field may be low. For this reason, a 2.0 L/day was assumed in the high end, farming, scenarios.

The contact fraction is defined as the fraction of total contact with an exposure media that is contact with contaminated media. For drinking water, this translates to the fraction of water ingestion that comes from the contaminated water source. In the example scenarios, it was assumed that the impacted water was a river which supplied water to the exposed individuals, perhaps through a public water system. The contact fraction of 0.75 for central scenarios is based on time use surveys which showed roughly this fraction of time spent in and around the home environment on the average. The upper recommended limit in EPA (1989) was 1.00; this was felt to be unrealistic for the example scenarios which involved relatively small sources and consequently the likelihood that contamination would not be widespread. Thus, a farmer would likely obtain some water from outside his home where the water supply was not contaminated. An assumption of 0.90 for farming familes was selected for the high end scenarios of this assessment.

The uncertaintainties associated with the water ingestion pathway are summarized in Table 7-16.

Sections 7.2.3.5 and 7.2.3.6 earlier addressed the capabilities of the models of this assessment to estimate fish tissue concentrations, by looking at measured fish concentrations and comparing them with modeled concentrations. In general, it was concluded that fish tissue concentrations estimated are consistent with those found in the literature, and differences in concentrations with differences in source strength (i.e., higher soil concentrations, higher effluent discharges) also appear to have been captured.

Section 7.2.3.2. looked at a comprehensive data set developed and supplied by the Connecticut Department of Environmental Protection which included soil concentrations, sediment concentrations of water bodies near where soil samples were taken, and fish concentrations from the same water bodies. Data on 2,3,7,8-TCDD, 2,3,7,8-TCDF, 2,3,4,7,8-PCDF, and total TEQ were examined. Soil concentrations of 2,3,7,8-TCDD were found to be in the low ppt range, which has been described in various places in this document as a range for "background" soil conditions. Sediment concentrations of the three congeners and total TEQ were generally in range of 2-3 times higher than soil concentrations, which was

Table 7-16. Uncertainties associated with the water ingestion pathway.

Assumption/

Method Approach Rationale Uncertainty Comments

Water See modeling approaches for the soil, literature data show few No major uncertainty

Concentrations stack emission, and effluent discharge occurrences of dioxin-like expected due to

source categories in Chapter 4. compounds at 1 pg/L detection; modeling of water

models estimate 10^-2 pg/L range concentrations

and lower; cannot therefore

easily ascertain uncertainty

due to modeling, although

little uncertainty expected due

low concentrations both found

and predicted.

Water ingestion 1.4 L/day The classically assumed EPA (1989) also noted that Not expected to be

rate 2.0 L/day was evaluated information on sensitive a critical factor for

in EPA (1989) and found subpopulations such as uncertainty

to be high as an average; laborers was unavailable;

1.4 L/day recommended in still, their analysis indicated

general assessments, so that 2 L/day corresponds to

it was used for central a 90% value; hence it is

while 2.0 L/d used for appropriate for high end

high end settings

Contact rate 0.75 & 0.90 0.75 recommended in EPA Not expected to be widely Same comment as

(1989) for general uses variable for rural settings. above

based on time spent at

home; recommended value

of 1.00 was felt to be

too high for high end

scenarios; chose 0.90

instead

Overall: Data in the literature suggests concentrations mostly below 1 pg/L, which is consistent with modeling of concentrations 10^-2 pg/L and lower in demonstration of all source categories. With this evidence, little uncertainty is expected due to modeling techniques. There also does not appear to be a wide range of possible values for ingestion and contact rate, and these are not expected to introduce significant uncertainty into water ingestion exposure estimates.

consistent with the demonstration of the on-site source category. This demonstration scenario had a basin-wide soil concentration of 1 ppt, and the sediment concentration was estimated at 2.8 ppt. The Biota Sediment Accumulation Factor, BSAF, from this field data was estimated to be 0.86 for 2,3,7,8-TCDD. This was higher than the assumed 0.09 in the demonstration scenarios. Two explanations were offered for this difference. One was that the fish sampled were bottom feeders, which would put them in more contact with contaminated sediments compared to column feeders, and the 0.09 was justified based on data from column feeders; higher impact from contaminated sediments is expected from bottom feeders as compared to column feeders. Two, the 0.86 may have been skewed from two (of seven) sites in the Connecticut data which had high BSAFs at greater than 1 and 3. Although the soil sampling in this data set was generally sparse, the result that bottom sediment concentrations exceeded surface soil concentrations by 1.6-3.9 times generally supports the model's algorithms for estimating sediment concentrations in areas with low basin-wide concentrations.

Section 7.2.3.5 looked at fish concentrations in background areas and where point source impacts to water bodies were identified. A principal source of information was EPA's National Study of Chemical Residues in Fish (EPA, 1992b; abbreviated NSCRF). The range of fish tissue concentrations measured for (perhaps) background conditions in this study, 0.56 - 1.02 ppt, were comparable to the fish tissue concentration estimated assuming the low (perhaps) background soil concentration of 1 ppt soil concentration, 0.6 ppt. It may also be appropriate to make the same observation for the source categories assuming higher soil concentrations as compared to measured concentrations. In this case, the range of measured concentrations, 1.4 - 30.02 ppt, compares with the modeled 3 ppt. Specific field data were not available for more direct model validation. However, the magnitude of concentrations appears to have been captured, and the approximate order of magnitude difference between background and higher source strength categories of the NSCRF also appears to have been duplicated.

While the modeled PCDD/PCDF fish concentrations seem reasonably in line with measured concentrations, this assessment may have underestimated concentrations of 2,3,3',4,4',5,5'-HPCB in the demonstration scenarios. Concentrations for fish in the Great Lakes Region were in the tens to hundreds of ppb range, while this assessment derived estimates all under 1 ppb. However, an examination of bottom sediment concentrations of PCBs in the literature showed them to be roughly three orders of magnitude higher than estimated with the algorithms of this assessment. This mirrors the difference in observed vs. estimated fish tissue concentrations. The Biota Sediment Accumulation Factors, BSAFs, for PCBs also was noted to be variable, with values below 1.0 to values over 20.0 (see Section 4.3.4.1, Chapter 4). The BSAF for the example PCB congener in this assessment was 2.0. Higher BSAFs would also increase PCB concentrations estimated for fish.

Section 7.2.3.6 evaluated the model for estimating fish tissue concentrations for the effluent discharge source category, using data from the 104-mill study. Comparing model predictions of fish tissue concentrations with observed concentrations, it was found that there was generally an underprediction of observed fish tissue concentrations, although the average predicted concentration 7 ppt cannot be considered significantly different then the observed concentration of 15 ppt. An important qualifier is that this exercise assumed that the effluent discharges were the sole source of contaminants which may have impacted the water bodies. Also, the maximum "observed" fish tissue concentration of 143 ppt was matched by a predicted concentration of 89 ppt. Finally, there was discussion that the BSSAF (biota suspended sediment accumulation factor) assigned value of 0.09 for 2,3,7,8-TCDD, the same value used for the BSAF, might be low for the effluent discharge source category. The justification for this hypothesis concerns the differences between past and ongoing water body impacts, and the fact that the 0.09 value was based on field data for a water body where impacts are speculated as principally occurring in the past (see Section 7.2.3.6 for a further discussion of this issue). When the BSSAF was "calibrated" to 0.20, the average predicted fish concentration of 15 ppt for 2,3,7,8-TCDD now matched the observed fish tissue concentration.

The model did not perform as well for pulp and paper mills discharging into the largest receiving water bodies. The average fish tissue concentration observed for 21 fish was about 7 times higher than predicted concentration. No precise conclusion can be reached with this result. However, it may be true that large water bodies are likely to be ones having multiple sources rather than small water bodies. Therefore, the assumption that one or more proximate mills are solely responsible for observed fish concentrations is most likely to be flawed for large water bodies.

In summary, the evaluations for model performance regarding fish tissue concentration estimation seem to lend credibility to the approaches taken. The sensitivity analyses exercises on the algorithms to estimate fish tissue concentration discussed the variability and uncertainty with the parameters required for the algorithms. Generally the most sensitive input was the source strength characteristics - soil concentrations, contaminant discharge rates in effluents, and so on. A single order of magnitude or less range in predicted concentrations would result with singular changes in all other model parameters.

An exposure parameter of paramount importance in estimating exposure to contaminated fish is the fish ingestion rate. Although fish consumption surveys are available and are discussed in EPA (1989), this assessment uses a different approach to estimate the consumption of fish from an impacted water body. The approach is recommended for use when site-specific survey or other information is unavailable (EPA, 1989). Briefly, assume a meal size of between 100 and 200 g/meal - this assessment assumed 150 g/meals - and estimate the number of fish meals that may be recreationally caught from the impacted water body. An estimate of 3 meals/year was made for central exposure scenarios, and 10 meals/year was made for high end exposures. Ingestion of contaminated fish is therefore, estimated as 1.2 and 4.1 g/day, respectively (150 g/meal * 3 meals/yr * 1/(365 d/yr) = 1.2 g/d).

Surveys of recreational fisherman near large water indicates that these estimates are low for this subgroup. As noted in Chapter 2, EPA (1989) estimates that a typical rate of ingestion of recreationally caught fish for this subgroup is 30 g/day, with a 90% estimate of 140 g/day. Chapter 2 also summarizes the USDA 1977-78 National Fod Consumption Survey (USDA, 1983), a three-day total diet survey which showed a range of 0.00 g/day ingested (i.e., survey respondants reported no fish consumption for the three-day period) up to 146 g/day fish including shellfish. The range of 30-140 g/day may be more appropriate, therefore, if estimating fish ingestion exposure for recreational fisherman near a large, impacted water body. If using any of these estimates in exposure exercises, assumptions on percent of total consumption which is recreationally caught and/or impacted by dioxin-like compounds needs to be made.

A key trend noted for the example scenarios in Chapter 5 is that fish, along with beef and milk ingestion, led to the highest exposure estimates for the dioxin-like compounds. Obtaining site-specific information for fish ingestion is critical for this pathway. The ingestion rates made in this assessment are very likely low by an order of magnitude or more for use to a subgroup of recreational fisherman obtaining fish from a nearby large water body.

A summary of the uncertainties associated with the fish ingestion pathway is given in Table 7-17.

Table 7-17. Uncertainties associated with the fish ingestion pathway.

Assumption/

Method Approach Rationale Uncertainty Comments

Bioaccumulation Modeled bottom Bioaccumulation approaches It is not clear Evaluations of literature reports

approaches for and suspended rather than bioconcentration whether BSAFs developed in Sections 7.2.3.2, 7.2.3.5,

fish tissue concentrations, are appropriate for lipo- from one set of field and 7.2.3.6 speak well for the

concentration multiplied them philic persistent organic data are transportable algorithms estimating fish tissue

estimation by BSAF or contaminants; water-based to other water bodies; concentrations; predicted and

BSSAF rather than sediment-based uncertainty/variability observed conc. range from less than

approaches could be used. associated with sediment 1.0 ppt for background sources to the

concentration modeling. single to two digit ppt concentrations for more substantial point sources.

Fish ingestion 1.2 and 4.1 Based on 3 and 10 meals A low estimate compared Example scenarios were for

rate g/day per year caught from the to surveys of recreational "rural", agricultural settings;

impacted water, and or subsistence fisherman higher rates of ingestion

150 g/meal near large water bodies appropriate for other purposes

was not deemed appropriate

for such settings.

EVALUATION: Comparison of fish concentrations generated in the demonstration scenarios with literature values of fish concentrations of dioxin-like compounds shows them to be comparable. The test run with the effluent discharge source category suggests that fish tissue concentration predictions may be low, but not by a significant amount. It would appear that procedures to estimate fish tissue concentrations, while still obviously containing uncertainties and variabilities, obtain results that are consistent with source strengths and in the same order of magnitude as measured in field settings. Much higher sediment concentrations of PCBs were noted in actual water bodies than were modeled in the demonstration scenarios - hence much lower fish tissue concentrations of PCBs were estimated. The BSAFs were assigned based on field measurements for dioxins, furans, and PCBs. PCB BSAFs were an order of magnitude and more higher than dioxin and furan BSAFs. Alternate modeling approaches based on water column concentrations show comparable fish concentration estimations. Fish concentrations estimations vary by less than an order of magnitude with changes in model parameters, except for source strength terms. The fish ingestion rates assumed were low in comparison to estimates given for subsistence fisherman or others who live near large water bodies where fish are commercially caught. A lower ingestion rate is appropriate for settings where large water bodies containing edible fish are not present.

This section will address the uncertainty associated with vapor and particulate phase inhalation exposures. Sources addressed in this assessment include stack emissions and contaminated soils; this section will only address contaminated soils. The fate and transport of dioxin-like compounds from stack emissions to exposure sites, and the resulting air concentrations, are discussed in Chapter 3.

The respiration rate of 20 m³/day used for inhalation exposures is within the standard range of 20-23 m³/day (EPA, 1989). The contact fraction is 0.75 for central scenarios and 0.90 for high end scenarios. Like the water ingestion contact fractions, these were based on time at home surveys. The inhalation rate and contact fractions are not expected to introduce much uncertainty into inhalation exposure estimates.

Another exposure parameter critical for the inhalation pathway is exposure durations, which is 9 years for central and 20 years for high end exposures. The uncertainties associated with this parameter in its use as an exposure parameter are discussed above in Section 7.3.1. However, exposure duration is additionally critical for the inhalation pathway, as estimated volatilization flux is a function of the time during which volatilization is occurring. Essentially, the model assumes that contamination is at the soil surface at time zero, and over time, residues which volatilize originate from deeper in the profile leading to lower volatilization fluxes after time, and also lower average volatilization flux as the averaging time increases. The sensitivity analyses exercises in Chapter 6, Section 6.3.3.1., evaluated the sensitivity of air concentration predictions to changes in exposure duration. It was shown that there is roughly a factor of four difference between concentrations predicted over one year duration to a seventy year duration. Therefore, there is both a direct and an indirect impact from changing the exposure duration in these procedures. The direct impact from changing exposure duration is in the exposure equation - increasing the exposure duration increases the exposure estimate. What is seen also with increases in exposure, however, is a decrease in the estimated average air concentrations to which individuals are exposed. The impact in the exposure estimates is more driven by having more years of exposure rather than being exposed to a lower average air concentration, as expected.

Vapor-phase emissions are estimated with a volatilization flux algorithm. The procedures were developed in Hwang, et al. (1986). A near-field dispersion model estimates air concentrations for the on-site source category - the category addressing soil contamination at the site of exposure. For the off-site source category, where the site of contamination is located distant from the site of exposure, the same volatilization flux model is used. Exposure site concentrations for these sources are estimated using a far-field dispersion model.

Sensitivity analyses in Chapter 6 showed that the air concentration varied roughly over an order of magnitude with testing of key contaminant parameters, the organic carbon partition coefficient, Koc, and the Henry's Constant, H. Air concentration predictions are also sensitive to other key parameters, including those associated with source strength (area of contamination, concentration), geometry, (distance to receptor in off-site source category), and climate (average windspeed). However, these might be expected to be known with a reasonable degree of certainty for a site-specific application. If they are, it can be concluded that the most uncertainty associated with the vapor phase algorithm is in the contaminant parameters, and it would appear that a range of about an order of magnitude difference in predicted air concentrations might be expected with different pairs of these parameters.

The model's predictions of vapor phase air concentrations in the demonstration scenarios, with all parameters as selected, were compared with air concentrations that were found in the literature earlier in Section 7.2.3.7. This was clearly not a validation test, but might be called a reality test. It was found that air concentrations resulting from low, background levels of 2,3,7,8-TCDD, 1 ppt, were orders of magnitude lower than levels of 2,3,7,8-TCDD found in urban air samples, when 2,3,7,8-TCDD was measured in such samples. It was also found that air concentrations found with elevated soil concentrations of 2,3,7,8-TCDD, 1 ppb, soil concentrations which are more typical of Superfund and related sites, are comparable to noted urban air samples. The claim made was that this leant some credibility to model predictions - other possible outcomes such as air concentrations from low background soil concentrations being equal to urban air concentrations or concentrations from contaminated soil being higher than urban air concentrations, would appear to be inconsistent, and so on.

However, this examination also suggests that air concentration estimated with the volatilization/dispersion algorithms of the soil source categories may be underestimating air concentrations by an order of magnitude. Evidence here came in a few different forms. First, air concentrations of 2,3,7,8-TCDD taken in a "remote countryside" in Sweden showed concentrations an order of magnitude higher than are predicted for the on-site demonstration scenario, where soil concentrations were set at 1 ppt. This soil concentration was developed from literature data where researchers sampled soils in areas described as "background" or "rural". The soil concentration corresponding to the Swedish remote countryside data was unavailable, but it should be at least equal to these background or rural settings, if not lower. Another piece of evidence came in an examination of above ground plant:soil ratios as generated by the models and found in experimental testing. The models underestimated these ratios by 1 to 2 orders of magnitude as compared to the literature when vegetations in the literature were grown in soils with concentrations in the ppt range, a range typical of background settings. Since the models operate by estimating air concentrations, both particle and vapor concentrations, followed by air-to-plant impacts, this would be further evidence that the models are underestimating air concentrations, perhaps by the same 1-2 orders of magnitude difference.

While these pieces of evidence would seem to indicate that the model is underpredicting air concentrations resulting from soil contamination, the exact amount of this shortfall cannot be quantified. Arguments presented in Volume II and summarized in Volume I of this assessment indicate that the ultimate source of dioxins in soil, vegetations, and food products are air emissions from industrial sources, followed by long-range transport. If this is true, than the measured air concentrations and the vegetations in the experiments discussed above, are impacted not only by soil releases, but by long range transport from other sources. This assessment only models the incremental additions due to soil releases. The difference between the incremental addition from soil releases and the amount attributable to long range source cannot be ascertained at this time.

An alternate model for volatilization flux and an alternate model for air dispersion were evaluated in Section 7.2.4.4 above. It was found that the alternate volatilization model predicted about a third as much volatilization as the Hwang model, but that the alternate dispersion model predicted air concentration that may by 8 times higher than the models predicted in this assessment.

There was no data on concentrations of air-borne contaminants in the particle phase only. The procedures used to estimate the suspension of particles were developed from information on highly erodible soils. As such, fluxes and hence concentrations are expected to be higher than might be seen on the average. Still, inhalation exposures to contaminants sorbed to air-borne particulates were 1 to 2 orders of magnitude lower than exposures to contaminants in the vapor phase, and along with water ingestion exposures, were the lowest exposures estimated for the on-site and off-site soil source categories. In this regard, certainty with regard to estimating exposures due to inhalation of airborne contaminated particulates may be a small concern.

However, the sensitivity analysis exercises in Chapter 6 did indicate a two order of magnitude range in estimated concentrations depending on the assumptions concerning wind erodibility of the soil. Also, several issues of uncertainty concerning the suspension of contaminated particles and relationship between air-borne vapor and particle phases were examined. It was noted that the total reservoir of suspended contaminated particulates was likely to be underestimated because the algorithm for wind erosion was developed only for inhalable size, < 10 m m, particles, which is appropriate for inhalation exposures but would lead to an underestimate of the depositions onto vegetation, including fruits/vegetables for consumption and grass/feed for the beef/milk bioconcentration algorithm. Vegetation concentrations might also be low because the impact of rainsplash on transferring soil to the lower parts of vegetation was not considered.

A critical assumption made was that volatilized residues remained in the vapor phase and did not sorb to airborne particles. This led to a dominance of vapor phase contaminants - 90% and more of the total airborne reservoirs (vapor + particle phases) estimated for the on-site and off-site soil source categories were in the vapor phase. A model by Bidleman (1988) suggested that the fraction of 2,3,7,8-TCDD that would exist in the particulate phase in background settings (i.e., rural, non-urban) might range from 26% (average background) to 45% (average background with local sources), and in urban settings, would be as high as 72%. Transferring portions of the vapor phase contaminants to the particulate reservoir to get balances suggested by Bidleman's model would not change total inhalation exposures, but would impact concentrations in above ground vegetations. Currently and even with transfers such as these, vapor phase transfers dominate plant concentrations. Because vapor phase reservoirs would be reduced after transferring a portion to the particle phase, such transfers translate to reductions in plant concentrations, and for grass and feed, subsequent reductions in beef and milk estimations.

Perhaps the most critical assumption which could be questioned is that airborne vapor and particle phase contaminants at the site of exposure originate only from the site of contamination in the off-site soil source category. Meanwhile, soils at the exposure site are impacted - concentrations in the air at the exposure site do not consider possible fluxes from exposure site soils, or from soils between the contaminated and exposure sites. A test was conducted for this assumption using the demonstration scenario for the off-site soil source category, which had a 4-ha site at 1 ppb 2,3,7,8-TCDD 150 meters from an exposure site of the same size. The soil concentrations at the exposure site were 0.28 ppb for a 5-cm notill mixing depth and 0.08 ppb for a 20-cm tilled mixing depth. These concentrations were then input as soil concentrations for the on-site soil source algorithms to determine what air concentrations would results. These exposure site air concentrations were compared with exposure site air concentrations generated with the off-site algorithms. It was found that on-site air concentrations with soil concentrations at 0.28 ppb exceeded exposure site vapor and particle air concentrations estimated for a 1 ppb contaminated site 150 meters away by a factor of 3-5. When the same test was run using a tilled concentration of 0.08 ppb, concentrations predicted using the on-site algorithm and this concentration were similar to the concentrations predicted using off-site algorithms and a starting concentration of 1 ppb.

Several uncertainties were discussed, but a lack of data and a complete understanding of atmospheric processes for dioxin-like compounds precludes any final quantitative judgements on uncertainties in the air concentration algorithms. Some of the uncertainties imply that procedures and assumptions adopted overestimate pertinent environmental media, and others imply that such media concentrations were underestimated. The assumption that air-borne reservoirs of contaminant originate only at an off-site area of contamination and not from other soils should be examined further.

A summary of the uncertainties associated with the vapor and particle inhalation routes is given in Table 7-18.

Consumption rates of 200 g/day for vegetables and 140 g/day for fruit were derived in EPA (1989) and recommended for general assessment purposes. They include all fruits and vegetables and were derived from two principal sources: Foods Commonly Eaten by Individuals: Amount Per Day and Per Eating Occasion (Pao, et al. 1982), and 2) Food Consumption: Households in the United States, Seasons and Year 1977-1978 (USDA, 1983). Pao, et al. (1982) used the data from the USDA survey, which included interview responses from 37,874 individuals, to estimate total consumption and percentiles of home-grown fruits and vegetables. EPA (1989) identifies two principal sources of uncertainty with Pao's estimates:

• These data are from all consumers, only a small percentage of whom are also home gardeners. Those who home garden may have higher total rates of consumption.

Table 7-18. Uncertainties and sensitivities associated with estimating vapor and particle-phase air concentrations from contaminated soils.

Assumption/

Method Approach Rationale Uncertainty Comments

Exposure parameters inhalation rate of range of inhalation not much uncertainty uncertainty introduced by

23 m³/day; contact rates typically given expected due to these exposure durations of 9 and

fraction 0.75 in as 20-23 m³/day; con- choices 20 years because of their

central and 0.90 in factions based on time their role in volatilization

high end parameters at home surveys algorithm; otherwise

uncertainty more due to

methodologies estimating

air concentrations

Volatilization followed Used model developed Like PCBs, dioxin-like Chemical parameters H An analysis of model per-

by near or far-field by Hwang (1986) for are highly sorbed and and Koc are most un- formance suggests that the

dispersion for vapor volatilization of PCBs; persistent certain with an order soil to air algorithms may be

phase contaminants standard area-source of magnitude range underestimating air concen-

dispersion algorithms in estimated concentra- trations by a factor of 10.

for concentration tions; estimations also The amount of under-

sensitive to area, estimation cannot be

distance, and frequency known, since measurements

wind blows to receptor. would include soil emissions

and long-range transport

from other sources.

Wind erosion followed Used model based Assuming highly erodible Parameters associated No data to evaluate model

by same near or far-field on highly erodible soils may tend to over- the erodibility of soils results; however, particle

dispersion algorithms soils for dust flux estimate flux, but not can lead to a 2 order inhalation exposures were

for particle phase to estimate fluxes considering particles magnitude range for 1-2 orders of magnitude

contaminants for particle sizes of size > 10 m m would estimated concentrations; lower than vapor phase

< 10 m m underestimate total much less sensitivity exposures - certainty

airborne reservoir noted for other parameters may be less of an issue

Volatilization contaminants eroding if delivered contaminants As noted, the key uncer- More consideration of

or resuspension to exposure site volatilize or resuspend tainty is in the fate of the fate of delivered

of eroded assumed not to at site of exposure, of delivered residues contaminants is warranted.

contaminants volatilize or resus- then exposure site air

not considered pend to contribute would increase by a factor

to exp. site air of 2 to over a factor of

concentrations 10.

Evaluation: Model estimations of vapor-phase 2,3,7,8-TCDD and 2,3,4,7,8-PCDF in a rural setting resulting from emissions from low, background soil concentrations are orders of magnitude lower than observed urban air concentrations of these contaminants. The fact that they are lower is to be expected. However, an analysis of other available data suggests that, in fact, the models predicting air concentrations over soils may be underestimating such concentrations by an order of magnitude or more. This is only a speculation on the degree of underestimation, and in fact that underestimation is occurring. No data could be found on air concentrations over soils where it is definitely known that the soil is the only source of the dioxin-like compounds. The hypothesis put forth in this assessment is that the ultimate source of dioxin-like compounds in soils, vegetations, and food products are emissions into the air from industrial sources. Therefore, literature reports on air and vegetation concentrations are not only impacted by soil emissions, but by industrial emissions and long-range transport. Sensitivity analysis showed estimations to be sensitive to Koc and H, and also to key source strength and delivery terms such as areas of contamination and wind speed. Assuming these non-chemical specific parameters can be known with reasonable certainty for site-specific applications, the most uncertainty lies with chemical specific data. Alternate approaches for volatilization and air dispersion generally estimate comparable air concentrations. Approaches to estimate particulate phase concentrations are empirical and based on field data. They are based on highly erodible soils but are specific to inhalable size particles, those less than 10 m m. As such, they may overestimate inhalation exposures, but may underestimate the total reservoir of particulates, which becomes critical for the particle deposition to vegetation algorithms. Another area of uncertainty is the assumption that volatilized contaminants do not become sorbed to airbone particles - this is also critical because vapor phase transfers dominate plant concentration estimation. A final key area of uncertainty is that transported contaminants from a contaminated to an exposure site via erosion are assumed not to volatilize or resuspend at the exposure site - air borne exposure site concentrations may be underestimated as a result.

• USDA's survey only included information for 3 days from each respondent: products eaten the day before, the day of, and the day after the interview. Therefore, the results on a per day basis only include information for three days from respondents; what is required for long term exposure assessments is an amount eaten per average day over the course of a long time period, such as a year or a duration of exposure. EPA (1989) did not discuss whether this aspect of uncertainty might render the 200 and 140 g/day estimates over- or underestimates.

These total consumption rates were reduced considering fruit and vegetables which are "protected" and "unprotected". Protected fruit, for example, included citrus and cantaloupe, whereas unprotected fruit included peaches or apples. This distinction was made because evidence indicates very little translocation or residues to within the plant. It was assumed that there would be no exposure when the produce was protected. Again using data from Pao, et al. (1982) as summarized in EPA (1989), it was estimated that 44% of total fruit ingestion was ingestion of unprotected fruit and 74% of total vegetable ingestion was unprotected vegetables.

A final distinction was required which divided unprotected fruit/vegetables to those which grow underground and those which grow above ground. Different algorithms were used to transfer soil residues to plants depending on whether they were above or below ground. Using the same data once again, it was estimated that no fruits were grown underground (unprotected or protected), and that 37% of unprotected vegetables were grown underground.

The result of these two distinctions was to estimate total consumptions rates of unprotected fruit as no below ground and 88 g/day above ground consumption; for unprotected vegetables, total consumption included 76 g/day above ground and 28 g/day below ground.

The overall average fraction of total vegetable and fruit consumption which is homegrown is estimated as 0.25 and 0.20, respectively (EPA, 1989). EPA (1989) recommends 90th percentile assumptions for these parameters of 0.40 (vegetables) and 0.30 (fruit), which were assumed in the high end scenarios of this assessment. EPA (1989) notes a wide range of fraction homegrown for individual vegetables, 0.04-0.75, and fruits, 0.09-0.33.

All these assumptions discussed: total consumption rates, protected or unprotected, above or below ground, and fraction home grown, are probably reasonable for general assessment purposes as long as exposures are to the broad categories of fruits or vegetables, and not for individual fruits or vegetables. For a site specific assessment, there will likely be wide variability on the types of produce grown at home, what percentage of that is unprotected, and so on. Finally, and as is also true for beef and milk exposures, this assessment only considers the impact of home-grown fruits and vegetables. In rural settings, it is plausible that a large percentage of an individual's total fruit and vegetable intake comes from nearby and impacted sources, more than the 20-40% assumed in this assessment. If that is the case, than contact fractions should be set at 1.0, and exposures would increase 2-5 times from what they are estimated as in this assessment.

Several issues of uncertainty pertinent to the estimation of concentrations in below and above ground vegetation have been examined in other parts of this document and are not repeated here. Key issues include: 1) the uncertainty associated with empirical parameters, VG_ag and VG_bg, 2) the assumption that residues which volatilize from contaminated soils remain in the vapor phase and not partially partition into the vapor phase, 3) the possible underestimation of total particle reservoirs of contaminant in the air resulting from wind erosion of contaminated soils because the wind erosion algorithm only estimated suspension of inhalable size and not all particulates, and also because the possible effect of rainsplash onto vegetables low to the ground such as lettuce, was not considered, 4) for the stack emission source, uncertainties associated with air dispersion and deposition modeling using the COMPDEP model as discussed in Section 7.2.2., and therefore the subsequent impacts of soil-to-plant transfers, 5) for the stack emission and off-site soil source categories, air borne concentrations in the vapor and particle phases at the exposure site are assumed to only originate at the source of contamination (the off-site contaminated soil and stack emissions) and not on impacted soil at the exposure site - considering additional fluxes from impacted soils could lead to up to an order of magnitude higher concentrations in the vapor and particle phases.

Quantitative judgements as the uncertainties associated with these issues are difficult to make. An examination of experimental data in Section 7.2.3.8, where most of the vegetations were grown in well characterized conditions implied that the soil contamination models may be underestimating concentrations in both above and below ground vegetations. For above-ground vegetations, other evidence suggests that the models estimating air concentrations over contaminated soils may be underestimating such concentrations, which would explain the underestimation of above ground vegetations. On the other hand, the air-to-beef validation exercise described in Section 7.2.3.9 does lend quantitative credibility for the air-to-plant algorithms. While the soil contamination model may be underestimating vegetation concentrations, the literature evidence suggesting that below ground vegetations have higher plant:soil ratios than above ground vegetations, and that perennials have higher concentrations than annuals, was duplicated by the modeling approaches.

A summary of uncertainties associated with the fruit and vegetable ingestion exposure pathway is provided in Table 7-19.

Concentrations in beef and milk are a function of cattle ingestion of contaminated soil, pasture grass, and cattle feed. Therefore, previous sections on soil contamination, soil transport algorithms, and plant concentration estimation, are relevant to estimating beef and milk concentrations. Section 7.2.3.9 above is particularly relevant. This section described an exercise where air concentrations of dioxin-like compounds were routed through the food chain model to estimate concentrations in beef. Generally, that section showed that an air concentration of 0.019 pg TEQ/m³, speculated to be an appropriate air concentration for rural environments where cattle are raised for beef, translates to a whole beef TEQ concentration of 0.36 ppt, using the models and parameters of this assessment. The observed whole beef concentration, from three studies in the United States where TEQ concentrations in beef were taken from grocery store beef samples, averaging 0.48 ppt (when non-detects in the sample set were estimated as 1/2 detection limit; 0.28 ppt when they were estimated as 0.0). Section 7.2.3.1. on off-site soil impacts, including erosion from a site of contamination to another site and deposition of stack emitted particulates onto a site, describes uncertainties with estimating soil impacts from a distant source of contamination. Section 7.3.7. above summarizes uncertainties associated with estimating grass and feed concentrations, with further information on vegetation concentration uncertainty in Section 7.2.3.8.

For the bioconcentration algorithm itself, there is uncertainty with the parameters

Table 7-19. Uncertainties associated with vegetable and fruit ingestion exposure algorithms.

Assumption/

Method Approach Rationale Uncertainty Comments

Rates of fruits: 88 g/day above Protected/unprotected Much variability expected All parameters assumed are

ingestion ground unprotected, 0 distinction because resi- when using approach for a evaluated as reasonable for

g/day below ground unp., dues not expected to specific site when actual general exposure to broad

0.20-0.30 home grown; translocate; above/below home gardening can be categories of fruits and

veg: 76 g/day abv grd ground because procedures ascertained. vegetables.

unp. 28 g/day bel. grd. for soil transfers are

unp.; 0.25-0.40 home different

grown.

Below ground Uses empirical Separating below ground The VG_bg empirically des- Comparison with the literature

vegetable Root Concentration with above ground vegeta- cribes the difference in suggests estimates of below

concentration Factor which is function was critical and supported barley roots and bulky ground vegetables may be low

of Kow, VG_bg, an by the literature; approach underground vegetables; by an order of magnitude; how-

empirical correction based on laboratory experi- although assignment of ever, the trend that below

factor, and soil water ments with barley roots. 0.01 is rationally based, ground vegetables have higher

concentrations arguments presented could transfers from soil to plant

estimate it instead at as compared to above ground

0.10 or 0.001; algorithm vegetables was correctly

also a function of Kow, captured.

which is uncertain by

2 orders of magnitude;

most likely change in Kow

decreases concentration by

up to an order of magnitude

Vapor Phase Uses air-to-leaf factor transfer factor is Empirical correction factor, Limited literature data suggests

Transfer developed in laboratory a function of Kow and VG_ag, is necessary, but that above ground vegetation

for Above conditions for 14 com- H as experimentally values could also be low impacts from contaminated soil

Ground pounds transferred to derived for 14-compound or high, as above; like may be underestimated. The

Vegetation azalea leaves; empiri- experiment; air-to-beef algorithm above, transfers hypothesized cause is soil to

cally corrects for plants exercise in Sec. 7.2.3.9 critically a function of Kow; air impacts, not air to plant

like fruit/veg. that have shows that the empirical most likely alternate Kow impacts. The air-to-leaf

much less transfer to adjustments by the match would increase concentra- transfer factors are the most

inner parts as compared of predicted and observed tion estimates by up to critical since vapor transfers

to leaves; also empi- concentrations; results an order of magnitude. dominate above ground impacts. rically corrects for show that vapor trans- Experimental evidence recently

the demonstrated fers dominate plant developed by McCrady (1994)

high rate of transfer concentrations justifies use of VG_ag and

of the 14-compound numerical value of 0.01 used

experiments. for bulky fruits/vegetables.

(continued on next page)

Table 7-19. (cont'd)

Assumption/

Method Approach Rationale Uncertainty Comments

Particulate Model for soil contami- Algorithm developed for Wind erosion algorithm The basic approach given con-

Phase nation solves for air- radionuclide impact to developed for particle sizes taminant deposition rates is

Deposition borne particulate phase agriculture (Baes, et al., < 10 m m and might therefore defensible; model results imply

for Above concentration and applies 1984) but applied to other underestimate total deposi- particulate deposition is much

Ground deposition rate; COMPDEP contaminants sorbed to air- tions - not the case for less an important process than

Vegetation estimates deposition rate borne particulates; parti- COMPDEP modeling which vapor transfers for impacts to

for stack emission source culate deposition as mecha- simulates the range of above ground vegetations.

source category; plant con- nism of plant contamination particles; also does not

centrations based on wet speculated to be of concern consider rainsplash.

+ dry deposition rates, for 2,3,7,8-TCDD in early

plant mixing volumes, literature. Model results

canopy cover, retention show particulate deposition

of wet deposition, and less critical than vapor

washoff. transfers for plant impact.

Overall: All ingestion parameters assumed are evaluated as reasonable for general exposure to broad categories of fruits and vegetables. However, great variability is expected if using these procedures on a specific site where home gardening practices can be more precisely ascertained. A contaminant concentration ratio was defined as the concentration of contaminant in vegetation divided by the concentration in the soil. A comparison of the modeled ratios with those found in the literature showed that the modeled ratios tended to be lower for all vegetation (above and below ground fruit and vegetation, grass and cattle feed) by 1-2 orders of magnitude, although the literature data was not consistent. For example, the literature data showed different ratios as a function of soil concentration - lower soil concentrations had higher ratios, while higher soil concentrations had lower plant:soil ratios. Trends noted in the literature which were duplicated by the model include higher below ground vegetable ratios as compared to above ground vegetable ratios, and higher ratios for perennials (grasses, e.g.) as compared to vegetables. A key assumption in the vegetation algorithm, that dioxin-like compounds do not translocate from root to shoot, was verified by two experiments, although a third recently completed experiment (Huelster and Marschner, 1993) contracticted this conventional wisdom for zucchinis and squash. Vapor-phase transfers dominate vegetation concentrations. Evidence suggests that the methodologies and/or parameters used in this assessment may have underestimated the vegetative concentrations that result from contaminated soils. Further, the evidence suggests that the models underestimate air concentrations above soils by an order of magnitude, which leads to lower vegetation impacts. Other evidence suggests that the air to plant transfer algorithms do, in fact, estimate above ground vegetation concentrations appropriately. A critical empirical parameter was the above and below ground correction factors, VG_ag and VG_bg, both set at 0.01 for fruits/vegetables. These factors are justified for dioxins based on the fact that the experiments for derivation of the below ground empirical transfer factor and the above ground empirical transfer factor were conducted with thin barley roots and azalea leafs, respectively. Whole plant concentrations for these vegetations are likely to be much less than whole plant concentrations of bulky fruits and vegetables; hence the introduction of the VG parameters. Recent experimental evidence by McCrady (1994), where the uptake rates of vapor-phase 2,3,7,8-TCDD into different vegetations including grass, azalea, kale, tomato, pepper, and apple, did in fact show a much lower uptake rate for these bulky vegetations. The ratio of uptakes between the bulky fruit/vegetables and grass leaves was between 0.02 and 0.08, justifying the use of the VG_ag of 0.01 for fruits/vegetables in this assesment. A different assumption for VG, such as 0.10, would increase estimated concentrations and perhaps make concentration ratios more in line with literature values. Other experimentally derived empirical factors describing the transfer of compounds from soil to below ground vegetables and vapor-phase air to above ground vegetation were a function of contaminant Kow and H (H for above ground transfers). An alternate value of log Kow for 2,3,7,8-TCDD would more likely be higher than lower, given a literature range of 6.15 to 8.5, and a selected value of 6.64. Increasing log Kow tends to decrease below ground vegetation, by as much as an order of magnitude, while increasing above ground vegetation by as much as an order of magnitude.

estimating beef and milk concentrations: the beef/milk bioconcentration factor BCF, the soil bioavailability factor, B_s, and the parameters describing the cattle diet which include dietary fractions in soil, grass, and feed (the sum of the three adding to 1.00), and the degree to which these three are impacted by on-site soil and deposition conditions. Section 6.2.3., Chapter 6, described the results of sensitivity analysis of these parameters on beef and milk concentrations. It was shown that there is a small range of possible values for B_s and a small impact on results. Data indicates that range of values for BCF for 2,3,7,8-TCDD is 1 to 10, with a concurrent order of magnitude difference between the upper and lower values. The parameters describing cattle exposure to soils and vegetation at the site are also critical, with up to an order of magnitude difference in concentrations for the example exposure situations examined in Section 6.2.3. It is expected that cattle exposure assumptions can be reasonably described for a specific site. Therefore, the most uncertainty in the bioconcentration algorithm itself lies with the bioconcentration factor, BCF.

The whole beef and milk concentrations of 2,3,7,8-TCDD estimated with the stack emission source were lower than the other sources at 0.0005 ppt and 0.00006 ppt, respectively. The on-site demonstration scenario, where soil concentrations were set at background levels of 1 ppt, estimated beef and milk concentrations in 10^-2 and 10^-3 range, respectively. This is consistent with literature data on the concentrations of 2,3,7,8-TCDD in whole beef and milk. There was some literature data showing beef and milk concentrations near incinerators to be higher than concentrations where no incinerators or other known sources were present. Comparisons between impacts as noted in these references with the results of the demonstration scenarios cannot be done because information on the source strength in these references is not available. What can be stated, however, is that the emission factors (mass contaminant emitted per mass contaminant incinerated) from the hypothetical incinerator are comparable to emissions from incinerators having a high level of air pollution control, e.g., scrubbers with fabric filters, and that the feed rate of 200 metric tons per day is a midrange value (for more detail on the example emissions, see Section 3.3.3, Chapter 3). The literature articles noting more impact in the vicinity of incinerators were from the 1980s from Europe, and it is certainly plausible that the incinerators did not have a comparable level of air pollution control. Also, it should be remembered that actually measured concentrations of these compounds are the result of multiple sources impacting the cattle; the methodologies of this assessment such as the stack emission source category evaluate only the incremental impact for that source.

One other literature comparison that was made was comparing beef fat:soil and milk fat:soil concentration ratios developed for PBBs with those estimated for 2,3,7,8-TCDD in the demonstration scenarios. Such a comparison is thought to be valid since PBBs are similar in fate and bioconcentration tendencies to the dioxin-like compounds. In this comparison, differences in beef and milk bioconcentration tendencies appear to be captured. Fries (1985) found body fat:soil PPB and milk fat:soil PBB concentration ratios for dairy heifers to range from 0.10 to 0.37, and from 0.02 and 0.06, respectively. For body fat of beef cows, these ratios were 0.27 and 0.39. Analogous ratios were derived for the contaminated soil scenarios, and for beef and milk fat. For the contaminated soil demonstration scenarios, Scenarios 1-3, beef fat:soil and milk fat:soil ratios were 0.12 and 0.06, respectively. These appear a bit lower than the PBB ratios derived by Fries (1985). The interpretation of this result was that, again here was some evidence that models may be underestimating the impacts of soil contamination to air, and hence air to plants and plants to animals.

Section 7.2.4.6 evaluated other beef and milk bioconcentration models. It was found that most earlier efforts are quite similar to the model of this assessment, with simple mathematical transformations. Other efforts had considered cattle inhalation exposures and cattle ingestion of impacted water, and found them to be of minimal importance in estimating beef and milk concentrations. They were not considered in this assessment. Two efforts, that of Stevens and Gerbec (1988) and Fries and Paustenbach (1990), evaluated the practice of placing beef cattle on a grain-only diet for fattening prior to slaughter. Both assumed that the reduction in beef concentrations could be modeled as a first-order process with a half-life of around 115 days. With grain only diet periods of 120-130 days, they showed beef concentrations to be reduced by about 50%. A similar approach could be adopted for the models of this assessment. The general result that the fattening regime was estimated to reduce body fat concentrations by 50% was used in the air-to-beef validation exercise described in Section 7.2.3.9.

The air-to-soil algorithms of the stack emission source category, and the soil-to-air algorithms of the soil contamination source categories have both been highlighted as algorithms which may have uncertainties. These uncertainties are detailed in Chapter 6, Sections 6.3.3.7, 6.3.3.8, and 6.3.3.10. They uncertainty with regard to soil to air impacts is also discussed in this Chapter in Sections 7.2.3.7 and 7.2.3.8. Generally, it was found that the air-to-soil algorithms may be underestimating soil concentrations, while the soil-to-air algorithms may be underestimating air concentrations. As a result, an examination of model trends show a key dichotomy in the way the stack emission source category performed as compared to the soil contamination source categories. Specifically, soil alone accounted for about 90% of the milk and beef impacts for the soil source category, whereas soil accounted for only about 5% of the milk and beef impacts for the stack emission source category. Refinements to the model algorithms or the model parameters which would increase air concentrations resulting from soils, and increase soil concentrations resulting from depositions would narrow this gap.

Data on rates of milk and beef consumption were taken from surveys summarized in EPA (1989). Whereas the survey data may lead to adequate estimates for per capita consumption of these products, EPA (1989) cautions that farm families who home slaughter or who home produce dairy products may have higher consumption rates. Data is unavailable for these situations. Another consideration for application to real world rural situations is that farming and non-farming families may be obtaining cattle food products from local farms which may also be impacted by dioxin-like compounds. This possibility was not addressed in this assessment.

The fractions of meat or milk intake coming from the farmer's home supplies was determined in a survey of 900 rural farm households (USDA, 1966). The 0.44 (44%) of meat and 0.40 of dairy contact fractions from this survey were, appropriately, proportions of total dietary intake that is home-produced and consumed by farming families. Therefore, more certainty is expected for these contact fractions as compared to ingestion rates.

The trend analysis for the example scenarios in Chapter 9 indicated that the greatest exposures occur for beef, milk, and fish. Therefore, the rate of consumption of impacted beef and milk is critical. The range of beef fat consumption noted in surveys summarized in EPA (1989) is 14.9 to 26.0 g/day, but a single high consumption rate of 30.6 g/day was noted. If this high rate is more typical of home-producing farm families, then the value of 22 g/day selected for this assessment may be 28% low. The single high rate of 35 g/day of milk fat is significantly higher than the 8.9-10.7 g/day range noted in EPA (1989) and the 10 g/day ingestion rate for milk fat may be low.

A summary of uncertainties associated with the beef and milk ingestion pathways is given in Table 7-20.

The purpose of this discussion is to 1) briefly discuss how Monte Carlo procedures work and could be applied in exposure assessments and 2) summarize recent efforts by three investigators to apply Monte Carlo procedures to assessments involving dioxin-like compounds.

Basically, Monte Carlo is a generic statistical method which generates a distribution for an analytical output of a mathematical model using the distributions of the input variables. Computer simulations are used to repeatedly generate outputs based on parameter inputs, where values for parameters are selected from their distributions. The outputs are compiled and expressed as a frequency distribution. In the context of exposure assessment, a Monte Carlo application could involve developing distributions for each of the parameters in the exposure equation and generating a distribution showing how the exposure levels vary in the exposed population. The final distribution can be interpreted as the probabilities of one individual (randomly selected from the exposed population) experiencing various exposures. Since exposure levels are not only a function of the exposure parameters but also of the concentration in exposure media, another application of the Monte Carlo method would be to estimate the distribution of exposure media concentrations using mathematical models for fate and transport.

Monte Carlo techniques can be a powerful tool for expressing variability and evaluating scenarios in exposure assessments. However, its use requires detailed input data which is frequently unavailable. Although the procedure may make an analysis look more elegant, it may actually yield misleading results if based on poor data. Accordingly, exposure assessors should be very cautious when trying to apply Monte Carlo techniques or interpreting the results.

Generally, Monte Carlo procedures should be applied only when credible distribution data are available for most of the key variables. Distribution data refers to empirical information on the statistical variation of the variable that is relevant to the site assessed. Usually this data should be obtained from surveys conducted at the site of interest. However, data on human behavioral characteristics could be obtained from survey

Table 7-20. Uncertainties associated with beef and milk ingestion exposure algorithms.

Assumption/

Method Approach Rationale Uncertainty Comments

Ingestion rates 22 g/day beef fat Literature showed 14.9- Shape of distribution of Beef and milk home producers

10 g/day milk fat 26.0 g/day beef fat, and consumption not well may tend to ingest more than

18.8-43 g/day milk fat; defined; study and survey average families.

ranges developed from 3 showing 43 g/day milk fat

surveys less well documented than

other 2 surveys.

Contact rates 0.44 for beef fat Data from USDA survey Likely to be substantial Again, home producers may

0.40 for milk fat including percent of differences between families; obtain more than 44 or 40% of

annual consumption of some may not home slaughter. of beef and milk from their

beef and milk homegrown. own supplies.

Beef and milk Model of Fries and Paus- A key premise was Uncertainties associated with Section 7.2.4.6 shows how

fat concentra- tenbach (1990) used; bio- that 2,3,7,8-TCDD soil, pasture grass, and feed current approach is the same

tion model concentration factor bioconcentrates equally carry over into beef and milk as earlier approaches which used

multiplied by propor- in beef and milk fat; fat concentrations; other whole beef and milk biotransfer

tionally weighted concen- Fries and Paustenbach uncertainties with parameters factors and similar models for

tration in cattle diet, also developed key as noted below. particle deposition impact to

which is composed of parameters used here soil and vegetation. Also,

soil, grass, feed. as well. air-to-beef validation exercise lends important credibility to

approach (Section 7.2.3.9)

Key parameters Bioconcentration factor, BCFs developed from Of three noted, BCF most un- Fattening of beef cattle prior

and assumptions BCF for dioxin-like com- data in Mclachlin, et certain; cattle diet assump- to slaughter could result in 50%

pounds; soil bioavaila- al. (1990); Bs from tions also critical; but site- or more reductions in fat concen-

bility Bs of 0.65; and Fries and Paustenbach specific information could trations; considered in air-to-

cattle diet fractions (1990); diet fractions reduce uncertainty due to beef validation, but not in

in soil, grass, and generalized from infor- cattle exposures. general assessment.

feed on lactating and grazing

cattle.

Key associated For soil source categories, the BCF is applied to for soil models, uncertainties A key dichotomy which arises

models these are the soil-to-air, a weighted average in air concentration of vapors from these key associated models

and air-to-plant algorithms; concentration of dioxin- and particles; for stack emission is the role of soil in the beef

for stack emissions, these like compound in cattle source, soil concentrations may concentration; for the soil source

are air-to-soil and air-to- diet, which consists be underestimated. category, cattle soil ingestion

plant of soil, grass, feed explains about 90% of beef fat

concentrations; for the stack

emission source, it explained

only about 5% of the

concentration.

Table 7-20 (cont'd).

Overall: The air-to-beef validation exercise described in Section 7.2.3.9 lends credibility to the algorithms estimating transfers of airborne dioxin-like compounds to vegetations cattle consume, and also to the bioconcentration model taking vegetation and soil concentrations and translating them to beef concentrations. However, uncertainties appear to exist for the soil source categories in modeling the soil to air transfers of dioxin-like compounds leading to an underestimation of air concentrations. This might lead to a requisite underestimation of beef/milk concentrations for the soil source category. Section 7.2.3.9 also shows that the air to soil deposition algorithms may be underestimating soil concentrations. Since beef concentrations are dominated by vegetation contributions to their diet - soil diet fractions are less than 10% - an underprediction of soil impacts for the stack emission source category may not have a great effect on beef/milk concentrations. Another literature comparison was with beef fat:soil and milk fat:soil concentration ratios, where Fries (1985) had developed such ratios for a farm known to be contaminated with PBBs, compounds similar in fate and persistence, and bioaccumulation tendencies, as the dioxin-like compounds. Field data showed ratios of 0.10-0.39 for beef and dairy cow body fat:soil, and 0.02-0.06 for milk fat:soil. In contrast, modeled ratios in the example soil contamination scenarios for 2,3,7,8-TCDD were 0.12 for beef fat:soil and 0.06 for milk fat:soil. Comparison with earlier modeling approaches showed that the current approach is the same as earlier approaches, although mathematically formulated differently. Earlier approaches also estimated cattle dose of 2,3,7,8-TCDD from contaminated air (directly) and contaminated ground water - these earlier estimations showed these contributions to be minimal, and they were not considered in this assessment. Early efforts in the literature did not consider vapor transfers to vegetations; one later assessment did include vapor transfers, and a key result in that assessment, as well as this one, is that vapor transfers are critical for beef/milk impacts. Finally, earlier assessments considered the practice of fattening beef cattle prior to slaughter by feeding them residue-free grains. These efforts estimated over a 50% reduction in beef concentration due to residue degradation or elimination and/or dilution with increases in body fat. The demonstrations scenarios in this assessment did not consider this practice.

information based on populations distant from the site, if comparability can be established.

Paustenbach et. al. (1992a) used Monte Carlo procedures to develop soil cleanup levels for 2,3,7,8-TCDD at residential and industrial sites. The following exposure pathways were included: dermal contact, soil ingestion, dust inhalation and fish ingestion. For each parameter a range of values was identified (on the basis of reported values in the literature) and a uniform distribution assumed. These assumptions are summarized in Table 7-21. For the residential scenario, the soil level corresponding to the 50th percentile (defined as 50% of the population being exposed below a risk of 10^-5) was 17 ppb and the 95th percentile was 7 ppb. For the industrial scenario (outdoors), the soil level corresponding to the 50th percentile was 160 ppb and the 95th percentile was 50 ppb.

Anderson et. al. (1992) used Monte Carlo procedures to describe the distribution of exposures to 2,3,7,8-TCDD occurring in various U.S. population segments as a result of ingesting fish caught near pulp and paper mills. The populations considered were all U.S. residents, all sportfishermen, U.S. residents living near (within 50 km) mills, and sportfishermen living near mills. The distributions for the various parameters were derived by either fitting idealized curves to empirical data or using personal judgement. These distributions are summarized in Table 7-22. The distribution of 2,3,7,8-TCDD concentrations in fish was derived from data collected in EPA's National Study of Chemical

Table 7-21. Distributions for a Monte Carlo exercise which developed soil cleanup levels at residential and industrial sites.

Source: Paustenbach et. al. (1992a); uniform distributions assumed over ranges shown.

Table 7-22. Summary of Monte Carlo distributions used in a fish consumption assessment.

Source: Anderson et. al. (1992).

as exposure parameters. Distributions were developed for input factors and Monte Carlo Residues in Fish (EPA, 1992b). The following 50th and 95th percentile risks were estimated (using EPA cancer potency values):

all US residents - 1 x 10^-9 & 3 x 10^-7

near mill residents - 4 x 10^-8 & 2 x 10^-6

all sportfishermen - 2 x 10^-8 & 3 x 10^-6

near mill sportfishermen - 6 x 10^-7 & 2 x 10^-5

McKone and Ryan (1989) developed an exposure assessment procedure based on simple steady state transfer factors called PEFs or pathway exposure factors. These factors were applied to two paths: air/plant/food and soil/plant/food. This is an example of Monte Carlo techniques being applied to estimate exposure media concentrations as well techniques were used to estimate the distribution of exposures. The procedure was demonstrated using 2,3,7,8-TCDD and four pathways: ingestion of fruit/vegetables, grains, meat and dairy products. The distributions used for the various input parameters are summarized in Table 7-23.

The three articles discussed above differ widely in how they have applied Monte Carlo methods, particularly in the selection of input parameter distributions. In some cases, it appears that uniform distributions were assumed due to the lack of data needed to support more complex distributions. The central values in these ranges probably occur more often than those near the ends, so the uniform distribution assumption probably underestimates the occurrence of central values and overestimates the occurrence of values near the ends of the distribution. Clearly more data are needed to better support input parameter distributions.

These three articles are just a small set of the growing body of literature on the topic of applying Monte Carlo methods to exposure and risk assessments. For example, the application of Monte Carlo methods to problems involving contaminated groundwater and related exposure pathways such as ingestion, indoor air inhalation and dermal contact with water has recently been examined (McKone and Bogen, 1991). Although this work does not deal specifically with dioxin, it may be informative to readers generally interested in Monte Carlo procedures. Similarly, Paustenbach has published additional articles dealing with the application of Monte Carlo methods to exposure problems involving other chemicals (Pasutenbach et al. 1991; Paustenbach, et al., 1992a). Burmaster has also published numerous articles on this topic which may be of general interest to readers (ie. Burmaster and Stackelberg, 1991).

Table 7-23. Summary of Monte Carlo distributions used in food chain study.

1. Geometric Standard Deviation

Source: McKone and Ryan, 1989.

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