Maturity 2
5 MERCURY
Prior to the analysis in this study, it was uncertain whether a relationship between the concentrations of mercury in soil and plants from multiple studies would be significant. Both the speciation of mercury and the uptake route via air were expected to contribute large uncertainty bounds to any empirical relationship. In contrast to other metals, most mercury in above-ground plant tissue is taken up as volatile, elemental mercury through the leaves (Bysshe 1988, Siegel and Siegel 1988, Lindberg et al. 1979), with limited accumulation from the soil via the roots and transpiration stream. However, significant relationships between soil and plant mercury have been observed previously. For example, a significant correlation between soil mercury and tissue concentrations was observed for several plant
species found in mining areas (Siegel et al. 1987) and near chloralkali plants (Lenka et al. 1992 and Shaw and Panigrahi 1986).
6 NICKEL
In contrast to the results of this study, an association of nickel concentrations in plants and pH has
previously been observed. Sims and Kline (1991) found significant multiple regression models between nickel in wheat and soybean and soil metal concentrations and pH, but not with soil metal concentrations alone. Reducing the pH of soils led to increased uptake in several plant species (Sauerbeck and Hein 1991). Thus, it is surprising that pH did not contribute significantly to the variability in the present multiple regression model.
7 SELENIUM
Major determinants of the uptake of selenium include chemical form and soil properties. Selenate
is taken up more effectively than selenite (Banuelos 1996, Hamilton and Beath 1963, Gissel-Nielson and Bisbjerg 1970, Smith and Watkinson (1984)), and the uptake of organic selenium is lower than that of inorganic forms (Hamilton and Beath 1963). Banuelos (1996) suggests that soils of high redox in arid regions probably have selenate as the primary species in solution, whereas acid or neutral soils are not likely to have much selenate. Thus, because the present regression model was generated predominantly using data from western sites, the uptake of selenium by plants may be somewhat lower in non-arid environments.
8 ZINC
pH has commonly been observed to be a controlling variable in the uptake of zinc. An increase in
soil pH was associated with a decrease in the zinc content of radish tops (Lagerwerff 1971). Similarly, a decrease in soil pH was associated with an increase in the concentration of zinc in kidney bean (Phaseolus vulgaris), though the mass taken up was unchanged with pH, because the pH decrease was associated with a reduced yield (Xian and Shokohifard 1989). Both of these results are consistent with the relationship derived from data in this study. In contrast, in a study of the uptake of zinc by radish (Raphanus sativus), the regression was improved by including pH as a variable (Davies 1992). However, the positive value that was obtained for the pH term would suggest that raising soil pH increases accumulation of zinc, a result opposite to that found here.
RECOMMENDATIONS
Measurements of contaminant concentrations in plants at a specific waste site are always superior
to estimates of these concentrations for assessing risks to herbivorous or omnivorous wildlife. Even a small number of samples (e.g., 10 or 20) from which site-specific uptake factors can be developed would probably give more precise and accurate estimates of concentrations of chemicals in plants at the site than the use of models recommended below. However, in the absence of these data, regression models or uptake factors should be used. Our study demonstrates that regression models are generally superior to uptake factors for estimating concentrations of chemicals in plants from concentrations in soil.
Single-variable regressions of the natural log-transformed chemical concentration in plant on the
log-transformed concentration in soil are recommended as good tools for estimating concentrations of contaminants in plant tissues for all eight chemicals tested (Table 10). Multiple regressions with chemical concentration in soil and pH are recommended as good tools for estimating the uptake of cadmium, mercury, selenium, and zinc. Although multiple regressions were good predictors of plant concentrations of copper and lead in the validation dataset, pH was not a significant variable in the final combined models. For mercury, the multiple regression with pH was the best predictor of the plant concentrations in the validation data.
Both the 90th percentile uptake factor and the 95% upper prediction limit for the single-variable
regression were adequately conservative for screening ecological risk assessments. Indeed, for data from the two validation studies, these models were arguably too conservative, overpredicting 100% of the measured values for most chemicals. The appropriate level of conservatism should be agreed upon by regulatory agencies, risk assessors, and site managers in the DQO sessions and work plan approval process.
The 95% upper prediction limit for the single-variable regression is recommended as the better of
the two models for providing conservative estimates of plant uptake of contaminants. The method
provided the best, conservative estimate for four of eight chemicals. For three others, the 90th percentile uptake factor provided the best conservative estimate, though one of these comparisons (for mercury) was based on only three samples. The 95% upper prediction limit would be expected to be the better model for a wide range os soil concentrations. The log-transformed regression models consistently proved to be better than uptake factors for estimating chemical concentrations in plants, and the slopes were apparently different from one, indicating that uptake factors are not the best models to use.
Therefore, conservative bounds on the regression models should be better conservative estimates of uptake for most random datasets than the uptake factors.
Tuesday, March 24, 2009
Monday, March 23, 2009
Maturity 1
Maturity 1
Measurements of accumulation of chemicals by plants are usually taken at a single time without
knowledge of whether or not vegetation may be in equilibrium with the soil with respect to chemical movement. However, longer exposure does not necessarily lead to higher plant concentrations. Both the age of the plant and seasonal processes apparently affect uptake. For example, for all leafy and root crops grown in a muck soil, heavy metal concentrations were greater in young crops in the early summer than in mature crops (Hutchinson et al. 1974). Moreover, the selenium content of birdsfoot trefoil exposed to natural levels of the element decreased with each cutting until midsummer, after which it remained constant (Lessard et al. 1968). On the other hand, selenium uptake by timothy increased until maturity.
In the sections below, the regressions of plant concentration on soil concentration (and pH) are
discussed. In addition, potential sources of variability in uptake of the chemicals by plants are discussed.
1 ARSENIC
As with most inorganic chemicals, the uptake of arsenic by crop plants has been observed to vary
with plant species and soil type (Otte et al. 1990). Additionally, phosphorus concentrations in soil have a large and complex effect on the uptake of arsenic by plants. The arsenic concentration in ryegrass (Jiang and Singh 1994) and that in the roots of Urtica dioica (Otte et al. 1990) were positively correlated with phosphorus in the soil, but in the latter case, negatively correlated with the concentration of arsenic in soil. In a second species, Phragmites australis, arsenic concentrations in the plant were measured at a level that was not correlated with concentrations of arsenic or phosphorus in soil (Otte et al. 1990). A better regression may have been obtained in this study if soil phosphorus were included as
a variable.
2 CADMIUM
The uptake of cadmium has been observed to vary with plant species (Haghiri 1973). Cadmium
uptake by plants has been shown in numerous studies to decrease with increasing pH (He and Singh 1994, Miller et al. 1976), so it is not surprising that the multiple regression with pH was significant in this study. Uptake by soybeans is also related to the sorptive capacity of soil (Miller et al. 1976). Lead has been widely observed to increase cadmium uptake; for example, the addition of both lead and cadmium increased the foliage content of each contaminant in American sycamore over the uptake values observed with a single metal added (Carlson and Bazzaz 1977). Lead has also increased the uptake of cadmium in rye and fescue (Carlson and Rolfe 1979) and in corn shoots (Miller et al. 1977). However, Miles and Parker (1979) found only low-level and inconsistent synergistic and antagonistic effects among cadmium, lead and other heavy metals in uptake by little bluestem and black-eyed Susan. A better regression may have been obtained in this study if soil lead were included as a variable.
3 COPPER
Prior to this study it was not known whether a significant regression of plant concentration on soil
concentration could be derived. Copper is a plant nutrient, and plants would be expected to exert control over uptake at certain ranges of soil c ncentration. As with other chemicals, in some previous investigations, no correlation was found between copper in plant foliage and underlying soil (Burton et al. 1984, Davies 1992). In contrast to the results in this study (in which pH did not contribute significantly to the multiple regression), pH has sometimes been shown to contribute to the variability in uptake of copper from different soils. Sims and Kline (1991) found a significant regression model between copper in wheat and soybean and soil copper and pH, but not with the copper concentration in soil alone.
4 LEAD
Lime has been observed to reduce the uptake of lead by lettuce and oats (John and Laerhoven
1972), suggesting that pH is a variable which controls the uptake of the element from soil. In contrast, Davies (1992) found that lead uptake by radish was best predicted by total lead in soil, and the regression of plant lead on soil lead concentration in that study was not improved by adding other soil characteristics. Similarly, in this study, pH did not contribute significantly to the multiple regression. The uptake of lead by plants has been found to be increased (Carlson and Bazzaz 1977), unaffected (Carlson and Rolfe 1977), and decreased (Miller et al. 1977) by increased concentrations of cadmium. Additional contributors to the variability in uptake of lead are: exposure time (Nilsson 1972) and plant taxon. While the attempt was made to exclude aerial exposure of lead, the use of lead in gasoline may have contributed to aerial exposure of plants to lead in some studies.
Measurements of accumulation of chemicals by plants are usually taken at a single time without
knowledge of whether or not vegetation may be in equilibrium with the soil with respect to chemical movement. However, longer exposure does not necessarily lead to higher plant concentrations. Both the age of the plant and seasonal processes apparently affect uptake. For example, for all leafy and root crops grown in a muck soil, heavy metal concentrations were greater in young crops in the early summer than in mature crops (Hutchinson et al. 1974). Moreover, the selenium content of birdsfoot trefoil exposed to natural levels of the element decreased with each cutting until midsummer, after which it remained constant (Lessard et al. 1968). On the other hand, selenium uptake by timothy increased until maturity.
In the sections below, the regressions of plant concentration on soil concentration (and pH) are
discussed. In addition, potential sources of variability in uptake of the chemicals by plants are discussed.
1 ARSENIC
As with most inorganic chemicals, the uptake of arsenic by crop plants has been observed to vary
with plant species and soil type (Otte et al. 1990). Additionally, phosphorus concentrations in soil have a large and complex effect on the uptake of arsenic by plants. The arsenic concentration in ryegrass (Jiang and Singh 1994) and that in the roots of Urtica dioica (Otte et al. 1990) were positively correlated with phosphorus in the soil, but in the latter case, negatively correlated with the concentration of arsenic in soil. In a second species, Phragmites australis, arsenic concentrations in the plant were measured at a level that was not correlated with concentrations of arsenic or phosphorus in soil (Otte et al. 1990). A better regression may have been obtained in this study if soil phosphorus were included as
a variable.
2 CADMIUM
The uptake of cadmium has been observed to vary with plant species (Haghiri 1973). Cadmium
uptake by plants has been shown in numerous studies to decrease with increasing pH (He and Singh 1994, Miller et al. 1976), so it is not surprising that the multiple regression with pH was significant in this study. Uptake by soybeans is also related to the sorptive capacity of soil (Miller et al. 1976). Lead has been widely observed to increase cadmium uptake; for example, the addition of both lead and cadmium increased the foliage content of each contaminant in American sycamore over the uptake values observed with a single metal added (Carlson and Bazzaz 1977). Lead has also increased the uptake of cadmium in rye and fescue (Carlson and Rolfe 1979) and in corn shoots (Miller et al. 1977). However, Miles and Parker (1979) found only low-level and inconsistent synergistic and antagonistic effects among cadmium, lead and other heavy metals in uptake by little bluestem and black-eyed Susan. A better regression may have been obtained in this study if soil lead were included as a variable.
3 COPPER
Prior to this study it was not known whether a significant regression of plant concentration on soil
concentration could be derived. Copper is a plant nutrient, and plants would be expected to exert control over uptake at certain ranges of soil c ncentration. As with other chemicals, in some previous investigations, no correlation was found between copper in plant foliage and underlying soil (Burton et al. 1984, Davies 1992). In contrast to the results in this study (in which pH did not contribute significantly to the multiple regression), pH has sometimes been shown to contribute to the variability in uptake of copper from different soils. Sims and Kline (1991) found a significant regression model between copper in wheat and soybean and soil copper and pH, but not with the copper concentration in soil alone.
4 LEAD
Lime has been observed to reduce the uptake of lead by lettuce and oats (John and Laerhoven
1972), suggesting that pH is a variable which controls the uptake of the element from soil. In contrast, Davies (1992) found that lead uptake by radish was best predicted by total lead in soil, and the regression of plant lead on soil lead concentration in that study was not improved by adding other soil characteristics. Similarly, in this study, pH did not contribute significantly to the multiple regression. The uptake of lead by plants has been found to be increased (Carlson and Bazzaz 1977), unaffected (Carlson and Rolfe 1977), and decreased (Miller et al. 1977) by increased concentrations of cadmium. Additional contributors to the variability in uptake of lead are: exposure time (Nilsson 1972) and plant taxon. While the attempt was made to exclude aerial exposure of lead, the use of lead in gasoline may have contributed to aerial exposure of plants to lead in some studies.
Friday, March 20, 2009
MODELING RESULTS
MODELING RESULTS
Based on preliminary analyses, regressions of plant concentrations versus soil concentrations using salt data differed significantly from those using field data. Significance values (p) were 0.035 for arsenic, 7.9 x 10-15 for cadmium, 4.5 x 10-5 for copper, 0.0017 for lead, 0.0059 for mercury, 0.00011 for nickel, 7.2 x 10-29 for selenium, and 0.0013 for zinc. For some of the chemicals (arsenic and nickel), the salt uptake data were within the 95% prediction limit of the field data regressions (Appendix C). However, or most chemicals, several data points were outside of these bounds. Most concentrations of selenium in plants when the source of selenium was selenate or selenite were higher than most concentrations of selenium in plants in the field studies (Appendix C, Fig. C.7). For some chemicals, such as arsenic, cadmium, and zinc, the plant concentrations associated with salts additions were comparable to the highest plant concentrations from the field dataset. For other chemicals, such as mercury, the range of
soil concentrations in the salts dataset was simply too narrow to give a good regression line (Appendix C, Fig. C.5). However, because for some chemicals, salts-amended soils were generally associated with higher chemical concentrations in plants than chemicals in field soils, the decision was made to exclude salts data from the models for uptake of all chemicals by plants. All results below are for field data only. Soil-plant regression models and uptake factors were developed for eight inorganic chemicals: arsenic, cadmium, copper, lead, mercury, nickel, selenium, and zinc (Figs. 1 to 8). In the initial dataset with salts data excluded, the number of observations ranged from 99 for arsenic to 164 for zinc (Table
1). The number of studies incorporated in the models ranged from seven for nickel to twenty for zinc. Six of eight distributions of uptake factors fit a lognormal distribution more closely than a normal distribution, though only the distribution of uptake factors for arsenic, lead, selenium, and zinc fit the lognormal form well (Table 1). Median uptake factors for all chemicals were less than one; however, the maximum uptake factor for all chemicals exceeded one. The distributions of uptake factors for the eight chemicals spanned at least two orders of magnitude; e.g., for copper the range of uptake factors was less than three orders of magnitude and for arsenic the range was greater than five orders of magnitude. An example of the cumulative distribution of uptake factors for selenium is presented in Fig. 9. [Note: the mean and standard deviation of the natural-log-transformed uptake factors are presented as parameters for describing the uptake factor distributions for chemicals where the distribution is lognormal .Whereas these untransformed uptake factors are best fit by a lognormal distribution, the natural-logtransformed uptake factors are normally distributed. These parameters may be used in two ways. They may be applied to normal distribution functions in Monte Carlo simulation software, however the output from the sampling from this distribution must be back-transformed. Alternatively, the parameters may be incorporated into the LOGNORM2 function in the @RISK1 Monte Carlo simulation software (Palisade Corp. 1994b). Use of the LOGNORM2 function requires no back-transformation. Comparable results are obtained using either approach.
Based on preliminary analyses, regressions of plant concentrations versus soil concentrations using salt data differed significantly from those using field data. Significance values (p) were 0.035 for arsenic, 7.9 x 10-15 for cadmium, 4.5 x 10-5 for copper, 0.0017 for lead, 0.0059 for mercury, 0.00011 for nickel, 7.2 x 10-29 for selenium, and 0.0013 for zinc. For some of the chemicals (arsenic and nickel), the salt uptake data were within the 95% prediction limit of the field data regressions (Appendix C). However, or most chemicals, several data points were outside of these bounds. Most concentrations of selenium in plants when the source of selenium was selenate or selenite were higher than most concentrations of selenium in plants in the field studies (Appendix C, Fig. C.7). For some chemicals, such as arsenic, cadmium, and zinc, the plant concentrations associated with salts additions were comparable to the highest plant concentrations from the field dataset. For other chemicals, such as mercury, the range of
soil concentrations in the salts dataset was simply too narrow to give a good regression line (Appendix C, Fig. C.5). However, because for some chemicals, salts-amended soils were generally associated with higher chemical concentrations in plants than chemicals in field soils, the decision was made to exclude salts data from the models for uptake of all chemicals by plants. All results below are for field data only. Soil-plant regression models and uptake factors were developed for eight inorganic chemicals: arsenic, cadmium, copper, lead, mercury, nickel, selenium, and zinc (Figs. 1 to 8). In the initial dataset with salts data excluded, the number of observations ranged from 99 for arsenic to 164 for zinc (Table
1). The number of studies incorporated in the models ranged from seven for nickel to twenty for zinc. Six of eight distributions of uptake factors fit a lognormal distribution more closely than a normal distribution, though only the distribution of uptake factors for arsenic, lead, selenium, and zinc fit the lognormal form well (Table 1). Median uptake factors for all chemicals were less than one; however, the maximum uptake factor for all chemicals exceeded one. The distributions of uptake factors for the eight chemicals spanned at least two orders of magnitude; e.g., for copper the range of uptake factors was less than three orders of magnitude and for arsenic the range was greater than five orders of magnitude. An example of the cumulative distribution of uptake factors for selenium is presented in Fig. 9. [Note: the mean and standard deviation of the natural-log-transformed uptake factors are presented as parameters for describing the uptake factor distributions for chemicals where the distribution is lognormal .Whereas these untransformed uptake factors are best fit by a lognormal distribution, the natural-logtransformed uptake factors are normally distributed. These parameters may be used in two ways. They may be applied to normal distribution functions in Monte Carlo simulation software, however the output from the sampling from this distribution must be back-transformed. Alternatively, the parameters may be incorporated into the LOGNORM2 function in the @RISK1 Monte Carlo simulation software (Palisade Corp. 1994b). Use of the LOGNORM2 function requires no back-transformation. Comparable results are obtained using either approach.
Analitic Methods
Analitic Methods
Field and greenhouse studies which report concentrations of arsenic, cadmium, copper, lead,
mercury, nickel, selenium, or zinc in both surface soil and collocated, aboveground plant tissue were identified. Most plant species were agricultural crop plants. For some elements, many studies were pot studies in which inorganic salts were added to soil. Information regarding soil and plant concentrations, soil parameters, exposure time, chemical form, dry or wet weight, extraction method, plant species, and plant part was compiled in a spreadsheet. Only studies in which concentrations were expressed on a dry weight basis were used. Some soils were air dried rather than oven dried. Although most studies reported that plant material was washed, studies were not excluded if the extent of washing was not stated in the paper. Studies were used even if the individual investigators observed no correlation between concentrations of contaminants in soils and plants (e.g., arsenic in Norway spruce, Wyttenbach et al. 1997; copper in Sitka-spruce seedlings, Burton et al. 1984; copper in radish foliage, Davies 1992). Concentrations of chemicals in soil or plants were sometimes estimated visually from a figure, but only
if estimates could be made within about 10%. Studies were not used if the only plants tested were those known to hyperaccumulate elements.
Each plant species or variety, soil type, location, concentration of the test element in soil, and form of an added element represented an independent observation in the dataset. Differences in exposure duration or above-ground plant part did not constitute separate observations. That is, concentrations in soils or plants that differed on the basis of one of these two variables were averaged. (The number of observations in these means, which ranged between 1 and 6, was not retained in the subsequent statistical analysis.) For example, concentrations of nickel in upper and lower leaves of bush bean (Sajwan et al. 1996) and concentrations of lead in corn leaves and stalks (de Pieri et al. 1997) were averaged and each constituted a single observation. Also, concentrations of lead in spruce needles (Nilsson 1972) and cadmium in clippings of red fescue (Carlson and Rolfe 1979) after different periods of exposure were averaged. A pattern of higher levels of accumulation with increased exposure time was not generally observed. The database of bioaccumulation concentrations is presented in Appendix A.
Concentrations of contaminants in soil at the time of plant sampling were used if known. If these
concentrations were not measured (as was often the case in pot studies), the initial concentration of the element measured in or added to soil was assumed to be equivalent to the final oncentration. In field experiments, the change in soil concentration of an element over time was assumed to be minimal (e.g., selenium in van Mantgem et al. 1996). However, total soil concentrations of elements in pot studies have been observed to change as much as twenty percent during an experiment. The concentration of an element in soil prior to the addition of the salt in a pot study was often not stated. Thus, the added concentration was often assumed to be equivalent to the total concentration. Experimental treatments or field studies in which aerial contaminants potentially contributed to uptake were excluded from the database. In some early field studies with lead, aerial exposure to lead additives from gasoline was likely (e.g., Parker et al. 1978). In other field studies, ongoing exposure to metal contaminants from smelters or other sources was possible, though data from the vicinity of a smelter or other air source were not used unless it was demonstrated in the study that air was not a significant route of contamination.
Observations were included in the database if the total chemical concentration in soil was measured, either by extraction with strong acid or by extraction with moderately strong acid (e.g., 4 N sulfuric acid) sometimes accompanied by heat. In one study, it was shown that extraction of arsenic with 6M HCl for 2 h under constant rotation gave the same recovery as digestion in aqua regia, a mixture of concentrated nitric and hydrochloric acids (Otte et al. 1990). Studies in which concentrations of contaminants in soil were determined by a partial extraction with DTPA (diethylene triamine pentaacetic acid), weak acids or water were excluded from analysis, unless DTPA was used only to extract the background fraction of the element, and salts were added. Although concentrations of DTPA-extracted contaminants from soils sometimes correlate with those taken up by plants (Sadiq 1985), this estimate of bioavailability has been observed not to be valid for some metals (Sadiq 1985, Sadiq 1986, Hooda and Alloway 1993) or for comparisons of soils of varying pH.
Field and greenhouse studies which report concentrations of arsenic, cadmium, copper, lead,
mercury, nickel, selenium, or zinc in both surface soil and collocated, aboveground plant tissue were identified. Most plant species were agricultural crop plants. For some elements, many studies were pot studies in which inorganic salts were added to soil. Information regarding soil and plant concentrations, soil parameters, exposure time, chemical form, dry or wet weight, extraction method, plant species, and plant part was compiled in a spreadsheet. Only studies in which concentrations were expressed on a dry weight basis were used. Some soils were air dried rather than oven dried. Although most studies reported that plant material was washed, studies were not excluded if the extent of washing was not stated in the paper. Studies were used even if the individual investigators observed no correlation between concentrations of contaminants in soils and plants (e.g., arsenic in Norway spruce, Wyttenbach et al. 1997; copper in Sitka-spruce seedlings, Burton et al. 1984; copper in radish foliage, Davies 1992). Concentrations of chemicals in soil or plants were sometimes estimated visually from a figure, but only
if estimates could be made within about 10%. Studies were not used if the only plants tested were those known to hyperaccumulate elements.
Each plant species or variety, soil type, location, concentration of the test element in soil, and form of an added element represented an independent observation in the dataset. Differences in exposure duration or above-ground plant part did not constitute separate observations. That is, concentrations in soils or plants that differed on the basis of one of these two variables were averaged. (The number of observations in these means, which ranged between 1 and 6, was not retained in the subsequent statistical analysis.) For example, concentrations of nickel in upper and lower leaves of bush bean (Sajwan et al. 1996) and concentrations of lead in corn leaves and stalks (de Pieri et al. 1997) were averaged and each constituted a single observation. Also, concentrations of lead in spruce needles (Nilsson 1972) and cadmium in clippings of red fescue (Carlson and Rolfe 1979) after different periods of exposure were averaged. A pattern of higher levels of accumulation with increased exposure time was not generally observed. The database of bioaccumulation concentrations is presented in Appendix A.
Concentrations of contaminants in soil at the time of plant sampling were used if known. If these
concentrations were not measured (as was often the case in pot studies), the initial concentration of the element measured in or added to soil was assumed to be equivalent to the final oncentration. In field experiments, the change in soil concentration of an element over time was assumed to be minimal (e.g., selenium in van Mantgem et al. 1996). However, total soil concentrations of elements in pot studies have been observed to change as much as twenty percent during an experiment. The concentration of an element in soil prior to the addition of the salt in a pot study was often not stated. Thus, the added concentration was often assumed to be equivalent to the total concentration. Experimental treatments or field studies in which aerial contaminants potentially contributed to uptake were excluded from the database. In some early field studies with lead, aerial exposure to lead additives from gasoline was likely (e.g., Parker et al. 1978). In other field studies, ongoing exposure to metal contaminants from smelters or other sources was possible, though data from the vicinity of a smelter or other air source were not used unless it was demonstrated in the study that air was not a significant route of contamination.
Observations were included in the database if the total chemical concentration in soil was measured, either by extraction with strong acid or by extraction with moderately strong acid (e.g., 4 N sulfuric acid) sometimes accompanied by heat. In one study, it was shown that extraction of arsenic with 6M HCl for 2 h under constant rotation gave the same recovery as digestion in aqua regia, a mixture of concentrated nitric and hydrochloric acids (Otte et al. 1990). Studies in which concentrations of contaminants in soil were determined by a partial extraction with DTPA (diethylene triamine pentaacetic acid), weak acids or water were excluded from analysis, unless DTPA was used only to extract the background fraction of the element, and salts were added. Although concentrations of DTPA-extracted contaminants from soils sometimes correlate with those taken up by plants (Sadiq 1985), this estimate of bioavailability has been observed not to be valid for some metals (Sadiq 1985, Sadiq 1986, Hooda and Alloway 1993) or for comparisons of soils of varying pH.
Chemicals-plant
Chemicals-plant
The major pathway of exposure of terrestrial wildlife to contaminants in soil is through food ingestion. The prediction or estimation of risks to wildlife requires knowledge of their diets, body weights, habitats, and concentrations of contaminants in all ingested media (food, soil, and water). The direct measurement of chemical concentrations in wildlife food is advisable to minimize uncertainty in ecological risk assessments. However, site-specific data on the bioaccumulation of contaminants in vegetation and other biota that comprise wildlife diets are often not available because of constraints in
funding or time. At a minimum, concentrations of inorganic and organic chemicals in soils are measured at contaminated sites prior to a risk assessment. The challenge is to develop models that estimate concentrations of chemicals in plants from these concentrations in soil. The simplest linear model for estimating the concentrations of chemicals in vascular plants is the soil-plant uptake factor, the ratio of the concentration of a chemical in a plant or portion of a plant to that in soil. The concentration of a contaminant in plants at a particular location is estimated by multiplying the measured concentration in soil by the soil-plant uptake factor. Chemical concentrations in plants and soil are assumed to be at equilibrium; thus, exposure time is not considered. The usefulness of uptake factors lies in the ease by which distributions can be developed and conservative (e.g., 90th percentile) values chosen. However, the evidence below suggests that uncertainty in uptake model predictions may be minimized if:
(1) nonlinear models are employed and
(2) environmental factors and other sources of variability are
incorporated in the model.
Uptake factors would be most useful if they did not vary with soil concentration. Although the uptake relationship between soil and plants is probably valid for narrow ranges of chemical concentration in the relatively nontoxic range (e.g., Jiang and Singh 1994, Carlson and Bazzaz 1977), some evidence demonstrates that uptake factors are dependent on soil concentration. Baes et al. (1984), who developed soil-vegetative tissue uptake factors that are often used in human health and ecological risk assessments,
found that the uptake factors for copper and zinc were inversely correlated with soil concentration. These metal contaminants are also nutrients, and it is not surprising that they would be regulated by plants. Alsop et al. (1996) showed that the use of Baes factors underpredicted the uptake of lead and zinc by oats at concentrations within background ranges in soil and overpredicted metal concentrations in the plants at concentrations exceeding background levels. Clearly, nonlinear models would sometimes be
more useful for risk assessments than the Baes factors. Both Neuhauser et al. (1995) and Sample et al. (1998a) have obtained significant regressions for the uptake of inorganic elements by earthworms using log-transformed concentrations, so it is reasonable to assume that log-transforming soil and plant concentrations could result in a statistically significant relationship. Inorganic chemicals are passively taken up by plants from soil water, with the additional possibility of active uptake in the case of required nutrients, such as copper and zinc. Soil properties such as pH, clay content, and organic matter strongly affect the concentrations of inorganic chemicals in soil solution. For example, the amount of zinc in soil water and plant tissues is generally observed to increase with
decreasing pH and cation exchange capacity (Bysshe 1988). Cadmium uptake by plants has been shown in numerous studies to decrease with increasing pH (He and Singh 1994, Miller et al. 1976). Sims and Kline (1991) found significant multiple regression models between nickel, copper, and zinc in wheat and soybean and soil metal concentrations and pH, but not with soil metal concentrations alone. The type of soil is significant for accumulation of chemicals by plants, with arsenic uptake in crops dependent on soil type (Jiang and Singh 1994) and cadmium uptake by soybeans related to the sorptive capacity of soil
(Miller et al. 1976)
The major pathway of exposure of terrestrial wildlife to contaminants in soil is through food ingestion. The prediction or estimation of risks to wildlife requires knowledge of their diets, body weights, habitats, and concentrations of contaminants in all ingested media (food, soil, and water). The direct measurement of chemical concentrations in wildlife food is advisable to minimize uncertainty in ecological risk assessments. However, site-specific data on the bioaccumulation of contaminants in vegetation and other biota that comprise wildlife diets are often not available because of constraints in
funding or time. At a minimum, concentrations of inorganic and organic chemicals in soils are measured at contaminated sites prior to a risk assessment. The challenge is to develop models that estimate concentrations of chemicals in plants from these concentrations in soil. The simplest linear model for estimating the concentrations of chemicals in vascular plants is the soil-plant uptake factor, the ratio of the concentration of a chemical in a plant or portion of a plant to that in soil. The concentration of a contaminant in plants at a particular location is estimated by multiplying the measured concentration in soil by the soil-plant uptake factor. Chemical concentrations in plants and soil are assumed to be at equilibrium; thus, exposure time is not considered. The usefulness of uptake factors lies in the ease by which distributions can be developed and conservative (e.g., 90th percentile) values chosen. However, the evidence below suggests that uncertainty in uptake model predictions may be minimized if:
(1) nonlinear models are employed and
(2) environmental factors and other sources of variability are
incorporated in the model.
Uptake factors would be most useful if they did not vary with soil concentration. Although the uptake relationship between soil and plants is probably valid for narrow ranges of chemical concentration in the relatively nontoxic range (e.g., Jiang and Singh 1994, Carlson and Bazzaz 1977), some evidence demonstrates that uptake factors are dependent on soil concentration. Baes et al. (1984), who developed soil-vegetative tissue uptake factors that are often used in human health and ecological risk assessments,
found that the uptake factors for copper and zinc were inversely correlated with soil concentration. These metal contaminants are also nutrients, and it is not surprising that they would be regulated by plants. Alsop et al. (1996) showed that the use of Baes factors underpredicted the uptake of lead and zinc by oats at concentrations within background ranges in soil and overpredicted metal concentrations in the plants at concentrations exceeding background levels. Clearly, nonlinear models would sometimes be
more useful for risk assessments than the Baes factors. Both Neuhauser et al. (1995) and Sample et al. (1998a) have obtained significant regressions for the uptake of inorganic elements by earthworms using log-transformed concentrations, so it is reasonable to assume that log-transforming soil and plant concentrations could result in a statistically significant relationship. Inorganic chemicals are passively taken up by plants from soil water, with the additional possibility of active uptake in the case of required nutrients, such as copper and zinc. Soil properties such as pH, clay content, and organic matter strongly affect the concentrations of inorganic chemicals in soil solution. For example, the amount of zinc in soil water and plant tissues is generally observed to increase with
decreasing pH and cation exchange capacity (Bysshe 1988). Cadmium uptake by plants has been shown in numerous studies to decrease with increasing pH (He and Singh 1994, Miller et al. 1976). Sims and Kline (1991) found significant multiple regression models between nickel, copper, and zinc in wheat and soybean and soil metal concentrations and pH, but not with soil metal concentrations alone. The type of soil is significant for accumulation of chemicals by plants, with arsenic uptake in crops dependent on soil type (Jiang and Singh 1994) and cadmium uptake by soybeans related to the sorptive capacity of soil
(Miller et al. 1976)
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