0.34
0.0040 0.0033 0.0030 0.0046 0.0031
0.00038 0.00037 0.00062 0.0013 0.00070
OAll values are fresh mass. *Based on the standard deviation among the means of the 114 sample units used by Yang and Nelson (6). 'Obtained from the standard error under the assumption that there are 214 adults and 56 children in each of the 114 sample units. dThe range of comparative values reflects the range of values published in ICRP-23 (3), Rupp (4), and Regulatory Guide 1.109 (5). The lower value expresses the reference value; the upper values are used to estimate maximum annual average exposures.
in food intake. However, because of the large number of individuals used in the USDA study and the use of data from four seasons and different geographical regions, we expect the estimate of food intake variability based on this data is rather precise relative to other parameters in our analysis. Yang and Nelson (6) divide dairy product intake into fresh cow's milk (82% of the total) with the remainder consisting of dry milk, cheese, and yogurt. Meat includes beef (51%),pork (16%), poultry (18%))and other meats (15%). The total intake of fruits and vegetables includes leafy vegetables (14%),exposed produce (30%),protected produce (53%1, and other produce (3%1. Leafy vegetables, including such items as lettuce, cabbage, spinach, celery, and cauliflower, are likely to be exposed to contaminants deposited from the atmosphere. Exposed produce, including apples, pears, berries, grapes, peaches, tomatoes, string beans, and corn, are also susceptible to atmospheric deposition but are likely to have a lower surface-to-mass ratio. Protected produce, including carrots, beets,potatoes, melons, legumes, and citrus fruits, are not considered to be as efficient at transferring atmospherically deposited contaminants to food products as exposed produce. We assume grains are similar to exposed produce in their ability to intercept and transfer atmospheric contaminants to food. The last column in Table I1 gives our estimate of the mean and standard deviation for food intake per unit body weight. The variance and thus standard deviation in this ratio is calculated by using the standard deviation in body weight in each age group and the standard deviation in food intake by age group and food type. The variance in food intake per unit body weight is calculated by using a covariance that is derived from the assumption that food intake scales with body weight to the two-thirds power. Properties of Cattle. In order to calculate human exposures to chemicals in dairy products and meat we need estimates of inhalation and ingestion rates for both beef and dairy cattle. Table I11 lists properties of cattle that were obtained from eight papers that addressed the transfer of contaminants from air, vegetation, or soil to beef and dairy cattle. Because we had no basis by which to rank the quality of information in these papers, we gave them equal weight when developing input data distributions and elected to represent these data by using uniform distributions. We also found that the uniform distribution gave us a good fit to the upper and lower bounds, the mean, and 1156
Environ. Sci. Technol., Vol. 23, No. 9, 1989
Table 111. Properties of Beef and Dairy Cattle property vegetation intake, kg(dry mass)/day
mean f SD" soil ingestion, kg/day
mean f SD" inhalation, m3/day mean f SD"
beef cattle 8.0 9.2 15.0 6.1 13.2 16.5 17.5 12.2 f 4.4 0.1 0.25 0.50 0.72 0.39 f 0.27 85 130 150 122 f 33
dairy cattle 16.0 12.0 17.0 15.0 25.0 16.4 16.5 17.5 16.9 f 3.7 0.1
0.25 0.48 0.50 0.72 0.41 0.24 85 130 150 122 f 33
*
ref 7 8 9 5 10 11 12
13 7 10, 14 8 9 12 13 15 13
"The arithmetic mean ( p ) and standard deviation (a) of the reported values are given. In the simulation of output uncertainty, each of these parameters is treated as a uniform distribution on the range (2,y) where x = u - 1 . 7 3 and ~ y = u + 1.730.
the standard deviation of the reference data.
Food Exposures Attributable to Air Particle Concentrations Contaminants in air can be present attached to soil or other particles suspended in air or as part of the atmospheric gas phase. For purposes of exposure estimates, air concentrations are expressed as mg(contaminant)/m3(air) and divided into two fractions-a gas fraction, C,, and a particle fraction, C In this section we develop expressions for calculating patgway exposure factors for the following pathways: air particles to vegetables and fruits, Fpv;air particles to grains, Fw;air particles to cattle to meat, Fpt; air particles to cattle to dairy products, Fpk Fruit, Vegetable, and Grain Exposures. The factors Fa,,F,,, FaB,and Fw account for the ingestion of contaminants as a result of uptake by food products from the atmosphere. The daily average ingestion exposure e,(C,, C,) in mg/kg.day attributable to the atmospheric con-
centrations C, and C, (in mg/m3) is given by the expression evg(Cs,Cp) = (Fa, + J’JCa + (Fpv + Fpg)Cp (1) We focus in this paper on the factors F and FWassociated with atmospheric particles. In or& to calculate the transfer of atmospheric particles from atmosphere to fruits, vegetables, and grains, we consider the balance between material that deposits on the exposed and edible portion of food crops and is removed by weathering and senescence. With this approach, the PEFs for transfer from atmosphere to food crops is set up as the ratio of intake to atmospheric concentration Fpv= (daily contaminant intake)/C, = I,Cv/C, (2) and the steady-state concentration in vegetation is obtained under the assumption that gains equal losses vdpcp
= MfRvcv
(3)
so that the P E F for fruits and vegetables is given by Fpv
= (0.47IvVdpfv)/ (MfRv)
(4)
and, similarly, the P E F for grains is given by Fpg
= ( I g v d p f g ) / (Mf&)
(5)
In eq 2-5,0.47 is the fraction of the total mass of ingested fruits and vegetables that consists of leafy vegetables and exposed produce, no units; I, is the human intake per unit body weight of fruits and vegetables, kg(fresh mass)/ kgday; Ig is the human intake per unit body weight of grains, kg(fresh mass)/kgday; C, is the contaminant concentration in fresh vegetation, mg/kg(fresh mass); v d is the deposition factor of atmospheric particles unto f o d crops, m/day; f, and fg are, respectively, the fractions of the target population’s vegetables and fruits (v) and grains (g) that come from the area with atmospheric concentration, C,, no units; Mf is the annual average inventory of food crops per unit area, kg(fresh mass)/m2; and R, is the rate constant for the removal of chemicals from vegetation surfaces as a result of weathering and senescence, l/day. This rather simple steady-state model allows preliminary estimates of lifetime exposure. This type of approach has been used to assess exposures to airborne radioactive contaminants (16). Values for I, and I are taken from Table 11. The factor 0.47 is taken from t a n g and Nelson (6) and assumed to have a small enough variance to be ignored relative to the variance in other parameters for the uncertainty analysis. The fractions f, and fgof food coming from the contaminated area are assigned the value 1. This allows us to focus on the significance of uncertainty in other parameters. The task of quantifying the values and likely ranges off, and fg.is not addressed in this paper. The deposition factor is the ratio of deposition rate on vegetation in mg/m2.day to the air concentration in mg/m3. The deposition factor vd, includes both wet and dry deposition processes vd, = U g bR W , (6) where u is the dry deposition velocity onto vegetation, m/day; is the fraction of material retained on vegdtation from wet deposition; R is the annual average rainfall rate, m/day; and W, is the volumetric washout factor for particles, no units. According to Peterson (161,the retention factor b depends on the intensity of rainfall and is in the range 0.1-0.3. We assume an annual rainfall on the order of 1 m. Bidleman (17)reported that W, was highly variable and controlled by meteorological factors and particle size and that a large number of measurements of W, for trace metals in air and rain had geometric means in the
1
range 2 X 105-1 X lo6 and geometric standard deviations in the range 2.2-3.2. Dry deposition velocities are influenced by numerous factors and there is a wide range of reported values, for example from 3 to 4900 m/day reported for deposition rates of particles from air to vegetation surfaces (9). Schroeder and Lane (18) reported that dry deposition velocities measured for gases span 4 orders of magnitude, from 0.002 cm/s (1.7 m/day) to 26 cm/s (22 000 m/day). They reported deposition velocities measured for particles in the range from 0.001 cm/s (0.86 m/day) to 180 cm/s (155000 m/day). For particles less than 5 pm, McMahon and Denison (19) reported deposition velocities in the range 0.003-1 cm/s (2.6-860 m/day). According to Whicker and Kirchner (9), the fraction of deposited particles intercepted by vegetation can be calculated from the total deposition velocity ut as ug = u t [ l - exp(-aMV)] (7) where a is the foliar interception constant, m2/dry kg, and M, is the dry mass inventory of vegetation per unit area, kg/m2. Using a fixed foliar interception constant of 2.8 (9) and an annualaverage dry-mass inventory of 0.6 kg/m2 (ZO), we estimate that ug is on the order of 0.8vt for food and pasture crops. On the basis of the observations above, we represent uncertainty in the deposition factor v d p from air particles (less than 5 pm) to vegetation surfaces using a log-normal distribution having a geometric mean of 300 m/day and a geometric standard deviation of 3.0. It is important to note that uncertainty in the deposition factor comes from both the natural variability in the underlying proceases and lack of complete information on the parameters in the model. To the extent that one can make chemical- and site-specific measurements to reduce the latter source of uncertainty, the overall uncertainty about deposition might be reduced. However, for many cases a risk assessment must be completed before such measurements can be made. Whicker and Kirchner (9) and Bowen (20) reported the inventory of standing and mature biomass in agricultural landscapes to be on the order of 0.63 kg(dry mass)/m2 or 3.0 kg(fresh mass)/m2. Layton et al. (21) suggested that the variation in standing biomass is within a factor of 3 higher or lower than this value. Whicker and Kirchner (9) provided weathering data with which we estimate that the rate constant R, is on the order of 0.03 day-l with a range of 0.01-1.0 day-’. Because these parameters have values that vary by a factor of 10 or more and for which there is no other information indicating the character of the distribution, we represent the uncertainty in them using log-uniform distributions. Meat and Dairy Product Exposures. The PEFs Fat, Fpt,Fak,and Fpkaccount for human contaminant exposure8 attributable to meat- and milk-producing cattle as a result of inhalation of gases and particles and ingestion of gases and particles deposited onto pasture. The expressions that link atmospheric concentrations C, and C, and the four PEFs to human exposure (in mg/kgday) through meat, e,(C,, C,), and dairy products, ek(Ca,C,), are as follows: et(Ca, Cp) = FatCa + F p t c p (8) d C a , Cp) = FakCa
+ Fpkcp
(9)
The PEFs in eq 8 and 9 are related to human ingestion of meat and dairy products, cattle properties, deposition factors, and meat and dairy-product partition factors. For the transfer of particle-bound contaminants (C,) to meat and milk, the assumption that gains equal losses for the Environ. Sci. Technol., Vol. 23, No. 9, 1989
1157
cattle and vegetation compartments at equilibrium gives rise to the following PEFs expressions: (10) F p t = It[Inc + (IvbcVdp)/(M$v)lftBt
Fpk = Ik[Inc + (IvdcVdp)/(M&v)]fkBk
(11)
where I , is human intake per unit body weight of meat, kg(fresh mass)/kg.day; I k is human intake per unit body weight of dairy products, kg/kg.day; In,is the inhalation rate for cattle (beef and dairy), m3/day; Ivb,is the ingestion rate of pasture grasses by beef cattle, kg(dry mass)/day; Ivdc is the ingestion rate of pasture grasses by dairy cattle, kg(dry mass)/day; Mp is the annual average inventory of pasture crops per unit area, kg(dry mass)/m2;ft and fk are, respectively, fractions of the target population’s meat and dairy products that come from the area with concentration C,, no units; B, is the biotransfer factor from cattle intake to meat, which is the steady-state contaminant concentration in meat divided by the animal’s daily contaminant intake, (mg/kg)/(mg/day); and Bk is the biotransfer factor from cattle intake to dairy products, which is the steadystate contaminant concentration in dairy products divided by the animal’s daily contaminant intake, (mg/L)/ (mg/ day). Other parameters have been defined above. Human intakes of meat and dairy products I, and I k are given in Table 11. The parameters In,,Ivbc, and Ivdc are given in Table 111. The distribution of Vd, is assumed to be the same here as in the previous section. The parameters f , and fk are both assigned the value unity. The density of dry mass pasture is assumed to be on the order of 0.3 kg/m2 (9). the parameters B, and Bk are dependent on the chemical form of the contaminant species. Values and uncertainties for B, and Bk for several chemical elements (including arsenic) have been published by Ng (22). He reviewed a number of biotransfer measurements for radioactive elements and observed that the geometric standard deviation for these measurements is in the range 1.3-3.8. The lower values are for the essential elements, whereas the higher values are for the nonessential elements. We use 3 for the geometric standard deviation of the arsenic biotransfer factors. For organic chemicals the biotransfer coefficients B, and Bk can be related to the fat/diet partition coefficient by the following expressions: Bt = Kfd(0.4/Ivbc) (12) Bk = Kfd(O*05/lvdc)
Environ. Sci. Technol., Vol. 23, No. 9, 1989
Food Exposures Attributable to Soil Concentrations Soil is composed of liquid, solid, and gas phases. According to Bowen (20), roughly 80% of the soil mass is composed of solid particles and the remaining 20% is mostly soil solution. For purposes of estimating exposure, we use the total contaminant concentration in the combined liquid and solid phases as the representative value C,, in mg(contaminant)/kg(soil), for expressing soil concentration. In this section we develop expressions for calculating pathway exposure factors for the following pathways: soil to vegetables and fruits, Fa,; soil to grains, FSg;soil to cattle to meat, Fat;and soil to cattle to dairy products, Fsk
Fruit, Vegetable, and Grain Exposures through Soil. The factors F,, and Fagaccount for the ingestion of contaminants as a result of plant uptake from soils through roots. The daily average ingestion exposure e,(C,) in mg/kg-day attributable to the soil concentrations C, (in mg/kg) is given by the expression evg(C,) = V, + F,,)C, (17) In a steady-state system the rate of transfer from plant to soil by senescence is balanced by uptake from soil by growth rates. Under these conditions, the PEFs for food crops are given by the following expressions: Fsv= (I,f&,,)0.25 (18)
(13)
where Kfdis the fat/diet partition coefficient and expresses the ratio of contaminant concentration in animal fat to that in animal feed, (mg/kg)/[mg/kg(dry mass)] and 0.4 and 0.05 are the fractional fat content of, respectively, meat and dairy products (21). Variance in fat-content values is likely to be small relative to the variance in fat/diet partition coefficients. Fat/diet partition coefficients can be estimated by a correlation developed by Kenaga (23) log Kfd = 0.5 log KO, - 3.457 f 2.0 (n = 23, r = 0.79) (14) in which KO,is the octanol/water partition coefficient and the f range reflects the 95% confidence bound for this estimate. Travis and Arms (7) reviewed biotransfer factors for 36 organic chemicals in meat and 28 organic chemicals in milk. For these two pathways they have developed geometric mean regressions for the biotransfer factors in terms of KO,. Their reported regressions are log B, = log KO, - 7.6 (n = 36, r = 0.81) (15) log Bk = log KO, - 8.1 (n = 28, r = 0.74) (16) 1158
Travis and Arms (7) have not estimated the uncertainty in their correlation. In analyzing their data, we have estimated that the 95% prediction interval for both B, and Bk in their correlation formula is approximately 2 orders of magnitude and thus similar to the range reported by Kenaga (23). However, the B, and Bk values listed in the Travis and Arms data ( 7 ) and used in the example below for TCDD are not estimated from the regression equations above but are based on experimental data. In order to estimate the geometric standard deviation of biotransfer factors measured for TCDD, we assume that biotransfer experiments with organic compounds can be no more precise than those with nonessential radioactive elements and assign a geometric standard deviation of 3 to these parameters. This is lower than the geometric standard deviation of 10 associated with the regression estimation in eq 14.
Values for I, and Ig are taken from Table 11. K,,, is the steady-state partition coefficient between vegetation dry mass and soil ([mg/kg(dry mass vegetation)]/mg/kg(in soil)) and 0.25 is the amount of dry mass in vegetation per unit fresh mass (2). Because the uncertainty in the latter factor is lower than other parameters, we treat this value as constant. A correlation between log K,, and log KO, for organic compounds has been published by Travis and Arms (7). For inorganic compounds, such as arsenic, K,, can be estimated from the health-physics literature (16,22) or by using geochemical abundances (20). According to Ng (22), K,, for the common radionuclides can be estimated for food and forage crops over all agricultural soils with a geometric standard deviation on the order of 3.5-4. However, he points out that knowledge of the dominant crops and soil characteristics of an area may be useful in reducing the uncertainty of Ksy We assume a geometric standard deviation of 4 for this parameter in our analysis. Meat and Dairy Product Exposures through Soil. The PEFs Fa,and Fskaccount for human contaminant exposures attributable to ingestion of soil and pasture
Table IV. Type of Distributions and Distribution Moments Used for the Input Parameters parameter
expected value
geometric mean
GSD"
distribn type
0.0086 0.0046 0.0075 0.0031 0.0044 0.0030 0.015 0.0033 500 0.10 0.002 0.0002 0.03 0.10 0.02
0.0081 0.0045 0.0074 0.0030 0.0044 0.0029 0.014 0.0033 300 0.04 0.001 0.0001 0.013 0.055 0.010
1.4 1.3 1.2 1.2 1.1 1.2 1.2 1.1 3.0 4.0 3.0 3.0 4.0 3.0 3.0
log normal log normal log normal log normal log normal log normal log normal log normal log normal log normal log normal log normal log normal log normal log normal
Zv(child) Zv (adult) Zg (child) Zg (adult) Zt(child) Z, (adult) Zk (child) Zk
(adult)
"d,
K,, (As) Bt (As) Bk (As) K,,(TCDD) B, (TCDD) Bk (TCDD) parameter
expected value
lower bound
upper bound
distribn type
MI
3.9 0.39 0.039 120 12 17 0.46
1.0 0.1 0.01 63 4.0 11 0.10
10.0 1.0 0.1 177 20. 23 0.83
log uniform log uniform log uniform uniform uniform uniform uniform
MP
RV
Ill, IVbC Ivdc
I,,
" GSD, geometric standard deviation. vegetation by cattle. The expressions that link soil concentrations C, and the PEFs to human exposure (in mg/kgday) through meat, et(C8),and dairy products, ek(C,), are as follows:
et(C,) = F,tC,
(20)
ek(C8) = F&Cs
(21)
The PEFs in eq 20 and 21 are related to human ingestion of meat and dairy products, cattle properties, plantuptake factors, and meat and dairy-product partition factors as shown in the following expressions:
Fat = Fsk
+ (Ivb&p)lftBt
= Ik[Isc + (Ivd&sp)]ffik
(22) (23)
where I,, is the ingestion rate of soil by cattle (beef and dairy), kg/day, and other parameters are as defined above.
Application to Exposure Estimates for TCDD and Arsenic In order to examine the magnitude and sources of uncertainty associated with the combined air and soil media and the multiple ingestion pathways described above, we calculate here probability distributions for the exposures attributable to unit concentrations of arsenic and TCDD. These are contaminants that could be found in air and soil as a result of hazardous waste incineration or as a result of land disposal of municipal or industrial wastes. Table IV lists the mean, standard deviation, and type of distribution used to represent each of the parameters in the equations that define PEFs. We use the log-normal distributions to represent the intake per unit body weight data, because these parameters are positive and based on the quotient of uncertain data. Log-uniform distributions are used to represent the uncertainty in parameters that are expected to have a value that varies by a factor of 10 or more and for which there is no other information indicating the character of the distribution. We found that the data in Table I11 on the properties of cattle are best fit by uniform distributions. Following the suggestion of Ng (22), we represent uncertainty in biotransfer coeffi-
cients using log-normal distributions. Distributions in Table IV are used in a Monte Carlo simulation to produce the distributions of PEFs and exposures. The Monte Carlo simulations are carried out with latin hypercube sampling using the LHS program (24) and the SIMSYS program developed by Ryan and Lek (W). The LHS program is used to produce 1000 stratified sample values for each parameter used in the defining equation for a given PEF. The restricted pairing procedure of the LHS program is used to minimize spurious correlations between input parameters. The SIMSYSprogram is used to read the LHs-generated data, construct the distribution of PEFs and exposure estimates, and evaluate the properties of the output distributions. Table V lists the outcome of this simulation in terms of PEFs by contaminant, age group, and pathway. Shown in Table V are the expected value, geometric standard deviation, the geometric mean value, the 90% value, and the 95% value of the output distributions of PEFs. For the PEF distributions described in Table V, the expected value is always greater than the median and (in some cases) close to (within a factor of 2 of) the 90% value. This reveals that the output distributions are skewed. Figure 2 shows the frequency and cumulative distributions of lo00 simulations used to estimate the distribution of the pathway exposure factor Fp4 for children. We use the PEF distributions to calculate sample exposures by arbitrarily assigning the contaminants concentrations of 1 mg/kg (1ppm) in soil, 1 X lo4 mg/m3 bound to particles in the air, and 0 mg/m3 as gases in the air. Table VI presents the results of this exercise. Shown in Table VI are the geometric standard deviation, geometric mean, and expected value estimates for adult and child exposures obtained with assigned concentrations and the parameter distributions in Table IV.The third column in Table VI gives E*, which is the exposure obtained by using the assigned concentrations and the expected values of each parameter in Table IV to calculate PEFs. Also listed in Table VI are the 95% upper bound values for each exposure estimate. It is important to note that the standard deviations for these exposure estimates are relatively large. For some Environ. Sci. Technol., Vol. 23, No. 9, 1989
1159
Frequency Dinribution
nns.
I"
50
. 0
F s
. m
e
Expcled value
o Median value A 10.27
Calculated lromlhe mean value 01 the inputs
IL
0
