Volume III describes procedures for conducting site specific exposure assessments to estimate potential dose. A potential dose is defined as a daily amount of contaminant inhaled, ingested, or otherwise coming in contact with outer surfaces of the body, averaged over an individual's body weight and lifetime. The general equation used to estimate potential dose normalized over body weight and lifetime is as follows:

Lifetime Average Daily Dose (LADD) = (exposure media concentration x

contact rate x contact fraction x exposure duration ) /

(body weight x lifetime)

This procedure is used to estimate dose in the form needed to assess cancer risks. Each of the terms in this exposure equation is discussed briefly below:

• Exposure media concentrations: These include the average concentrations in the media to which individuals are exposed. Media considered in this assessment include soil, air, water, vegetables/fruits, fish, beef, and milk.

• Contact rate: These include the ingestion rates, inhalation rates, and soil contact rates for the exposure pathways.

• Contact fraction: This term describes the distribution of total contact between contaminated and uncontaminated media. For example, a contact fraction of 0.8 for inhalation means that 80% of the air inhaled over the exposure period contains dioxin-like compounds in vapor form or sorbed to air-borne particulates.

• Exposure duration: This is the overall time period of exposure, mostly pertinent to adult exposures. Another exposure duration considered in this methodology is one associated with a childhood pattern of soil ingestion. The exposure duration in this case is 5 years.

• Body weight: For all the pathways, the human adult body weight of 70 kg is assumed. This value represents the United States population average. The body weight for child soil ingestion is 17 kg (EPA, 1989).

• Lifetime: Following convention, and because cancer risk slope factors are derived based on a 70-year human lifetime, the average adult lifetime assumed throughout this document is 70 years.

Before making exposure estimates, the assessor needs to gain a more complete understanding of the exposure setting and the contamination source. The approach used for this assessment is termed the exposure scenario approach. A "road map" of that procedure including identification of chapters in Volumes II and III where key information can be found, is shown in Figure III-1. Brief descriptions of 7 steps in this approach are:

Step 1. Identify Source: Three principal sources are addressed in this document: contaminated soils, stack emissions, and effluent discharges.

Step 2. Estimate Release Rates: Estimating the release of contaminants from the initial source is the first step towards estimating the concentration in the exposure media. Releases from soil contamination include volatilization, and wind and soil erosion. Stack emissions and effluent discharges are point source releases into the environment.

Step 3. Estimate Exposure Point Concentrations: Contaminants released from soils, emitted from stacks, or discharged into surface waters move through the environment to points where human exposure may occur, and/or to impact environmental media to which humans are exposed. Various fate, transport, and transfer models are used to predict exposure media concentrations given source releases.

Step 4. Characterize Exposed Individuals and Exposure Patterns: Exposed individuals in the scenarios of this assessment are individuals who are exposed in their home environments. They are residents who breathe air at their residence, fish recreationally, have a home garden, farm, and are children ages 2-6 for the soil ingestion pathway. Exposures which are occur at the workplace or other locations are not discussed in this assessment, although the procedures could be adapted for other exposure sites. Each of these pathways are evaluated separately. Since it is unlikely that single individuals would experience all of these pathways, the exposures across pathways are not added. Each pathway has a set of exposure parameters including contact rates, contact fractions, body weights, exposure durations, and a lifetime.

Figure III-1. Road map for assessing exposure and risk to dioxin-like compounds.

Step 5. Put It Together in Terms of Exposure Scenarios: A common framework for assessing exposure is with the use of "settings" and "scenarios." Settings are the physical aspects of an exposure area and the scenario characterizes the behavior of the population in the setting and determines the severity of the exposure. A wide range of exposures are possible depending on behavior pattern assumptions. An exposure scenario framework offers the opportunity to vary any number of assumptions and parameters to demonstrate the impact of changes to exposure and risk estimates.

Step 6. Estimate Exposure: The end result of having followed the above 5 steps are estimates of individual exposures to a characterized source of contamination.

Step 7. Assess Uncertainty: Uncertainties should be considered when applying procedures in this document to a particular site. Pertinent issues explored in this assessment include: 1) model predictions of exposure media concentrations compared to field measurements, 2) similarities and differences for alternate models for estimating exposure media concentrations, 3) sensitivity of model results to a range of values for methodology parameters, 4) mass balance checks, and 5) qualitative and quantitative discussions on the uncertainties with the model parameters and exposure estimates generated for the demonstration scenarios.

Literally hundreds of fate and transport models have been published which differ widely in their technical sophistication, level of spatial or temporal resolution, need for site specific parameterization, and so on. This makes selection of the most appropriate one for any particular situation very difficult. For this assessment, relatively simple, screening level models are used to model fate, transport, and transfer of dioxin-like compounds from the source to the exposure media. Simple assumptions are often made in order to arrive at the desired result, which is long-term average exposure media concentrations. Perhaps the most critical of the assumptions made is that the source strength remains constant throughout the period of exposure.

It is important to understand that EPA is not endorsing the algorithms of this assessment as the best ones for use in all dioxin assessments. They are suggested as reasonable starting points for site-specific or general assessments. All assumptions for the models and selection of parameter values are carefully described. If these assumptions do not apply to a particular situation, or where assessors require more spatial or temporal resolution, more complex models should be selected. Finally, it cannot be overemphasized that measured concentrations are generally more reliable than modeled ones. Assessors should use measured concentrations if available and if such measurements can be considered spatially and temporally representative for the exposed populations.

Figures III.2 through III.5 provide an overview of algorithms used to evaluate the fate, transport, and transfer of dioxin-like compounds from contaminated soil, stack emissions, and effluent discharge (called "source categories" in this document). Algorithms are presented which link each of these sources to estimated concentrations in a number of media which may be contaminated as a result, and are therefore potential "exposure media": 1) surface soils, 2) surface-water associated media: suspended and bottom sediment and dissolved phase concentrations, 3) air including the vapor phase and in particulate form, and 4) biota including beef, milk, fruit and vegetables, and fish. The remainder of this section describes how each potential exposure medium can be affected by each source, and the algorithms used to make this link.

• Surface soils: Exposure to contaminated soil may be a result of direct contact with soil on the site of the "source" contamination, or indirectly after the contaminated soil has been transported off-site. These cases are known as the "on-site" scenario and the "off-site" scenario, respectively. In either case, soil concentrations are specified for the contaminated source. For the on-site scenario, the soil at the residence or farm (where exposures occur) is contaminated. In the off-site scenario, soil contamination is assumed to be adjacent to an accessible area known as the "exposure site". Examples here would include a landfill or a Superfund site. Residues which reach the exposure site mix with soil already there; the mixing is assumed to take place to either a "tilled" depth or a "non-tilled depth". The tilled depth is assumed to be 20 cm (approximately 8 inches), typical of soil mixing for growing below-ground vegetables. The concentrations derived from using a 20 cm mixing depth are also used to estimate concentrations for dermal contact for individuals in farming families (i.e., dermal contact is assumed to occur as a result of

farming activities). The non-tilled mixing depth is assumed to be 5 cm (approximately 2 inches) when erosion transports residues to a site of exposure where deep tilling or plowing does not routinely occur. The concentrations derived using this mixing depth are used for dermal contact exposures in residential settings, for childhood soil ingestion in residential and farm settings, and for cattle soil ingestion (used in estimation of beef and milk concentrations).

Exposure site soils can also be impacted from stack emissions due to air transport of either vapor or particulate residues from the stack to the exposure site. Deposition modeling for particles allows for estimation of tilled and non-tilled soil concentrations. When stack emissions are the source, however, the nontilled depth of mixing is assumed to 1 cm (about 0.4 inch) instead of 5 cm, on the assumption that particle deposition is a less turbulent process than soil erosion. A key assumption for evaluating the exposure site as a result of both off-site erosion and stack emissions is that contaminants impact a thin layer of soil and do, in fact, dissipate. For the on-site soil scenario, on the other hand, the contamination is assumed to extend into the soil and surface concentrations are not dissipated over time. Dissipation processes could include volatilization, photolysis, or other processes. A soil dissipation half-life of ten years is assumed for all dioxin-like compounds.

• Surface Water: The principal assumption driving the solutions for the soil and stack emission source categories is that the suspended and bottom sediments of water bodies originate as watershed soils, which are subsequently eroded. For the stack emission source category, a portion of the sediments also originates from directly-depositing particulates. The process of erosion transports soils within the watershed to the water body. Unit rates of erosion along with watershed size determine the total potential amount of soil which could be delivered to the water body. Sediment delivery ratios reduce that potential amount. A mass balance assures that soil eroding on an annual basis becomes either suspended or bottom sediment within an annualized volume of surface water. "Enrichment" of eroded soil is assumed, which means that eroded soil from a contaminated source is assumed to be higher in concentration of dioxin-like compounds than in situ, off-site soils. Once in the water body, a standard partitioning model based on the organic carbon partition coefficient, Koc, determines the concentration of contaminant in the water in truly dissolved form and the concentration on suspended sediments. The organic carbon normalized concentrations of suspended and bottom sediment are assumed to be equal. Watershed soil concentrations are model input parameters for determining the effect on surface water from contaminated soils. For stack emissions, a total (dry + wet) deposition rate of contaminant which represents average depositions onto the watershed is specified as an input parameter, as well as a mixing depth representing the watershed. In this way, average watershed soil concentrations are calculated for the stack emission source category.

For effluent discharges as sources, watershed soils are not considered. An amount of contaminant is discharged into an annual flow volume to obtain a simple dilution concentration. This total concentration is partitioned into a truly dissolved phase and a phase sorbed to suspended sediments using the organic carbon partition coefficient, the Koc. Bottom sediments are not considered for effluent discharges.

• Soil to Air: From contaminated soils, residues become airborne via the processes of volatilization and wind erosion. For on-site soil contamination, these vapor and particle phase fluxes are translated to ambient air concentrations using a near-field dispersion model. For the off-site scenario, the same approach is used to estimate ambient air exposure site concentrations, except that a far-field dispersion model is used. These airborne reservoirs are the basis for inhalation exposures, and are also used to estimate plant concentrations for vegetable ingestion and in grass and feed for estimating beef and milk concentrations.

• Stack Emissions, Atmospheric Transport Modeling: Air dispersion/deposition models consider the basic physical processes of advection, turbulent diffusion, and removal via wet and dry deposition to estimate the atmospheric transport, resulting ambient air concentration, and settling of particles. Volume III uses the COMPDEP model for air dispersion and deposition modeling. Besides discussions in Volume III, further discussions on the COMPDEP model can be found in EPA (1990d).

COMPDEP contains modifications of the Industrial Source Complex model (Short-Term version), and COMPLEX I to incorporate algorithms to estimate dispersion, and resulting ambient air concentrations, and wet and dry deposition flux. COMPLEX I is a second level screening model applicable to stationary combustion sources located in complex and rolling topography (EPA, 1986). The model was developed specifically to evaluate the effects of complex terrain that exceeds the stack height of the source as developed by Turner (1986). To account for pollutant deposition, the concentration algorithms in COMPLEX 1 were replaced with those from the Multiple Point Source Algorithm with Terrain Adjustments Including Deposition and Sedimentation (MPTER-DS) model (Rao and Sutterfield, 1982). The MPTER-DS algorithms incorporate the gradient transfer theory described by Rao (1981), and are extensions of the traditional Gaussian plume algorithms. The dispersion algorithms contained in the Industrial Source Complex, Short-term version (ISCST), have been incorporated in COMPDEP to analyze ground-level receptors located below the height of the emission plume. COMPDEP uses the generalized Briggs (1975, 1979) equation to estimate plume-rise and downwind dispersion as a function of wind speed and atmospheric stability. A wind-profile exponent law is used to adjust the observed mean wind speed from the measurement height to the emission height for the plume rise and pollutant concentration calculations. The Pasquill-Gifford curves are used to calculate lateral and vertical plume spread (EPA, 1986). These curves are based on Pasquill's definitions of atmospheric stability classes, e.g., extremely unstable, moderately unstable, slightly unstable, neutral, slightly stable, and moderately stable, that correspond to various intensities of solar radiation and wind speeds (Seinfeld, 1986). The incorporation of these two basic models into COMPDEP permits analysis of a source located in all types of terrain. Further details on the use of the COMPDEP model are:

1. Emission factors: The first step in the use of the COMPDEP model is to determine "emission factors" for dioxin-like congeners. These factors are defined as the m g (or other mass unit) congener emitted per kg (or other mass unit) feed material combusted. Once assuming a rate of feed material combusted in appropriate units, kg/day, these emission factors can be translated to the units appropriate for atmospheric transport modeling, m g/sec. This assessment promotes the generation of specific congener emission factors, rather than TEQ or homologue group emission factors. A TEQ concentration can be generated for exposure media concentrations once congener-specific concentrations are estimated using the Toxicity Equivalency Factor (TEF) scheme. This recommendation is made because fate, transport, and transfer parameters, and TEFs, are different for specific congeners, leading to a TEQ exposure media concentration which would be different but more accurate than, say, assuming only a TEQ emission factor and one set of parameters for further modeling. Emission factors for the demonstration were generated from actual test data from an incinerator burning organic wastes (source otherwise unspecified). Emission estimates for this example incinerator are similar to emissions that are known to be emitted from combustors employing sophisticated air pollution control devices (e.g., scrubbers combined with fabric filters). In order to place the demonstration scenario in context, the emissions from the hypothetical incinerator were ranked with other types of waste incinerators that are well controlled with some combination of a scrubber device and/or a fabric filter, as follows:

1. Medical waste incineration: 25 - 200 ng TEQ/kg waste combusted.

2. Hazardous waste incineration: 0.18 - 119 ng TEQ/kg waste combusted.

3. Hypothetical waste incinerator: 4.5 ng TEQ/kg waste combusted. 4. Municipal solid waste incineration: 0.05 - 3 ng TEQ/kg waste combusted.

5. Sewage sludge incineration: 0.002 - 0.03 ng TEQ/kg sludge combusted. 2. Vapor/Particle Partitioning: The second step in atmospheric transport modeling is to determine the percent of totally emitted dioxin-like congener which is in a vapor phase, and the percent which is in the particle phase. The partitioning of stack emissions into these two phases was examined by reviewing stack testing data, ambient air sampling data, and a theoretical approach developed in Bidleman (1988). A summary of the vapor/particle (V/P) partitioning surmised from these three sources is given in Table III-1. From this review, it is generally concluded that:

a. Stack gas sampling: The stack gas sampling methods in use today to monitor and measure the concentration of CDDs/CDFs emitted to the air from combustion sources do not provide a credible basis for assuming the vapor phase and particle bound partitioning at the point of release. There is no consistent pattern to the interpretation of V/P based on where the CDD/CDF segregates in the instrument, e.g., the glass fiber filter or the XAD resin. Factors that may contribute to this are: the relatively long residence time spent traversing the stack interior; the probe to the instrument is inserted into a relatively hostile environment of the hot combustion gas; the static temperature of the particulate filter caused by heating the particulate filter housing; the fact that located between the particulate trap and the vapor trap is a condensing section consisting of glass

Table III-1. Percent distribution of CDDs and CDFs between vapor-phase (V) and particulate-phase (P) as interpreted by various stack sampling methods, ambient air monitoring, and ambient air theoretical partitioning.

4CDD 5CDD 6CDD 7CDD 8CDD 4CDF 5CDF 6CDF 7CDF 8CDF

Stack V 76 70 71 73 63 76 66 64 62 73

Testing¹ P 24 30 29 27 37 24 34 36 38 27

Ambient air V 87 69 30 10 4 83 65 35 11 2

Monitoring² P 13 31 70 90 96 17 35 65 89 98

Theoretical³ V 55 26 4 2 1 71 36 7 3 1

P 45 74 96 98 99 29 64 93 97 99

tubing surrounded by an ice bath.

b. Ambient air sampling: On the other hand, the ambient air sampling methods do give an approximate indication of the V/P ratio that seems to be responsive to changes in temperature, and degree of chlorination of the CDDs/CDFs. This is in accordance with what would be expected from their individual vapor pressures. There is no artificial heating or cooling of any component of the sampler. The sampler is exposed to actual temperature, pressure, and humidity of the ambient air. This reduces the possibility that the vapor phase-particle bound partitioning operationally defined as the compound segregating to the particulate trap and vapor trap is actually an artifact induced by artificial heating and cooling within the system. Therefore, the methods present a realistic picture of partitioning under variable ambient conditions. However, the method has certain limitations that currently prevent deriving a true measurement of V/P partitioning in the ambient air:

• The glass fiber filter is designed to capture and retain particulate matter greater than or equal to 0.1 µm diameter. Particles less than this diameter may pass through the filter and be retained in the polyurethane foam vapor trap downstream. If this is the case, the amount of CDDs/CDFs observed to be particle bound would be underestimated, and the amount observed to be in vapor phase would be overestimated.

• The relatively high sampled volume of air passed through the system (200 to 400 m³ of air per 24 hours) may redistribute the more volatile congeners from the filter to the adsorbent trap by a process known as 'blow-off'.

c. Theoretical partitioning: Until sampling methods are improved and modified such that they give results that indicate the true V/P ratio of CDDs/CDFs in ambient air, the theoretical construct described by Bidleman (1988) is used to calculate the V/P ratio for purposes of air dispersion and deposition modeling of emissions from the hypothetical case demonstrated in Chapter 5 of Volume III. Key advantages to the theoretical approach are that the theoretical construct relies on current adsorption theory, considers the molecular weight and the degree of halogenation of the congeners, uses the boiling points and vapor pressures of the congeners, and uses the availability of surface area for adsorption of atmospheric particles that correspond to a variety of ambient air shed classifications having variable particulate matter densities. Four air shed classifications are described in Bidleman (1988): "clean continental", "background", "background plus local sources", and "urban". The classification used for the example scenarios in Chapter 5 of Volume III, and shown in Table III-1, is "background plus local sources".

3. Two runs of the COMPDEP model: In order to provide estimates of vapor and particle phase concentrations of dioxin-like compounds, as well as estimates of wet/dry particle deposition flux, it is necessary that to run the COMPDEP model twice. Both model runs should assume a "unit emissions release rate", e.g., 1 g/s. Results from these unit runs can easily be transformed to final outputs given assumptions on emissions in vapor and particle forms. A vapor phase run involves turning wet/dry deposition switches to the "off" position. This inactivates a plume depletion equation that subtracts out losses in ambient air concentration due to particle deposition. What is left are the Gaussian dispersion algorithms. The vapor phase concentrations are used for inhalation exposures and also for vapor transfers onto vegetation for food chain modeling. A second run of COMPDEP with wet/dry deposition switches turned to the "on" position is considered a simulation of particle-bound contaminant. Outputs from this run include wet and dry deposition rates, and air concentrations of contaminants in the particulate phase. The depositions are used in soil and food chain modeling, and the concentrations are added to the vapor phase concentrations from the first COMPDEP run to arrive at the total air-borne reservoir for inhalation exposures.

4. Assumed particle size distributions of emitted particles: In order to estimate deposition flux, certain inferences must be made concerning the distribution of particulates according to particle diameter (µm). The distribution of particulate matter by particle diameter will differ from one combustion process to another, and is greatly dependent on the type of feed material, conditions of combustion, and the efficiency of various air pollution control devices. For purposes of demonstration, three particle size categories were generalized from available data on particle fractionation: Category 1: < 2 µm, Category 2: 2 to 10 µm, Category 3: > 10 µm. By using data on the proportion of total particles emitted per size category, and conducting a surface area to volume calculation, it was estimated that 87.5% of the emission rate of particle-bound dioxin-like congener is associated with particles less than 2 µm in diameter, 9.5% is associated with the particle size of 2 to 10 µm, and only 3% is associated with particles greater than 10 µm. Finally, the particle size distribution is further simplified by assuming a median particle diameter to represent each broad particle size category, as follows:

· Particulate category 1 = 1 µm particle diameter

· Particulate category 2 = 6.78 µm particle diameter

· Particulate category 3 = 20 µm particle diameter

5. Dry deposition: The COMPDEP estimates dry deposition flux based on the model developed by Dumbauld, et al. (1976). This model assumes that a fraction of the particulate comes into contact with the ground surface by the combined processes of gravitational settling, atmospheric turbulence, and Brownian diffusion. The COMPDEP model contains enhancements to calculate dry deposition flux using a computerized routine developed by the State of California Air Resources Board (CARB, 1986). The routine is based on a summary of dry deposition velocity curves developed by Sehmel (1980) for a broad range of particle diameters. For the example application of the COMPDEP model in Chapter 5 of Volume III, particles less than 2 m m, represented by a 1 m m size, were assumed to deposit at a velocity of 0.00711 cm/sec. Particles between 2 and 10 m m, represented by a 6.78 m m size, were assumed to deposit at 0.287 cm/sec. Finally, particles greater than 10 m m, represented by a 20 m m size, were assumed to deposit at a velocity of 2.47 cm/sec.

6. Wet deposition: Wet deposition flux depends primarily on the fraction of the time precipitation occurs and the fraction of material removed by precipitation per unit of time by particle size. Based on these relationships, scavenging coefficients were developed by Cramer (EPA, 1986) for varying types and intensities of precipitation relative to different particle diameters by incorporating the observations of Radke, et al. (1980) in a study of scavenging of aerosol particles by precipitation. The principal assumptions made in computing wet deposition flux are: (1) The intensity of precipitation is constant over the entire path between the source and the receptor; (2) The precipitation originates at a level above the top of the emission plume so that the precipitation passes vertically through the entire plume; (3) The flux is computed on the bases of fraction of the hour precipitation occurs as determined by hourly precipitation measurements compiled by the National Weather Service. The remaining fraction (1-f) is subject only to dry deposition processes. Thus no dry deposition occurs during hours of steady precipitation, and dry deposition occurs between the periods of precipitation.

• Biota: Simple bioconcentration/biotransfer approaches are used to estimate biota concentrations in this assessment. Specifics for each biota considered are:

1. Fish - The soil contamination and stack emission source categories estimate the concentration of contaminant on bottom sediments of water bodies. A fish lipid concentration is estimated based the organic carbon normalized bottom sediment concentration and a BSAF, or Biota Sediment Accumulation Factor. Whole fish concentrations for exposure estimation then equal this lipid concentrations times a whole fish lipid content (or a fillet lipid content). For the effluent discharge source category, fish lipid concentrations are estimated as a function of organic carbon normalized concentrations and the closely related BSSAF, or Biota Suspended Solids Accumulation Factor. This recently introduced bioaccumulation factor (EPA, 1993) is analogous to the BSAF, and it is suggested in EPA (1993) that, as a first estimate, it take on the same chemical-specific numerical value as the BSAF.

2. Vegetation - Concentrations in three types of vegetation are considered in this assessment: below ground vegetables (carrots, potatoes, e.g.), above ground vegetables/fruits (tomatoes, apples), and above ground grass and cattle feed which are required for estimation of beef and milk concentrations. Assumptions critical to all three include: above ground vegetation is impacted by vapor phase transfers and particle deposition - there is no root to shoot translocation, outer portions of the vegetation are only impacted with minimal within plant translocation, a steady state is reached between vapor phase contaminants in air and vegetation, particle bound contaminants deposit onto and mix in a vegetative reservoir and are subject to a fourteen-day dissipation half-life which represents particle washoff, and vegetables/fruits which have an outer protective layer (peas, citrus e.g.) are unimpacted by dioxin-like compounds. Below ground vegetable concentrations are estimated from soil water concentrations and a Root Concentration Factor, or RCF. Above ground concentrations due to vapor phase transfers are a function of the vapor phase air-borne reservoir, an air-to-leaf transfer factor, B_vpa, and a surface area to volume reduction factor, VG, which is equal to 1.00 for grasses and other leafy vegetation and less than 1.00 for bulky vegetation.

3. Beef and Milk - Weighted average concentrations of dioxin-like compounds in the diets of cattle raised for beef or lactating cattle are multiplied by a congener-specific bioconcentration factor, BCF, which yields the concentrations in the fat of beef or milk. The same congener-specific BCF is used for beef and milk. This presumes that dioxin-like compounds bioaccumulate equally in body fat and milk fat of beef and dairy cattle. While there is expected to be some difference in bioaccumulation tendencies, the literature was not clear on this issue. Fries and Paustenbach (1990) discuss the importance of the dietary habits of cattle raised for beef versus those raised for dairy products; beef cattle tend to be grazed substantially more, while dairy cattle tend to be barn-fed for a greater proportion of their dietary intake. Like this assessment, Fries and Paustenbach (1990) model beef and milk concentrations using a single BCF for 2,3,7,8-TCDD. They used a BCF of 5.0 for 2,3,7,8-TCDD. A set of BCFs for all dioxin-like congeners for this assessment were based on a set of data on a lactating cow (i.e., dietary intakes of dioxin congeners, concentrations in milk, and other pertinent quantities; McLachlan, et al., 1990). The BCF for 2,3,7,8-TCDD from this data set was 4.32. Beef and dairy cattle diets are described in terms of proportions in pasture grass, cattle feed (silage, grains), and soil. Models described above estimate concentrations in these cattle intakes.

EPA (1992a) states, "In exposure scenario evaluation, the assessor attempts to determine the concentrations of chemicals in a medium or location and link this information with the time that individuals or populations contact the chemical. The set of assumptions about how this contact takes place is an exposure scenario." These assumptions can be made many different ways producing a wide variety of scenarios and associated exposure levels. The number of people exposed at different levels form a distribution of exposures. Ideally assessors would develop this entire distribution to fully describe the exposed population. Since the necessary information for developing a population distribution is rarely available, EPA (1992a) recommends developing a central and high end scenario to provide some idea of the possible range of exposure levels.

The basic setting for which the methodologies are demonstrated is a rural setting which contains both farms and non-farm residences. The three principal sources of contamination, the soil (both on-site and off-site), stack emission, and effluent discharge, categories, are assumed to exist in such a setting. "Central" scenarios are based on typical behavior at a residence and "high end" scenarios are comprised of a farm family that raises a portion of its own food. Key distinguishing features between the high end and central scenarios include: 1) individuals in high end scenarios are assumed to be at their home a greater proportion of the day than the central scenarios (which impacts assignment of contact fraction), 2) individuals in high end scenarios are exposed to impacted beef and milk which they raise on their farm while these exposures are not considered for the central scenarios, 3) the exposure duration for individuals in the high end scenario is 20 years compared to 9 years for the central scenario, and 4) certain exposure parameters, such as water ingestion rate which is 1.4 L/day for the central scenarios and 2 L/day for the high end scenario, are different.

The example scenarios were carefully crafted to be plausible and meaningful, considering key factors such as source strength, fate and transport parameterization, exposure parameters, and selection of exposure pathways. However, it should be clearly understood that the purpose of the demonstration scenarios is to provide users of this methodologies with a comprehensive example of their application. The demonstration exposure scenarios were:

Surface soils on a 4,000 m² (1-acre roughly) rural residence (Scenario 1) and on a 40,000 m² (10-acres) small rural farm (Scenario 2) contained residues of the three example contaminants. The concentrations of the contaminants are uniformly set at 1 part per trillion, which was evaluated as reasonable background levels.

A 40,000 m² rural farm is located 150 m (500 ft) from a 40,000 m² area of bare soil contamination; an area that might be typical of contaminated industrial property. The surface soil at this property is contaminated with the three example compounds to the same concentration of 1 part per billion. This is evaluated as reasonable for industrial sites of contamination of dioxin-like compounds, and three orders of magnitude higher than concentrations for Scenarios 1 and 2.

A 4,000 m² rural residence (Scenario 4) is located 5000 meters downwind from a stack emission source, and a 40,000 m² rural farm (Scenario 5) is located 500 meters from the same stack emission source. The emissions of dioxin-like compounds were evaluated as within the range observed for various stack emission sources which have sophisticated air pollution control devices (e.g., scrubbers combined with fabric filters).

As has been discussed, this source category is different from others in that the air, soil, and vegetation at a site are not impacted. Rather, only surface water impacts, and exposures to ingestion of drinking water and fish, are considered. The source strength was developed from data on pulp and paper mill discharges of 2,3,7,8-TCDD. Discharge rates were based on data from EPA's 104-mill study (EPA, 1990c), and then reduced considering recent improvements in the bleaching process which have reduced discharges.

Three compounds were demonstrated for the two soil source categories, on- and off-site soil contamination, and for the effluent discharge source category. For purposes of illustration, one compound was arbitrarily selected from each of the major classes of dioxin-like compounds. They are: 2,3,7,8-tetrachlorodibenzo-p-dioxin (abbreviated 2,3,7,8-TCDD), 2,3,4,7,8-pentachlorodibenzofuran (2,3,4,7,8-PCDF), and 2,3,3',4,4',5,5'-heptachloro-PCB (HPCB).

For the stack emission demonstration, Scenarios 4 and 5, a different approach was taken. Exposures to 2,3,7,8-TCDD alone are determined, as in the other demonstrations. Emission rates for all dioxins and furans with non-zero toxicity equivalency factors (abbreviated TEFs) were available for the demonstration of the stack emission source category. Use of the full suite of emissions allowed for the opportunity to demonstrate an appropriate methodology for estimating TEQ exposures. The framework takes the individual deposition rates and concentrations for the individual congeners and models the exposure media concentrations individually with unique fate and bioaccumulation parameters, and then determines a final TEQ exposure media concentration using TEFs.

III.4.1. Results from the Demonstration of the Stack Emission Source Category

For brevity, only the results from the stack emission source category will be summarized. Table III-2 gives the exposure media concentrations estimating for 2,3,7,8-TCDD and for TEQs for Example Scenario #5, the high end scenario for the stack emission source category. Table III-3 gives the estimated Lifetime Average Daily Doses, LADDs, for the exposure pathways modeled in this assessment.

Much of the differences between exposure pathways and scenarios is due to differences in exposure media estimation. Therefore, the discussion below on trends for LADD follows directly from how the methodologies estimate exposure media concentrations. It is important to understand that exposure estimates generated for the demonstration scenarios are specific to the site conditions assumed for the examples and are not generalizable to other sites. Following are some key observations:

1) The highest exposures were associated with the off-site soil contamination scenario, Scenario #3. This scenario had the highest exposure media concentrations for all exposure media. The source of contamination was a 40,000 m² land area with soil concentrations initialized at 1 ppb for the three example compounds. The lowest LADDs

Table III-2. Exposure media concentrations estimated for the demonstration of the stack emission source category¹.

Exposure media

concentration 2378-TCDD TEQ

1. Concentration of contaminants in soil

for soil ingestion and dermal contact

pathways, ng/kg 1*10^-3 2*10^-2

2. Concentration of contaminants in air

for inhalation pathway, pg/m³ 1*10^-5 2*10^-4

3. Concentration of contaminants in water

for water ingestion pathway, pg/L 4*10^-6 5*10^-5

4. Concentration of contaminants in

fish for fish ingestion pathway, ng/kg 6*10^-5 1*10^-3

5. Concentration of contaminants in below

ground vegetables, ng/kg fresh weight 8*10^-8 1*10^-6

6. Concentration of contaminants in above

ground fruit and vegetables, ng/kg

fresh weight 3*10^-6 1*10^-4

7. Concentration of contaminants in

beef for beef ingestion pathway,

ng/kg whole beef (22% fat) 5*10^-4 1*10^-2

8. Concentration of contaminants in

milk for milk ingestion pathway,

ng/kg whole milk (3.5% fat) 6*10^-5 1*10^-3

Soil dermal contact 5*10^-11 1*10^-9

Inhalation 1*10^-9 2*10^-8

Water ingestion 3*10^-11 4*10^-10

Fish ingestion 1*10^-9 2*10^-8

Fruit ingestion 3*10^-10 2*10^-8

Vegetable ingestion 4*10^-10 2*10^-8

Beef ingestion 9*10^-8 2*10^-6

Milk ingestion 3*10^-8 6*10^-7

were estimated for the demonstration of the stack emission source category. Although the intensity of the source strength between a stack emission source and a soil source cannot be directly related, it is noted that the releases of 2,3,7,8-TCDD and TEQs used to demonstrate the stack emission source were comparable to other stack emission sources with sophisticated air pollution control devices. Exposures to 2,3,7,8-TCDD were about 5% of exposures to TEQs. This mirrors the comparison of the 2,3,7,8-TCDD release rate and total TEQ release rate from the stack. Only a fish and a water ingestion pathway were considered for the effluent discharge source category. The exposures estimated for these two pathways were similar in magnitude to the fish and water ingestion exposures estimated for demonstration of the on-site soil source category, demonstrations #1 and #2. For those demonstrations, watershed soils were initialized at 1 ppt, a concentration that researchers have found for 2,3,7,8-TCDD in background settings.

2) Differences between analogous "central" and "high end" exposures for the on-site soil source demonstration scenarios were near or less than an order of magnitude. "Analogous" exposures are those estimated for both scenarios. They include inhalation, soil ingestion and dermal contact, water, vegetable/fruit, and fish ingestion exposures. Only beef and milk are not analogous since they were only estimated for the high end scenario. Analogous exposures were within an order of magnitude of each other because the exposure parameters used to distinguish typical and high end exposures, the contact rates, contact fractions, and exposure durations, themselves did not differ significantly, and these were the only distinguishing features for the central and high end demonstrations of the on-site soil source category. In the stack emission scenario, placing exposed individuals either 500 or 5000 meters away from the incinerator did significantly impact the results. In this case, the difference was closer to 2 orders of magnitude for all analogous exposures except water and fish exposures, which were not a function of distance from the stack. The order of magnitude difference in distance added about an order of magnitude difference in exposure media concentrations and hence LADD estimates.

3) It is inappropriate to compare and rank exposure pathways across all scenarios because the source terms are different. However, relationships between different pathways within each scenario can be discussed. Table III-4 was constructed by summing the LADDs for all pathways, and then determining the percent contribution by each pathway. Before the summation, LADDs were corrected to account for absorption - all ingestion LADDs assumed 50% absorption and inhalation LADDs assumed 75% (data on bioavailability from animal feeding studies, suggests that the absorption of 2,3,7,8-TCDD is around 50%; 75% for inhalation reflects a general assumption of greater absorption for this pathways; both simple assumptions made only for the purpose of this comparative exercise). The dermal contact LADD was the only one where absorption was already considered in its estimation: absorbed dose was estimated as 3% of dose contacting the body. Also, this exercise assumes all pathways occur simultaneously. Table III-4 was generated only for the 2,3,7,8-TCDD example compound, and the rows are listed generally from the highest to lowest percentage contribution. The following observations are made: • In high end scenarios which assumed exposure to home grown beef, milk, and fish, Scenarios 2, 3, and 5, exposures to these three foods dominated the results. In Scenarios where beef and milk were not considered, but fish was considered, Scenarios 1, 4, and 6, fish exposures dominated. The general dominance of beef, fish, and milk exposures underscores the importance of food chain exposures.

• Milk exposures were lower than beef exposures because of less milk fat

Table III-4. Percent contribution of the different exposure pathways within each exposure scenario.*

Scenario #

Exposure Pathway 1 2 3 4 5 6

Meat Ingestion NA 26 50 NA 72 NA

Fish Ingestion 56 44 2 27 1 95

Soil Ingestion 36 15 32 23 3 NA

Milk Ingestion NA 6 11 NA 23 NA

Soil Dermal 4 8 4 0 0 NA

Vegetable Ingestion 1 0 0 5 0 NA

Fruit Ingestion 0 0 0 5 0 NA

Water Ingestion 3 1 0 1 0 5

Vapor Inhalation 0 0 0 39 1 NA

Particle Inhalation 0 0 0 NA NA NA

* Assumes exposed individual experiences all relevant pathways and exposures are additive.

ingestion (10.5 g/day milk fat versus 22 g/day beef fat) and lower concentrations in milk as compared to beef.

• Fish was the principal impacted media for the effluent discharge source category, with fish ingestion 19 times higher than water ingestion, the only two pathways considered for the effluent discharge category. However, fish is much less important than beef or milk for the high end stack emission scenario which had a beef and a milk pathway, and when a small site of contamination is near a farm raising a portion of the farming families beef and milk ingestion.

• Soil ingestion exposures were also noteworthy, particularly in scenarios that did not consider beef and milk, the central on-site scenario, #1, and the central stack emission scenario, #4. Soil ingestion was also the second highest pathway in the scenario evaluating the impact of nearby soil contamination, #3, ranking higher than milk or fish ingestion. Dermal exposures were non-trivial, but ranked behind the four ingestion pathways previously discussed: beef, milk, fish, and soil.

• Inhalation was the highest impact for the stack emission scenario when farm animal products were not considered, in Scenario #4. Fruit and vegetable exposures were noteworthy only in this same scenario. These trends imply that, where farm animal products are not being produced near a stack emission source, fish and vegetative food products still may dominate the overall exposure, but inhalation exposure can become critical.

• Water ingestion exposures were very low in comparison to the other exposures in these scenarios.

These demonstration scenarios represent only one approach to scenario development; other approaches might consider the quality of exposure media not associated with the home environment. For example, if the bulk of an individual's ingestion of produce comes from local farms, and local farms may be impacted by an stack emission source, then perhaps 90-100% of an individual's fruit and vegetable ingestion, rather than the 20-40% assumed in this assessment, should be considered impacted.

This section discusses three issues pertinent to use of the methodologies. The first subsection below discusses the use of the parameter values selected for the demonstration scenarios for other applications. The next subsection is a sensitivity analysis exercise on the parameters required for algorithms estimating exposure media concentrations. The last subsection addresses the issue of mass balance with regard to the source strength terms of the four source categories.

Table 6.1 in Chapter 6 of Volume III lists all the parameters, including names, definitions, and units, that are required for the methodologies of this assessment except the exposure parameters. Exposure parameters are given in Table 2.1 in Chapter 2 of Volume III. Table 6.1 also gives four additional pieces of information for each parameter listed. Three are numerical values which were used in the sensitivity analysis exercises that are described below. One of those parameters is labeled "selected", which were the ones used in the demonstration exposure scenarios. High and low values of parameters selected for sensitivity analysis were carefully developed and might be considered a reasonable range of values for other uses of the methodology (with obvious exceptions such as areas of contamination, distances from contaminated to exposure site, and so on). The chemical specific parameters are those only for 2,3,7,8-TCDD. The fourth piece of information is a qualitative judgement on the part of the authors of this document as to the appropriateness of using the "selected" parameter values for other assessments. This judgement is categorized in three ways:

1) First Order Defaults: As defaults, these parameters are independent of site specific characteristics. As first order defaults, it is felt that the values selected for the demonstration scenarios carry a sufficient weight of evidence from current literature such that these values are recommended for other assessments. Several of the chemical specific parameters, such as the Henry's Constant, H, and the organic carbon partition coefficient, Koc, fall into this category. The qualifier above, "current literature", indicates that new information could lead to changes in these values.

2) Second Order Defaults: Like the above category, these parameters are judged to be independent of site specific characteristics. However, unlike the above category, the current scientific weight of evidence is judged insufficient to describe values selected for demonstration purposes as first order defaults. Parameters of principal note in this category are the bioconcentration parameters specific to the chemicals, such as the Biota Sediment Accumulation Factor, or BSAF. This parameter translates a bottom sediment concentration to a fish tissue concentration. Users should carefully review the justification for the SOD values selected for the demonstration scenarios before using the same values. 3) Site Specific: These parameters should or can be assigned values based on site-specific information. The information provided on their assignment for the demonstration scenarios, and for selection of high and low values for sensitivity analysis testing, is useful for determining alternate values for a specific site. A key class of SS parameters which are the source strength terms - the soil concentrations, effluent discharge rates, and stack emission rates. If users are unable to obtain site-specific information, or their use of the methodologies is for general purposes, they should review the justification for selection of values for methodology demonstration, as well as information provided giving ranges of likely values for model parameters.

The exposure parameters can be categorized as have the contaminant fate and transport/transfer parameters. Assignment of these values are critical as LADD estimates are linearly related to parameter assignments - doubling exposure duration assumptions double LADDs, and so on. Some of the exposure parameters are appropriately described as first order defaults. These include: lifetime, body weights, water ingestion rates, inhalation rates, and an exposure duration for a childhood pattern of soil ingestion. All of the other exposure parameters are better described as either second order defaults or site-specific. All exposure parameters were developed based on information and recommendations in EPA's Exposure Factors Handbook (EPA, 1989) and Dermal Exposure Assessment: Principals and Applications (EPA, 1992c). Attaining site-specific information is recommended for exposure parameters.

Sensitivity analysis was undertaken in order to evaluate the impact to exposure media concentration estimations with changes in fate and transport/transfer model parameters. Figure III-5 shows an example of sensitivity analysis conducted. This figure describes the impact of key factors for the stack emission source category for determining biota impacts. The x-axis contains the names of the parameters evaluated. The key below the figure gives the definition of the parameters and the values selected for the demonstration scenarios. The y-axis shows the numerical change to the key model result, in this case, vegetable and beef concentrations, to the changes made in the parameter. These changes are noted above and below the bars. For example, vegetable concentration is about 3 times higher at 200 ft from the stack emission source than it is at 500 meters from the source, the distance used in the demonstration scenario. Some of the observations made for this test, typical for the type of observations which were made for sensitivity testing, include:

1) Mixing depth, described by the parameter d_not, has very little impact on final beef concentrations.

2) Nearer to and further from the stack had different impacts for above and below vegetable concentrations as compared to beef concentrations. The farm was assumed to

be 500 meters from the stack. Nearer to the stack at 200 meters, ambient air concentrations and dry deposition amounts were lower, but wet deposition was at its maximum. One effect of this was that vegetable concentrations increased. Below ground vegetables increased by about a factor of 4, due to the same increase in soil concentration as a result of much higher wet deposition. Above ground vegetation increased by about 50%. Particle depositions dominated above ground vegetable/fruit concentrations. Therefore, an increase in overall particle depositions due to an increase in wet depositions led to increased above ground vegetable/fruit concentrations. However, the trend was not the same for beef and milk fat. The reason for this was that grass and other cattle feeds were dominated by vapor contributions, not particle depositions, as were above ground vegetables. For bulky above ground vegetables, vapor phase impacts were empirically reduced considering the difference in bulk for these vegetables compared to the leafy grass and azalea leaf for which the air-to-leaf vapor transfer factor was developed. Therefore, a drop in ambient air vapor phase concentrations at 200 meters as compared to 500 meters dominated the result, and the net impact was to reduce beef fat concentrations. Further from the stack at 5000 meters, all biota concentrations were lower. Vapor phase air concentrations were roughly halved, and dry and wet deposition were lower by 60 and 80% respectively. This led to substantial reductions in vegetable concentrations. Interestingly, beef concentrations were lower at 5000 meters than at 200 meters, but not by much. This is because vapor phase concentrations at 5000 meters were, in fact, greater than they were at 200 m. The net results, according to the modeled depositions and air concentrations, is that beef and milk fat impacts are ironically fairly similar at 200 and 5000 meters.

3) Changing the vapor/particle partitioning assumption also had inverse effects for above and below ground vegetables as compared to beef. The baseline vapor/particle partitioning for 2,3,7,8-TCDD was 55% vapor/45% particle. When decreasing the vapor to 10% and increasing the particle to 90%, both vegetations increased. Below ground vegetables increased because below ground vegetables were not a function of vapor phase concentrations, only of soil concentrations, which were a function of particle depositions. Above ground vegetable concentrations increased as well, as they are dominated by particle depositions. As noted above, however, cattle vegetations are driven by vapor transfers. Therefore, increasing the vapor portion tended to increase these vegetations and hence beef concentrations.

Following are key overall observations from the sensitivity analysis:

1) Source terms are the most critical for exposure media impacts. Source terms include soil concentrations, stack emission rates, and effluent discharge rates. In all cases, the impact to exposure media is linear with changes to source terms. Proximity to the source term can be important as well, as demonstrated with differences in distance from the stack emission source.

2) Chemical-specific parameters, particularly the bioconcentration/biotransfer parameters, are the second most critical model inputs. Some of these have lesser impacts within the range tested, such as the organic carbon partition coefficient, Koc, for surface water impacts. Generally, at least an order of magnitude in range in possible media concentrations is noted with the range of chemical-specific parameter ranges tested. The impact of changes to bioconcentration/biotransfer parameters is mostly linear. This is because these transfer factors estimate media concentrations as a linear transfer from one media to another. For example, fish lipid concentrations are a linear function of the organic carbon normalized concentration of contaminants in sediments. These transfer parameters are also identified as uncertain parameters. Tested ranges sometimes spanned over an order of magnitude for 2,3,7,8-TCDD.

3) All other parameters had less of an impact as compared to source strength and chemical specific parameters; nearly all impacts were within an order of magnitude for the range of tested values. Part of the reason for this trend is that there is a reasonably narrow range for many of the non-chemical specific or source term parameters - soil properties, wind speeds, vegetation yields, and others.

4) The sensitivity analysis exercises unearthed a dichotomy in model performance between the soil source category and the stack emission source category. The on-site soil source category was demonstrated with a 1 ppt soil concentration of 2,3,7,8-TCDD, a concentration similar to measured concentrations of 2,3,7,8-TCDD in rural settings. Air concentrations are esimtated to be 4*10^-5 pg/m³ (vapor+particle phases summed). Atmospheric transport modeling in the demonstation of the stack emission source category resulted in an exposure site air concentration (vapor+particle phases summed also) at 500 meters from the stack to be 1*10^-5 pg/m³. With similar air concentrations predicted to occur at the exposure site for the demonstration of the soil and stack emission categories, one might hypothesize that all subsequent impacts would be similar. That was not the case. The stack emission source algorithms deposited particulates onto soil to estimate a soil concentration that was in the 10^-3 ppt range for the 1-cm untilled depth and the 10^-5 range for the 20-cm tilled depth. This compares to the 1 ppt concentration for the on-site soil source category demonstration. With similar air concentrations but a 3+ order of magnitude difference in soil concentrations in the demonstration of the soil and the stack emission sources, the following trends were noted:

• Below ground vegetables had much higher concentrations for the soil source demonstration scenario.

• Soil-related exposures (dermal contact and soil ingestion) were much higher for the soil source demonstration scenario.

• Soil was significantly more critical in predicting beef and milk fat concentrations in the soil source category. The following shows the relative impact of soil versus vegetations (grass and cattle feed) for the on-site soil demonstration and the stack emission demonstration:

Percent impact due to ingestion of:

Description Soil Grass Feed

Soil contamination, beef 90 7 3

Soil contamination, milk 87 2 11

Stack emission, beef 5 59 32

Stack emission, milk 3 15 82

Subsequently, beef and milk concentrations were almost two orders of magnitude higher for the soil source category as compared to the stack emission source category.

• Because above ground vegetations are driven by air concentrations, above ground vegetables/fruit and grass/cattle feed concentrations were similar for both demonstrations.

Further examination of the results and other model testing did suggest that the air-to-soil algorithm may be underestimating soil concentrations, and the soil-to-air algorithms may be underestimating air concentrations. If both these observations are correct, and model or parameter adjustments corrected these underestimations, then model performance would be more similar for the two source categories.

The evidence for the air-to-soil underestimation came in an air-to-beef food chain model exercise (see Table III-5 below on model testing for a summary of this test). An air-borne reservoir of dioxin-like compounds was crafted to be typical of rural environments. Depositing this reservoir onto soil resulted in a predicted concentration about an order of magnitude lower than observed concentrations in rural settings. Speculated causes include: 1) the 10-year half-life for dioxin-like compounds may not be long enough, 2) vapor-phase transfers to soils were not modeled, and 3) detritus input to soils was not considered. Empirical evidence for the possible underestimation of air concentrations over soils came in two forms. One, plant:soil ratios modeled in the soil source demonstration scenario appeared lower than experimentally determined plant:soil ratios by about an order of magnitude. This could be due to an underestimation of air concentrations. Two, air concentrations of 2,3,7,8-TCDD predicted to occur over a 1 ppt soil concentration was lower than by an order of magnitude for concentrations found in a "remote" area of Sweden, and about two orders of magnitude lower than crafted to be typical of rural setting in the United States.

While the soil-to-air algorithm may be underestimating air concentrations, it is also possible that they are not underestimating these concentrations. The expectation that releases of dioxins from soils in background settings should result in air concentrations typical of background settings may not be a realistic expectation. The argument was developed in Section II.3.3. Conclusions for Mechanisms of Impact to Food Chain earlier in this Executive Summary that the food chain is impacted via atmospheric depositions, and that industrial emissions followed by long range transport ultimately explain media concentrations in background settings. What is not known is, what portion of air concentrations in rural settings can be attributed to long range transport and what portion attributed to suspension of reservoir sources (soil and other reservoir sources). What is really needed to test the soil to air algorithm are measured concentrations over soils not known to be otherwise impacted by dioxin like compounds. Such information could not be found in the literature.

The purpose of this exercise is to evaluate whether a principal of mass balance will be violated with the models and parameters used for the demonstration of the off-site soil source category - that principal being that dioxin releases from a site cannot exceed the original amount at the site (assuming no replenishment). A simplifying assumption for the off-site soil source category was that the soil concentration remained constant over the period of exposure - there was not a systematic depletion of the reservoir over time due to modeled dissipation processes.

First, an estimate of the "reservoir" of 2,3,7,8-TCDD that is implied with the demonstration parameters was made. Then, an estimate of the rate at which this reservoir dissipated using the solution algorithms for dissipation: volatilization and wind erosion flux from soils, and soil erosion, was made. Other routes of dissipation that were examined are the soil ingestion by cattle and children, losses in runoff and leaching, the loss via dermal contact, and the removal via harvest of below ground vegetation. These were shown to be minuscule in comparison to air and soil erosion. The premise examined was that, if it takes substantially more time than the exposure period to dissipate the reservoir, then it may be fair to conclude that the assumption of a constant soil concentration may be suitable for purposes of exposure assessments. On the other hand, complete dissipation within a time period less than or even near to the period of exposure would mean that exposures and risks are being overestimated. This analysis led to a conclusion that the reservoir modeled in the exercise above would take more than 90 years to dissipate.

This was not a definitive exercise, by any means, but it does lend some confidence that a principal of mass balance may not have been violated for the soil source categories, and for the assumption of 20 years exposure duration.

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 Volume III 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 the uncertainty evaluation is to elucidate those uncertainties inherent to the exposure assessment tools presented. 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 with human exposure patterns (contact rates and fractions, exposure durations, etc.).

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 the exposed population. 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 a hypothetical rural population.

A 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. Three recent Monte Carlo studies which have been done for exposure to 2,3,7,8-TCDD were reviewed. Assumptions on distributions of exposure patterns and fate and transport parameter distributions are described, as are the results of their analyses. Monte Carlo procedures require distributions for the input parameters used in the assessment. Such distributions have not been established by the Agency. Decisions on the use and definition of such distributions affect assessments of all chemicals and cut across all Agency programs. Thus, it is not appropriate to establish such polices in this document.

The Agency does have efforts underway to evaluate these generic issues. For example, the Office of Health and Environmental Assessment (OHEA) is in the process of revising the Exposure Factors Handbook and held public review meetings in 1993. In addition, OHEA is developing a guidance document on generating exposure scenarios. Several offices have projects specific to Monte Carlo:

• Office of Health and Environmental Assessment - A Workshop on approaches to evaluating uncertainty (including the use of Monte Carlo) was held in 1992.

• Office of Policy, Planning and Evaluation - A workshop on using Monte Carlo methods was held in 1993.

• Office of Pollution Prevention and Toxics - A handbook on the use of Monte Carlo is being developed for publication at a later date.

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. While Volume III does demonstrate the methodologies developed for sources of release of dioxin-like compounds into the environment 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. Comprehensive comparisons and rankings of different sources and exposure patterns are generally not available, although pieces of the puzzle are beginning to come together. Volume II of this assessment does estimate national releases of dioxin-like compounds from several sources. References to EPA and other assessments on dioxin-like compounds have been made throughout Volumes II and III of this assessment, such as those related to soil exposures (Paustenbach, et al., 1992), exposures to contaminated fish (EPA, 1991), and exposures resulting from land disposal of sludges from pulp and paper mills (EPA, 1990b).

There was a concerted effort to evaluate the capabilities of the fate, transport, and transfer algorithms by comparing key outputs from these models - predictions of concentrations and ratios of media to media concentrations - with literature reports. A summary of key comparative tests is given in Table III-5.

Table III-5. Summary of key tests of the fate, transport, and transfer models.

Description of Test Summary of Results

Predicted vs. observed Air concentrations resulting from 1 ppt background 2,3,7,8-TCDD

air concentrations concentration were about three orders of magnitude lower than observed urban air concentrations of these contaminants, two orders of magnitude lower than a concentration speculated to be more typical of rural settings in the United States, and one order of magnitude lower than a measured air concentration in a "remote" setting in Sweden. This suggests that the volatilization/dispersion algorithms for soil contamination may be underestimating air concentrations. Air concentrations resulting from a 1 ppb soil concentration, more typical of Superfund sites, were comparable to urban air concentrations.

Plant concentration to A comparison of ratios for the soil contamination source category showed

soil concentration ratio the modeled ratios tended to be lower for all vegetation (above and below ground fruit and vegetation, grass and cattle feed) by about one order of magnitude. This could partly be due to underestimations or air concentrations, as described above. A complication in understanding the measured data, however, was that as soil concentrations increased, plant:soil ratios decreased - that is, proportionally less transfer from soil to plant was occurring as soil concentration increased. No explanation was available for this phenomena, and the models cannot duplicate it. The observation made above that modeled ratios tended to be lower was true for lower experimental soil concentrations, in the low ppb to ppt range.

Background soil concentration The Connecticut Department of Environmental Protection (CDEP, 1992)

to bottom sediment concen- monitored ambient air, soils, surface water bottom sediments, and fish in

tration ratio the vicinity of seven resource recovery facilities and one background site. Six of the eight sites were characterized as "rural"; sites in Hartford and Bridgeport might be more appropriately characterized as suburban or urban. The average concentration in all soil samples (n = 77; assuming non-detects were half detection limits with a detection limit at 0.1 ppt; soil samples were all within 3 miles of sampled water bodies) was 0.77 ppt, which also supported the hypothesis that soils in the area of these RRFs were near background levels. The average concentration in sediment samples (n = 346; same detection limits and procedures for average concentration estimation) was 2.16 ppt. The sediment to surface soil concentration ratio was 2.8 (2.16 ppt/0.77 ppt). The sediment to surface soil concentration ratio for 2,3,7,8-TCDD was also 2.8 for the demonstration of the on-site source category, where basin-wide soil concentrations were set at 1.0 ppt and bottom sediment concentrations were modeled as 2.8 ppt using the soil to sediment algorithms of the soil contamination source categories. This exercise lends some credibility to an enrichment ratio - soils eroding into water bodies are enriched in comparison to in-situ soils - and an assignment of 3.0 to the enrichment ratio in this assessment.

(cont'd on next page)

Table III-5. (cont'd)

Description of Test Summary of Results

Predicted vs. observed With background soil concentrations of 2,3,7,8-TCDD of 1 ppt, estimated

fish tissue concentrations fish concentrations were 0.6 ppt. With a bounded site of 1 ppb soil concentrations, fish concentrations were 3.0 ppt. These were compared with analagous results from the National Study of Chemical Residues in Fish (NSCRF; EPA, 1992b). For NSCRF sites that were evaluated as comparable to background settings, fish concentrations ranged from 0.56 ppt to 1.02 ppt. Average fish tissue concentrations from National Priority List (NPL) and similar industrial contaminated sites ranged from 1.4 to 30.0 ppt, with the 30 ppt average from National Priority List (NPL) sites and all other site averages under 4.4 ppt. The comparison indicates that the magnitude of concentrations appears to have been captured, and the magnitude of difference between background and higher source strength categories of the NSCRF also appears to have been duplicated.

 

Predicted vs. observed The "sources" of 2,3,7,8-TCDD loadings into surface water were pulp and

fish concentrations paper mills of the 104-mill study (EPA, 1990c). A complete set of

for the 104-mill pulp "observed" data (fish concentrations from the NCSRF described above, paper mill study 2,3,7,8-TCDD discharges other than non-detects, water body characteristics, etc.) were available for only 47 mills and 95 fish samples (in some cases, more than one fish was identified downstream of a mill). A dichotomy in model performance was observed for 9 mills (and 21 associated fish samples), which differed from the other 38 in that the receiving water body flow volumes were significantly larger. The average for these 9 mills was 3*10¹⁰ L/hr, while the average for the other 38 was 5*10⁸ L/hr. The average predicted whole fish tissue concentration of 2,3,7,8-TCDD for the 38 mills was 7 ppt, and the average observed concentration in 74 fish was 15 ppt. For the 8 mills and 21 fish, the average predicted fish concentration was 0.7 ppt compared to an observed 5.3 ppt. The correlation over all mills and samples was low, at r² = 0.41. However, the merit of generating this descriptor should be considered: it assumes that the single observed discharge of 2,3,7,8-TCDD represents long term discharges for a given mill, that the single or the few fish samples represent observed impacts from the mill, and so on. One pertinent result was that the maximium "observed" fish tissue concentration of 143 ppt was matched by the maximum predicted concentration of 89 ppt. The key assumption was that the pulp and paper mills were the only sources impacting fish tissue concentrations; it is suggested that other sources impacting the large water bodies explain why observed fish concentrations were about an order of magnitude higher than model predictions for these water bodies.

(cont'd on next page)

Table III-5. (cont'd)

Description of Test Summary of Results

Predicted vs. observed Data in the literature suggests concentrations of dioxin-like compounds water concentrations mostly below 1 pg/L. Models predicted concentrations of 10^-2 pg/L and

lower in demonstration of all source categories.

Predicted vs. observed A profile of "observed" air concentrations of dioxin-like compounds was beef concentrations crafted from available air concentration data. An urban air profile of TEQs developed in Volume II was 0.095 pg/m³, and based on evidence that rural air concentrations (which are the ones most appropriate for beef concentrations) are 4-6 times lower than urban air concentrations, a rural air profile was crafted, totalling 0.019 pg TEQ/m³. These concentrations were routed through the food chain model to arrive at beef TEQ concentrations which were compared with a TEQ beef concentration profile generated from measurements in Volume II. A predicted TEQ concentration of 0.36 ng/kg whole beef concentration (19% fat) was compared to the observed 0.48 ng TEQ/kg in whole beef. Also evaluated were the capabilities of the model to evaluate air to leafy vegetation transfers (vapor and particle) by looking at model predictions and comparing them a single set of observations taken in a rural location in Minnesota (Reed, et al., 1990). Model predictions and observations also compared favorably, except for octa congeners, where predictions were much lower than observations. However, the model for vapor/particle partitioning indicated that the octa congeners would reside fully on particles, i.e., f (particle fraction) = 1.00. In fact, the f for both octa congeners equalled 0.998. Allowing calibration for f , values equalled 0.9998 for OCDD and 0.998 for OCDF, and leafy vegetation predictions, as well as octa beef measurements, now closely matched observations. An air-to-soil evaluation was also done, comparing model predictions of dioxin congener soil concentrations with measurements taken in the United States in rural settings. It was found that the model generally underpredicted soil concentrations by about an order of magnitude, although a more close match would not have greatly affected the predictions in beef since soil is only a small part of the cattle diet. Speculations for why the model was underpredicting soil concentrations included: 1) vapor transfers to soils were not considered, 2) detritus contributions to soil concentrations were not considered, and 3) the assumed half-life of 10 years for this exercise might not be long enough.

Predicted vs. observed Fries (1985) had developed fat:soil ratios for a farm known to be

beef fat:soil and contaminated with PBBs, compounds similar in fate and persistence, and milk fat:soil concentration bioaccumulation tendencies, as the dioxin-like compounds. Field data

ratios showed ratios of 0.10-0.39 for beef and dairy cow body fat:soil, and 0.02-0.06 for milk fat:soil. Modeled ratios in the both soil contamination (on and off-site) example scenarios for 2,3,7,8-TCDD were 0.12 for beef fat:soil and 0.06 for milk fat:soil.

 

A summary of key discussions from the uncertainty evaluation is now presented. First is a summary of three exposure parameters common to all pathways:

1. Lifetime, Body Weights, and Exposure Durations: Of these three parameters, the exposure duration is the most uncertain. The estimates of 9 and 20 years were made in this assessment for non-farming residents in rural settings, and farming residents in rural settings. These values were based on assumptions of time living at one residence. A critical assumption of a constant soil concentration for contaminated soil sites should be carefully considered for site-specific assessments. Data on degradation indicates very slow rates of degradation, and only photolysis as a possible degradation mechanism, which would not impact residues below the surface. A mass balance exercise on the demonstration of the off-site source category (where a 40,000 m² area had soil concentrations averaging 1 ppb 2,3,7,8-TCDD) indicates that it would take 90 years to dissipate a reservoir of 2,3,7,8-TCDD extending 6 inches into the soil. An adult body weight of 70 kilograms and a lifetime of 70 years are standard assumptions for exposure and risk and, although variability is recognized for these parameters, these variations are not expected to add significant uncertainty in exposure estimates. The same is true for the 17 kg child body weight in the childhood exposure pattern of soil ingestion.

2. Soil Ingestion and Soil Dermal Contact: Soil ingestion for older children and adults were not considered, which may have underestimated lifetime soil ingestion exposures. Pica soil ingestion patterns were not evaluated in this assessment. The ingestion rates (200 mg/day for central scenarios and 800 mg/day for high end scenarios, during ages 2-6) considering this appear reasonable. For the soil dermal contact pathway, key uncertain parameters include the soil adherence (0.2 mg/cm²-event for the central residential scenario and 1.0 mg/cm²-event for the high end farming scenario) and the absorption fraction (0.03 for dioxin-like compounds).

A major area of uncertainty for both pathways is the estimation of soil concentrations where the source of contamination is located distant from the site of exposure. For this assessment, this includes the off-site soil source category and the stack emission source category. Results from sensitivity analysis exercises for the erosion algorithm suggests that the 0.28 ppb soil concentration (within a 5-cm layer) used for soil ingestion and dermal contact, and which resulted from the 1 ppb nearby (150 m) soil contaminated site, may be high. Specifically, when all parameters for the erosion algorithm remained constant except the dissipation half-life, which initially was 0.0693

yr^-1 (half-life of 10 years) and then was reduced by a factor of ten to 0.00693 yr^-1 (half-life of 100 years), the soil concentration 150 meters away at the site of exposure increased to slightly above 1.00 ppb. While dissipation of surface residues which have arrived at an exposure site from a distant source is an appropriate assumption, the outcome of a higher soil concentration 150 meters from a site of soil contamination when no dissipation is assumed (albiet assuming infinite time such that a steady state is reached) is questionable. Key uncertain parameters identified include the dissipation rate (0.0693 yr^-1), the mixing depth (5 cm), and the use of an enrichment ratio (equal to 3.0) which increases the concentration of dioxin-like compound on eroded soil relative to in-situ soil. This latter parameter was speculated to the one most likely to be inaccurate for evaluation of off-site soil impacts. Its assignment was not based on data specific to dioxin-like compounds, but rather to general literature data on enrichment ratios for soil nutrients and pesticides showing a range of between 1 and 5. On the other hand, support for an enrichment ratio of 3.00 came in a data set including background soil and concurrent bottom sediment data in receiving water bodies in Connecticut (see Table III.5 for a summary of this data set). There, the ratio of sediment concentrations of 2,3,7,8-TCDD to soil concentrations was 2.8, suggesting that bottom sediments are enriched in comparison to surface soils. The model for bottom sediment impact from watershed soils includes the enrichment ratio, which was set at 3.00, and the demonstration scenarios did show a sediment:soil ratio of 2.8, like the observed data.

An uncertain outcome was also identified for the particle deposition algorithm used for the stack emission source category. An analysis suggests that the soil concentration in a 1-cm layer resulting from depositing particles may be underestimated by about an order of magnitude. The pertinent analysis for this observation came from the air-to-beef food chain model validation exercise conducted for dioxin-like compounds (further details of this exercise are found in Table III-5). There, a rural air profile of dioxin-like compounds were deposited onto soils, and the resulting concentrations of dioxin-like compounds were compared against observations from four United States reports on soil concentrations in rural areas. Generally, the model underpredicted soil concentrations by about an order of magnitude. Suggested causes for this underprediction include: 1) the model does not consider vapor phase transfers to soils, 2) the model does not consider detritus contributions to soil, and 3) the half-life of 10 years may not be long enough for dioxin-like compounds.

In summary, principally identified uncertain parameters for the algorithms transporting eroding soil and depositing particles include: the mixing zone depth for untilled situation of 1 and 5 cm, the dissipation half-life of 10 years, the lack of consideration of vapor phase depositions and detritus additions to soils, and the use of an enrichment ratio for eroded soil of 3.0.

3. Ingestion of Water: A comparison of alternate modeling approaches for estimating water concentrations showed similar results to the models adopted for this assessment. There also does not appear to be a wide range of possible values for water ingestion rate (1.4 L/day for central scenarios and 2.0 L/day for high end scenarios) and contact fraction (0.75 for central scenarios and 0.90 for high end scenarios), and these are not expected to introduce significant uncertainty into water ingestion exposure estimates.

4. Inhalation: The inhalation rate assumed for both central and high end scenarios was 20 m³/day. The distinction in the scenarios was in the contact fractions: central scenarios assumed a contact fraction of 0.75 and high end scenarios had a 0.90 contact fraction. These fractions correspond to time at the home environment. These fractions and the inhalation rate are not expected to add significant uncertainty in inhalation exposure estimates.

Sensitivity analysis showed air concentrations resulting from soil emissions 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 tested included the volatilization approach developed by Jury, et al. (1983) and the box model for dispersion calculations. The Jury model predicted about 1/3 as much volatilization flux (given the selection of parameters, made equal to or most analogous to the models of this assessment) as the Hwang, et al. (1986) model of this assessment. The box model predicted about 6 times higher air concentrations than the near-field dispersion approach of this assessment. This reasonable comparison lends some credibility to the models selected.

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 or from soils between the contaminated and the exposure site - air borne exposure site concentrations may be underestimated as a result.

5. Fruit and Vegetable Ingestion: 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. Concepts of below and above ground vegetations were developed to accomodate soil to root algorithms and soil to air to vegetation algorithms. Protected vegetations - those with outer inedible protections such as citrus or corn - were assumed not to be impacted by dioxin-like compounds.

A key assumption in the vegetation algorithm, that dioxin-like compounds do not translocate from root to shoot, was verified by two experiments. Vapor-phase contributions to vegetation dominated the contaminated soil and stack emission source categories, with one exception. Particle depositions were more important for above ground fruit/vegetable concentrations for the stack emission source.

A critical empirical parameter was the above and below ground correction factors, VG_ag and VG_bg, both set at 0.01 for fruits and vegetables. These factors were 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 higher than whole plant concentrations of bulky fruits and vegetables; hence the introduction of the VG parameters. VG for grass was set at 1.00, which assumes that grass leaves and azalea leaves are analagous with regard to vegetative bulk. VG for cattle feed was set at 0.50, which assumes that some cattle feed is leafy (hay), while some is bulky (corn silage). A different assumption for VG of fruits and vegetables, such as 0.10, would increase estimated concentrations and perhaps make plant:soil concentration ratios more in line with literature values (see Table III-5).

Experimental evidence that a VG_ag for vapor transfers of dioxin-like compounds is justified came in a recent study by McCrady (1994). McCrady experimentally determined uptake rate constants, termed k₁, for vapor phase 2,3,7,8-TCDD uptake into several vegetations including kale, grass, pepper, spruce needles, apple, tomato, and azalea leaves. The uptake rate for an apple divided by the uptake rate for the grass leaf was 0.02 (where uptake rates were from air to whole vegetation on a dry weight basis). For the tomato and pepper, the same ratios were 0.03 and 0.08. The VG_ag was 0.01 for fruits and vegetables in this assessment. McCrady (1994) then went on to normalize his uptake rates on a surface area basis instead of a mass basis; i.e., air to vegetative surface area instead of air to vegetative mass. Then, the uptake rates were substantially more similar, with the ratio of the apple uptake rate to the grass being 1.6 instead of 0.02; i.e., the apple uptake rate was 1.6 times higher than that of grass, instead of 1/50 as much when estimated on an air to dry weight mass basis. The ratios for tomato and pepper were 1.2 and 2.2, respectively. In his article, McCrady (1994) concludes, "The results of our experiments have demonstrated that the exposed surface area of plant tissue is an important consideration when estimating the uptake of 2,3,7,8-TCDD from airborne sources of vapor-phase 2,3,7,8-TCDD. The surface area to volume ratio (or surface area to fresh weight ratio) of different plant species can be used to normalize uptake rate constants for different plant species." McCrady does caution, however, that uptake rates are only part of the bioconcentration factor estimation, and is unsure of the impact of surface area and volume differences on the elimination phase constant, k₂ (personnal communication, J. McCrady, US EPA, ERL-Corvallis, Corvallis, OR 97333). Still, his recent experiments do appear to justify the use of a VG parameter since the air-to-leaf transfer parameter was developed on an air-to-whole-plant-mass basis, and his results are consistent with the assignment of 0.01 for fruits and vegetables.

An uncertain experimentally derived empirical factor described the transfer of compounds from soil to below ground vegetables, the Root Concentration Factor, RCF. An analagous uncertain parameter describes the transfer of vapor-phase dioxin-like compounds from air to above ground vegetations, the air-to-leaf transfer factor, B_vpa. Both of these parameters are estimated as functions of the contaminant properties; both used contaminant octanol water partition coefficient, Kow, and the B_vpa also used contaminant-specific Henry's Constant, H. The B_vpa was developed in a series of experiments by Bacci, et al. (1990, 1992) using 14 different organic contaminants and azalea leaves. Adjustments to the B_vpa as formulated by Bacci were suggested by the experiments on the transfer of 2,3,7,8-TCDD to grass leaves by McCrady and Maggard (1993). The adjustments dealt with the impact of photodegradation, which was not considered in the experimental design of Bacci, and in the different plant species used by McCrady and Maggard. Those adjustments were made for the dioxin-like compounds in this assessment. The range of log Kow for 2,3,7,8-TCDD found in the literature was 6.15 to 8.5. An alternate value of log Kow for 2,3,7,8-TCDD would more likely be higher than lower, given the 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.

5. Ingestion of Fish: The key exposure parameter for this pathway was the fish ingestion rate. The rates assumed in the demonstration scenarios were low in comparison to estimates given for subsistence fisherman or others who live near large water bodies where fish are commercially caught. The justification for the lower ingestion rate for demonstration purposes was that the setting demonstrated was described as rural, containing farms and non-farm residences, where the emphasis is on agriculture. A relatively small watershed with a small impacted water body was assumed. Daily ingestion rates of 1.2 (central) and 4.1 (high end) g/day were assumed, based on an assumption of 3 fish meals per year (150 g/fish meal) obtained from the water body for the central scenario and 10 fish meals per year for the high end scenario. Other fish ingestion rates that can be considered for exposure assessments include: 6.5 g/day characterized as a national average ingestion rate for freshwater and estuarine fish and shellfish (EPA, 1984), and 30 and 140 g/day, which are described as 50th and 90th percentile rates for recreational fisherman in areas where large water bodies are present (EPA, 1989).

Other models for estimating fish concentration based on water column concentrations, rather than suspended sediment concentrations, were described in EPA (1993) and demonstrated in this assessment. Results indicated that the water column approaches would predict similar whole fish concentrations compared with the sediment concentration approaches of this assessment. However, the various models would respond differently to changes in model parameters. For example, a bioaccumulation parameter based on whole water concentration (total contaminant, the sum of sorbed and dissolved amounts, divided by water volume) will be mostly insensitive to changes in organic carbon content of sediments. In contrast, this is a critical parameter for bioaccumulation parameters which are based on sediment concentrations (as in this assessment) or dissolved-phase water column concentrations.

A key uncertain parameter for estimating fish tissue concentrations is the Biota Sediment Accumulation Factor, or BSAF, and the Biota Suspended Sediment Accumulation Factor, or BSSAF. A range of 0.03 to 0.30 for 2,3,7,8-TCDD is hypothesized for column feeding fish, while the Connecticut data (CDEP, 1992) and some other data on bottom feeding fish indicate higher BSAFs ranging up to 0.86 for 2,3,7,8-TCDD. A value of 0.09 for 2,3,7,8-TCDD for BSAF and BSSAF is used in this assessments. Data is scarce for BSAF and BSSAF for other dioxin-like compounds, although available data does suggest that these parameter values decrease as the degree of chlorination increases. A key parameter is the fish lipid content, which can vary from below 0.05 to above 0.20. The model estimates a fish lipid concentration. Multiplying fish lipid concentration by fish lipid content arrives at a whole fish concentration or an edible fish concentration, depending on the user's assignment and characterization of the fish lipid content variable. For this assignment, the fish lipid content was assigned a value of 0.07 for the demonstration scenarios, based on lipid content of fish in EPA's Lake Ontario study (EPA, 1990a).

7. Beef and Milk Ingestion: The rates of beef and milk fat ingestion are 22 and 10.5 g/day, respectively. The median whole beef and whole milk ingestion rates are given as 100 and 300 g/day, respectively (EPA, 1989), and these were assumed for the demonstration scenarios. Beef fat and milk fat contents are assumed to be 22% and 3.5%, respectively. Only the high end demonstration scenarios included beef and milk ingestion pathways. These scenarios were farm settings, and the assumption was that farming families would obtain a portion of their ingestion of these foods would come from home produced beef and milk. The assumptions for contact fractions for beef and milk (fractions of their total consumption that comes from home supplies) was 0.44 and 0.40, respectively. These were average consumption fractions for farming families, whether or not the farm families home consumed, and were developed from a USDA (1966) survey of farming families. Since exposure estimates from these pathways are linearly related to ingestion rate and contact fraction, these are critical exposure parameters for site specific applications.

Comparison with earlier modeling approaches showed that the current approach to estimating beef and milk concentrations 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 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. However, this practice was considered in the air-to-beef food chain validation exercise. There, a 50% reduction in beef concentrations due to feedlot fattening was assumed.

Key uncertain and variable parameters for beef/milk concentrations include: 1) the assumptions concerning vapor/particle partitioning for the stack emission source category, 2) the air-to-leaf transfer parameter, B_vpa, for vapor phase contaminants, 3) beef cattle exposure assumptions, 4) the weathering factor for particles depositing on vegetations which cattle consume, and 5) uncertainties as discussed above for air to soil algorithms and soil to air algorithms.

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