Environ. Sci. Technol. 2001, 35, 4522-4529
Novel Model Describing Trace Metal Concentrations in the Earthworm, Eisenia andrei J E N N I F E R K . S A X E , †,‡ C H R I S T O P H E R A . I M P E L L I T T E R I , †,§ WILLIE J. G. M. PEIJNENBURG,| AND H E R B E R T E . A L L E N * ,† Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware 19716, and Laboratory for Ecotoxicology, National Institute of Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands
We developed a novel model describing Eisenia andrei body concentrations for Cd, Cu, Pb, and Zn as a function of pH, metals, and soluble organic carbon (SOC) in soil extracts for potential use in predicting values in contaminated field sites. Data from 17 moderately contaminated Dutch field soils in which earthworms were cultured were used in model development. Model parameters quantify biological phenomena important for metal bioavailability, and soil variables quantify relevant soil chemistry characteristics. Earthworm body concentration (EBC) was modeled so that soil metal soluble at bulk soil pH was considered available for dermal exposure, and gut exposure was due to soil metal in solution near neutral regulated gut pH. The efficiency parameter values indicated that metals are biologically regulated in the following order (most to least): Zn ∼ Cu > Pb > Cd. The values determined for the exposureroute constant indicate that Cd, Cu, and Pb EBCs are almost exclusively (>96%) due to dermal exposure and that only 18% of Zn EBC was due to gut exposure. The minimum healthful EBCs determined were Zn > Cu > Pb > Cd, and the values for Pb and Cd were near zero. The Cu model was normalized by soluble organic carbon to be meaningful. The model was most accurate in describing Zn behavior.
Introduction Earthworms are ecologically important for their role in soil ecosystem health through improvement of soil texture, through aeration of soil, and as important prey for secondary consumers such as birds and some small mammals. Because of their intrinsic importance and their role in transferring materials from soil throughout the food web as prey, understanding and quantifying the movement of potentially harmful materials from soil into earthworms is a valuable area of study. Trace metals from soil are potentially toxic if bioavailable levels lead to high doses in organisms. Many studies have shown that elevated trace metal levels in * Corresponding author phone: (302)831-8449; fax: (302)831-3640; e-mail:
[email protected]. † University of Delaware. ‡ Present address: Gradient Corporation, 238 Main St., Cambridge, MA 02142. § Present address: U.S. EPA National Risk Management Research Laboratory, 5995 Center Hill Ave., Cincinnati, OH 45224. | National Institute of Public Health and the Environment. 4522
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earthworm tissue may occur in soils near roadsides, amended with biosolids, or under other environmentally relevant circumstances (1-6). Although there is no one soil extractant that is accepted as predictive of bioavailability, there is some evidence that soluble substances in soil are those bioavailable to earthworms (7, 8). Solubility of trace metals is dependent upon solution pH. The optimal pH for earthworm enzymes associated with digestion is near neutrality (9), and pH in several earthworm species’ guts have been shown to be buffered near neutral pH (10). The guts of many earthworm species are well-buffered due to the secretion of CaCO3 by glands responsible for the elimination of excess Ca and CO2 (11). Several studies agree that earthworms regulate metal concentrations in their tissues. Spurgeon and Hopkin (8) concluded that Zn is regulated. They also found that tissue Zn concentrations were not well-correlated with total soil Zn concentrations, in support of biological regulation. Neuhauser et al. (4) suggest that Cd, Cu, Pb, and Zn are regulated while Ni uptake was not. Ireland and Richard (12) found that Cd uptake was regulated. Prediction of metal uptake by earthworms has been modeled by one-compartment models (13, 14) and by linear regression (15). We have found no published model that attempts to explain or predict earthworm body burdens for trace metals in terms of both earthworm biology and soil chemistry simultaneously. The goal of this research is to combine information about earthworm biology with knowledge of metal solubility in soil to synthesize a novel descriptive model with the potential for predictive generalizability for trace metal body burdens in earthworms.
Experimental Section Earthworm bioassays were completed using the species Eisenia andrei exposed in 20 Dutch field soils, processed and analyzed according to the procedures described in two papers by Janssen et al. from which the earthworm concentration data was taken for this study (15, 16). Subsamples of 18 of these soils were provided to the University of Delaware laboratory for further chemical characterization. No earthworm data were available for one of these soils (soil L), so data treatment here is limited to 17 of the 20 soils used in developing the previously published models (15). Briefly, soils had been originally collected from the top 20-cm layer after removal of litter and grass at sites suspected to contain elevated concentrations of trace metals. The soil was air-dried, and aggregates were crushed and sieved to remove particles larger than 2 mm (16). Ten adult earthworms were exposed to soil through inhabitation for 3 weeks in duplicate 1-kg remoistened subsamples of soil (i.e., 20 earthworms total per soil). Aggregated earthworm samples from each duplicate soil subsample were washed and depurated overnight before further processing. Freeze-dried, microwave-digested earthworm tissue was analyzed for metals using atomic absorption spectrometry (15). The 18 Dutch soils, which were further characterized for this study, underwent extraction with unbuffered deionized (DI) water and DI water in which the pH was initially adjusted using TraceMetal Grade HCl or NaOH (Fisher Scientific, Fairlawn, NJ) to achieve a range of equilibrium pH values with soil. This extraction procedure is described in detail in Impellitteri et al. in which metal and organic matter partitioning in these soils is examined (17). Briefly, samples of soil (3 g) were extracted 24 h through shaking (60 rpm) with 30 mL of extractant solution prepared in DI water. Samples were centrifuged, and the supernatant was filtered 10.1021/es0109038 CCC: $20.00
2001 American Chemical Society Published on Web 09/29/2001
(0.45 µm cellulose fiber; Fisher Scientific) and analyzed for pH and metals using inductively coupled plasma optical emission spectrometry (ICP; Spectro Analytical Instruments, Kleve, Germany). Total organic carbon (TOC) in the filtered supernatant was determined using a TOC analyzer (model DC 190, Rosemount Analytical, Inc., Dohrmann Division, Santa Clara, CA). Soils were also digested using EPA Method 3051 (19), and the resulting metal concentrations in digestate (determined using ICP) are operationally defined to be the total metal in these soils (see Table SI-1, Supporting Information). Statistical analysis including nonlinear least-squares regression for the determination of model parameters and confidence intervals was completed using the S-plus software package (Insightful Corp., Seattle, WA).
Results and Discussion Modeling of Trace Metals in Earthworms. A previously published model was developed to predict copper and zinc accumulation in the earthworm, E. andrei, using soil measurements only. This empirical regression model, described in detail in Janssen et al. (15), was developed using data from 20 Dutch soils in which earthworms were exposed. The Janssen models describing metal concentration in E. andrei take the following form:
log
[Me]worm [Me]solid
) a + b log(SC1) + c log(SC2) + ... (1)
in which SCx is a measured soil characteristic, which may include pH, clay fraction, organic matter content, aluminum oxide content, or iron oxide content. Soil characteristics included for each metal differed, as determined by multivariate regression. For example, clay was required in the models for Cd, Cu, Pb, and Zn, whereas pH was used only for Cd and Zn (among these four metals); iron oxide was required for Cu and Pb; organic matter was required for Cd only (surprisingly not for Cu); and aluminum oxide was required for Zn only. This evaluation was strictly empirical, and there is no clear scientific basis through which each soil characteristics’ inclusion or relative importance can be justified. It is notable, however, that the same soil characteristics used in these models also were predictive of the water-soluble pool of metal in soil (13). This provides empirical evidence to support the idea that earthworms obtain metals from this soluble portion in soil. It is also notable that the quantity modeled is the worm tissues’ metal concentration normalized by total soil metal or the bioconcentration factor (BCF), which is also termed the biota-soil accumulation factor (BSAF). This formulation qualitatively implies that bioconcentration of copper and zinc in the earthworm is independent of the concentration of metals in soil, although this has been shown not to be the case (4). A critical evaluation of the use of generic BCF values for biologically regulated metals can be found in Chapman et al. (18). No quantitative external validation of the Dutch model was performed due to a lack of available published data. An alternative, mechanistically based consideration we are proposing for the system is to regard soluble metal at soil pH to be bioavailable for the dermal route of exposure and soluble metal at the organism’s gut pH value to be bioavailable through the gut wall. Eukaryotic organisms are able to regulate their internal nutrient-metal concentrations by changing the quantity and/or the efficiency of ion carriers in their cell membranes and through active efflux of excess metal ions (20). An animal that has approached steady-state with surrounding soil will be able to maintain a constant body concentration of each metal or be damaged or killed.
FIGURE 1. Bioconcentration factor of zinc in earthworms (EBC: total soil concentration) is strongly correlated to the total soil concentration (A). log(BCF) ) -0.88 log(ZnTOT) + 1.9; r2 > 0.99. The regression equation in panel A was used to calculate EBC explicitly from total recoverable zinc, and model performance was plotted (B). Modeled ) 88 + 0.36 × measured. Because of this, the BCF exhibits an inverse relationship with measures of biologically useful but potentially toxic metals in soil. Organisms accumulate these nutrients above soil concentrations when they are scarce for use in synthesis of necessary enzymes. At the same time, when available soil metals are very high in concentration, an organism seeks to exclude much of what is available to protect itself from possible toxic effects (4). For E. andrei, a strong inverse linear relationship between BCF and soil zinc content is observed when data are log-transformed (e.g., Zn in Figure 1). Because of this relationship, the concentration of metal in the worm body can be calculated explicitly using eq 2, resulting in modeled earthworm body concentrations. This was done for the example of Zn and is shown in Figure 1 (regression equation in Figure 1A used to determine values for Figure 1B):
[Me]worm [Me]solid
) 10a log[Me]solid + b
(2)
This model for predicting earthworm body concentration directly from total soil metal concentrations is poor. Although VOL. 35, NO. 22, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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the correlation shown in Figure 1A is strongsthe example of zinc is depicted because it has the most significant correlation among the metals (r2 ) 0.98, 0.96, 0.78, and 0.12 for Zn, Cu, Cd, and Pb, respectively)smathematical rearrangement is less sensitive for the variable of interest (earthworm body concentration, EBC, Figure 1B). Earthworm BCF is not independent of total soil metal, which can be used alone as a predictor of BCF for Cu, Zn, and to some degree Pb. However, the relationship does not adequately predict EBC, so development of a new model is warranted. Development of a New Model. We developed a mathematical representation for the alternative approach where doses originating from dermal and gut exposure routes, which represent different pH regimes, sum to represent the total dose in earthworms:
Meworm ) Medermis + Megut - Meefflux
(3)
where Meworm represents the mass of metal in the earthworm body and the subscripts dermis and gut refer to the mass obtained due to each of these two routes of exposure. Once within the earthworm’s body, it is assumed that there is no longer any difference between the fractions due to each exposure route (i.e., earthworm tissue represents one compartment). It is also assumed that metal efflux from the organism occurs as a first-order function of internal metal concentration only. Aqueous pH is arguably the most important variable determining metal solubility (21). Comprehensive characterization of endogeic earthworm species’ revealed that soil passing through the gut is subjected to biologically regulated pH conditions that are near neutrality (10, 11). No such study has been performed on epigeic species’ such as E. andrei. However, digestive enzymes isolated from this species exhibit maximal activity in the neutral pH region (9), indicating that similar regulation may also occur in E. andrei. The pH of moist soil contacting an earthworm’s dermis is governed by soil chemistry and is not biologically regulated. Additionally, there is evidence supporting the concept that aqueous soluble metals constitute the major fraction available for uptake by earthworms (8). Because of the pH difference between bulk soil and soil within the gut, different pools of metal will be available for uptake through each exposure route. Combined with this effect, dermis and gut cells may have substantially different effective permeabilities to metals. Earthworms are lubricated and protected when moving through soil by excretion of a dermal mucous layer through which metals most likely must move before dermal uptake is possible. Ireland and Richard (12), for example, found that Cd uptake was regulated and that 68% of the measured Cd in earthworms was associated with the outer surface mucous. The intensity of the exposure due to each route may be estimated empirically from aqueous extracts of soil conducted in the applicable pH regimes:
[Me]dermis ) a[Me]SpH + z1
(4)
[Me]gut ) c[Me]GpH + z2
(5)
and
Equations 4 and 5 imply that [Me]SpH, or metal soluble at soil pH, and [Me]GpH, or metal soluble at earthworm gut pH, are proportional to the dose experienced by the earthworm due to each of these exposure routes. Because there is limited data available regarding the variability of earthworm gut pH, [Me]GpH was chosen as the average over pH 7.0 ( 0.5. Models using narrower pH ranges, also bracketing neutral, were initially considered, but they did not perform as well as pH 7.0 ( 0.5 and were subsequently discarded. The constants 4524
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a and c each represent a parameter that quantifies effective permeability of the dermal or gut surface to the metal and quantity of pore water contacted. The constants zj each represent nonzero values that account for an organism’s ability to acquire necessary amounts of nutrient elements even in low-concentration soils where soluble metals may be undetectable using these soil extractions. Once again, this relates to the observation that the BCF is high in lowmetal soils, likely as a result of high-affinity ion carriers that develop in cells in response to low environmental nutrient concentrations (14, 20). Combining eqs 4 and 5 results in the following expression for metal concentration in earthworm tissue:
[Me]worm ) a[Me]SpH + c[Me]GpH + z3
(6)
Once again, the constant z remains to account for the case in which no metal can be extracted from soil yet organisms are able to grow. Under conditions where essential nutrient metals are scarce, earthworms’ uptake efficiency increases and depuration is reduced. A positive z-term is the de facto case if earthworms are indeed surviving in a soil where there are little or no extractable essential metals. This constant physically represents the lowest concentration observable in a live, but perhaps stressed, test organism and will be species-dependent. Any amount over and above the value given by z is modeled as originating from soil solution, as this is less energetically expensive to obtain and will be the favored uptake mechanism for the organism. The EBC greater than z is modeled as if obtained proportionally from the two pHdependent soluble pools. The dose experienced by an earthworm through each of these routes depends also upon the volume of pore water contacted and the net permeability (influx - efflux) of the surface to the metal. The influx of metal to the earthworm is modeled as a function of external metal concentration; when external concentration and influx rate increase, high-affinity ion transport routes are biologically deactivated. The effective permeability is determined by the efflux of metal as well, which is dependent on EBC only, regardless of the route of uptake. All of these processes are considered to be first order, so their constants have been lumped. We thus developed a new expression, shown as eq 7:
[Me]worm ) η(k[Me]SpH + (1 - k)[Me]GpH) + z
(7)
The intensity of the metal exposure is proportioned between dermal and gut routes of entry, and its dependence on soil characteristics, including pH, is accounted for empirically by using the concentration removed from soil during extraction procedures as input variables. The constant k accounts for relative differences in net permeability between dermis and gut, including transport across membranes and differences between their surface areas, and will be speciesdependent. The absolute volume of soil encountered by the organism (total influx potential) plus the efflux constant are lumped as η, which represents the efficiency of EBC maintenance. The minimum concentration required for life, and the level at which the species will maintain internal concentration using alternative means regardless of what is soluble in pore water, is separated out as the speciesdependent constant, z. The model represented in eq 7 was tested using the earthworm data of the Janssen et al. (15) study combined with data from additional soil analyses conducted specifically for this study. Model Performance. The earthworm metal concentration data used in model development and previously published (15) included values for seven elements: As, Cd, Cr, Cu, Ni, Pb, and Zn. Duplicate worm samples were raised in each
soil, and coefficients of variation were reported for most of the concentration measurements. On the basis of the published information, 95% confidence intervals for the earthworm concentrations were calculated. The reliability of the data for earthworms, from most reliable to least, is as follows: zinc, mean 95% confidence interval (CI95) is 25.0% of the measured value; copper, mean CI95 is 40.6%; lead, mean CI95 is 49.2%; cadmium, mean CI95 is 49.8%; arsenic, mean CI95 is 73.6%; chromium, mean CI95 is 116%; and nickel, mean CI95 is 142%. Measurements for all seven of these elements were made in soil extracts for this study. Nonlinear least-squares regression was used to determine model parameters for all seven elements, but the model parameters for As, Cr, and Ni were both highly uncertain (standard error greater than the parameter value) and outside of a physically meaningful range (e.g., k > 1 or k < 0). For this reason, models for Cd, Cu, Pb, and Zn only are presented here. The model proposed in eq 7 requires that the modeled element be biologically regulated or efflux-dominated in the organism so that quantification of the difficult-to-measure surface area of soil encountered by the earthworm is not necessary. Previous studies indicate that Cu, Cd, Pb, and Zn may be regulated while Ni is not (4). It is usually the case that organisms have evolved more efficient biochemical regulation strategies for elements whose presence exerted selective pressure during evolutionary processes. That pressure may have been exerted either through abundance, causing possible toxicity (e.g., Pb), or through scarcity causing malnourishment in the case of trace nutrients (e.g., Cu and Zn). It was previously shown that earthworms raised for 63 days in these soils were still linearly accumulating Cd in four of the soils (E, F, H, and J) and Pb in of the six soils (A, H, I, K, M, and N) (13). For this reason, the models for Cd and Pb were developed with and without inclusion of the data from soils in which steady state was not achieved. Subsamples of the soils used in earthworm bioassays for the Janssen et al. (15) experiments were extracted with unbuffered basic, acidic, and neutral aqueous solutions covering a range of pH values bracketing neutral (earthworm gut pH). These extracts were then analyzed for copper and zinc using ICP spectrometry and for soluble organic carbon (SOC) using a TOC analyzer. The resulting data for soluble metals and organic carbon, treated as functions of pH only, significantly fit parabolic equations (second-order polynomial equation) in almost every case (see Table SI-2, Supporting Information). These data and the modeled polynomial relationships are shown for Cd, Cu, Pb, Zn, and SOC in Figure 2 for soil P. This soil was chosen for its representation of how the empirical second-order polynomial expression was able to adequately describe many cases; for example, the semiparabolic distribution (Cd) and cases of pH ranges where no measurable metal was soluble (Pb, Zn). The values for soluble metal or organic carbon at the approximate pH expected in earthworm guts (7.0 ( 0.5) were interpolated from the modeled best-fit parabolic equation for each soil. The soluble metal or organic carbon concentration values were calculated at 0.1 pH-unit intervals between pH 6.5 and pH 7.5 inclusive and averaged. In some cases, the empirical best-fit parabolic equation included negative values, in which case a value of zero was substituted. The values for metal soluble at pH 7.0 ( 0.5, calculated according to the empirical relationship between metal and pH, were substituted for the variable [Me]GpH from eq 7 for model parameter determination. As a part of the soil extraction procedure, each of the Dutch soils was extracted with unbuffered DI water, representing that soil’s natural pH. The values for the concentration of metal removed from soil using this process were substituted for the variable [Me]SpH in model development. A nonlinear least-squares regression was performed for eq 7 using the data shown in Table 1 to
determine the model parameters η, k, and z for Cd, Cu, SOCnormalized Cu, Pb, and Zn concentrations in soil extracts. The model parameters determined are listed in Table 2. The use of data based on more stringent extractions of soil (i.e., 0.01 M CaCl2 or HNO3 solutions) was tested as a possible alternative to using the water-soluble concentration for describing the bulk bioavailable fraction, but this did not improve model performance. Natural organic matter can form stable complexes with metals, particularly copper and lead, the examination of which is a matter under current investigation in soil and, more often, in natural waters (22-24). Natural organic matter in soil may be present as a solid or in a soluble form. The pH of the soil environment largely determines the solubility of the organic matter present; the concentration of SOC from these soil extracts significantly fit a parabolic model using pH as the dependent variable (e.g., Figure 2E; Table SI-2, Supporting Information). The concentration of SOC in extracts at each of the pH values (soil and gut) was determined. The SOC-pH relationship that was experimentally derived was used as with the metals for interpolating at gut pH. An alternative formulation of the model from eq 7 for the metals was developed by normalizing extract metal concentrations by the organic matter concentration, as expressed by SOC, present in those extracts. The reason for doing this was that some of the organic matter-bound copper or lead is likely to be stable and not bioavailable, regardless of the fact that the metal is soluble (22). Normalizing metal concentrations in extracts by SOC at the same pH was an attempt to account for this effect so that the relevant chemical conditions at the two routes of exposure are more adequately described. Among all of the metals investigated, only in the case of copper did SOC normalization improve the model performance from that using metal extract values alone. SOC normalization decreased model performance for Cd and Zn, and virtually no change was evident in the Pb model. For this reason only, the Cu/SOC model is presented here. The best-fit model parameters (Table 2) are interesting because the model formulation requires that they have physical significance. The regulation efficiency parameter, η, is a lumped parameter accounting for exposure potential and net influx and efflux of the element. Greater values for η imply that the metal is less well-regulated by the organism and that soil chemistry and not biology is likely to dominate the observed EBC. The results here show that biological regulation is important in the following order: Zn ∼ Cu > Pb > Cd. This result is not surprising as Cu and Zn are micronutrients, and as such, it is likely that organisms have developed biochemical mechanisms for their internal regulation. The regulation efficiency parameter is an order of magnitude greater for Cd and Pb, implying that their regulation is less well managed so soil chemistry may be more important than earthworm biology in determining Cd and Pb doses from soil. The exposure-route proportionality constant, k, is an estimate of the proportion of metal dose obtained through dermal entry versus that obtained through gut wall entry. When the copper model was not SOC-normalized, the bestfit value for k was not meaningful (greater than unity). No mathematical limitations were placed on possible values for k during regression analysis, so values outside the range of 0-1 were allowed. If values less than zero or greater than unity were calculated, however, this provided evidence that the model was invalid for that system. That was the case for Cu, supporting the use of the SOC-normalized model that does not violate the meaningful range of values for k. The values determined for k indicate that there is likely very little contribution to EBC through the gut wall pathway for Cd, Cu, and Pb. In each of these cases, greater than 96% of metals was supplied through the dermal route of exposure. Even in VOL. 35, NO. 22, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Soluble (A) Cd, (B) Cu, (C) Pb, (D) Zn, and (E) organic carbon modeled as a parabolic function of pH. Results for soil P presented here. See Table 1 and Supporting Information Tables SI-1 and SI-2 for model and chemical characterization. the case of zinc, dermal exposure dominates. Only 18% of zinc measured in earthworms was attributable to exposure through the gut. The minimum required concentration for each element is explained by the z parameter. Only Cu and Zn among the metals modeled are required elements, so z for these two 4526
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were expected to be positive numbers, which they are. A negative value for z (e.g., censored Cd model, Table 2) implies that earthworms are able to completely exclude metal below a certain threshold. For example, rearrangement of eq 7 allows calculation of this threshold: [Cd]worm is zero at the threshold value where the quantity of extractable metal, expressed as
TABLE 1. Cd, Cu, Pb, and Zn Measured in Earthworms (E. andrei) and in 17 Dutch Soils Used in Model Development earthworma soil ID A B C D E F G H I J K M N O P R S
soil
pHb
3.52 5.02 5.26 7.90 6.66 6.36 7.30 4.52 4.34 6.28 5.06 3.74 4.57 6.02 6.90 7.45 7.47
soil pH-extractableb
gut pH-extractablee
[Cd]c
[Cu]c
[Pb]c
[Zn]c
[Cd]d
[Cu]d
[Pb]d
[Zn]c
[Cd]d
[Cu]d
[Pb]d
[Zn]c
5.5 5.40 7.42 16.1 25.0 23.2 29.1 1.21 10.9 173 4.38 8.21 8.09 109 7.19 25.1 25.7
13.0 10.3 16.0 15.4 12.1 18.4 26.8 9.28 8.45 18.2 5.15 9.47 8.58 13.0 21.6 27.7 19.5
33.2 2.07 2.07 22.8 89.1 24.9 18.6 58.0 16.6 18.6 456 20.7 51.8 6.22 16.6 24.9 18.6
104 101 101 250 364 271 214 124 114 161 94.7 105 114 118 194 194 140
36.9 32.8 37.2 33.3 52.7 62.5 36.0 35.5 33.9 277 31.5 42.7 32.4 79.5 31.4 31.7 32.4
62.4 104 737 86.9 99.2 303 323 33.7 17.4 319 19.5 106 28.0 145 105 182 132
383 18.0 82.0 6.52 374 102 14.7 109 83.7 192 674 80.6 58.8 47.1 30.7 17.8 18.9
0.744 0.632 0.602 0.592 9.70 18.8 0.176 0.487 0.147 14.6 0.087 8.02 3.08 1.21 0.120 0.047 0.115
21.3 0 0 276 279 20.8 1410 0 0 33.0 0.263 15.4 11.6 0 340 381 661
177 0 1800 467 187 579 1.36 94.1 68.9 1501 25.5 18.5 0.227 523 192 218 0
0 0 0 6610 7160 206 2570 0 88.8 487 0 0 0 85.2 1010 6960 2360
0.061 0 0 129 121 10.7 95.4 0.04 0 6.16 0 0.424 0.013 0.044 31.9 40.6 47.2
a Values from Janssen et al. (16). b pH and metal concentrations determined after 24-h batch extraction in unbuffered DI water. c Values are in units of mg/kg. d Values are in units of µg/kg. e Soluble metal concentration predicted at pH 7.0 ( 0.5. Values listed as zero are modeled gut pH-extractable metal concentrations where the empirical second-order polynomial equation used for interpolation of these values was less than zero.
TABLE 2. Model Parameters (Value ( Standard Error), Residual Model Standard Error (SE), and Leave-One-Out (LOO) Cross-Validation Coefficient (q2) Determined for the Two-Exposure Route Model (Equation 7) for Metal Concentration in the Earthworm, E. andreia η k z SE q2
Cd
Cdb
Cu
Cu/SOC
Pb
Pbc
Zn
713 ( 84 0.98 ( 0.02 -11.2 ( 7.2 18.9 NDe
2120 ( 222 0.99 ( 0.01 -63.6 ( 9.2 9.69 0.31f
24.9 ( 10.7 1.25 ( 0.15 11.9 ( 2.0 5.86 ND
12.6 ( 0.97 ( 0.01 10.9 ( 1.8 4.91 0.39
112 ( 38 0.97 ( 0.02 10.4 ( 6.4 17.5 ND
151 ( 24 0.97 ( 0.01 0.13 ( 3.6 8.23 0.61
7.67 ( 1.33 0.82 ( 0.04 102 ( 10 30.4 0.73
4.9d
a η units are kg of soil/kg of worm except as noted; k is unitless; z units are mg of metal/kg of worm. b Data from soils E, F, H, and J removed; linear, non-steady-state Cd uptake for these soils (13). c Data from soils A, H, I, K, M, and N removed; linear, non-steady-state Pb uptake for these soils (13). d Parameter units altered due to normalization by SOC: η units are mg of SOC/kg of worm. e ND, not determined. f LOO trial in which soil E data were omitted did not converge to a stable solution and is not included in this calculation.
0.99[Cd]SpH + 0.01[Cd]GpH, is equal to z/η, in this case 63.6/ 2120 or 0.03 mg of Cd/kg of soil. It was expected that z for nonessential Cd and Pb would be less than or equal to zero, which is the case for the censored data versions. This model requires data that are not routinely measured in soils, and there are data from no published studies through which this model could be externally validated. For this reason, the same data used in model development was used to demonstrate model performance (Figure 3). Linear, forced zero-intercept least-squares fit curves were calculated for modeled versus measured EBC data and are depicted in Figure 3. Additionally, leave-one-out (LOO) cross validation was performed where model parameters were determined n different ways for n - 1 soils, and then each of these models was used to predict results in the soil that was omitted in model development. The LOO cross validation correlation coefficient, q2, was calculated and included in Table 2. The LOO q2 values indicate that the models for Zn and Pb are useful predictors. The model for Cu/SOM described 39% of the data variation, incurring most error in overpredicting low-concentration data. The model for Cd is dominated by the high-concentration data for this set of soils and may not have good predictive ability at lower concentrations. The lead concentration measured in earthworms cultured in soil K was more than 5-fold greater than the next highest value, and its absolute uncertainty was an order of magnitude greater than the next highest value (EBC ) 456 ( 125 mg/ kg). This resulted in the model regression being driven
predominantly by a single, uncertain value, increasing model parameter uncertainty unacceptably. In response, this value was removed from consideration and is not shown in Figure 3C, nor is it included in deriving the parameters listed in Table 2. No other high-concentration data were candidates for removal due to measurement uncertainty. Ireland and Wooton (25) showed that there were seasonal variations in metal uptake by earthworms and that the optimal conditions for increased metal uptake was during the cool, moist season. This concurs with other studies (26) showing that increased metal concentrations in worm tissues coincide with periods of peak activity. It has been estimated that a particular bolus of soil could take anywhere from 6 to 24 h to pass through the worm gut depending on whether the worm is actively feeding (6 h) or simply moving through the soil (24 h) (11). This indicates that there is a possibility that when internal worm metal concentrations are poorly regulated, results are likely to be significantly affected by the surface area of soil through which an organism moves. When this is the case, the model given by eq 7 would be expected to fail since its basic and necessary assumption, that a steady state is reachable, is violated. Both the Cd and Pb results are markedly superior when the data from soils in which earthworms that did not reach a steadystate Cd or Pb concentration were removed, as would be expected. When soil conditions cause toxicity to earthworms, either through large metal doses or due to the presence of other toxicants, biological changes in earthworms are likely to cause VOL. 35, NO. 22, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Modeled metal concentration in earthworms plotted vs measured values. Data from Table 1 and model parameters from Table 2 were substituted into eq 7: (A) cadmium, (B) steady-state censored Cd data set omitting linear uptake data, (C) lead, (D) steady-state censored Pb data set omitting linear uptake data, (E) SOC-normalized copper, and (F) zinc. Cadmium data (A and B) are plotted on a log-log scale in order to better obtain better resolution of the low concentration values. changes in the steady-state EBC, and results may deviate from those demonstrated for these soils. Additionally, in heterogeneous field soils, earthworms’ preferential feeding on organic-rich particles and avoidance of regions where 4528
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toxicity may occur may change the results from those obtained in these homogenized soils. Models capable of predicting doses of metal to organisms using only measurements of soil chemical characteristics for
input are potentially useful in saving money and time and increasing reproducibility of measurements for use in risk analyses of potentially contaminated sites. Inclusion of information relevant to biological phenomena as well as to soil chemistry is necessary for a model’s potential generalizability and was the goal of this exercise. The model developed here requires only measurements of soil chemical characteristics as inputs. The model parameters are used to characterize the important biological processes. The values determined for the Cd, Cu, Pb, and Zn model parameters qualitatively and quantitatively (by rank) conform to the values expected based on knowledge of earthworm biological function. At the same time, this model formulation is novel in that it takes into account relevant biology and chemistry while maintaining its transparency and simplicity. Model validation was not completed, however, due to a dearth of availability of adequate data in the literature.
Acknowledgments The International Copper Association, the International Lead-Zinc Research Organization, and the United States Environmental Protection Agency funded this work.
Supporting Information Available Two tables showing the total recoverable metal in the soils used in model development and the correlation coefficients for the parabolic empirical model. This material is available free of charge via the Internet at http://pubs.acs.org.
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(5) Ireland, M. P. In Earthworm Ecology from Darwin to Vermiculture; Satchell, J. E., Ed.; Chapman and Hall: New York, 1983; pp 247-265. (6) Brewer, S. R.; Barrett, G. W. Bull. Environ. Contam. Toxicol. 1995, 54, 120-127. (7) Beeby, A. Environ. Pollut. 2001, 112, 285-298. (8) Spurgeon, D. J.; Hopkin, S. P. Pedobiologia 1996, 40, 80-96. (9) Merino-Trigo, A.; Sampedro, L.; Rodriguez-Berrocal, F. J.; Mato, S.; de la Cadena, M. P. Soil Biol. Biochem. 1999, 31, 1735-1740. (10) Doube, B. M.; Brown, G. G. In Earthworm Ecology; Edwards, C. A., Ed.; CRC Press: Boca Raton, FL, 1998; pp 179-211. (11) Michel, C.; DeVillez, E. J. In Physiology of Annelids; Mill, P. J., Ed.; Academic Press: New York, 1978; pp 509-554. (12) Ireland, M. P.; Richard, K. S. Environ. Pollut. 1981, 26, 69-78. (13) Peijnenburg, W. J. G. M.; Baerselman, R.; Groot, A. C. d.; Jager, T.; Posthuma, L.; Veen, R. P. M. V. Ecotoxicol. Environ. Saf. 1999, 44, 294-310. (14) Spurgeon, D. J.; Hopkin, S. P. Appl. Soil Ecol. 1999, 11, 227-243. (15) Janssen, R. P. T.; Posthuma, L.; Baerselman, R.; Hollander, H. A. D.; Veen, R. P. M. V.; Peijnenburg, W. J. G. M. Environ. Toxicol. Chem. 1997, 16, 2479-2488. (16) Janssen, R. P. T.; Peijnenburg, W. J. G. M.; Posthuma, L.; Van Den Hoop, M. A. G. T. Environ. Toxicol. Chem. 1997, 16, 24702478. (17) Impellitteri, C. A.; Lu, Y.; Saxe, J. K.; Allen, H. E.; Peijnenburg, W. J. G. M. Sci. Total Environ., submitted for publication. (18) Chapman, P. M.; Allen, H. E.; Godtfredsen, K.; Z’Graggen, M. N. Environ. Sci. Technol. 1996, 30, A448-A452. (19) U.S. EPA. Method 3051: Microwave assisted acid dissolution of sediments, sludges, soils, and oils. In SW846 Test Methods for Evaluating Solid Waste, Physical/Chemical Methods, 2nd ed.; U.S. Government Printing Office: Washington, DC, 1997. (20) Eide, D. J. Adv. Microb. Physiol. 2000, 43, 1-38. (21) Sauve´, S.; Hendershot, W.; Allen, H. E. Environ. Sci. Technol. 2000, 34, 1125-1131. (22) McBride, M B.; Martinez, C. E.; Topp, E.; Evans, L. Soil Sci. 2000, 165, 646-656. (23) Carballeira, J. L.; Antelo, J. M.; Arce, F. Environ. Sci. Technol. 2000, 34, 4969-4973. (24) Ma, H. Z.; Allen, H. E.; Yin, Y. J. Water Res. 2001, 35, 985-996. (25) Ireland, M. P.; Wooton, R. J. Environ. Pollut. 1976, 10, 201-208. (26) Ma, W. Environ. Pollut. 1984, 33A, 207-219.
Received for review April 30, 2001. Revised manuscript received August 9, 2001. Accepted August 16, 2001. ES0109038
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