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CONTINUED 4.3.2. Vapor-Phase Air Concentrations

. Dc:

Molecular diffusivities in air of the example compounds could not be found in the literature. However, diffusivities of one compound can be estimated from another with the following (Thibodeaux, 1979):

Equation V3 4-18

Thibodeaux (1979) lists the molecular diffusivity of diphenyl at 25 C at 0.068. Given the molecular weight of diphenyl of 154 g/mole, the diffusivities of the example compounds are:

  • 2,3,7,8-TCDD (MW = 322) = 0.047;
  • 2,3,4,7,8-TCDF (MW = 340) = 0.046; and
  • 2,3,3',4,4',5,5'-HPCB (MW = 396) = 0.043.

. Um:

Mean annual windspeeds vary from between 2.8 and 6.3 m/s (EPA, 1985b). An assumption of 4.0 m/s in the absence of site-specific average wind speeds was made for the example scenarios of this assessment.

. a, b, z, and x:

Simple assumptions can be made to assign values to the length terms above: a, b, z, and x. Assuming a square-shaped contaminated site, a equals b which equals the square root of the area of the site. A common assumption for z, the height of the exposed individual, is 2 m. The x term can be assumed equal to a side length (a or b), or can equal the side length plus the distance to the exposed individual if the contamination is not on-site and dispersion is modeled as "near field." For the residence and farm setting examples in Chapter 5, where the contamination was on-site, the x term was equal to a side length.

4.3.3. Particulate-Phase Air Concentrations

The method for determining the flux of soil particles due to wind erosion for on-site conditions was developed in EPA (1985b) based on Gillette's (1981) field measurements of highly erodible soils. A key assumption for this solution is that the soil surface is assumed to be exposed to the wind, uncrusted, and to consist of finely divided particles. This creates a condition defined by EPA (1985b) as an "unlimited reservoir" and results in maximum dust emissions due to wind only. This wind erosion flux is given as (EPA, 1985b):

Equation V3 4-19

The following is offered as guidance specific to on-site conditions:

. V:

For a "residence" or "farm", grass or crops are likely to substantially cover the soil, and the fraction of vegetative cover can range from 0.5 (minimal coverage) to 0.9 (more lush coverage). For the residence example settings, V was set at 0.9 which assumes a continual grass cover over the contaminated soil. The V for the farm settings was instead 0.5. The area of contamination for the example farm settings was larger than the residence setting, 10 acres to 1 acre. The land where crops were grown was also contaminated; the 0.5 value for V assumes that the cropland is totally or partially bare at some times - perhaps during spring land preparation and fall harvest.

. Um, Ut:

As given above in Section 4.3.2. on vapor phase air concentrations, the mean annual wind speed, Um, assumed in the example scenarios was 4.0 m/s. The threshold wind velocity, Ut, is the wind velocity at a height of 7 m above the ground needed to initiate soil erosion. It depends on nature of surface crust, moisture content, size distribution of particles, and presence of non-erodible elements. It can be estimated on the basis of the following procedure (EPA, 1985b):

Step 1. Determine the Threshold Friction Velocity

This is the wind speed measured at the surface needed to initiate soil erosion. EPA (1985b) shows how it can be determined as a function of soil aggregate size distribution. However, for the "unlimited reservoir" approach for which Equation (4-19) was developed, soil particles are assumed to be fine at 1.5 mm or less as an average. This translates to a threshold friction velocity of 75 cm/s and less. A value of 50 cm/s might be reasonably assumed to be representative of these types of surfaces, and was assumed for this assessment.

Step 2. Estimate the "Roughness Height"

EPA (1985b) graphically shows the roughness height for a range of possible conditions. Included in this range are a roughness height of 0.1 cm for natural snow, 1.0 cm for a plowed field, 2.0-4.0 cm for grassland, 4.0 cm for a wheat field or for suburban residential dwellings, and up to 1000 cm for high rise buildings. The assumption made for the residence and farm example settings was 4.0 cm, following the information given for a wheat field or a suburban residence.

Step 3. Estimate Ratio of Threshold Wind Speed at 7 m to Friction Velocity

A chart provided by EPA (1985b) shows this ratio as a function of roughness height. For a roughness height of 4.0 cm, this ratio is seen to be 13.

Step 4. Estimate Threshold Wind Speed

This is simply the product of the ratio given in step 3 above and the friction velocity. Using values given above, 50 cm/sec * 13 = 6.5 m/sec.

. F(x):

 

The model-specific function, F(x), is determined by first calculating the dimensionless ratio x, where x = 0.886 Ut/Um, and finding F(x) from a chart of F(x) versus x, as provided in EPA (1985b). For Ut = 6.5 and Um = 4.0, x = 1.44 and F(x) = 1.05.

The unit dust flux is easily converted to a total contaminant flux by multiplying by soil concentration and area:

Equation V3 4-20

The next step in estimate particulate-phase contaminant concentration is to estimate the dispersion term. The model that is used is the same as the one used to estimate on-site vapor phase dispersion given in Equation (4-16) above. The following two changes obtain the correct "FLUX" term for use in Equation (4-16):

1) Instead of WE, a total flux term presented here in units (g/sec) consistent with other particulate flux terms discussed in this chapter, an appropriate "FLUX" for Equation (4-16) is a unit flux term: Cs*Ee (ng/m2-hr). Since the algorithm for Ee was developed for 10 m m size particles, the multiplication of Ee by Cs assumes that the concentration of contaminant on 10 m m size particulates is the same as that for the soil overall.

2) Cs*Ee is still not in the right units for Equation (4-16). The conversion term of Equation (4-16), 1010, should instead be, .00028.

Substituting Cs*Ee for FLUX, and .00028 for 1010 in Equation (4-16) will allow for the estimate of Cpa, the particulate phase concentration of contaminant in air, in units of m g/m3.

4.3.4. Biota Concentrations

This section summarizes the algorithms to estimate contaminant concentrations in fish, vegetation (including vegetables for human consumption and pasture grass or fodder grown on contaminated soil for beef cattle consumption), beef, and milk. As will be shown, all algorithms are simple empirical equations which relate an environmental media concentration to a biota concentration, using a "biotransfer" or "bioaccumulation" factor.

4.3.4.1. Fish concentrations

The procedure and supportive data for the algorithm to estimate fish tissue concentrations can be found in Cook, et al. (1991), and more recently in an assessment of risk of 2,3,7,8-TCDD to aquatic life and associated wildlife (EPA, 1993) which EPA is conducting as part of its reassessment of dioxin-like compounds. The information in those reference focuses on 2,3,7,8-TCDD, although there is discussion on the related compounds covered in this assessment including other PCDDs, PCDFs, and PCBs. These compounds share a high degree of hydrophobicity that increases as the degree of chlorination increases. Cook, et al. (1991) note that this corresponds in general to an increase in lipophilicity and an increase in ability to bind to organic carbon in sediments and to dissolved organic matter in water.

However, these tendencies are not paralleled by an increase in bioaccumulation. Only the 2,3,7,8-chlorine-substituted congeners are substantially bioaccumulated by fish, although large quantities of other PCDD and PCDF congeners are found in sediments. This pattern of bioaccumulation results because of higher rates of metabolism of PCDDs and PCDFs in fish as compared to the 2,3,7,8-chlorine-substituted congeners (EPA, 1992; Cook, et al., 1991, with references to Muir et al., 1986; Gobas, 1990). While the highly chlorinated 2,3,7,8-substituted congeners are very slowly accumulated, they have very slow elimination rates.

2,3,7,8-TCDD and other planar polyhalogenated aromatic hydrocarbons often have not been detected in water from aquatic ecosystems even when biota are highly contaminated. Because surface layers of bottom sediments are a good indicator of the relative amount of chemical in the system over the time scale involved for bioaccumulation of super-hydrophobic chemicals, a term known as the Biota to Sediment Accumulation Factor, or BSAF, has been offered as a measure of site-specific bioaccumulation potential. This term was recently proposed to replace equivalent terms which were known as the Bioavailability Index, or BI (Kuehl, et al., 1987; Cook, et al., 1991; EPA, 1990b), the Accumulation Factor, AF (Lake, et al., 1990) and the Biota to Sediment Factor, or BSF (Parkerton, et al., 1993; Parkerton, 1991; Thomann, et al., 1992). BSAF is defined as:

Equation V3 4-21

The surface water algorithms estimate concentration of contaminant in bottom sediments (see Section 4.3.1 above).

This concentration, Csed, can be converted to an organic carbon basis as a function of OCsed:

 

Equation V3 4-22

The organic carbon content of bottom sediments was assumed to 0.03; see Section 4.3.1. for the derivation of Csed.

Since the accumulation of contaminant is assumed to occur only in fish lipid, a correction term to estimate the whole fish tissue concentrations is needed since fish consumption in g/day refers to whole fish consumption. The correction term is simply flipid, and so whole fish concentrations are simply Clipid * flipid.

The BSAF was developed as a measure of bioaccumulation potential rather than as a predictor, as it is being used here. It is uncertain as to whether measured BSAFs are generally applicable to other water bodies. Efforts are underway to evaluate the general applicability of BSAFs (P. Cook, Duluth Environmental Research Laboratory, US EPA, 6201 Congdon Boulevard, Duluth, MN 55804, personal communication). Using the BSAF approach as a predictive tool greatly underplays the complexity of the processes transferring contaminants from aquatic ecosystems to aquatic organisms. EPA (1993) provides a comprehensive discussion on aquatic impacts and processes for 2,3,7,8-TCDD and related compounds. Following are some of the key issues to consider:

1) Resident vs. Migratory Species:
Parkerton (1991) applied a bioenergetics-based bioaccumulation model in an attempt to duplicate BSAFs for 2,3,7,8-TCDD found for carp and blue crabs in the Passaic River, New Jersey. He showed nearly a ten-fold difference in 2,3,7,8-TCDD BSAF calculated from data for resident species as compared to migratory species in the Passaic River. This would be expected for fish which also reside part of the time in relatively clean water bodies; migration would enable depuration of residues from fish. The possibility that migration patterns might explain some of the results for fish concentrations of 2,3,7,8-TCDD in the Lake Ontario bioaccumulation study was also raised (EPA, 1990b). That assessment also discussed a related issue of concern - to consider lakewide average sediment concentrations or concentrations near where sampled fish were captured in calculating the BSAF. Even within a large lake, more sedentary populations of fish may be impacted by localized contamination.

2) Past history of contamination:
If contamination of surface water bodies with hydrophobic compounds like the dioxin-like compounds has occurred principally in the past, then it can be expected that most of the contamination occurs in or near the bottom sediment layer and not within the water column. Furthermore, if inputs to water bodies are declining or low in comparison to past loadings, then sediments would be undergoing depuration - residue levels would be declining, and the system may not be equilibrium. EPA (1990b) noted that very low BAF*s (defined as a fish to sediment ratio not including the sediment organic carbon and fish lipid considerations of BSAFs) and BSAFs for 2,3,7,8-TCDD in Lake Ontario contrasts higher BAF*s for other hydrophobic compounds such as DDE or PCBs.

An explanation offered is that loadings to the Lake may be declining, such that there is a substantial disequilibrium between sediments, water, fish, and their prey. One parameter required in the bioenergetics model Parkerton (1991) used (referred to in the above bullet) was a ratio of contaminant concentration in bottom sediment to that in suspended sediment, rs/rw. In modeling exercises on the Passaic River, he found closer agreement between measured and predicted BSAFs with this ratio equal to 10 in contrast to 1, the only two values tested; a ratio of ten means that the concentration of contaminant in bottom sediment is ten times higher than it is in the suspended sediment. BSAFs predicted by the model were developed as the ratios in modeled fish lipid concentrations divided by modeled bottom sediment organic carbon normalized concentrations.

Measured BSAFs used actual Passaic River fish lipid and bottom sediment concentrations of 2,3,7,8-TCDD. BSAFs predicted with this ratio equal to 1 were roughly 4 times as high as measured BSAFs, and BSAFs found with rs/rw equal to 10 were twice as high as measured. A related result of his modeling exercise was that, at best fit between modeled and measured BSAFs where the rs/rw was 10, dietary exposures explained over 50% of the BSAFs for carp and 85% in blue crabs, in contrast to water column exposures. He speculates that prey organisms consist of benthic animals which ingest contaminated bottom sediment. If the food chain begins near bottom sediments, and if food chain exposures are a principal explanation for fish tissue dioxin concentrations, than it follows that a model would perform better when bottom sediment concentrations drive fish tissue concentrations rather than water column concentrations, or equivalently, when rs/rw = 10.

Finally, he notes that 2,3,7,8-TCDD contamination in Passaic river largely occurred as a result of historical loadings. The picture that emerges from Parkerton's modeling is as follows: sediments are serving as an internal source of contaminants due to past historical loadings, and the water column is in disequilibrium with bottom sediments and driven only by depuration of bottom sediment concentrations. The bioaccumulation of these compounds in carp and blue crabs appears to be mediated by trophic transfer via the benthic foodweb. In both the Lake Ontario and Passaic River studies, concentrations of 2,3,7,8-TCDD were higher in deeper bottom sediments as compared to surficial bottom sediments - this implies historical loadings and possibly depuration of surficial residues.

This issue is non-trivial for the methodology of this assessment, since an assumption for deriving suspended and bottom sediment concentrations is that the contamination is ongoing, and that the hypothetical water body may be closer to a state of equilibrium as compared to situations where contamination was principally in the past. The BSAF assumed for 2,3,7,8-TCDD in the demonstration scenarios of 0.09 is more in line with data from EPA (1990b) on Lake Ontario and from Parkerton (1991) from data in Passaic River, then with other data (presented later) where historical loadings are not as clear a principal source of bottom sediment contamination. The issue of ongoing versus historical contamination should be considered when assigning site-specific BSAFs.

3) Variations among fish species:
Feeding habits, age, migratory patterns, and lipid contents (including lipid content of edible vs. inedible fish tissues) are just a few of the interacting factors which determine a site-specific BSAF as a function of fish species. The demonstration of this approach in Chapter 5 assigns a single BSAF to each of the three example contaminants. Although not unlike other simplifications of this assessment, such approaches are recognized as oversimplifications.

4) Study designs to obtain BSAFs:
Although there is some evidence that BSAFs specific to a contaminant may be applicable to other aquatic settings, data to evaluate such a hypothesis is still sparse. Even data sets that do exist need to be carefully evaluated before deriving BSAFs. Such an evaluation should consider sediment as well as fish species data. Critical factors for sediment sampling include location, number, depth of sampling, variability, availability of organic carbon fraction information, and so on. Similar issues are pertinent for fish sampling and analysis.

Following now are guidance for the terms required for estimating fish tissue concentrations.

. BSAF:

Table 4-1 summarizes literature from which biota sediment accumulation factors for dioxin and furan congeners could be developed. Only five sets of data were found in the literature. The data from the Wisconsin River (Kuehl, et al. 1987) and that from 1 lake in Sweden (Kjeller, et al. 1990) both show decreasing BSAF with increasing chlorination. The BSAF of 2.94 for 2,3,7,8-TCDD determined from a lake in Sweden should be questioned since it is more than an order of magnitude different than any of the other data. Causes for this discrepancy could be manifold. Some observations from Kjeller, et al. (1990) might shed some light on this result. Although sediment data was from three water bodies, 8 of the 9 Pike samples (pike samples were composites of 2-5 fish from one location in the water body) were from one of the water bodies. This is why only data from the one water body was summarized in Table 4-1.

This water body, Lake Vanern, was clearly the most contaminated of the three water bodies studied. A paper mill was located at the northern part of this lake and the authors concluded that discharges from this mill impacted the lake. The average of 2,3,7,8-TCDD and 2,3,7,8-TCDF organic carbon normalized concentrations for five sediment samples from this lake was 297 pg/g; the analogous average concentration for 10 samples taken from another lake, Lake Vattern (6 samples), and a river, Dala (4 samples), was 65 pg/g.

A similar disparity between Lake Vanern and the other water bodies is found with the penta-CDD/CDF concentrations: 205 pg/g vs. 108 pg/g, with similar comparisons for the hexa-, hepta, and octa-CDD/CDF. The sediment and corresponding pike sample nearest this mill had the highest concentrations reported - pike samples were given as 3000 and 833 pg/g lipid normalized 2,3,7,8-TCDF and 2,3,7,8-TCDF (a composite from 5 pike taken at this sampling station), respectively, and sediment was 1800 and 244 pg/g organic carbon normalized for 2,3,7,8-TCDF and 2,3,7,8-TCDD. Note the BSAF for 2,3,7,8-TCDD implied from this data point is 3.41.

Another consideration for high BSAFs might be the source of contamination. Speculation from the Lake Ontario and Passaic River field data was that contamination principally occurred in the past, whereas in the Swedish data, contamination appears to have been ongoing at the time of sampling. This might be one indication that BSAFs for aquatic systems where contamination is ongoing might be greater than from systems where the contamination is primarily historical.

table Table 4-1 Available Biota to Sediment Accumulation Factors, BSAF, for dioxin-like compounds.
The Swedish data also illustrates some of the complexities of interpreting literature data. First, the sediment data was expressed concentrations normalized to "sediment contents of organic material" (sic).

This was interpreted as organic matter normalized, not organic carbon normalized. Parkerton (1991) assumed that organic carbon was 45% of organic matter to derive BSAFs when organic carbon data was unavailable; following this lead, organic matter normalized concentrations in Kjeller, et al. (1990) were divided by 0.45 to arrive at organic carbon normalized concentrations.
expand table Table V3 4-1

Also, there was not an exact match in "sites" between sediment samples and fish samples; these sites were physical locations within the large lake where samples were taken.

There were five sites where sediment samples were taken, and five sites where composited pike samples were taken in Lake Vanern. However, one of the sediment and one of the pike samples were from unique sites; only four sites had both sediment and pike samples. The results in Table 4-1 were derived using average sediment and pike concentrations from only these four sites. Another way to have derived BSAFs would be to average all lake sediment and pike concentrations; since there may be some relationship between sediment and pike concentrations based on lake location, it was decided to include only the four sites with both fish and sediment samples. Finally, there were two sets of results listed for 1,2,3,4,6,7,8-HpCDF as though there were two unique sets of analyses for the same congener; this is why there are two entries for this congener in Table 4-1.

A complete discussion of the data generated by the Connecticut Department of Environmental Protection (CDEP, 1992) is included in Chapter 7, Section 7.2.3.2. Generally, water bodies tested were mostly in rural/suburban settings rather than urban settings. Concentrations of 2,3,7,8-TCDD in surface soils and bottom sediments were in the low ppt level, indicating background impacts. BSAFs generated with that data were 0.24 to 0.85 for TEQs, 2,3,7,8-TCDD, 2,3,7,8-TCDF, and 2,3,4,7,8-PCDF.

Excluding the Swedish data, there are 26 reported BSAFs for dioxin-like congeners in Table 4-1. These range from 0.009 to 0.93, with lower BSAFs associated with higher chlorinated congeners. A BSAF of 0.09 will be assumed for 2,3,7,8-TCDD in the demonstration scenarios in Chapter 5. Although there is indications of declining BSAFs with increasing chlorination, there is probably not sufficient grounds to assign a BSAF for the second example compound, 2,3,4,7,8-PCDF, significantly different from that of 2,3,7,8-TCDD. The BSAF for this example furan will also be 0.09. In demonstrating the suite of dioxin-like congeners for the stack emission scenario, a profile of BSAF values is crafted generally reflecting the trend of lower BSAF for higher chlorinated congeners, but this profile cannot be rigorously defended, for obvious reasons.

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

EPA (1990b) estimates BSAFs for PCBs and other selected chemicals (DDE, HCB, etc.) for Lake Ontario from several data sets. Parkerton, et al. (1993) summarizes BSAFs for PCBs and other compounds from other water bodies using other data sets. A selected summary by water body taken from these two sources for PCBs is given in Table 4-2.

Two trends are apparent. First, the BSAFs for PCBs appear to exceed those of the dioxin and furan congeners by an order of magnitude and more. Second, and from limited data, it would appear that BSAFs increase from dichloro- through hexa- or perhaps hepta-chloro PCBs, and then decrease thereafter. An assignment of a BSAF for 2,3,3',4,4',5,5'-HPCB is not apparent from the data summary below. The data point from Siskiwit for the single heptachloro-PCB, which was 2,2',3,4',5,5',6-HPCB, was estimated by Parkerton (1991) as 12.5. The BSAF for flounder from New Bedford Harbor estimated by Parkerton (1991) was 0.84, with BSAFs for lobster and crab as 1.29 and 2.74, respectively. A value of 2.00 is assigned to 2,3,3',4,4',5,5'-HPCB for the example scenarios in Chapter 5.

Finally, it should be noted that these assignments are based on data on vertebrate rather than invertebrate aquatic species. It is generally recognized that invertebrates do not possess the enzymatic capability to metabolize hydrophobic compounds as effectively as higher chordates. As a result, invertebrate species such as mussels, clams, oysters, shrimp, crabs and lobsters may have BSAF values much higher than those observed for fish. Parkerton (1991) and Parkerton, et al. (1993) reviewed the literature to estimate BSAFs of 1 to 5 for species including grass shrimp, sandworms, deposit feeding clams, and blue mussel for PCDD/PCDFs and PCBs.

table Table 4-2 Available Biota to Sediment Accumulation Factors, BSAF, for PCBs.  
expand table Table V3 4-2

flipid:

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

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

Different lipid contents have been reported for the same fish, so generalizations are difficult to make at this point. EPA (1990b) lists percent lipid contents for Lake Ontario fish including brown trout:

14.3%, lake trout:
21.1%, coho salmon:
6.45%, yellow perch:
5.2%, and white perch:
17.1%. Kuehl, et al. (1987) lists a range of percent lipid for carp taken at different days during a study of between 13.0 and 18.7%.