• f_lipid: Lipid contents of edible fish species have not been compiled, although such a compilation would clearly be useful if applying a BSAF in an assessment mode such as is done here. BSAFs are typically developed on the basis of whole fish lipid content, so estimates of whole fish concentrations should be made with a whole fish lipid content. Parkerton, et al. (1993) cautions, however, that lipid contents of edible portions of fish may be lower than lipid contents of some of the fish portions that were sampled and used to develop BSAFs. Non-edible high lipid content portions include, for example, liver and hepatopancreas. Parkerton, et al. (1993) develops the parameter, b , which is defined as the ratio of the lipid content of the edible portion and the sampled tissue. To demonstrate the impact of this ratio, Parkerton used data from Niimi and Oliver (1989) which included PCB and other halocarbon compound concentration in whole fish and fillets of fish taken from the Great Lakes. The b (defined here as the ratio of lipid in fillet to lipid of whole fish) for these fish, which included brown trout, lake trout, rainbow trout, and coho salmon, ranged from 0.22 to 0.51. The ratio of fillet to contaminant concentrations ranged from 0.20 to 0.54.

In the context of the current model, concentrations in fish for estimating exposure are estimated as the product of: organic carbon normalized bottom sediment concentrations * BSAF * f_lipid. BSAFs (in theory) are independent of fish tissue being sampled - they are ratios of the organic carbon normalized concentration and fish lipid concentration. Users should be aware, however, that the f_lipid value assigned should correspond to the fish concentration of interest - that could be whole fish if the model is used in validation exercises or edible fish if the model is used for exposure assessment. Cook, et al. (1990) and EPA (1993) assumed a lipid content of 0.07 for fish in discussions of BSAF and related methodologies for estimating bioaccumulation of 2,3,7,8-TCDD in aquatic ecosystems. This assessment will also assume a f_lipid of 0.07, and since its use in this context is in exposure assessment, this value could be thought of as a edible portion lipid fraction.

Different lipid contents have been reported for the same fish, so generalizations are difficult to make at this point. EPA (1990b) lists percent lipid contents for Lake Ontario fish including brown trout: 14.3%, lake trout: 21.1%, coho salmon: 6.45%, yellow perch: 5.2%, and white perch: 17.1%. Kuehl, et al. (1987) lists a range of percent lipid for carp taken at different days during a study of between 13.0 and 18.7%.

Vegetation concentrations are required for the estimation of exposure to homegrown fruits and vegetables, and also for the beef and dairy food chain algorithms. Three principal assumptions are made to estimate vegetative concentrations:

• Outer surfaces of bulky below ground vegetation are impacted by soils which contain dioxin-like compounds. Inner portions are largely unimpacted.

• Translocation of dioxin-like compounds from roots to above ground portions of plants are negligible compared to other mechanisms which impact above ground portions of plants. As such, translocation into above ground portions will be assumed to be zero.

• Similar to the assumption concerning transport of contaminants from outer to inner portions of below ground vegetation, it will be assumed that outer and not inner portions of above ground bulky vegetation are impacted.

Concentration of contaminants in below ground vegetation is only required for vegetables (carrots, potatoes, e.g.) grown underground. The basis for the below ground algorithm is the experiments of Briggs, et al. (1982) on uptake of contaminants into barley roots from growth solution, and their elaboration of a Root Concentration Factor. The below ground concentration is given by:

where:

C_bgv = fresh weight concentration of below ground vegetables, mg/kg

C_s = contaminant concentration in soil, ppm or mg/kg

Kd_s = soil-water partition coefficient, L/kg

= Koc*OC_sl

Koc = contaminant organic partition coefficient, L/kg

OC_sl = fraction organic carbon in soil, unitless.

RCF = root concentration factor equaling the ratio of the contaminant concentration in roots (fresh weight basis) and the concentration in soil water, unitless

VG_bg = empirical correction factor for below ground vegetation which accounts for the differences in the barley roots for which the RCF was derived and bulky below ground vegetables, unitless

Two processes, air-borne vapor phase absorption and air-borne particle deposition, are assumed to contribute to above ground vegetation concentrations:

where:

C_abv = concentration in above-ground vegetation, expressed on a dry weight basis, mg/kg or ppm

C_vpa = contribution of concentration due to vapor-phase absorption or airborne contaminants, mg/kg or ppm

C_ppa = contribution of concentration due to wet plus dry deposition of contaminated particulates onto plant matter, mg/kg or ppm.

The basis for a vapor-phase bioconcentration factor for various airborne contaminants, including 1,2,3,4-TCDD, from the atmosphere to vegetation was developed by Bacci, et al. (1990, 1992), with amendments suggested by McCrady and Maggard (1993), and McCrady (1994). Bacci and coworkers conducted laboratory growth chamber experiments on the vapor-phase transfer of 14 organic compounds from air to azalea leaves, and developed a generalized model to predict the vapor-phase bioconcentration factor based on a contaminant Henry's Constant, H, and octanol water partition coefficient, Kow. A similar experiment by McCrady and Maggard (1993) conducted for 2,3,7,8-TCDD vapor transfer to grass leaves suggested that the Bacci empirical algorithm to estimate the transfer factor would greatly overestimate it. Further details on these experiments are in the section below on this critical bioconcentration parameter, termed B_vpa in this assessment. The algorithm estimating plant concentrations as a function of vapor-phase air concentrations is:

where:

C_vpa = contribution concentration due to vapor-phase absorption or airborne contaminants, mg/kg or ppm

B_vpa = mass-based air-to-leaf biotransfer factor, unitless [(m g contaminant/kg plant dry)/(m g contaminant/kg air)]

C_va = vapor-phase concentration of contaminant in air, m g/m³

VG_ag = empirical correction factor which reduces vegetative concentrations considering that B_vpa was developed for transfer of air-borne contaminants into leaves rather than into bulky above ground vegetation

d_a = density of air, kg/m³, 1.19

1/1000 = converts resulting concentration from m g/kg to mg/kg.

Several exposure efforts for 2,3,7,8-TCDD (Fries and Paustenbach, 1990; Stevens and Gerbec, 1988; Connett and Webster, 1987; Travis and Hattemer-Frey, 1991), have modeled the accumulation of residues in vegetative matter (grass, feed, vegetables) resulting from deposition of contaminated particulates. Key components of their approach, as well as the one for this assessment, include:

• Vegetative concentrations result from particulate deposition onto plant surfaces.

• Vegetative dry matter yield is the reservoir for depositing contaminants; this reservoir varies according to crop.

• Not all particulate deposition reaches the plant, some goes directly to the ground surface; the "interception fraction", less than 1.0, reduces the total deposition rate. This fraction can be related to the percent ground that is covered by the vegetation. • Weathering processes, such as wind or rainfall, remove residues that have deposited onto plant surfaces via particle deposition, and this process is reasonably modeled as a first-order exponential loss with an associated weathering dissipation rate. All the above references have justified a dissipation rate derived from a half-life of 14 days (based principally on field measurements described in Baes, et al. (1984)); this is the value used for all dioxin-like compounds in this assessment as well. As well, a portion of particles depositing as wet deposition are not retained on the vegetation after the rainfall. A retention factor reduces total wet deposition considering this.

• Vegetative concentrations may not reach steady state because of harvesting or grazing, but a steady state algorithm is used.

The steady state solution for plant concentrations attributed to wet plus dry particle deposition is:

where:

C_ppa = Vegetative concentration due to settling of contaminated particulates onto plant matter, mg/kg or ppm

F = unit contaminant wet plus dry deposition rate onto plant surfaces, m g/m²-yr

k_w = first-order weathering dissipation constant, 1/yr

Y_j = dry matter yield of crop j, kg/m²

1/1000 = converts m g/kg to mg/kg

The unit contaminant wet plus dry deposition rate, F, is given as:

where:

F = unit contaminant wet plus dry deposition rate onto plant surfaces, m g/m²-yr

C_pa = air-borne particulate phase contaminant concentration, m g/m³

V_d = deposition velocity, m/yr

I_j = fraction of particulates intercepted by crop j during deposition, unitless

RN = annual rainfall, m/yr

R_w = fraction of particles retained on vegetation after rainfall, unitless

W_p = volumetric washout factor for particulates, unitless

Following is brief guidance on assignment of values to the terms in Equations (4-23) to (4-27).

• C_s and Kd_s: This is the soil concentration and soil/water partition coefficient, respectively. The soil concentration is specified for the on-site source category. For the two source categories where soil contamination is distinct from the site of exposure, the soil concentration at the site of exposure is estimated. As discussed in Section 4.4.1 below, two soil concentrations including one for a no-till and one for a tilled situation, are estimated. For estimating below ground vegetable concentration, the tilled concentration is required. The soil partition coefficient is a function of the contaminant organic carbon partition coefficient, Koc, and the soil organic carbon fraction, OC_sl, as discussed above in Section 4.3.1. Division of C_s by Kd_s results in the equilibrium soluble phase concentration of the contaminant, in mg/L.

• RCF: Briggs, et al. (1982) conducted experiments measuring the uptake of several compounds into barley roots from growth solution. He developed the following relationship for lipophilic compounds tested (lipophilic compounds were identified as those tested that had log Kow 2.0 and higher (n=7, r=0.981):

where:

RCF = root concentration factor equaling the ratio of the contaminant concentration in roots (fresh weight basis) and the concentration in soil water, unitless

Kow = contaminant octanol water partition coefficient, unitless

Since his experiments were conducted in growth solution, the RCF is most appropriately applied to soil water in field settings. This is why the C_s was divided by Kd_s in Equation (4-23).

• VG_bg: This correction factor and the one used to correct for air-to-leaf transfer of contaminants, VG_ag, are based on a similar hypothesis. That hypothesis for VG_bg is that the uptake of lipophilic compounds into the roots of this experiments is due to sorption onto root solids. High root concentrations were not due to translocation to within portions of the root hairs. Direct use of the RCF for estimating concentrations in bulky below ground vegetation would greatly overestimate concentrations since an assumption (stated above) is that there is insignificant translocation to inner parts of below ground bulky vegetation for the dioxin-like compounds. Concentrations in outer portions of edible below ground vegetation would mirror concentrations found in barley roots, by this hypothesis.

VG_bg can be estimated by assuming that the outer portion, or skin, of below ground vegetables would contain concentrations that can be predicted directly using the RCF, but that the inner portions would effectively be free of residue. The correction factor can be estimated as the ratio of the mass of the outer portion to mass of the entire vegetable:

where:

VG_bg = below ground vegetation correction factor, unitless

MASS_skin = mass of a thin (skin) layer of below ground vegetables MASS_vegetable = mass of the entire vegetable

Simplifying assumptions are now made to demonstrate this ratio for a carrot and a potato. First, it will be assumed that the density of the skin and of the vegetable as a whole are the same, so the above can become a skin to whole vegetable volume ratio. The thickness of the skin will be assumed to be same as the thickness of the barley root for which the RCF was developed. Without the barley root thickness in Briggs, et al. (1982), what will instead be assumed is that the skin thickness is equal to 0.03 cm. This was the thickness of a leaf from broad-leaved trees assumed by Riederer (1990) in modeling the atmospheric transfer of contaminants to trees. The shape of a carrot can be assumed to be a cone. The volume of a cone is given as (p /3)r²l, where r is a radius of the base and l is length. Assuming a carrot base radius of 1 cm and a length of 15 cm, the volume is 16 cm³. The curved surface area of a cone is given as: p r(r² + l²)^1/2, which equals 47 cm², given the r and l assumptions. The volume of the cone surface area is 47 cm² * 0.03 cm, or 1.41 cm³. The skin to whole plant ratio for this carrot is 0.09 (1.41/16). A similar exercise is done for a potato, assuming a spherical shape with a radius of 3 cm. The volume is given as 4/3p r³, or 113 cm³. The surface area of a sphere is 4p r², or 113 cm², and the volume of this surface area is 3.39 cm³. The skin to whole plant ratio for the potato is 0.03.

This exercise indicates upper bounds for such an empirical parameter. For exposure assessments, other factors which reduce vegetative concentrations should also be considered and will be considered in this empirical correction factor in this assessment. Additional reductions in concentration result from peeling, cooking, or cleaning, for example. Wipf, et al. (1982) found that 67% of unwashed carrot residues of 2,3,7,8-TCDD came out in wash water, and 29% was in the peels. A peeled, washed carrot correction factor might instead be, 0.09*0.04, or 0.004 (0.09 from above; 0.04 = 100% - 67% - 29%). A 96% reduction in the estimated VG_bg for the potato (the potato is cleaned and the skin is not eaten; additional reductions possibly when cooking the potato) would equal 0.001. In a site-specific application, the type of vegetation, preparation, and so on, should be considered. The VG_bg for underground vegetables for this assessment is assumed to be 0.01. This is less than the estimates of 0.09 and 0.03 for the carrot and potato above, but greater than it might be if based on this discussion on cleaning, washing, peeling, and so on.

• C_va, C_pa: The vapor-phase concentration of contaminant in air, C_va, used in this algorithm is estimated using procedures described in Section 4.3.2 above. The particle-phase concentration of contaminant in air, C_pa, is estimated using procedures described in Section 4.3.3.

• B_vpa: Bacci, et al. (1990, 1992) conducted laboratory experiments on the air-to-leaf transfer of vapor-phase concentrations of 14 organic contaminants to azalea leaves. With their results, they developed an empirical relationship for a vapor-phase bioconcentration factor from air to azalea leaves, termed in this assessment the B_vpa, but which was termed BCF by Bacci and coworkers. They related the B_vpa to the chemical octanol-water and air-water partition coefficients, Kow and Kaw. The air-water partition coefficient, Kaw, is a dimensionless form of Henry's Law constant, H, derived by dividing H by the product of the ideal gas constant, R, and the temperature, T. The most general form of the air-to-leaf transfer factor is on a unitless volumetric basis: [ng contaminant/L or leaf]/[ng contaminant/L of air], and is given as:

where:

B_vol = Bacci volumetric air-to-leaf biotransfer factor, unitless [(m g contaminant/L of wet leaf)/(m g contaminant/L air)]

Kow = contaminant octanol water partition coefficient, unitless

H = contaminant Henry's Constant, atm-m³/mol.

R = ideal gas constant, 8.205 x 10^-5 atm-m³/mol-deg K

T = temperature, 298.1 K

-1.654 = empirical constant

Bacci, et al. (1990) showed that the volumetric transfer factor can be transformed to a mass-based transfer factor by assuming that 70% of the wet leaf is water, the leaf density is 890 g/L, and the air density is 1.19 g/L:

where:

B_vpa = mass-based air-to-leaf biotransfer factor, unitless [(m g contaminant/kg plant dry)/(m g contaminant/kg air)]

B_vol = Bacci volumetric air-to-leaf biotransfer factor, unitless [(m g contaminant/L of wet leaf)/(m g contaminant/L air)]

Bacci's experiments were conducted under conditions which would not account for photodegradation of his test chemicals from the leaf surfaces. A recent study by McCrady and Maggard (1993) which investigated the uptake and photodegradation of 2,3,7,8-TCDD sorbed to grass foliage suggests a significant difference in experimental B_vol for grass plants. The authors note that the log B_vol for 2,3,7,8-TCDD and azalea plants, using Bacci's empirical relationship, was estimated as 8.5. The experimental log B_vol for 2,3,7,8-TCDD and grass plants reported by McCrady was 6.9 when photodegradation was accounted for, and 7.5 in the absence of photodegradation. Since the photodegradation experiments by McCrady best represent outdoor conditions, their work suggests that the air-to-leaf transfer factor estimated by Bacci's algorithm may be 40 times too high for vapor-phase transfer of 2,3,7,8-TCDD onto grass leaves.

While McCrady's experiments included consideration of photodegradation of 2,3,7,8-TCDD, it is uncertain as to how their results can be generalized to other dioxin-like compounds and vegetations other than grass. There is very little information in the literature on the photodegradation of dioxins and furans on plant surfaces. McCrady and Maggard (1993) cite Crosby and Wong (1977) as the only other work measuring photodegradation of 2,3,7,8-TCDD from leaf surfaces. In that work, 2,3,7,8-TCDD was applied as a 15 ppm concentration in Agent Orange, and McCrady speculated that the rapid photodegradation measured in those experiments occurred because the herbicide formulation contained carriers and organic solvents that may have promoted photodegradation. Some experiments conducted in organic solvents (Crosby, et al., 1971; Buser, 1976) and in water (Friessen, et al., 1990) noted reductive dechlorination resulting in dioxin compounds of lower chlorination. Other experiments did not find such reductive dechlorination (Dulin, et al., 1986; Friessen, et al., 1990 who found reductive dechlorination in one experiment, but not in another). An important issue to consider, at least, for the process of photodegradation of dioxins and furans on leaf surfaces is the possible formation of lower chlorinated congeners of non-zero toxic equivalency.

Another issue discussed by McCrady is that the theoretical time for the grass tissue to reach a steady state in his experiments is much shorter than that indicated in the Bacci experiments. Using Bacci's results, McCrady noted that the azalea leaves theoretically take greater than 400 days to reach equilibrium, in comparison to less than 20 days to reach equilibrium for the grass plants in his experiments. This difference is not entirely due to photodegradation. McCrady (personal communication, J. McCrady, Corvallis Environmental Research Laboratory, EPA) suggests that the 50-day exposure time used in Bacci's experiments may allow for considerable diffusion into the newly formed plant surface wax. The sorbed TCDD residues may be trapped and unable to volatilize. Thus, for estimating contaminant concentrations in animal feeds such as relatively short-lived grass plants, the equilibrium B_vol from the Bacci azalea model may overestimate the contaminant concentration in grass. On the other hand, McCrady's experiments may have been conducted in too short a time frame, with the sum of uptake and elimination phases being less than 10 days in the various experimental designs. The volatilization and photodegradation rates reported by McCrady may be higher than what might occur for the longer exposure times expected in real world situations, where growth and residue trapping may occur.

These arguments are being presented to demonstrate the uncertainty in choosing either of the two reported B_vol values for estimating plant contaminant concentrations. McCrady's results pertaining to 2,3,7,8-TCDD cannot be generalized to other dioxin-like compounds or other contaminants in terms of commonly available contaminant parameters such as H or Kow. Therefore, a McCrady framework similar to Bacci's for estimating congener-specific B_vpa cannot be offered at this time. On the other hand, their work strongly suggests that the Bacci model may be inappropriate for terrestrial vegetations of this assessment, including vegetables/fruits and vegetations of the beef/milk food chain model, and Bacci's experiments, because of their length of time, the use of an azalea leaf of high wax content, and lack of an artificial light source simulating photodegradation, are likely to have overestimated the air to leaf transfers.

What will be done for this assessment is to first estimate a congener-specific B_vol using the Bacci algorithm of Equation (4-30) above. Then, it will be transformed into a mass-based B_vpa as in Equation (4-31), except that the assumptions McCrady and Maggard used for fraction of grass plant that is wet weight, 85%, and the grass leaf density, 770 g/L, will instead be used as more representative of vegetations of this assessment. Most importantly, the B_vpa calculated this way will be empirically reduced by a factor of 40 for all dioxin-like congeners as suggested by the difference in McCrady's experiments as compared to Bacci's.

It should be noted that all bioconcentration or biotransfer parameters, such as the B_vpa, are qualified as second order defaults for purposes of general use. Section 6.2. of Chapter 6 discusses the use of parameter values selected for the demonstration scenarios, including a categorization of parameters. Second order defaults are defined there as parameters which are theoretical and not site specific, but whose values are uncertain in the published literature. The parameter values in this category should be considered carefully by users of the methodology.

• VG_ag: The same discussion for this correction factor for below ground vegetation applies here. Fruits such as apples, pears, plums, figs, peaches, and so on, can be approximated by spheres, and upper bound estimates of correction factors would be less than 0.05. Peeling, cooking, and cleaning further reduces residues. The VG_ag for unspecified above ground fruits and vegetables in this assessment is assumed to be 0.01. Like VG_bg, this value is assigned considering that it should be less than estimated just based on surface volume to whole fruit volume ratios.

Two other VG_ag values are required for this assessment. One is for pasture grass and the other for other vegetations consumed by cattle. Both are required to estimate concentrations in these vegetations consumed by cattle in order to estimate beef and milk concentrations. A VG_ag value of 1.0 was used to estimate pasture grass concentrations since there appears to be a direct analogy to exposed azalea and grass leaves. However, VG should be less than the other general category of cattle vegetations defined in this assessment, "hay/silage/grain". Recognizing that pasture grass is important in terms of amount consumed in the lifetime of a beef cow, and the fact that it is a leafy vegetation, it is considered seperately, whereas other cattle vegetations are lumped together in this second category. As described below in Section 4.3.4.3, this second general category of non-grass cattle vegetations include some thin leafy (hay) as well as bulky (corn silage and other grains) vegetations to consider. A volume ratio of outer surface to whole surface area to volume vegetation could be used to assign a value to VG, if specific assumptions concerning proportions of each type of vegetative cattle intake were made. An appropriate assumption for a fully protected vegetation such as grain would be zero. Silage can be considered part protected and part leafy. Since specific assumptions concerning hay/silage/grain intake are not being made for this exercise, a simple assumption that VG equals 0.50 for hay/silage/grain is instead made, without rigorous justification.

The only experimental evidence that a VG_ag for vapor transfers of dioxin-like compounds is justified came in a recent study by McCrady (1994). McCrady experimentally determined uptake rate constants, termed k₁, for vapor phase 2,3,7,8-TCDD uptake into several vegetations including kale, grass, pepper, spruce needles, apple, tomato, and azalea leaves. Recall that the similar experimental design of both McCrady and Maggard (1993), and Bacci, et al. (1990; 1992), included an initial phase where vegetations in experimental chambers were exposed to the vapor-phase organic chemicals. The uptake which occurs during this initial phase is described with the rate constant, k₁. A second "elimination" phase then occurs where organic vapors are removed from the chambers and the chemicals allowed to volatilize or otherwise dissipate from the vegetation. The rate constant for this phase is termed k₂. A steady state bioconcentration factor, or B_vpa in this assessment, is then estimated as k₁/k₂. The uptake rate constants from air to the whole vegetations estimated in the recent experiments by McCrady (1994) demonstrate the concept behind the VG parameter. The uptake rate for an apple divided by the uptake rate for the grass leaf was 0.02 (where uptake rates were from air to whole vegetation on a dry weight basis). For the tomato and pepper, the same ratios were 0.03 and 0.08. The VG_ag was 0.01 for fruits and vegetables in this assessment, but note above that the simple exercise with a conical carrot and spherical potato estimated a surface volume to whole fruit volume ratio of 0.09 (carrot) to 0.03 (potato); a value of 0.01 for fruits and vegetable empirically considers factors such as washing or peeling which would reduce exposures. McCrady (1994) then went on to normalize his uptake rates on a surface area basis instead of a mass basis; i.e., air to vegetative surface area instead of air to vegetative mass. Then, the uptake rates were substantially more similar, with the ratio of the apple uptake rate to the grass being 1.6 instead of 0.02; i.e., the apple uptake rate was 1.6 times higher than that of grass, instead of 1/50 as much when estimated on an air to dry weight mass basis. The ratios for tomato and pepper were 1.2 and 2.2, respectively. In his article, McCrady (1994) concludes, "The results of our experiments have demonstrated that the exposed surface area of plant tissue is an important consideration when estimating the uptake of 2,3,7,8-TCDD from airborne sources of vapor-phase 2,3,7,8-TCDD. The surface area to volume ratio (or surface area to fresh weight ratio) of different plant species can be used to normalize uptake rate constants for different plant species." McCrady does caution, however, that uptake rates are only part of the bioconcentration factor estimation, and is unsure of the impact of surface area and volume differences on the elimination phase constant, k₂ (personnal communication, J. McCrady, US EPA, ERL-Corvallis, Corvallis, OR 97333). Still, his recent experiments do appear to justify the use of a VG parameter since the B_vpa were developed on an air to whole plant mass basis, and his results are consistent with the assignment of 0.01 for fruits and vegetables.

• kw: Fries and Paustenbach (1990) note that this approach may overestimate concentrations because crops can be harvested or pastures grazed before the plant concentrations reach steady state, and that a kw based on a weathering half-life of 14 days may be too long given experimental results of Baes, et al. (1984) which showed a range of 2-34 days, and a median value of 10 days. Stevens and Gerbec (1987) considered harvest intervals by including the exponential term, (1-e^-kt), and assigning values of t based on harvest intervals of different crops. This assessment uses a kw of 18.02 yr^-1, which is equivalent to a half-life of 14 days.

• I_j and Y_j: Interception values and crop yields were determined in the afore-

mentioned assessments based on geographic-specific crop yield data provided in Baes, et al., (1984) and the following types of crop-specific relationships estimating interception fraction based on yield (Y), also presented in Baes, et al., (1984):

corn silage: I = 1 - e^-0.768Y

hay/grasses: I = 1 - e^-2.88Y

lettuce: I = 1 - e^-0.068Y

Judgments by Fries and Paustenbach (1990) on high, medium, and low yields of silage, hay, and pasture grass, and the use of the first two interception equations above (the first for silage, and the second for hay and grass), can give some guidance on interception fractions and yields for these crops:

Yield Intercept

(kg/m²) Fraction

corn silage 0.30 (low) 0.20

0.90 (med) 0.50

1.35 (high) 0.64

hay 0.25 (low) 0.51

0.45 (med) 0.73

1.30 (high) 0.98

grass 0.05 (low) 0.13

0.15 (med) 0.35

0.35 (high) 0.64

This information can be used for cattle intake of vegetation, and the resulting beef and milk concentrations. The medium values for grass, 0.15 kg/m² yield and 0.35 interception, were used for the example setting in Chapter 5. An average of the medium values for hay and silage, 0.63 kg/m³ yield and 0.62 interception, were used for the second category of cattle vegetations for Chapter 5, the hay/silage/grain category.

Stevens and Gerbec (1988), using yields obtained from the Minnesota State Agricultural Office, derived the following yield and interception estimates, respectively, for vegetables for human consumption in their assessment: lettuce - 8.6,0.72; tomatoes - 12.0,0.55; and beans - 2.7,0.18. Average yields and interception fractions from their exercise: 7.8 kg/m² and 0.48, were used in the example setting in Chapter 5. These vegetable yields are fresh weight, so they need to be converted to a dry weight basis in order to estimate a C_ppa appropriate for use in Equation (4-23). Since vegetables are generally 80 ->90% water, a fresh to dry weight conversion factor of 0.15 was used, resulting in an average vegetable dry matter yield of 1.17 (7.8 * 0.15). This was used in the example settings in Chapter 5.

• V_d: Particles settle to the ground surface and plant surfaces due to the forces of gravity. Gravitational settling velocity is a function of particle size, with more rapid settling occurring with larger particles. The algorithm used to estimate the concentration of contaminated particulates in air estimates the suspension of particles less than and equal to 10 m m, which is commonly referred to inhalable size particles. Seinfeld (1986) listed a gravitational deposition velocity of 1 cm/sec for 10 m m size particles. This deposition velocity will be used in this assessment, and in units of m/yr, this equals 315,360 m/y.

• RN: Geraghty, et al. (1973) provides a map showing isolines average annual rainfall throughout the United States. This map shows low rates of 5 to 20 inches/year in the desert Southwest, moderate rates of 25 to 40 in/yr in the Midwest cornbelt, 40 to 60 in/yr in the South, and so on. The example scenarios of Chapter 5 were described as rural, with land in agricultural and non-agricultural settings. A rate of 1 m/yr (39 in/yr) will be used in the example scenarios.

• R_w: It is assumed that dry depositions fully adhere to plant surfaces; the weathering constant, k_w, models the loss of the vegetative reservoir of particle bound contaminants due to wind, rain, or other weathering process. However, it is not clear that wet deposition should also be assumed to fully adhere during a wet deposition event. Hence, the R_w parameter, or fraction of wet deposition adhering, was introduced. Prior modeling efforts of the impact of depositions of dioxin-like compounds to vegetations are unclear with regard to wet deposition. Stevens and Gerbec (1988), Fries and Paustenbach (1990), Webster and Connett (1990), and Travis and Hattemer-Frey (1991) all model particle deposition impacts of 2,3,7,8-TCDD to vegetations in air-to-beef/milk modeling. None of them discuss the distinction in wet and dry deposition, and model "total deposition" impacts, describing total as wet and dry deposition, total deposition, or simply as deposition. On the other hand, McKone and Ryan (1989) reduce the wet deposition portion of total deposition. They promote use of a "b", which they define as the fraction of material retained on vegetation from wet deposition. They recommend a value between 0.1 and 0.3.

The clearest indication of the fate of wet deposition of particles can be found in Hoffman, et al. (1992). In that field study, simulated rain containing soluble radionuclides and insoluble particles labeled with radionuclides was applied to pasture-type vegetation under conditions similar to those found during convective storms. The fraction of the labeled particles found to remain on the vegetation after the rainfall varied from 0.24 to 0.37. Nine values comprised this range, including particle sizes of 3, 9, and 25 m m, and cover described as clover, fescue, and mixed (a site with old field vegetations including fescue, grasses, weeds, and wild flowers). Based on this work, the R_w will be assumed to be 0.30 for all vegetations and dioxin congeners of this assessment.

• W_p: Washout ratios are generally defined as the concentration of contaminant in rain to the concentration of contaminant in air. Concentrations of contaminants in air and rain water can be derived as a mass of contaminant divided by a mass of air/water or a volume of air/water. Mackay, et al. (1986) shows that volume-based washout ratios (mass of contaminant mixing in m³ air or water, e.g.) exceed mass-based washout ratios (mass of contaminant mixing in kg of air or water) by a factor of 815, which is the ratio of water and air densities. The washout ratio used in this assessment is a volumetric ratio based on methodologies described by Bidleman (1988). Using a volumetric ratio then allows for direct use of contaminant concentrations estimated in this methodology since they are already on a m g/m³ volume basis.

Bidleman (1988) defines the overall washout ratio as: (mass contaminant/volume rain) ¸ (mass contaminant)/(volume air). Bidleman (1988) also discusses that fact that overall washout includes both wet deposition of particulates and scouring of contaminants in the vapor phase. He includes methodologies for estimating the vapor/particulate ratios for semi-volatile organic compounds (abbreviated SOCs) and also for estimating the washout ratios for vapors. However, he claims that if H is sufficiently high, vapor dissolution in droplets is negligible and only the particulate fraction is removed by wet deposition. He claims this to be the situation for n-alkanes, PCBs, chlordane, DDT, and 2,3,7,8-TCDD. Developing overall washout ratios for these and several other SOCs, he estimates that vapor scouring accounts for 1% of the overall washout ratio for 2,3,7,8-TCDD. For PCBs, he estimates similar percentages of 2, 4, and 28% for Aroclors 1260, 1254, and 1248, respectively. Based on this work, it will be assumed that vapor scouring of the dioxin-like compounds is small in comparison to wet deposition and the washout ratio for this assessment will only be applied to the air-borne particulate concentration of dioxin-like compounds.

Bidleman (1988) does not provide a chemical or site-specific equation which estimates the particle-phase washout ratio (which he does for the vapor-phase washout ratio). Rather, he summarizes available data and concludes that there is a wide range of the particle-phase washout ratio, W_p, for SOC: between 2x10³ and 1x10⁶. He claims that a typical range is 10⁵ to 10⁶, and uses a W_p of 2 x 10⁵ in his exercises to estimate the overall washout ratio for several SOCs.

Koester and Hites (1992) list vapor and particle scavenging ratios for congener groups of dioxin-like compounds. To derive these ratios, they used air concentrations for congener groups that were taken at one time period in Bloomington and Indianapolis, Indiana, and rainfall depositions of these compounds at these sites measured during a second period of time. Using the Bidleman vapor/particle partitioning model used in this assessment, they estimate the vapor/split for the air concentrations. With these observations and models, they conclude that the overall washout ratio (sum of vapor and particle ratios) ranges from 10⁴ to 10⁵, which contrasts the typical range of 10⁵ to 10⁶ noted above from Bidleman (1988). Also, their calculations indicate that vapor scavenging of dioxin-like compounds is comparable to particle scavenging, also in contrast to the Bidleman analysis summarized above. However, they did not state whether their washout ratios were volume or mass-based. If they were mass-based, then a conversion to volume based would put them in the 10⁷ to 10⁸ range, which seems improbable given the Bidleman summary above. Therefore, it will be assumed that are volume-based, and they are appropriate to use for this assessment. Since no clear trend for particle washout ratios with regard to the degree of chlorination increased appears in Koester and Hites' data, the midpoint of their calculated range, 5 * 10⁴, will be used for all example compounds in this assessment.

As a final note, the multiplication of the above terms, W_p * C_pa * RN * R_w, does result in wet deposition in appropriate units of m g/m²-yr, although that is not immediately obvious. First, multiplication of W_p, in (m g contaminant/m³ rain)¸ (m g/m³ air), and C_pa, in m g contaminant/m³ air, leaves a partial term in units of m g contaminant/m³ rain. Then, multiplication of this partial term times annual rainfall rate, thought of as m³/m²-yr instead of m/yr, gives the final quantity in the appropriate units.

When calculating concentrations in below ground fruit and vegetables using Equation (4-23), C_bgv is on a fresh weight basis since the RCF developed by Briggs, et al. (1982) is on a fresh weight basis, and no correction for estimating exposures is necessary. However, C_abv as estimated in Equation (4-24) is on a dry weight basis, and should be multiplied by a dry weight to fresh weight conversion factor when applied to above ground fruits and vegetables. A reasonable estimate for this parameter for fruits and vegetables is 0.15 (which assumes 85% water), which was used in this assessment. When using Equation (4-24) to estimate C_abv for the beef and milk food chain algorithm, a conversion to fresh weight is not required, however, since the algorithms were developed assuming dry weight concentrations.

The algorithm to estimate the concentration of contaminant in beef and/or milk was based on methods developed by Fries and Paustenbach (1990). They developed the beef bioconcentration factor for 2,3,7,8-TCDD, which is defined as the ratio of the concentration of the contaminant in beef fat to the concentration in the dry matter dietary intake of the beef cattle. They discussed bioavailability, which, as they define it, is the fraction of ingested contaminant which is absorbed into the body. It depends on the vehicle of ingestion - dioxin in corn oil has a bioavailability in the range of 0.7 to 0.8, in rodent feed it has an estimate of 0.5, while in soil it has a range of 0.3 to 0.4. They emphasized the importance of the differences in diet between cattle raised for beef and those which are lactating in explaining different food product concentrations. Although there is likely to be some difference in the bioconcentration tendencies for lactating cattle and beef cattle, Fries and Paustenbach in fact used the same bioconcentration for beef fat and milk fat, and the same will be done here.

The concentration in the fat of cattle products is given as:

where:

C_fat = concentration in beef fat or milk fat, mg/kg

BCF = bioconcentration ratio of contaminant as determined from cattle vegetative intake (pasture grass or feed), unitless

DF_s = fraction of cattle diet that is soil, unitless

B_s = bioavailability of contaminant on the soil vehicle relative to the vegetative vehicle, unitless

AC_s = average contaminant soil concentration, mg/kg

DF_g = fraction of cattle diet that is pasture grass, unitless

AC_g = average concentration of contaminant on pasture grass, mg/kg

DF_f = fraction of cattle diet that is feed, unitless

AC_f = average concentration of contaminant in feed, mg/kg.

The following is offered as brief guidance to these terms and also the justification for the values selected in the example Scenarios in Chapter 5.

• BCF: Fries and Paustenbach (1990) developed the concept of a beef/milk bioconcentration ratio and applied it to 2,3,7,8-TCDD. BCF is defined as the concentration of contaminant in fat of cattle products (i.e., dairy and beef) divided by the concentration in dry matter intake. The key difference in the dietary intake of cattle raised for beef versus cattle raised for dairy is that cattle raised for beef tend to be pastured more than dairy cattle and be more exposed to contaminated soil, whereas lactating cattle are more often fed high quality feed, including grains which are expected to be substantially residue free since they are a protected vegetation. Based on literature studies of cattle consuming feed contaminated with dioxin-like compounds, Fries and Paustenbach (1990) calculated a BCF of between 4 and 6, and assumed a value of 5.0 for 2,3,7,8-TCDD.  Being developed directly from data of cattle ingesting contaminated feed, this BCF value of 5.0 already considers the bioavailability of the experimental contaminated feed. It will be assumed that the bioavailability of the cattle vegetations in this assessment equal that of the experimental feed. Therefore, a value of 5.0 can go directly into Equation (4-32) when applied to concentrations in grass and pasture. However, this value should not be applied to soil, since it has been shown that TCDD on soil is less bioavailable than TCDD on other vehicles. This is why a B_s appears in Equation (4-32) - it adjusts BCF when applied to a soil concentration. The value of B_s is described below. The Fries and Paustenbach (1990) literature review is reproduced in Table 4-3, which additionally shows results generated based on information in McLachlan, et al. (1990).

Although the BCF of 5 determined by Fries and Paustenbach (1990) for 2,3,7,8-TCDD appears high based on the literature for this compound, Fries and Paustenbach (1990) discuss how short duration feeding trials (the 21 days of Jensen and Hummel

Table 4-3. Ratios of dioxins and furans in milk fat (MF) and body fat (BF) to concentrations in diets of farm animals.

Animal Days Compound BF:diet MF:diet Reference

Goats 56 2,3,7,8-TCDD - 2.8 Arstilla et al. (1981)

Cows 21 2378-TCDD - 4.4 Jensen &Hummel (1982)

Cows 70 123678-HxCDD 3.9 5.7 Firestone, et al. (1979)

1234678-HpCDD 0.4 0.6

OCDD 0.1 0.1

Steers 28 2378-TCDD 3.5 - Jensen, et al. (1981)

Heifers 160 123689-HxCDD 2.1 - Parker, et al. (1980)

1234678-HpCDD 0.2 -

OCDD 0.05 -

1234678-HpCDD 0.3 -

OCDF 0.1 -

Cow 92¹ 2378-TCDD - 4.32 McLachlan, et al. (1990)

12378-PCDD - 4.16

123478-HxCDD - 2.02

123678-HxCDD - 1.74

123789-HxCDD - 2.24

1234679-HpCDD - 0.20

1234678-HpCDD - 0.36

12346789-OCDD - 0.52

234/78-TCDF - 0.94

1234/78-PCDF - 0.73

23478-PCDF - 3.10

123478/9-HxCDF - 2.34

123678-HxCDF - 2.00

234678-HxCDF - 1.78

1234678-HpCDF - 0.41

1234789-HpCDF - 0.99

12346789-OCDF - 0.20

TEQ - 2.50

d = depth of mixing, m

• D₁ and D₂: The first step in deriving both these amounts of soil is to use the Universal Soil Loss Equation (USLE). This approach was described above. Justification was given for an assumption of unit soil loss from the contaminated site of 9.6 t/ac-yr (see Section 4.3.1). D₁ equals this unit loss times the area of contamination times a sediment delivery ratio. The example scenario in Chapter 5 assumed that the exposure site was 150 meters from the contaminated site, and using Equation (4-10), the sediment delivery ratio is 0.26. The unit loss assumed for the area between the contaminated site and the exposure site is 0.96 t/ac-yr. Since this area is adjacent to the exposure site, there is no sediment delivery, and D₂ equals this unit loss times the area between the contaminated and exposure sites.

D₂ and D₂ can now be expressed as:

where:

D_1,2 = mass of soil delivered from off-site contaminated source, D₁, and from the land area between contaminated source and exposure site, D₂, kg/yr,

SL_1,2 = average annual unit soil loss, Eng. tons/acre-year, equal to 9.6 t/ac-yr for SL₁ and 0.96 t/ac-yr for SL₂