Page 6 of 6 Vegetable and beef fat concentrations resulting from stack emissions

Results of sensitivity analysis of algorithms estimating above and below ground vegetables and beef fat concentrations resulting from stack emissions are shown in Figure 6-15. This examination only looks at impacts that are specific to the stack emission source category; earlier sections described impacts with the several other parameters required for these biota impact algorithms.

First, it should be noted that the trends for soil impacts are the same as those for below ground vegetables. This is because underground vegetable concentrations are a direct linear function of soil concentrations. Second, trends for milk fat concentration are not exactly the same, but very similar to those of beef fat. The only difference in the algorithms estimating beef and milk fat concentrations are the assumptions concerning cattle exposure. The general trends discussed below regarding the impact of soil, the distance from the stack, and the vapor/particle partitioning also occur with milk fat concentration estimation.

One trend to easily spot and explain is the impact of the assumption on the no-till mixing depth. This impacts beef concentrations because soil ingestion by cattle is assumed to occur on untilled soil. This test indicates that the increment of beef concentration due to soil ingestion by cattle is small; that diluting depositing residues into either 1 or 5 cm mixing depth does not impact results. This trend is different then that of the soil source categories, where soil ingestion is critical to beef/milk concentrations. The following shows the percent of beef and milk concentrations that are due to soil, pasture grass, and feed ingestion in example scenarios for on-site soil contamination (Scenario #2) and the stack emissions (Scenario #5):

Diagram V3 6-2

As seen here, soil only accounts for 5 and 3% of beef and milk concentration impacts from stack emissions in the example scenario. The principal cause for this difference in trends between the soil source and stack emission source is the differences in the soil to air trends for the soil source category versus the air to soil trends for the stack emission source category. The dichotomy in these model performances was discussed in Section above. Briefly, emissions from the soil source starting with a 1 ng/kg (ppt) soil concentrations resulted in air concentrations in the order of 10-11 m g/m3. The demonstration of the stack emission source also led to total air concentrations on the order of 10-11 m g/m3 at the farm site of exposure. However, depositions onto soils for the stack emission demonstration scenario led to no-till (1 cm mixing depth) soil concentrations on the order of 10-3 ng/kg. Since the soil source demonstration had much higher soil concentrations, its contribution to beef and milk concentration was higher as compared to the stack emission source category.

The other two model trends evaluated included impacts of different distances away from the stack emission, and the vapor/particle partitioning at the stack. The farm was assumed to be 500 meters from the stack for the high end demonstration of the stack emission source category. 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%. As seen on Table 6-2, 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. As seen on Table 6-2, vapor contributions dominated grass and feed concentrations. 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.

table Figure 6-12 Results of sensitivity analysis of algorithms estimating above and below ground vegetation, and beef and milk fat concentrations resulting from stack emissions.
From Figure 6-12, it is seen that beef fat concentrations were reduced by about one-half when using COMPDEP output from 200 m instead of 500 m. 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 fat 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.
expand table Figure V3 6-12

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.

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 seen on Table 6-2, above ground vegetables were more impacted by particle depositions than vapor transfers for the stack emission high end scenario, #5. Increasing the particle depositions would increase their overall dominance and lead to increases in vegetable concentrations.

Interestingly, the trend was different for beef fat impacts. In this case, beef fat concentrations increased when the vapor reservoir was increased, although the increase was small. The reason beef fat concentrations increased can be seen in Table 6-2. Grass and feed were more impacted by vapor phase transfers than particle depositions, the opposite trend noted for vegetables. Therefore, a rise in vapor phase concentrations will even further dominate grass and feed concentrations, the major components of beef cattle diet. With an increase in grass and feed concentrations, beef fat concentrations increase. Water and fish concentrations resulting from effluent discharges

The impacts of parameter changes for algorithms estimating water and fish concentrations are shown in Figure 6-13. First, it should be noted that fish and water impacts are included in the same graph because the impacts to both concentrations are exactly the same with the noted changes in parameters, with one exception. This exception is the partition coefficient, Koc. Increases in Koc result in higher suspended sediment concentrations, which lead to higher fish tissue concentration estimations, but lower water concentration estimations. Increasing Koc by an order of magnitude actually decreases water concentrations to 14% of its baseline value, or 0.14 on the y-axis of Figure 6-13. Decreasing Koc by an order of magnitude increases water concentrations by a factor of 2.4. Roughly, the location of the high and low Koc points on Figure 6-13 should be reversed for water concentration impacts. Also, the biota to suspended solids accumulation factor, BSSAF, and the fish lipid content, flipid, are specific to fish tissue estimations.

table Figure 6-13 Results of sensitivity analysis of algorithms estimating surface water and fish concentrations resulting from effluent discharge.
Clearly, both concentrations are mostly impacted by the loading rate - the impact is linear and direct.

Of all the parameters describing the effluent stream and the receiving water body, only the two order of magnitude change in receiving water body flow rate seems to have about an order of magnitude range of predictions.

The effluent flow rate is ultimately low in comparison to the receiving water body, so its impact is limited.

The range of organic carbon contents of the effluent and of the suspended solids in the receiving water are reasonably assigned and appear to have a small impact.
expand table Figure V3 6-13

Higher suspended solids content in the receiving water body can result in lower fish and water concentrations. This might be termed a "solids dilution effect". Few studies are available in the literature which support this result, but two studies were found which are consistent with a solids dilution effect. One "simulated field experiment" conducted by Isensee and Jones (1975) maintained a constant water concentration of 239 ppb, but reported a decrease in 2,3,7,8-TCDD concentrations in both mosquite fish (2200 ppb to 90 ppb) and catfish (720 to 90 ppb) as the amount of sediment increased from 20 to 440 g. Sherman (1992), in a review of this and other simulated field experiments and laboratory flow through experiments, points out that a bioconcentration factor for these simulated field experiments would decrease as the sediment increases.

He speculates that, in comparing water flow through experiments with field simulated data, the bioconcentration factors tend to be less in field simulated experiments because 2,3,7,8-TCDD may sorb to sediments and be less bioavailable. A second study supporting a solids dilution effect was conducted by Larsson, et al. (1992). They studied uptake of PCBs and p,p'-DDE in 341 northern pike in 61 lakes in southern Scandanavia. They found that the levels of these persistent pollutants in the fish decreased as productivity increased. Productivity was measured by total phosphorus, chlorophyll a, and lake water transparency, which was mainly influenced by phytoplankton biomass. Their hypothesis was that the levels decreased because humus adsorbs persistent pollutants, rendering them less available for uptake in fish.

The two order of magnitude range in Koc translates to about a one order of magnitude range in estimated fish and water concentration estimations. Fish tissue concentrations are linearly and directly related to the BSSAF and flipid. About an order of magnitude of concentration estimation is noted with about the same order of magnitude in likely values. Water and fish concentrations resulting from stack emissions

Results of sensitivity analysis of algorithms estimating surface water and fish concentrations resulting from stack emissions are shown in Figure 6-14. First, it is noted that the impact to both these media is the same with impacts to all parameters. The impact with changes in the deposition of particles onto the water body, RDEPp, and with the fraction of deposited particles remaining in suspension, fsd, is negligible. The assigned values to these parameters for the demonstration are, therefore, sufficient for any purpose.

It is importantly noted the COMPDEP model (or other atmospheric transport models) do not need to estimate the concentration of contaminants on emitted particles - all that is required are mass emissions of contaminants (in g/sec units) and the delineation of size fractions of particles emitted. The COMPDEP model does not require a particle emission rate. An assumption of a greater deposition of particles directly into surface waters might translate back to an assumption of particle emissions.

table Figure 6-14 Results of sensitivity analysis of algorithms estimating surface water and fish concentrations resulting from stack emissions.
The RDEPp is only required to maintain a mass balance of solids entering the surface water body, and as it turns out, particles entering surface waters by this route are only a miniscule part of the total solids entering the body. There are no impacts to water or fish concentration estimations with reasonable values for this parameter. The same appears true for fsd, which determines the extent to which directly depositing contaminants remain in suspension. The assigned value of 1.00 (meaning that all directly depositing contaminants remain in suspension), based on the argument that particles emitted from stack are likely to be lighter than eroding soil particles, appears sufficient for general purposes.
expand table Figure V3 14

Water body impacts are linearly related to the average watershed depth of mixing. The value assigned for the demonstration scenario was 0.1 m, which is midway between the value assumed for non-tilled conditions, 0.01 m, and tilled conditions, 0.20. The value of 0.10 m suggests that half the watershed is tilled. The linear relationship underscores the importance of this uncertain parameter, and also suggests that erosion drives water body impacts rather than direct deposition. This trend is also apparent for the tests on depositions to the watershed, the RDEPwat input, versus depositions directly onto the surface of the water body, the RDEPsw input.

The impact to changes in RDEPwat are roughly linear - doubling the watershed depositions doubles the water body impacts, and so on. Only a marginal impact was noted for the same change to RDEPsw. To further evaluate this trend, another test was undertaken. In this test, it was assumed that the water body was much larger and so was the amount of land draining into the water body. Further, the stack emitter was next to the water body.

Two groups of changes were made in this test. First were physical changes to the water body and the watershed - water body volume, Vwat, was increased from 1.52*1012 to 1.52*1014, the water surface area, Awat, was increased from 32200 to 322000 (a 100-fold increase in water body volume might assume, for example, water body surface area increasing by a factor of ten and depth increasing by a factor of ten), the land area draining into the water body was increased 2 orders of magnitude from 4000 ha to 400000 ha, and the sediment delivery ratio, SDw, was decreased from 0.15 to 0.04.

The second group was on contaminant deposition rates. The deposition rate onto watershed was that modeled to occur at 5000 meters, 1.5*10-7 m g/m2-yr, instead of 500 meters, 1.2*10-6 m g/m2-yr, and the deposition rate onto the water body was modeled for 200 meters, 2.2*10-6 m g/m2-yr, instead of that at 500 meters. The result in Figure 6-14, labeled "large watershed" indicates that these changes dropped water impacts by about an order of magnitude, with lower unit inputs from erosion contaminant delivered per hectare of land combined with the larger water body driving the result.

However, direct depositions were proportionally more important to impacts. For the base conditions, direct depositions only accounted for 3% of the to the water body. For this large watershed test, direct depositions accounted for 25% the impact. Other changes could minimize the impact from the watershed, such as erosion rates or lower sediment delivery ratios. Still, the implication from these tests that direct deposition is of, possibly, much less importance to water body compared to depositions onto soils followed by soil to surface water transport.

6.3.4. Key Trends from the Sensitivity Analysis Testing

These are as follows:

1) Source terms are the most critical for exposure media impacts.
Source terms include soil concentrations, stack emission rates, and effluent discharge rates. In nearly 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, 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 - fish lipid concentrations are a linear function of the 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 parameters in this range - soil properties, wind speeds, vegetation yields, and others. It is important, nonetheless, to carefully consider all the model parameters. While impacts were generally within an order of magnitude of the values selected for the demonstration scenarios, there was often an order of magnitude or more difference between plausible high and low values for individual parameters.

4) A principal trend of note concerns the air to soil algorithm for the stack emission source category compared to the soil to air algorithm of the soil source category.
With a 1 ppt soil concentration of 2,3,7,8-TCDD, air concentrations are estimated to be in the 10-11 m g/m3 range. Atmospheric transport modeling in the demonstration stack emission source resulted in a delivery of 2,3,7,8-TCDD to result in an air concentration of the same magnitude, 10-11 m g/m3. Both models would have predicted similar impacts to vegetations and beef/milk had the deposition rates from the atmospheric transport modeling led to soil concentrations around 1 ppt. However, the tilled and untilled soil concentrations estimated for the stack emission source were 5*10-5 and 10-3 ppt, respectively.

This had several primary and secondary impacts, such as below ground vegetables had much higher concentrations for the soil source demonstration scenario, soil was significantly more critical in predicting beef and milk fat concentrations in the soil source category as compared to the stack emission source category, and so on. Exercises described earlier in Section suggest two model performance trends which would narrow the gap in the air-to-soil algorithm of the stack emission source category and the soil-to-air algorithms of the soil contamination source category:

1) evidence suggests that the volatilization/dispersion algorithms of the soil source category are underestimating air concentrations, and
2) the deposition algorithms of the stack emission source category are underestimating soil concentrations. Lack of data allows for definitive conclusions for either of the hypotheses; further analysis of model performance is called for.


As has been discussed in this document more than once is the characterization of this methodology as a screening level methodology. Steady state, equilibrium partitioning, and assumptions of nondegradation of source strengths are key assumptions which lead to this qualification. Stacks are assumed to emit a constant amount of contaminant over a duration of exposure for the stack emission source category. Effluent discharges are assumed to continue unabated over a duration of exposure.

These are both reasonable assumptions for evaluating the long-term impacts of these sources where no change in practices occur. Any violation of mass balance principals will, therefore, not be examined for these sources. The same assumption of unabated and constant releases might be questioned, however, for the soil contamination source categories, the on-site and the off-site source categories. Soil concentrations are assumed to remain constant, despite mechanisms which would dissipate concentrations over time. Volatilization and transport off-site, and wind erosion and transport off-site, are two mechanisms which dissipate residues into the air and deplete the source strength. Soil erosion off the site to a nearby exposure site and to nearby water bodies also is a mechanism of release.

A key dissipation mechanism is soil degradation. There is evidence that photolysis is a mechanism of degradation of dioxin-like compounds, as discussed in Chapter 2 of Volume II of this assessment. However, this would only apply to those residues directly on the soil surface and, as such, it may be reasonable to make an assumption of nondegradation if a concurrent assumption is that residues exist below the soil surface. In any case, releases for a bounded area of soil contamination including volatilization, wind erosion, and soil erosion, which are estimated for purposes of estimating off-site impacts, are not also used to estimate dissipation of the reservoir of contaminant in the soil. Said another way, the amount lost via these pathways is not a function of a soil reservoir which decreases over time.

The purpose of this section is to examine this assumption for the case of a bounded area of high soil concentration. The demonstration of the off-site soil source category will be the focus of discussions below, although the same principals are relevant for the onsite soil source category. First, an estimate of the "reservoir" of 2,3,7,8-TCDD that is implied with the default parameters will be made. Then, an estimate of the rate at which this reservoir dissipates using the solution algorithms for dissipation: volatilization and wind erosion flux from soils, and soil erosion, will be made.

Other routes of dissipation that will be examined are the soil ingestion by cattle and children, the loss via dermal contact, and the removal via harvest of below ground vegetation. These will be shown to be minuscule in comparison to air and soil erosion. The loss of soluble residues via surface runoff or leaching will be evaluated. Surface water bodies and above ground vegetations are sinks for dioxin-like compounds and therefore are not mechanisms of soil dissipation. If it can be shown, for example, that it takes several hundred years to dissipate a given reservoir, then it may be fair to conclude that exposures assuming non-dissipation over a 20 or even a 70 year exposure period are not significant overestimates. 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.

As will be shown, the rates of reservoir dissipation are very important considerations for soil contamination. Users of this methodology should consider dissipation of available residues and the discussions below when determining the duration of exposure for site-specific assessments. A recommended rule of thumb for users of this methodology is to evaluate the time to dissipation using the methodology below, and if it is less than or even near the assumed period of exposure (2 years to dissipate versus 20 years of assumed exposure, e.g.), then it may be appropriate to assign a duration of exposure equal or less than the calculated time to residue dissipation.

One of the key parameters in determining how rapidly residues will dissipate is one which is not required for this methodology. This is the depth of contamination. This depth, plus the initial concentration and the areal extent of contamination, describe the full extent of the source strength. The exercises below have assumed a shallow depth of 0.15 meters, or 6 inches, in soil. The impact of this assumption is demonstrated below. Also, the exercises below are specific to 2,3,7,8-TCDD.

The demonstration of the off-site soil contamination source category were as follows: 40,000 m2 soil contaminated with an initial concentration of 1 m g/kg (ppb). It is assumed that the contamination extends to 0.15 meters (6 inches).

Diagram V3 6-3

Step 2. Now estimate the amounts lost by various routes of dissipation

- Volatilization:
Volatilization flux is a function of exposure duration, with less average flux calculated over longer durations - this is, in fact, the only model algorithm which accounts for reservoir depletion over time. The durations of exposure for the high end scenarios was 20 years. The release rate via volatilization is given as the term FLUX and is shown in Equation (4-13) in Chapter 4. Plugging in baseline parameter values for 2,3,7,8-TCDD and a duration of 20 years results in a calculated flux of 1.12x10-18 g/cm2-sec. Over a year and over the 40,000 m2 contaminated area, this translates to an annual dissipation rate of 0.014 g/yr of 2,3,7,8-TCDD.

- Wind erosion:
Unlike the volatilization algorithm, the flux due to wind erosion is not dependent on the duration of exposure. The wind erosion algorithm is described in Section 4.3.3 in Chapter 4. Plugging in baseline parameter values results in a flux of 2,3,7,8-TCDD of 5.74x10-20 g/cm2-sec, or an annual flux over the 40,000 m2 contaminated area of 0.0007 g/yr.

- Soil erosion:
The annual erosion rate off the contaminated site was 21515 kg/ha-yr. This rate was assumed to erode towards the exposure site as well as towards the impacted surface water body. However, it would not be appropriate to double that quantity since it is used in two different algorithms - the exposure site could be in the direction of the water body, for example. Or, if applied to a specific site, one could ascertain that the exposure site is upgradient from the contaminated site, and so on. In any case, 21515 kg/ha-yr can be translated to a cm/yr of soil erosion as follows:

Diagram V3 6-4

Therefore, 21515 kg/ha-yr translates to a loss of soil equal to 0.14 cm/yr. Given that 9 g 2,3,7,8-TCDD are estimated to occur in 15 cm, the annual loss of 2,3,7,8-TCDD is 0.084 g/yr.

- Runoff and Leaching:
Transport via water are not considered in this methodology since the dioxin-like compounds are so tightly sorbed that these are expected to be negligible. An estimate of loss via water will nonetheless be made for this exercise. Surface water body volume was estimated assuming a runoff rate of 15 in/yr, which was defined as all surface water contributions (surface runoff, interflow, and ground water recharge). This is a reasonable estimate for water-borne losses for this exercise. The annual amount of 2,3,7,8-TCDD lost in this water can be estimated using the soil partition coefficient, Kds, relationship, which is Cs/Cw. Kds is equal to 27,000 for 2,3,7,8-TCDD (organic carbon partition coefficient, Koc * soil organic carbon, OCsl), so the concentration in water, Cw, given a soil concentration, Cs, of 1 m g/kg, is 3.7x10-5 m g/L, or 3.7x10-11 g/L. Translated to a 40,000 m2 area, 15 in/yr equals 1.524x107 L, so the total annual loss in water equals 0.00056 g/yr 2,3,7,8-TCDD.

Except for soil degradation, these are the dissipation routes that would be considered for a site of soil contamination that is not used for any purpose - residence, agriculture, and so on. For the sake of completeness, other routes that will be looked at now include soil ingestion, soil dermal contact, and harvesting of underground vegetations.

- Soil Ingestion:
Soil ingestion by children in the high end scenario is 800 mg/day, or 0.29 kg/yr. Soil ingestion by cattle will also be considered. First, an assumption of how many cattle would be feeding on a 40,000 m2 area should be made. A daily cattle dry matter ingestion rate is 19 kg/day. For beef cattle that are assumed to principally graze, for 90% of their dry matter intake, the daily ingestion of grass would be 17.1 kg/day, and their daily intake of soil while grazing, 8% of total dry matter intake, is 1.52 kg/day. With this daily ingestion of grass, their annual need for grass would be 6200+ kg/yr. The yield of grass assumed for other purposes in this assessment was 0.15 kg/m2-yr dry weight, or 6000 kg/40,000 m2-yr. Therefore, it appears that one grazing cow requires the 40,000 m2 to himself (as a rough approximation). The annual intake of soil by this cow equals 555 kg/yr, which as expected, is much higher than child soil ingestion. The annual removal of 2,3,7,8-TCDD by cattle soil ingestion is 555 m g/yr, or 0.0006 g/yr.

- Dermal Contact:
The dissipation of 2,3,7,8-TCDD residues via dermal contact is estimated as, NE*CA*CR*Cs, where NE = number of dermal contact events per year, which equals 350 in the high end scenario, CA = contact area, which equals 1000 cm2 in the high end scenario, CR = contact rate, which equals 1 mg/cm2-event, and Cs = 2,3,7,8-TCDD concentration, which is 1 m g/kg, or in more convenient units, 10-12 g/mg. The annual loss via dermal contact is negligible at 3.5x10-7 g/yr.

- Underground Vegetation Harvest:
The yield of vegetables required for other algorithms of this assessment, is 7.8 kg/m2 fresh weight. The concentration in underground vegetables that would occur, given the algorithms of this assessment, is 0.0015 m g/kg fresh weight. Therefore, the removal per m2 is 0.0117 m g/m2, and the removal over 40,000 m2 in g/yr is 0.0005 g/yr if all the 40,000 m2 were devoted to underground vegetables.

This exercise has shown that the principal mechanism of removal is soil erosion at 0.084 g/yr 2,3,7,8-TCDD, with volatilization explaining 0.014 g/yr removal. The sum of these two routes is 0.098 g/yr, and the sum of all the other routes examined briefly above 0.002 g/yr, leading to a round total estimate of 0.1 g/yr. With an initial reservoir of 9 g/yr, it would take 90 years to dissipate the available reservoir, not including degradation and assuming that surface concentrations remain constant. These two latter considerations are screening level considerations in the sense that attempting to model both (i.e., degradation leading to lower soil concentrations, volatilization from deeper depths as surface concentrations decline, etc.) would lead to lower releases and lower off-site impacts. Modeling them both would also, however, lead 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. As this section began, the algorithms of this assessment are characterized as screening level methodologies. Users of this methodology should be cognizant, nonetheless, of the possibility of depleting a reservoir of soil contamination prior to an assumed duration of exposure.

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