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6.3.3.9. Estimation of below ground vegetation concentrations

One important factor to note up front about below ground vegetable concentrations as compared to above ground vegetable concentrations (no underground fruits are assumed in this assessment) is that below ground vegetable concentrations are about two orders of magnitude higher than above ground vegetable concentrations for the soil contamination demonstration scenarios. Since no fruit is assumed to be grown underground, this difference does not affect fruit ingestion exposures. However, 28 g/day of a total of 106 g/day vegetable ingestion is assumed to be underground vegetables. Given the difference in concentration estimations, below ground vegetables explain 97% of the total exposure via ingestion of impacted vegetables.  Sensitivity of underground vegetable concentrations to parameter changes for the soil contamination source category becomes important from this perspective.

table Table 6-3. Results of the sensitivity test of modeling vapor/particle partitioning for volatilized residues (note: soil concentration equals 1 ppt in tests below).
On the other hand, the trend for the stack emission source category is exactly the opposite - above ground vegetable concentrations exceed below ground vegetable concentrations by two orders of magnitude.

If air and soil concentrations were equal (or proportional) for the demonstration of both source categories, than the impact to vegetations would be equal (or proportional). Obviously, that was not the case.

The soil concentration of 2,3,7,8-TCDD of 1 ppt (ng/kg) for the on-site soil source demonstration scenarios translated to a total airborne reservoir of 5E(-11) m g/m3 (vapor plus particle phase reservoirs).
expand table Table V3 6-3

For the stack emission demonstration scenario 5, where the exposure site was 500 meters from the stack emission source, the vapor plus particle phase concentration of 2,3,7,8-TCDD was 1.4E(-12) m g/m3, which is reasonably similar to the air concentration of 5E(-11) m g/m3 modeled from a soil concentration of 1 ppt. However, the soil concentration for the stack emission demonstration scenario for the 20-cm mixing zone depth used for underground vegetation concentration was 0.00005 ppt, more than 4 orders of magnitude lower than the 1 ppt level for the soil contamination source categories. Therefore, below ground vegetables for the stack emission source category will be more than 4 orders of magnitude lower for the stack emission source category as compared to the soil source category.

This question now is whether this is a reasonable outcome. Should deposition from an airborne reservoir in the range of 10-11 m g/m3 2,3,7,8-TCDD result in a soil concentration closer to 1 ppt than 0.00005 ppt? Is the algorithm estimating soil impacts from depositions inherently underestimating soil concentrations? Likewise, should emissions from soils at 1 ppt result in air concentrations higher than 10-11 m g/m3? Is the soil emission/dispersion algorithms inherently underestimating air concentrations? If air to soil impacts were being underestimated, and/or if soil to air impacts were being underestimated, than more correct model performance would lead to more equivalent outcomes with regard to vegetation impacts. As stated above, if soil and air concentrations for each demonstration scenario were equal (or proportional), then impacts to vegetation would be equal (or proportional). Air-to-soil and soil-to-air model performances are now examined briefly.

In Section 7.2.3.1 in Chapter 7, the capability of the deposition algorithm to estimate soil concentrations is examined. The hypothesis examined is that air concentrations of 2,3,7,8-TCDD in a rural setting should correlate to untilled soil concentrations in a rural setting, and that hypothesis can be evaluated using the deposition algorithms of the stack emission source category. The conclusion from that section was that it would appear that the deposition algorithm may, in fact, be underestimating soil concentrations. The amount of underestimation was speculated to be about one order of magnitude. Uncertain model parameters identified in that section were the untilled mixing zone depth, the velocity of deposition, and the dissipation half-life for depositing residues. It is also noted that the algorithms of this assessment do not consider detritus production as an input to the soil reservoir. If the deposition algorithm is indeed underestimating soil concentrations, than in fact an airborne reservoir on the order of 10-11 m g/m3 might translate to a concentration higher than 0.00005 ppt, although a one order of magnitude increase to 0.0005 ppt is still much less than 1 ppt.

One could also even question the use of a contaminant dissipation rate which is applied to depositing residues which are assumed to be tilled in a home garden or an agricultural field. Routes of dissipation for dioxin-like compounds are physical transport, such as wind or soil erosion, or volatilization, or chemical, such as photolysis, which is the only degradation route shown to be relevant to these compounds. These are phenomena relevant to surface residues, not buried residues. The use of a 10-year dissipation rate for both tilled (a mixing zone depth of 20 cm) and untilled (a mixing zone depth of 1 cm) settings could be a fundamental flaw in the approach - perhaps a dissipation rate should only be applied to untilled soil impacts. In addition to the argument presented above, this is another reason suggesting tilled soil concentrations resulting from stack emissions should be higher than are estimated in this assessment, and hence underground vegetable concentrations should be higher than are estimated in this assessment.

The other hypothesis is that the soil to air algorithms of the soil source categories are underestimating air concentrations. In fact, evidence developed in other parts of this document suggest that air concentrations resulting from soil concentrations may be underestimated. One piece of evidence discussed in different sections of this Volume is that air concentrations modeled to result from background soil concentrations are lower than air concentrations measured in pristine settings. In one literature article measuring concentrations in an area described as a "remote countryside" in Sweden (Broman, et al. 1991), air concentrations of 2,3,7,8-TCDD were measured at 2*10-10 m g/m3.

The air concentration of 2,3,7,8-TCDD modeled in this assessment from a 1 ppt background soil concentration of 2,3,7,8-TCDD is nearly an order of magnitude lower than that at 4*10-11 m g/m3. This suggests that the models of this assessment underestimate air concentrations resulting from releases from soils. Another piece of evidence is developed in Section 7.2.3.8, which compares plant:soil ratios as determined by the model with those developed in experimental and field conditions. The soil contamination models of this assessment appear to be leading to above ground plant:soil ratios that are 1-2 orders of magnitude lower (i.e., plant concentrations may be 1-2 orders of magnitude underestimated) than analogous ratios for experiments where soil can be surmised to be the only source of dioxin. One possible explanation offered is that the air concentrations are being underestimated.

If air concentrations resulting from soil concentration would be modeled to be higher than they are currently, then the dichotomy identified above would be narrowed. If soil concentrations resulting from air depositions are modeled to be higher than they are currently, then the dichotomy identified would be even further narrowed. Evidence summarized above suggests that both are plausible. Clearly, more evaluation of the soil to air algorithms of the soil contamination source categories and the air to soil algorithms of the stack emission source category, is warranted.

Given a soil concentration, in any case, the impacts of parameter changes for the algorithm predicting concentrations in underground vegetables are shown in Figure 6-10. The two orders of magnitude range for the root concentration factor, RCF, translates to a two order of magnitude range of concentration estimation. The same is true for the empirical correction factor applied to below ground vegetables, VGbg, and the organic carbon partition coefficient, Koc. A smaller impact is noted for the organic carbon fraction of soil, OCsl. Koc and OCsl are required for this algorithm because vegetable concentrations are a function of soluble phase concentrations, not soil concentrations. Increasing Koc and/or increasing OCsl results in decreasing the water concentrations, explaining why the high values for these parameters reduce vegetable concentrations.

One final note is that the dry to fresh weight ratio, FDW, is not on this figure, while it does appear on Figure 6-8. This is because the RCF was developed on fresh weight basis already, so no conversion to a fresh weight is required.

6.3.3.10. Beef fat concentration estimation The impacts of parameter changes to beef fat concentration estimation for the soil source category is shown in Figures 6-11.

First, it is noted that changes in soil concentration result in linear changes in beef concentrations - a ten-fold increase in soil concentration results in the same ten-fold increase in beef concentration. This is because changes to soil concentration alone result in the same proportional change in the concentrations estimated to be in grass and cattle feed. Changes to grass and feed concentrations with no change in soil concentrations (which could result from different parameter selections in estimating grass and feed concentrations) do not result in as substantial a change - a tenfold increase in vegetation concentrations only increases beef concentration by a factor of two; a tenfold decrease in vegetation concentration only reduces beef fat concentrations by about 20%.

table Figure 6-10 Results of sensitivity analysis of algorithms estimating below ground vegetation concentrations resulting from soil to root transfers. table Figure 6-11 Results of sensitivity analysis of algorithms estimating beef fat concentrations resulting from soil contamination.
expand table Figure V3 6-10 expand table Figure V3 6-11

This is a key and insightful result. If the models and their parameterization are valid, it indicates that the bulk of impact to beef and milk fat is from ingestion of soil for the soil source categories. With a closer look at the results for the demonstration of the on-site soil category, demonstration scenario #2, it is found that soil ingestion explains 90% of the beef fat concentration, despite being only 4% of their diet. Grass (48% of their diet) and feed (48%) of their diet explain 7 and 3% of beef fat concentrations. The story is the same for milk fat concentrations. Soil explains 87% of the milk fat concentrations, despite being only 2% of the dairy cattle's diet. Feed (90% of their diet) and grass (8%) explain 11 and 2% of milk fat concentrations.

Changes to other parameters, or groups of parameters, result in expected changes to beef fat concentrations. A doubling (roughly) of the assigned beef bioconcentration factor, BCF, from 4.3 to 10 doubles beef concentrations; reducing it to 1 reduces concentrations by a factor of 5. The soil bioavailability factor, Bs, has a rather small impact around its 0.3 to 0.9 range. The other tests run on the beef and milk algorithm looked at cattle dietary exposures as modeled by the diet fraction parameters, and the parameters describing the fractions of intake that are contaminated. In one test, patterns of soil ingestion were examined. As noted above, soil ingestion is critical for both beef and milk concentration estimation.

In the high and low soil ingestion tests, diet fractions were altered to reflect high (15% of the beef cattle diet) and low (1%) soil ingestion patterns. The impact is nearly linear, with an increase from 4 to 15% nearly quadrupling beef fat concentrations, and a reduction from 4 to 1% reducing concentrations by almost 75%. The same trend is seen with changes in dietary exposures to reflect a lifetime of pasturing versus minimal lifetime pasturing. A lifetime of pasturing translated to an assumption of 90% pasture grass intake, and a greater proportion of incidental soil ingestion, from 4 to 8%.

With this rise in soil ingestion mainly, the beef fat concentration doubled. Similarly, with minimal pasturing and half as much soil ingestion at 2%, beef fat concentrations halved. A different test described a condition where exposure to contaminated dry matter was minimized. This was modeled with two modifications: 1) the soil ingestion patterns were low as modeled in the previous test and 2) only 25% of the feed was assumed to be impacted by the soil contamination - i.e., 75% of it was residue free perhaps from being externally purchased and not impacted by soil contamination.

The impact of the feed assumption was insignificant, and the fact that beef concentration were reduced by about 75% was due mainly to the reduced soil ingestion assumed - 1%, down from the initially assumed 4%. The final test was termed, low extent of contamination. In this test, 75% of the grazing land was residue free, which reduced both soil and pasture grass concentrations by 75%, and 75% of the feed was also assumed to be residue free. With 75% reductions in concentrations in dry matter intake, beef concentrations both were reduced by the same 75%. These tests demonstrate the importance of assumptions on cattle dietary exposures on estimated beef concentrations, and by analogy, milk concentrations.