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This chapter addresses uncertainty in dioxin exposure assessment performed with the methodologies presented in this document. Some discussion of the issues commonly lumped into the term "uncertainty" is needed at the outset. The following questions capture the range of issues typically involved in uncertainty evaluations:

(1) How certain are site specific exposure predictions that can be made with the methods?

(2) How variable are the levels of exposure among different members of an exposed local population?

(3) How variable are exposures associated with different sources of contamination?

The emphasis in this document is in providing the technical tools needed to perform site-specific exposure assessments. For the assessor focusing on a particular site, question (1) will be of preeminent importance. Therefore the emphasis of this Chapter is to elucidate those uncertainties inherent to the exposure assessment tools presented in this document. This chapter examines the capabilities and uncertainties associated with estimating exposure media concentrations of the dioxin-like compounds using the fate, transport, and transfer algorithms, and also identifies and discusses uncertain parameters associated with human exposure patterns (contact rates and fractions, exposure durations, etc.).

Section 7.2 focuses on the fate, transport, and transfer algorithms of this assessment, which has the following subsections:

1. the variability and uncertainty with chemical-specific parameters (Section 7.2.1);

2. uncertainty issues associated with the use of the COMPDEP model for air transport modeling for the stack emission source category (Section 7.2.2); discussions of the COMPDEP model and its application in this assessment is discussed in detail in Chapter 3;

3. the reliability of the models to predict exposure media concentrations, looked at by comparing estimated exposure media concentrations with those found in the literature (Section 7.2.3);

4. the similarity and differences for other modeling approaches (Section 7.2.4);

Section 7.3. discusses uncertainties and variabilities with key exposure parameters, and is organized on a pathway-by-pathway basis. This section also provides a general overview of all key uncertainties with each pathway.

A site specific assessment will also need to address the variability of risks among different members of the exposed population, the second key question above. The level of detail with which this can be done depends on the assessors knowledge about the actual or likely activities of these residents.

In this document, one approach to evaluating this variability is demonstrated. Separate "central" and "high end" scenario calculations are presented to reflect different patterns of human activities within an exposed population. "Central" scenarios are constructed to represent typical behavior patterns for residential exposures in a hypothetical rural setting. "High end" calculations focus on a farming scenario where individuals raise much food for their own consumption, in the same rural area. It should be emphasized that high end calculations could also have been developed for residential exposures by making, for example, higher range assumptions about duration of residence or contact rates with the contaminated media.

Indeed, this would be recommended for an assessment where considerable emphasis was placed on residential exposures. The key issue with regard to intra-population variability is that it is best (if not only) addressed within the context of a specifically identified population. If such information is available, a powerful tool that can be used to evaluate the variability within a population is Monte Carlo Analysis. Section 7.4. reviews 3 recent Monte Carlo studies which have done for exposure to 2,3,7,8-TCDD.

Assumptions on distributions of exposure patterns and fate and transport parameter distributions are described, as are the results of their analyses. Aside from this review, this chapter does not address question (2) in any further manner.

With regard to question (3), this document does not present a detailed evaluation of how exposure levels will vary between different sources of release of dioxin-like compounds into the environment. Chapter 2 of Volume II of this assessment does examine sources of release of dioxin-like compounds into the environment.

This document, Volume III, does present methodologies for three types of sources - soil, stack emissions, and effluent discharges into surface water bodies. While this document does demonstrate the methodologies developed for these sources with source strengths and environments crafted to be plausible and meaningful, there is still a great deal of variability on both the source strengths and on the environments into which the releases occur.

For example, the frequency with which farms and rural residences are near stack emissions of dioxin-like compounds is not addressed. The scenario calculations in Chapter 5 are intended to be illustrative; the exposure levels that are obtained there are not intended to be typical of actual exposures for the sources and pathways assessed.

Nonetheless, some readers might ideally wish information on both the magnitude of actual exposures and the variability of these exposures associated with different sources of dioxin-like compound release into the environment. However, the analysis presented in this chapter cannot support so broad a goal. Representative data to address the variation of dioxin exposures are becoming available for sources as well as exposure media.

The compilation of environmental and exposure media concentrations presented in Chapter 3 of Volume II of this assessment displays the range of measured concentrations in the environment. The careful selection of certain literature reports to represent concentrations of dioxin-like compounds in background conditions, described in Chapter 5 of Volume II, is one way such environmental measurements can be used.

More detailed examinations for specific sources was done in Volume II of this assessment. References to EPA and other assessments on dioxin-like compounds have been made throughout this document, such as those related to soil exposures (Paustenbach, et al., 1992), exposures to contaminated fish (EPA, 1991a), exposures resulting from land disposal of sludges from pulp and paper mills (EPA, 1990b), just to name a few.

Still, studies comparing and ranking different sources and exposure patterns, and elaborations on ranges of source strengths and exposures, are generally not available. Information in Volume II of this assessment and procedures for source specific evaluations in Volume III can provide others with information and tools to begin such analysis.


This section examines issues pertinent to the certainty with which exposure media concentrations can be estimated using the fate, transport, and transfer algorithms of this assessment. Section 7.2.1. gives a brief overview of the variability and uncertainty associated with model parameters specific to the compounds themselves. Section 7.2.2. discusses uncertainty issues associated with the COMPDEP model, which was used in the stack emission source category. Section 7.2.3 evaluates the capabilities of the fate, transport, and transfer algorithms estimating exposure media concentrations by making comparisons of modeled and measured concentrations. Section 7.2.4. reviews other modeling approaches for estimating environmental and exposure media concentrations, and compares their performance with that of the models used in this assessment.

7.2.1. Uncertainties and Variabilities with Chemical-Specific Model Parameters and Assumptions

This assessment assumed that levels of dioxin-like compounds in soil and sediment were constant over the period of exposure, with two exceptions. One circumstance was when contaminated soil eroded from one site and deposited on a site of exposure nearby - the off-site source category.

The other was when stack emitted particulates deposited onto a site of exposure - the stack emission source category. In both these instances, it is assumed that only a relatively thin layer of surface soil would be impacted, and that this thin layer is subject to dissipation processes - erosion, volatilization, possibly degradation. Data in Young (1983) implied a soil half-life of 10 years for surficial 2,3,7,8-TCDD residues, although the circumstances of the soil contamination were not analogous. Specifically, a 37 ha test area at the site had received an estimated 2.6 kg of 2,3,7,8-TCDD over a two year period.

Soil sampling which occurred over 9 years from the last application suggested that less than 1 percent remained at the test area. Although Young hypothesized that photodegradation at the time of application was principally responsible for the dissipation of residues, other mechanisms of dissipation including volatilization, erosion, and biological removal may also have contributed to the loss of residues. Soil sampling over time after application implied a dissipation half-life of 10 years for soil residues of 2,3,7,8-TCDD. This value was assumed to apply to the other dioxin-like compounds, introducing further uncertainty.

Section 2.6.1, Chapter 2 in Volume II of this assessment, reviewed the literature on degradation of dioxin-like compounds. As discussed, biological transformations as well as chemical processes (oxidation, hydrolysis, and reduction) do not appear to result in substantial degradation of these compounds. There is evidence of photolysis, particularly when dissolved in solution and when organic solvents are present. Most of these data are specific to 2,3,7,8-TCDD, introducing further uncertainty when applied to the other dioxin-like compounds.

Dissipation of surficial residues could translate to lower soil-related exposures including particulate inhalations, soil ingestion, and soil dermal contact. However, it is not clear that reductions in exposure would, in fact, occur, particularly if the soil is contaminated below the surface. Processes such as wind erosion, soil erosion, or volatilization originating from deeper in the soil profile, could serve, in a sense, to replenish reservoirs at the soil surface. Depositions back onto soils from other soils, or depositions from distant sources, also replenish soils. Given very low rates of degradation (for all degradation processes except photolysis), the assumption of no degradation is reasonable with moderate, but unquantifiable uncertainty.

In evaluating an assumption of no degradation, another issue to consider is the depletion of the original source of contamination. For the stack emission and effluent discharge source categories, the assumption is made that steady releases occur throughout the period of evaluation - the exposure duration. Therefore, depletion of the original source is not an issue. For the soil source category, it is assumed that the reservoir of contaminant is constant throughout the duration of exposure. If such a duration is assumed to be very long, then degradation or dissipation of soil residues would be more critical than if the duration were relatively short. Uncertainties associated with the duration of exposure are discussed in Section 7.3.1. below.

Also, Section 6.4 in Chapter 6 evaluated the assumption of a constant soil concentration by estimating the time it would take for a 6-inch reservoir of soil contamination to be depleted, using the dissipation algorithms of this assessment. These algorithms include volatilization, soil erosion, and wind erosion, with lesser releases due to biological uptake, and leaching and runoff. It was found that it would take over 90 years to deplete a 6-inch reservoir, lending some credibility to a non-degradation assumption if the exposure duration were in the range assumed for the demonstration scenarios of this assessment, 20 years.

A critical contaminant parameter required for the procedures in this assessment is the octanol water partition coefficient, Kow, although none of the fate and transport algorithms directly require a Kow. Two empirical biota transfer parameters are, however, a function of Kow. These are the RCF, or Root Concentration Factor, which estimates the transfer of contaminant from soil water to root, and the Bvpa, the air-to-leaf vapor phase transfer coefficient which estimates the transfer of vapor phase contaminants to vegetations.

Log Kow estimates for dioxin-like compounds range from 6.00 to 8.5, with higher log Kow associated with higher chlorination. However, this is not a certain parameter. Estimates in literature for 2,3,7,8-TCDD, for example, range from 6.15 to 8.5. Two biota transfer coefficients are used to estimate fish tissue concentrations based on water body sediment concentrations: the Biota Sediment Accumulation Factor, BSAF, and the Biota Suspended Solids Accumulation Factor, BSSAF. There are no empirical relationships which estimate these as a function of the more common Kow for dioxin-like compounds. Rather, values were assigned based only on experimental and field data.

Needless to say, most of the data available was for 2,3,7,8-TCDD, leaving large gaps for other compounds. Also, there is no data available for estimating the BSSAF, a parameter proposed in EPA (1993) which was used in the effluent discharge source category. The BSSAF was set equal to the BSAF for this assessment. Field data including bottom sediment concentrations and concurrent fish concentrations were used to determine values for BSAF.

The limited field data available for BSAF suggests values in the range of 0.03 to 0.30 for 2,3,7,8-TCDD, with higher values approaching 1.00 indicated for bottom feeders (catfish, carp, etc.), and decreasing values as the degree of chlorination increases - limited information suggests values in the 10-3 to 10-2 range for hexa- through octa- CDDs and CDFs. Data on PCBs suggest that BSAFs are higher than those of CDDs and CDFs by an order of magnitude and more, and that the trend with increasing degrees of chlorination is not the same. The data indicates that BSAFs for PCBs increase from dichloro- through hexa- or perhaps hepta-chloro PCBs, and decrease thereafter.

A bioconcentration factor, BCF, translates the average contaminant in the diet of the cattle into a beef or milk fat concentration. Experimental rather than field data was available for estimates of BCF for dioxin-like compounds. Farm animals were fed known quantities of these compounds and their body tissues and milk were monitored over time to arrive at BCFs. Data showed that the BCF decreased to below 1.0 as the degree of chlorination increased. A experimental data set, including analysis of 16 of the 17 dioxin-like congeners, described in McLachlin, et al (1990), was used to assign BCF values for this assessment. Limited data showed PCB BCFs to be the same order of magnitude, although trend data for increasing degrees of chlorination was not available.

Obviously, a degree of uncertainty is introduced when relying on these empirical bioconcentration or biotransfer coefficients to estimate concentrations in fish, beef, and milk. The variability in the data suggests an order of magnitude range of variation may results from use of these parameters.

Another important chemical-specific parameter that can be estimated from Kow or estimated experimentally is the organic carbon partition coefficient, Koc. Koc describes the steady state partitioning between soil or sediment organic carbon and water; it impacts the volatilization flux from soils, and the partitioning between suspended sediment and water in the water column. Koc is used to estimate in-situ partitioning using a fraction organic carbon in the soil or sediment, OCsl, OCsed, and OCssed, as Koc*OCsl, etc.

The resulting chemical-specific parameter is termed the soil (or sediment) partition coefficient, Kds (or Kdsed, Kdssed). The empirical equation used to estimate Koc from Kow in this assessment was derived by Karickhoff (1979). This equation was chosen over others available (Lyman, 1982) because it was derived from laboratory testing of 10 hydrophobic contaminants. Others available would have led to lower estimates of Koc.

For example, using a relationship developed by Kenaga and Goring (1980) would estimate a 2,3,7,8-TCDD Koc (given log Kow for 2,3,7,8-TCDD of 6.64) of 97,500. The Koc for 2,3,7,8-TCDD estimated for this assessment using Karickhoff's relationship was 2,700,000. Some data implies that this estimate itself may be low for 2,3,7,8-TCDD. Studies reviewed in Section 2.4.5., Chapter 2 of Volume II of this assessment, particularly those Jackson, et al. (1986) and Lodge (1989), indicate 2,3,7,8-TCDD Koc estimates in the range of 20,000,000 to greater than 30,000,000.

Another critical contaminant parameter is the Henry's Constant. Table A-1, Appendix A of Volume II of this assessment, provides estimates of Henry's Constants, H, for dioxin-like compounds, most of which were estimated given vapor pressure and water solubility data. As seen, the PCDDs and PCDFs were in the 10-6 to 10-5 atm-m3/mol range, while coplanar PCBs were in the 10-5 to 10-4 range, with one high value at 3x10-3 atm-m3/mol.

Finally, the contaminant molecular diffusivity in air is required for estimates of volatilization flux from soils. The molecular diffusivity in air is set at 0.05 cm2/sec for all dioxin-like compounds. Molecular diffusivity is a property of both the chemical and the medium. It represents the propensity of a chemical to move through a medium. It is recognized to be largely a function of molecular weight. The values selected are evaluated as reasonable for all dioxin-like compounds, since the molecular weight for these compounds are similar.

7.2.2. A Discussion of Uncertainty Issues Associated With Use of COMPDEP for Transport and Dispersion of Stack Emitted Contaminants

Air dispersion and deposition analysis was performed using the COMPDEP Model. The model is intended to give approximate estimations of atmospheric dispersion and wet and dry deposition flux, and does not give absolute values. The model is used in the context of predicting future states based on known characteristics of geographical location, local meteorological conditions, temporal rates of emissions, and the physical description of the facility.

Atmospheric dispersion in COMPDEP is modeled using the common Gaussian plume model. Downwind concentrations of the dioxin-like chemicals are calculated as a function of stack height, the mass emission rate, the wind speed, and general atmospheric conditions. The Gaussian model assumes that the emission concentrations predicted by the model will fit a normal distribution. The principal assumptions in the Gaussian model are (Kapahi, 1991):

. The air concentration of the chemical at a fixed distance from the source is directly proportional to the emission rate from the source;

· The air concentration of a given chemical is inversely proportional to the wind speed corresponding to the effective height of release of the chemical into the air;

· The predicted ground-level concentration of the chemical approaches zero at large distances from the initial point of release.

· The model is steady-state.

· The model assumes constant wind speed, wind direction, and atmospheric stability over time and space for a given time period.

In general the stochastic features of the Gaussian plume model have been shown to predict annual average ambient air concentrations of a chemical emission from an industrial source to within a factor of one-order of magnitude of measured values, and in some cases, within a factor of 3 to 4-fold of field measurements (Cohrssen and Covello, 1989). This modeling error spans both sides of the predicted concentration, that is, the actual concentration may be plus or minus this amount of the predicted value. The most sensitive aspects to variability in modeled predictions of ambient air impacts, if emissions are held constant, are stack height (height of the release), and terrain (flat verses complex topography).

To investigate modeling variability, EPA placed a prototype hypothetical hazardous waste incinerator in flat terrain and elevated terrain in geographical areas around the U.S. (EPA, 1991b; analysis conducted with the Industrial Source Complex, or ISC, model, which is coded into the COMPDEP model). Then the stack height was varied at these particular locations. Numerous runs were made at twelve specific sites to compare and contrast the influence of stack height and terrain on predicted ambient air concentrations of various mass emission rates of specific inorganic pollutants. A series of tables were developed from this sensitivity analysis from which the numerical estimation of the variability as a function of stack height and terrain can be inferred.

When the hypothetical hazardous waste incinerator was modeled in flat terrain, e.g., topography within a distance of 5 km is not above the height of the stack, and the stack height was varied from 4 meters to 120 meters, the variability in the predicted ambient air concentration spanned two orders of magnitude (100). The lower stack height resulted in a predicted ambient air concentration that was 100 times greater than the concentration predicted using the tallest stack height.

When the hypothetical hazardous waste incinerator was located in complex terrain over the same range of physical stack heights, the variability in estimated groundlevel concentration of the subject pollutant spanned two orders of magnitude (100-fold). In the latter case the stack height was computed as the terrain-adjusted stack height by subtracting from the physical stack height the influence of terrain on plume rise.

From the limited sensitivity analysis of hazardous waste incinerators, it can be assumed that the predictions of spacial ground-level ambient air concentrations of dioxin-like compounds could differ from values in Table 3-18 by two-orders of magnitude in consideration of changes in stack height or changes in terrain. For example, Table 3-18 shows that the maximum annual average ambient air concentration of 2,3,7,8-TCDD predicted near the hypothetical incinerator is approximately 10-11 m g/m3 for the stack height of 30.5 meters, and assuming flat terrain.

If only the stack height is varied from 20 meters to 120 meters, and all other modelling parameters are held constant, then the predicted ambient air concentration would be approximately 10 times greater and 10 times less than the estimated concentration, respectively. The uncertainty is broader when considering the influence of topography on predictability of the ground-level concentrations from the model.

If only terrain elevation is varied at a distance of 5 km from the hypothetical incinerator from zero elevation to 30.5 meters, e.g., the height of the stack, then the predicted ambient air concentration of 2,3,7,8-TCDD would be approximately ten times greater. The tables derived in the hazardous waste incineration analysis have a limitation of elevation of terrain to the height of the stack.

The most uncertain aspect to the modeling is the estimation of dry and wet deposition flux of dioxin-like compounds on the vicinity of the hypothetical incinerator. Contributing most to this uncertainty seems to be the settling velocities and scavenging coefficients estimated for specific particle size diameters (Cohrssen and Covello, 1989; Doran and Horst, 1985). Seinfeld (1986) found that particles over 20 microns in diameter settle primarily by gravity, whereas smaller particles deposit primarily by atmospheric turbulence and molecular diffusion. Considerable, but non-quantifiable, uncertainty exists with respect to deposition velocities of particles 0.1 to 1.0 microns in diameter (Seinfeld,1986).

The uncertainty is difficult to define. The wide variation of predicted deposition velocities as a function of particle size, atmospheric turbulence and terrain adds to this uncertainty (Sehmel, 1980). However, Gaussian plume dispersion models have been field validated for their ability to spatially predict dry deposition flux over some specified distance (Doran and Horst, 1985).

In a series of field experiments conducted by Pacific Northwest Laboratory (Doran and Horst, 1985), zinc sulfide was used as a depositing tracer gas, and sulfur hexafluoride was used as a non-depositing tracer gas to compare and contrast modeling results with field measurements of dry deposition and atmospheric diffusion of the gases. The tracer was released from a height of 2 meters, and all releases were made under relatively stable atmospheric conditions.

Five sampling stations were located downwind of the release from 100 to 3200 meters. The results of these experiments showed good agreement with the predicted verses the measured deposition of the tracer ZnS. The overall correlation coefficient between predicted and measured deposition concentration was found to be 0.82 (Doran and Horst, 1985), but the models marginally over-predicted deposition flux near the source of release, and under-predicted deposition flux at 3200 meters.

Travis and Yambert (1991) have evaluated the uncertainty in modeling the dry deposition flux of particulates using four standard Gaussian plume dispersion models. Since deposition flux is dependent on deposition velocity for a given particle mass and diameter, comparisons were made between model-generated deposition velocities and measured values found in the open literature for particles ranging from 0.01 to 30 microns in diameter.

It was found that measured deposition velocities for a given particle size in the scientific literature exhibit variability spanning roughly two orders of magnitude. The analysis of the mean predicted deposition velocities to mean measured values showed that most measured data exceeded the predicted data for all four models. Moreover, the models underestimated the mean deposition velocities for particles in the range of diameters from 0.05 to 1.0 microns.

Similar uncertainty probably exists with regard to scavenging of various diameter particles by various intensity of rainfall. Seinfeld (1986) has calculated scavenging coefficients in terms of the removal efficiency of particles of a given size by rain droplets having a given momentum. Seinfeld (1986) found that the scavenging coefficient of a given particle diameter corresponding to a given rainfall intensity can be calculated based on physical laws, but there is a complete absence of research data to verify these calculations. Hence it is not possible to address the accuracy nor uncertainty of the wet deposition flux estimated in Tables 3-19 and 3-20.

7.2.3. Comparing Model Estimations of Exposure and Environmental Media With Those Found in the Literature

The fate, transport, and transfer models presented in this document may attain a measure of credibility (beyond that due to the integrity of their formulation and careful assignment of model parameters) if it can be shown that estimations of environmental and exposure media concentrations are consistent with those found in the literature. Some of those comparisons can use the exposure media concentrations generated in the demonstration scenarios because the source strength terms of the demonstrations were crafted to be meaningful.

Specifically, the on-site source category was demonstrated with basin-wide soil concentrations of 1 ppt, which are characterized as background soil concentrations. The off-site source category was demonstrated with a bounded area of high soil concentrations of 1 ppb. This was also supported by literature showing this that sites of high soil contamination contained dioxin-like compounds in the ppb range. The effluent discharge source category had substantial data from the 104 pulp and paper mill to both assign model parameters (effluent and receiving water body flow rates, etc.) and source strengths (mg/hr release of 2,3,7,8-TCDD).

Other tests are conducted below which do not use results from the demonstration scenarios. It is clearly stated up front that discussions below are not described as validation exercises. Validation exercises would require specific data sets on source strengths, environmental characteristics, and environmental impacts. None of the tests contain complete and accurate site-specific information which would be required for validation testing. The one exception might be the exercises undertaken to evaluate the effluent discharge source category; details on that exercise can be found in Section below. The impact to soils of point source releases of dioxin-like compounds

For the stack emission source category, emitted contaminants settle onto exposure and watershed soils and a resulting soil concentration is estimated. For the off-site soil source category, contaminated soils from a bounded area of soil contamination are assumed to migrate via erosion and impact the soils of a nearby exposure site. This section examines the model algorithms for estimating impacts to nearby soils from these two sources.

No data could be found which linked stack emissions to an incremental impact to nearby soils. However, it may be possible to evaluate the algorithm estimating steady state soil concentrations of dioxin-like compounds given a rate of deposition - the deposition rate is determined in this methodology using atmospheric dispersion/deposition modeling. The soil impact algorithm mixes the modeled deposition rate, in mass/area-time units or m g/m2-yr, into a defined zone of mixing, in units of length or cm, and assigns a rate of dissipation to the depositing residues, in units if time-1 or yr-1, to estimate a steady state soil concentration. The zone of mixing for untilled soils in this assessment is 1 cm and the dissipation rate for dioxin-like compounds is assumed to be 0.0693 yr-1, which corresponds to a half-life of 10 years.

In evaluating the beef bioconcentration algorithm below in Section, a side evaluation looked at the algorithm for estimating soil concentration from deposition of dioxin-like compounds. Starting with an air profile crafted to be characteristic of rural environments, this evaluation showed model predictions of soil concentrations ranged from a factor of 2 to about a factor of 10 lower than observed soil concentrations typical of a rural environment (i.e., observed concentrations are twice as high to about ten times higher than predicted concentrations). This would imply that the model and/or the parameter assignments are in error for the algorithm for estimating depositional impacts to soils. Section below offered three most likely areas which would explain this shortfall:

1) 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 implies that such a half-life may be appropriate for dissipation of residues from a bounded area of contamination, but perhaps not appropriate for background evaluation as was done in Section below. In that case, dissipation mechanisms such as volatilization, wind or soil erosion, and so on, may not be losses from the background setting, while they would be losses from a bounded area. Therefore, the algorithm may not be in error, but rather the half-life of 10 years might be too short for estimating background depositional impacts,

2) the soil impact algorithms do not consider dry deposition of vapor-phase dioxins. No information could be found on such depositions, and the impact of not considering this loading could not be made, and

3) detritus recycling is not considered. A simple exercise in Section showed that this loading might be equal to about 20% of background deposition loadings.

For the off-site soil source algorithm, contaminated soils erode onto a nearby site of exposure site and mix into a depth of either 5 cm for untilled conditions or 20 cm for tilled conditions. The 5-cm depth was chosen for this source category based on the hypothesis that erosion is more turbulent process than atmospheric deposition, and that there may be mixing of contaminated soil from the site with clean soil between the contaminated and the exposure site. A contaminant concentration ratio is defined for purposes of this discussion as the ratio of soil concentration at the site of exposure to the soil concentration at the site of contamination. For example Scenario 3, soil eroded from a 40,000 m2 (10-acre) contaminated site was assumed to partially deposit onto a 40,000 m2 exposure site. The contaminant concentration ratio was 0.28 for the 5-cm depth of mixing at the site of exposure and 0.08 for the 20-cm mixing depth.

Data to rigorously validate the approach taken in this assessment is unavailable. However, there have been documented evidence of migration of 2,3,7,8-TCDD away from industrial sites with soil contamination of 2,3,7,8-TCDD, resulting in off-site soil contamination. Off-site soil concentrations of concern were identified in 7 of 100 Tier 1 and Tier 2 sites of the National Dioxin Study (EPA, 1987). The study noted that in most cases, 2,3,7,8-TCDD had not migrated off-site. Most, but not all, Tier 1 and 2 sites did have some off-site soil sampling without detection. It should be noted, however, that soil detection limits for most of these 100 Tier 1 and 2 sites were at 1 ppb; this would have precluded finding concentrations less than 1 ppb in some of the off-site soil sampling, particularly important for many of the sites where on-site detections were in the low ppb range. Summary data from the 7 sites noted above is provided in Table 7-1. Contaminant concentration ratios cannot be evaluated by this summary because of lack of detail provided in the National Dioxin Study.

Further detail on the 1984 sampling at the Dow Chemical site in Midland is provided in Nestrick, et al. (1986). An evaluation of the information in that reference is more informative than the Dow Chemical summary in Table 7-1. The entire site is 607 hectares. On-site sampling included areas identified as chlorophenolic production areas, a waste incinerator area, and "background" areas. Background areas were within the 607 ha site but away from production areas. Two of the on-site areas were further identified as areas with Localized Elevated Levels (LELs). These two areas comprise less than 0.5% of the total site area, but had the three highest occurrences of 2,3,7,8-TCDD at 25, 34, and 52 ppb. Including these three high occurrences in the total of 33 samples taken on-site at sites of concern (i.e., not including the background sites) leads to an average concentration of 4.3 ppb; excluding them leads to an average of 1.0 ppb. The average of 11 background samples (including two ND assumed to be 0.0) was 0.15 ppb. A contaminant concentration ratio of 0.035 is calculated assuming an average concentration for contaminated soil of 4.3 ppb (0.15/4.3 = 0.035), and a ratio of 0.15 is calculated if the average soil contamination concentration is more like 1.0 ppb rather than 4.3 ppb.

This ratio of 0.035 is about 1/8 as much as the 0.28 ratio estimated assuming the shallow 5-cm depth of contamination, although the ratio of 0.15 is only about half as much as this 0.28 ratio. The depth of 20 cm led to a modeled ratio of 0.08, which is more in line with the Dow contaminant ratios of 0.035 or 0.15. The 5-cm depth ratios are probably more pertinent for comparison, however, since it is unlikely that there were tillage operations (or other soil practices which would distribute residues) in background areas of the 607 ha Dow site.

table Table 7-1 Summary of off-site soil contamination from Tier and 2 sites of the National Dioxin Study
It appears reasonable that the no-till contaminant ratio of 0.28 is higher than the Dow ratios for several reasons. First, the contaminated areas sampled were those likely to be of concern and comprising only a small percentage of the total 607 hectare site.

That might question the representativeness of 4.3 ppb as average soil contamination in impacted areas; the three highest concentrations came from specifically identified LELs com

Second, a map provided in Nestrick, et al. (1986) including a distance scale clearly shows that all of the background samples were much further than
expand table Table V3 7-1

150 meters from the contaminated sample points, with several sample points hundreds to over a thousand meters from the contaminated sample points.prising only 0.5% of the 607 ha site area. The contaminant concentration ratio of example Scenario 3, 0.28, was estimated with a distance of 150 meters. Third, the example scenarios had specific assumptions about erosion which may or may not have been appropriate for application to the Dow site.

Ideally validation of the soil erosion model would involve direct application at the DOW site and comparison of predicted values to measured values. This was not feasible due to lack of information regarding the DOW site. Instead, this analysis has shown that the model predictions of contaminant concentration ratios differ logically from observed ratios at the DOW site.