Page 5 of 9 Summary of algorithms, key assumptions, and parameter values

All parameters associated with individual congeners are shown in Table 7-7, and all parameters not specific to the congeners are shown in Table 7-8. Following now are summaries of the algorithms and key assumptions of this exercise. Many of them have been described in earlier chapters of this Volume and Volume II, and are not repeated here.

1. Partitioning total concentrations into a vapor and a particle phase

As shown in Figure 7-3, this is the first key step in this modeling exercise. Chapter 3 of this volume described air monitoring studies which reported the partitioning of dioxins into a particle and a vapor phase. Arguments were presented as to why these studies would likely overestimate the portion in the vapor phase. Because of this, a theoretical model for estimating the fraction of total concentration in the particulate and vapor phases was recommended for use in this assessment. The model of Bidleman (1988) was presented and discussed in Chapter 3, and will be used here as well. Table 7-7 presents the vapor and particle fractions assumed in this assessment, based on the Bidleman model.

2. Particle Depositions to Vegetations and Soils

Chapter 4 described the wet and dry particle deposition algorithm used for this assessment. The dry deposition algorithm and the key parameter assignment of a dry deposition velocity of 0.2 cm/sec will be used for this exercise without change. However, the wet deposition algorithm described in that chapter includes assignment of an annual rainfall amount with a washout factor. This is more appropriate for a site-specific application, and because this exercise is based on a "representative" rural profile of air concentrations and an average beef concentration profile derived from three locations in the United States, a simplification of the wet deposition algorithm is used in this exercise. This simplifications is based on the measurements made by Koester and Hites (1992). ...

table Table 7-8. Model parameters used for all dioxin-like congeners..
... They measured wet deposition of total dioxins at two sites in Indianapolis and Bloomington, Indiana, and generally found wet deposition to be comparable to dry deposition. Specifically, the estimated annual wet deposition of dioxins at Indianapolis was equal to 0.7 times dry deposition, while at Bloomington, wet deposition was 1.3 times dry deposition. Therefore, it will be assumed that wet deposition equals dry deposition in this exercise. Crop yields and interceptions which were used for the demonstration scenarios of Chapter 5 are used for the deposition algorithms here as well. The soil deposition algorithm remains unchanged from the structure and parameter assignments described in Chapter 4 and demonstrated in Chapter 5.
expand table Table V3 7-8

3. Vapor Phase Transfers to Vegetations

The key parameters for this algorithm include the air-to-leaf transfer factor, the Bvpa, and the empirical adjustment parameter, VG, which reduces vapor transfers considering the difference in the thin azalea and grass leaves used in experiments to derive the Bvpa and the bulky and protected vegetations of the cattle diet, such as silages as grains. The values of these parameters are the same ones used in Chapter 5.

4. Bioconcentration Model

The bioconcentration model includes assignment of the congener-specific bioconcentration factor, BCF, and the soil bioavailability parameter, Bs. The parameter assignments for these parameters are the ones which were developed in Chapter 4, used for the demonstration scenarios of Chapter 5, and shown on Tables 7-7 and 7-8.

5. Dietary Exposure of Cattle to Dioxins

The final key areas in this model are the assumptions concerning cattle exposure to dioxin-like compounds through their diet. A related key issue is the impact of feedlot fattening on final beef concentrations. The general diet profile used for the demonstration scenarios for beef concentration estimations in Chapter 5 is used here as well. This included an assumption of equal proportions of pasture grass and non-grass feed such as hay, silage, or grain, and a small amount of incidental soil. As discussed in Chapter 4, a 4% soil ingestion rate was assumed, leaving 48% each for pasture grass and the second category of cattle vegetation intake, abbreviated hay/silage/grain. Chapter 4 also discussed the impact of feedlot fattening.

The demonstration scenario of Chapter 5 did not include feedlot fattening since the scenario was one of a farmer home slaughtering for personal consumption. For this exercise, however, it is likely that the commercial beef samples from which the "observed" concentration profile was derived came from cattle which had undergone a period of feedlot fattening. Chapter 4 summarized modeling efforts which attempted to characterize the impact of a period of fattening assuming residue-free intake for a period of 120 days.

Based on their results, these modeling efforts hypothesized that such a diet regime would reduce fat concentrations by one-half. This will be the assumption used here as well; beef concentrations estimated using all the modeling described above will be halved as a final step in the modeling process. Results and discussion

A final comparison of predicted versus observed whole beef concentrations is shown in Table 7-9. Total TEQ concentrations compare favorably, with observed total TEQ at 0.48 ppt and predicted TEQ at 0.36 ppt. The congeners of most toxicity also had the best match of predicted and observed concentrations: 2,3,7,8-TCDD - 0.03 ppt observed and 0.03 ppt predicted; 1,2,3,7,8-PCDD - 0.22 observed and 0.27 ppt predicted; 2,3,4,7,8-PCDF - 0.21 ppt observed and 0.17 ppt predicted.

The largest discrepancies, an order of magnitude and more, were for two of the HxCDDs and for all HpCDD/Fs and OCDD/Fs. The total concentrations did not compare as well as the TEQ concentrations, with observed total whole beef concentration of 8.15 ppt and predicted at 2.13 ppt.

As a way of further examining these results, limited examinations are now presented on the two key components of this food chain model - the air to vegetation algorithm, and the air to soil algorithms.

One data set in the literature allows some limited comparisons between model predictions and observations of vegetation concentrations. This data was from a rural setting in Elk River, Minnesota (Reed, et al., 1990). This site was mentioned in the section above describing the derivation of the rural air concentration profile. The reference listed air concentrations by congener grouping for a rural setting (2 air sampling sites) and near an incinerator (1 site).

It was noted that the average annual air concentrations near the incinerator was about 5 times higher than the average annual air concentration at the two rural sampling stations. The total PCDD/F air concentration in the rural setting was estimated at 1.54 pg/m3. The corresponding TEQ concentration cannot be estimated without knowing the concentration of the congeners with non-zero toxicity. Therefore, a comparison to the crafted 0.019 pg/m3 concentration for the rural setting in this paper cannot be made.

However, a data set earlier described from Sweden (Broman, et al., 1990), listed a total concentration of 0.42 pg/m3 and a corresponding TEQ concentration of 0.004 pg/m3 for a rural Swedish countryside. This ratio of 100 between total and TEQ concentrations indicates that the Elk River total concentration of 1.54 pg/m3 may translate to a TEQ concentration around 0.015 pg/m3, which would be consistent with the 0.019 pg TEQ/m3 developed in this paper.

This study also took samples of vegetations in this rural setting, including two hay and two corn samples. The limits of detection for these vegetation samples varied between 0.31 and 6.5 ppt on a congener-specific and site-specific basis. ...

table Table 7-9. Results of validation exercise showing observed and predicted concentrations of dioxin-like compounds in whole beef..
... With vegetation concentrations predicted to be in this range generally, the data therefore cannot be rigorously informative. The congener found with the highest concentration is OCDD, found at 72 (site 1) and 170 (site 2) ppt in two corn samples, and 270 (site 1) and 300 (site 2) ppt in two hay samples.

In addition to this higher finding in the hay samples, generally more positives were detected in hay rather in corn. This is consistent with discussions in this paper indicating that vegetation concentrations of dioxin-like compounds is a surface phenomena with little within plant translocation. Hay, in this observation, is considered a leafy vegetation, whereas corn is considered a bulky vegetation.

expand table Table V3 7-9

Table 7-10 lists the average congener specific hay concentrations observed in Elk River (the average of two hay samples, with non-detects counted as 0.0 when one of the two samples had a positive, and just listed as ND when both hay samples showed non-detects) compared against the model's predicted concentrations in grass. This is felt to be a valid comparison. It assumes that hay alone is reasonably similar to grass in that both are "leafy" vegetations and would be modeled similarly in the framework of this paper.

What is now available to interpret and analyze are the predicted and observed beef concentrations, the predicted and observed leafy vegetation concentrations, and further model trends. Several observations are now summarized based on these analyses:1) Given the range of the detection limit, 0.31-6.5 ppt for the hay sampling, the model's predictions of grass concentrations are generally consistent with observations, with the exception of the OCDD and OCDF concentrations. It is noted that the second highest congener observation of 30 ppt of 1,2,3,4,6,7,8-HpCDD is matched by the model's prediction of 20.7 ppt for 1,2,3,4,6,7,8-HpCDD.2) The analysis of the OCDD and OCDF results for hay is very telling.

First, it is noted that the crafted rural air concentrations of these two congeners matches very well with the observed air concentrations at this Elk River site: OCDD observed at 0.5 pg/m3 and crafted at 0.57 pg/m3; and OCDF observed at 0.09 pg/m3 and crafted at 0.034 pg/m3 (note: the observed concentrations for OCDD/F congeners is the average of four listed concentrations of OCDD/F congeners in Reed, et al. (1990) - rural sites 1 and 2 and winter and summer listings). Since the crafted air concentrations match well with the observed air concentrations, one would hope that the vegetative concentrations also match. An analysis of why they did not indicates the importance of vapor phase contributions to vegetative concentrations.

table Table 7-10. Comparison of concentrations of dioxin-like compounds found in hay in a rural setting with model predictions of grass concentrations.
According to the application of the Bidleman (1988) approach for estimating the bound fraction, f , in the air, both these congeners were assigned a f of 1.00. In fact, using the OCDD/F vapor pressures and melting points, these f values were both 0.998.

If one allows for the possibility that f for OCDD/F could be less than one, and calibrates f for OCDD/F for this exercise, one can show that small reductions in f result in better predictions of both grass and beef concentrations. Recall that the observed "grass" concentrations are, in fact, the hay concentrations found at Elk River, Minnesota, and that the observed beef concentrations are those which were generated using available data from around the country.
expand table Table V3 7-10
table Table 7-11. Calibration exercise showing improvements in grass and beef concentrations when the fraction sorbed parameter, f , drops minutely below 1.00 for OCDD and OCDF.

Table 7-11 shows the results of a calibration, where f is first 1.00 as initially assumed, and then calibrated so that grass/hay and subsequently beef are more in line. As seen, the calibrated f are 0.9998 for OCDD and 0.998 for OCDF, and the grass and beef concentrations predicted are now much closer to observations.

The main reason for these very large differences in model predictions of hay concentrations with seemingly small differences in the amount assumed to be in the particle phase is that the air-to-leaf transfer factor, the Bvpa, is 2 to 4 orders of magnitude higher for OCDD and OCDF as compared to all other transfer factors. For OCDD, it is also noteworthy that the total air concentration is 1 to 2 orders of magnitude higher than the concentrations for all other congeners.

expand table Table V3 7-11

3) The one congener whose air concentration is within an order of magnitude of OCDD is that of 1,2,3,4,6,7,8-HpCDD, at 0.116 pg/m3. Also, the calculated Bvpa for this congener is second in magnitude behind the OCDD/F congeners. Since 2% of this air concentration is, in fact, predicted to be in vapor phase according to the Bidleman model, vapor transfers are considered and the model predicted 21.0 ppt grass concentration, which compared favorably with the observed 30 ppt concentration.

4) Calibrations for some of the other congeners for which a discrepancy exists between hay/grass predictions and beef predictions were not attempted. However, one can see with the following how the trend between predicted grass to beef concentrations followed the observed grass to beef trend. That is, when the model underpredicted grass, it also underpredicted beef, and likewise for overpredicting:

Diagram V3 7-1

5) A simple analysis of model performance indicates that vegetation concentrations explain beef concentrations. Looking only at 2,3,7,8-TCDD, it is seen that cattle soil ingestion, 4% of total diet, explains only 8.5% of final beef concentration, with grass explaining 60.6% and hay/silage/grain 30.9%. The main difference in grass and hay/silage/grain, as discussed above, is that vapor transfers are halved for hay/silage/grain with the use of the empirical VG parameter. Further, grass and hay/silage/grain concentrations are overwhelmingly dominated by vapor transfers for 2,3,7,8-TCDD, explaining 93% (grass) and 94% (hay/silage/grain) of final plant concentration.

Since grass and hay/silage/grain explain over 90% of beef concentration, vapor transfers onto vegetations cattle consume are predicted to explain about 85% of final 2,3,7,8-TCDD beef concentrations in this exercise. Very similar predictions occur for all congeners, with the exception of OCDD/F where 100% was initially assumed to be in the particle phase. Allowing for the calibration described above, now the OCDD/F beef concentrations are dominated by vapor transfers. Further discussion of the importance of vapor-phase dioxins to vegetations and to beef/milk can be found in Section in Chapter 6.

table Table 7-12. Comparison of concentrations of dioxin-like compounds found in soils described as "rural" or "background" with model predictions of soil concentrations..
An air to soil examination begins with a comparison of predicted soil concentrations of the dioxin-like compounds and an observed concentration in soils, which is shown in Table 7-12.

The observed data originated from four studies in the United States where soils were characterized as "rural" or "background". As seen in Table 7-13, there is clearly an underprediction trend for air to soil impacts.

For the nine congeners where the literature allowed for a non-zero average soil concentration, the model appears to underpredict soil concentrations by a range of about 2 to 10 times (i.e., observed concentrations are twice as high to about ten times higher than predicted concentrations).
expand table Table V3 7-12

While this is a non-trivial result, in fact the model would not predict a substantially different beef concentration if soil concentrations were more in line with observations. If the soil concentrations were artificially increased by a factor of 10, than whole beef concentrations of total dioxins increase from 2.13 ppt to 3.62 ppt, and TEQ concentrations increase from 0.36 ppt to 0.45 ppt. The reason for this trend is that soil is only 4% of the beef cattle diet prior to feedlot fattening.

The observation made is that the current formulation and/or parameter assignments for an air to soil impact will underpredict soil concentrations of dioxins by about 2-10 times. If this observation is, in fact, a statement of truth, then the following is offered as the most likely causes for model underprediction:

1. The soil dissipation rate:
The dissipation rate of 0.0693 yr-1, corresponding to a half-life of 10 years, was developed from field data of 2,3,7,8-TCDD applied to soils in the herbicide 2,4,5-T (Young, 1983). This may be appropriate for a limited loading onto a bounded area of soil. However, mechanisms for dissipation from this bounded area, such as dust suspension and volatilization, may not directly apply for background settings where such losses may be redeposited downwind. According to the steady state algorithm for soil impacts from depositions, the estimated soil concentration is an inverse function of the dissipation rate. If the dissipation rate is reduced to 0.00693 yr-1, corresponding to a half-life of 100 years, than the soil concentrations are increased by an order of magnitude.

2. Depositions of vapors:
Koester and Hites (1992) developed the argument that their collection apparatus for dry deposition of dioxins would not scavenge vapor phase dioxins from the air; that they would only be measuring dry deposition of particle bound dioxins. Since the dry deposition velocities used in this paper originate from their work, and if their arguments are valid, then the algorithms of this paper do not consider the dry deposition of vapors. Their methods for measurement of wet deposition did not preclude the scavenging of vapors, although they do argue that rainfall is more effective at scavenging particle-bound dioxins compared to vapor-phase dioxins. Therefore, the assumption made that total annual wet deposition equals dry deposition made in this paper, based on the results of Koester and Hites, means that wet deposition of vapor phase dioxins are considered. In any case, algorithms to estimate the additional dry deposition loadings of vapor-phase dioxins to soil could not be found, so the impact of including them cannot be estimated.

3. Detritus recycling:
This is another loading not considered, and also a loading tied directly to vapor-phase dioxins. As discussed above, vegetation concentrations are dominated by vapor transfers. Barbour, et al. (1980) list a detritus production rate for a setting described as "tallgrass prairie" as 520 g/m2-yr. Given the concentrations predicted to occur in grass, one can estimate the loadings of dioxin corresponding to a detritus production of this magnitude. This was done and compared against the estimated total deposition rates from the air to soil of individual congeners. It was found that detritus loadings varied by congener, and was equal to a range of 2% of atmospheric deposition to 100% (equal to) of deposition. Summing the depositions and the detritus loadings
of all congeners, it was found that detritus loadings are equal to about 20% of atmospheric deposition loadings of dioxins. Conclusions

The beef bioconcentration algorithm of this assessment was tested in this section. A profile of air concentrations was crafted to be typical of rural environments where cattle are raised for production of beef. This profile was routed through the model to predict concentrations of dioxin-like compounds in beef. These predictions were compared with a profile of measured concentrations. An "observed" TEQ concentration of 0.48 ng/kg in whole beef was compared with a "predicted" 0.36 ng/kg.

An observed total concentration PCDD/Fs of 8.15 ppt in beef was compared against the predicted 2.13 ppt. Further evaluations of the air to vegetation algorithm indicate the model appears to predict vegetation concentrations consistent with one set of literature observations, with the exception of the octa congeners, OCDD and OCDF. However, when assuming only a minute amount of the airborne reservoirs of these congeners is in the vapor phase, model predictions of both vegetations and subsequently beef concentrations fall in line.

A final evaluation of the air to soil model indicates that the model and/or the parameter assignments tend to underpredict soil concentration by as much as an order of magnitude. Refinements to the model which would bring soil concentrations more in line with observations were offered. It was observed that while the model appears to be underpredicting soil concentrations, a more appropriate prediction would not change beef predictions significantly since soil is only a small part of the cattle diet. A major conclusion of this work is the overwhelming dominance of the vapor phase transfers to vegetations which cattle consume, which in turn implies that the appearance of these chemicals in beef and milk is due to vapor transfers.

Another and more broad conclusion offered is that the validation exercise in general demonstrates the validity of the air-to-beef model framework and parameter assignments. This is a cautious conclusion, obviously, given the uncertainty in the many parameter assignments and real world observations. This exercise would need refinement in several areas before ascribing any finality to the model structure and results.

Following is a summary of the key uncertainties of this exercise:

1. A characteristic rural air environment:
A profile of air concentrations of dioxin-like congeners in a rural environment in the United States could not be found for this exercise, and instead one was crafted given a representative profile for urban/suburban areas and a simple proportional reduction.

2. A characteristic profile of dioxin-like congeners in beef:
Only 14 samples from three literature references, one of which only reported on 2,3,7,8-TCDD and 2,3,7,8-TCDF, were found for this exercise.

3. Vapor/particle partitioning:
A theoretical modeling approach was used to partition the total reservoir of congeners into particle and vapor phase. A carefully designed monitoring experiment could shed some light on vapor/particle partitioning for dioxin-like compounds. This is obviously critical given the major conclusion of the dominance of vapor phase concentrations in explaining beef concentrations.

4. Vapor transfers to vegetations:
Like the partitioning issue, the quantification of transfers onto vegetations is critical. The generalized model of Bacci (1990, 1992) was used with an empirical refinement suggested by McCrady and Maggard (1993). To highlight the importance of this empirical reduction, consider the following which describes what predictions would be without the benefit of the McCrady adjustments. A factor of 40 difference was noted in the measured transfer of 2,3,7,8-TCDD, on a volumetric basis, to grass leaves in the McCrady experiments compared to the transfer which would be estimated using the empirical algorithm developed by Bacci and coworkers.

This factor of 40 was applied to the transfer factor of all dioxin-like compounds. The volumetric transfer factor was transformed to a mass-based transfer factor using plant densities and percent dry matter suggested by McCrady rather than those used by Bacci and coworkers for the azalea leaf. Together, the final mass-based Bvpa of this exercise, and this assessment otherwise, is about a factor of 20 lower than that which would be estimated using the Bacci mass-based algorithm. Said another way, the model would have predicted a whole beef concentration greater than 7 ppt, instead of 0.36 ppt. Also, a second empirical refinement reduced the transfer into bulky vegetations. While the need for both refinements is argued to be justified for dioxin-like compounds, the precise numerical adjustments used in the exercises above cannot be rigorously defended without further data.

5. Particle depositions onto vegetations:
The impact of wet deposition needs to be further investigated. A literature article suggesting that about 30% of particles depositing in rain are retained on the canopy after the rainfall justified the assignment of 0.30 to the parameter, Rw (fraction retained on vegetation from wet deposition). The weathering half-life of 14 days, while often used for dioxins, is also identified as uncertain. Finally, the deposition velocity of 0.2 cm/sec should be considered further.

6. Air-to-soil impacts:
The trend here is that the model appears to underpredict soil concentrations by an order of magnitude or less. Three aspects of the model were offered above as possible candidates for refinement and further research. These included: vapor impacts to soils, dissipation rate in soils, and detritus loadings to soils.

7. The bioconcentration factor:
Only one study was found from which congener-specific bioconcentration factors for the suite of congeners could be developed, and this was for one cow, for one lactating period, and was for milk and not beef. The differences in bioconcentration between beef and milk need to be further investigated and quantified.

8. Cattle diet and the impact of feedlot fattening:
A cattle diet was simplistically assumed to consist of 4% soil and equal parts of grass and non-grass feeds. Perhaps a more representative diet could be crafted, which would lead to a different exposure pattern by the beef cow prior to feedlot fattening. Equally if not more important is the impact of this feedlot fattening. It is clear that commercial beef cattle in the United States undergo a period of feedlot fattening. However, before and after monitoring quantifying the impact of this practice could not be found. Two modeling studies, which assumed that dilution and depuration were occurring during feedlot fattening, estimated that concentrations were halved due to this process. This was the assumption also made in this paper, and it needs to be further evaluated. Comparison of modeled beef and milk concentrations with concentrations found

The example scenario in Chapter 5 demonstrating the on-site source category (where the soil at the place of residence/farming/exposure is the source of contamination) had soil concentrations initialized at 1 ng/kg (ppt) 2,3,7,8-TCDD. This concentration was chosen because it was similar to concentrations of 2,3,7,8-TCDD found in studies where researchers had measured what they characterized as "rural" or "background" soils. Beef and milk fat concentrations of 2,3,7,8-TCDD estimated with this soil concentration were 0.12 and 0.06 ppt 2,3,7,8-TCDD, respectively.

Assuming fat contents for beef and milk of 0.22 and 0.035, respectively, whole beef and milk concentrations are estimated as 0.03 and 0.002 ppt. Beef and milk fat concentrations for an exposure site located 500 meters from a hypothetical incinerator, another of the example scenarios in Chapter 9, were 0.0024 and 0.0017 ppt. Corresponding whole beef and milk concentrations were 0.0005 and 0.00006 ppt.

The other source category was a site of higher soil concentration located near a site of exposure. It was termed the off-site source category, and the demonstration scenario had a 4 hectare site contaminated with 2,3,7,8-TCDD at 1 m g/kg (ppb) located 150 meters from an exposure site. This concentration was selected based on similar 2,3,7,8-TCDD concentrations found in sites of elevated contamination, such as Superfund sites. No-till soil concentrations at the site of exposure, the concentrations which beef and dairy cattle were exposed to, were estimated to be 0.28 ppb, or 280 ppt. Concentrations in beef and milk fat were 38 and 19 ppt, respectively, which corresponds to whole product concentrations of 17 and 0.7 ppt.

A limited number of studies were available to estimate concentrations of dioxin-like compounds in beef suitable for background exposure estimations. Data from these studies is summarized in the previous section, Section From this limited data, the concentration of 2,3,7,8-TCDD in beef/veal fat was estimated at 0.134 ppt when non-detects were assumed to equal one-half the detection limit and 0.060 ppt when non-detects were assumed equal to 0.0. A single report containing milk concentrations (Lafleur, et al., 1990) indicated a concentration of 0.054 ppt in milk fat. This compares to the 0.12 ppt estimated for beef fat and 0.06 ppt estimated for milk fat for the demonstration scenario based on a background soil concentration of 1 ppt.

The example scenario results from the stack emission source estimated beef and milk concentrations over a factor of ten lower than for the background soil concentration scenarios. In interpreting this result, it is important to note that the emission rates assumed in this example scenario were characterized as typical of incinerators with a high level of air pollution control, e.g., scrubbers with fabric filters. The TEQ emission factor (mass TEQs emitted per mass feed material combusted) for the demonstration scenario was 4.5 ng/kg, which was compared to a crafted range of 0.3 ng/kg (for a municipal solid waste incinerator) to 200 ng/kg (for a medical waste incinerator) which had similar high levels of air pollution control. Also, the 200 metric tons per day feed material assumed for the example scenario is considered midrange (see Chapter 3 for more details).

Some articles in the public literature suggest a greater impact to milk when the milk is produced near incinerators or urban centers, although a direct comparison obviously is not warranted without a careful evaluation of source strengths from these literature articles, which is not done here. A study sampling remote farms in England also sampled two farms near incinerators and two farms near industrial centers.

Whereas samples from remote farms averaged 0.009 ppt for whole milk, two concentrations near the incinerators were 0.034 and 0.036 ppt 2,3,7,8-TCDD, and the samples near the industrial centers were 0.043 and 0.081 ppt (Startin, et al., 1990). A study from Switzerland which sampled milk from locations remote from 2,3,7,8-TCDD sources, and did not find detectable residues, also sampled three locations that were within 1000 meters of incinerators (Rappe, et al., 1987). Whole milk concentrations near the incinerators were 0.021, 0.038, and 0.049 ppt.

Sampling of beef and milk near areas of elevated soil concentrations, or where cattle were raised on soils with known high concentrations of 2,3,7,8-TCDD, were not found in the literature. Therefore, the beef fat concentration of 38 ppt (whole beef equal to 8 ppt) estimated to occur near an area where soil concentrations of 2,3,7,8-TCDD were 1 ppb cannot easily be evaluated. There are some studies on other animals indicating high tissue concentrations in areas of high soil contamination of 2,3,7,8-TCDD. Lower, et al. (1989) studied animal tissues for wild animals in the abandoned town of Times Beach, Missouri, and compared their results for similar wild animals tissue concentrations found in Eglin Air Force Base in Florida; Seveso, Italy; and Volgermeerpolder, Holland. With 2,3,7,8-TCDD soil levels in these areas in the hundreds to thousands of ppt, tissue levels for earthworm, mouse, prairie vole, rabbit, snake, and liver samples from some of these animals, were in the tens to thousands of ppt.

There is an episode of beef and dairy cows being raised on lots where the soil was heavily contaminated with polybrominated biphenyls (PBB; details can be found in Fries and Jacobs, 1986; and Fries, 1985). Soil concentrations to which dairy and beef cows were exposed were 830 and 350 m g/kg (ppb), respectively. Body fat of the dairy cows had PBB concentrations of 305, 222, and 79 ppt (dairy heifers, primiparous dairy, and multiparous dairy, respectively). Body fat for the beef cows exposed to 350 ppb soil levels were 95 (cows) and 137 ppt (calves). Milk fat concentrations from the primiparous dairy and multiparous dairy cows exposed to 830 ppb soil levels were 48 and 18 ppt.

Fries estimated a quantity which is also useful for purposes of comparison - this quantity is the ratio of concentration in animal fat to concentration in soil to which the animal is exposed. His justification for deriving this ratio is that soil was speculated as the principal source of body burdens of PBB in the data listed above. For the source categories where contaminated soil is the source of dioxin-like compounds, the on-site and off-site source categories, a similar assumption is warranted. Ratios he derived for body fat of dairy heifers ranged from 0.10 to 0.37, while it was 0.02 and 0.06 for milk fat.

For body fat of beef cows, these ratios were 0.27 and 0.39. Fries also measured a ratio of 1.86 for sows and gilts. He attributes much higher sow ratios to their tendencies to ingest more soil. Analogous ratios can be derived for the contaminated soil source categories, and for beef and milk fat. For the onsite source category with low soil concentrations, beef fat to soil and milk fat to soil ratios were 0.12 and 0.06, respectively. For the off-site source category, ratios were similar at 0.14 for beef fat and 0.07 for milk fat. The milk fat ratios compare favorably with PBB ratios derived by Fries (1985), although the beef fat ratios appear generally lower.

This is, once again, some indirect evidence that the soil to air models may be underestimating air concentrations. This had been discussed earlier in Section on air concentrations and on soil to plant relationships. For the current discussion, a higher beef fat:soil ratio would result if air concentrations were increased and hence the cattle vegetation concentrations would increase.