Exposure−Effect Model for Calculating Copper Effect Concentrations

SQGs should account for contaminant exposure from both water-filtration and ... Rainbow (6) proposed that toxicity would occur when the rate of metal ...
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Environ. Sci. Technol. 2005, 39, 7089-7096

Exposure-Effect Model for Calculating Copper Effect Concentrations in Sediments with Varying Copper Binding Properties: A Synthesis STUART L. SIMPSON* Centre for Environmental Contaminants Research, CSIRO Energy Technology, Private Mailbag 7, Bangor, NSW 2234, Australia

An exposure-effects model is described for calculating copper effect concentrations for benthic organisms in sediments with varying copper-binding properties. It is based on a bioenergetic-based kinetic model that describes the rate of assimilation of copper by benthic organisms from dissolved and particulate phases. During acute exposures, the total copper assimilated by the organisms was used as a measure of the organisms’ exposure to bioavailable copper, and toxicity occurs when the copper exposure exceeds a threshold value. Exposure-effects models were developed for nine benthic organisms and were used to predict the effect of sediment-water partitioning (Kd) and copper assimilation from ingested solids on toxic effects and how these factors will influence derived sediment quality guideline (SQG) concentrations. Species sensitivity distributions were used to calculate SQG concentrations for copper for sediments of varying copper binding properties. The modeling indicated that “single value” SQG concentrations would be ineffective for predicting the toxicity of metals in sediment. It is proposed that, for all contaminants (not just metals), a better approach would be to have SQG concentrations, or ranges, that are applied to different sediment types. SQGs should account for contaminant exposure from both water-filtration and particulate-ingestion exposure routes, as can be achieved by models of sediment-water contaminant partitioning (Kd) and the contaminant assimilation efficiency (AE) of ingested particles. The study indicates that improved mechanistic models of contaminant exposure, as influenced by both organism physiology and sediment properties, are needed to predict toxic effects in sediments.

Introduction For benthic organisms, effect concentrations for metal contaminants are dependent on the metal bioavailability in the water (via exposure to pore water, burrow water, or overlying water) and particulate phases (via assimilation of ingested particles) and on the sensitivity of the organism to these metal exposures (1-5). The bioavailability of metals in sediments is dependent on (i) speciation (e.g., metal binding with sulfide, iron hydroxide, organic matter), (ii) sedimentwater partitioning relationships (e.g., Kd ) [Sediment-Cu, * Corresponding author phone: +61-2-97106807; fax: +61-297106837; e-mail: [email protected]. 10.1021/es050765c CCC: $30.25 Published on Web 08/06/2005

 2005 American Chemical Society

mg/kg]/[Water-Cu, mg/L), (iii) organism physiology (uptake rates from waters, assimilation efficiencies from particulates), and (iv) organism feeding and other behavior (feeding selectivity, burrow irrigation) (5-10). Simpson and King (5) demonstrated that measurements of lethal effect concentrations (LC50s) and bioaccumulation following water-only and whole-sediment exposures of the amphipod, Melita plumulosa, and the bivalve, Tellina deltoidalis, to copper, could be combined with bioenergeticbased kinetic models of exposure pathways, to explain causality in whole-sediment toxicity tests. For both organisms, lethal body concentrations (LBCs) were greater for water-only exposures than for sediment exposures and indicated that the rate of copper accumulation and/or the mode of toxicity of copper assimilated were different for dissolved and particulate phases. The LBC approach, which assumes toxicity occurs when a threshold body concentration (accumulation) is exceeded (11), could not explain the observed toxicity. Exposure-effects models that were based on the net assimilation (net uptake) of copper, expressed as a lethal exposure concentration (LEC) that was independent of the postexposure copper efflux, provided a mechanistic explanation for the observed toxicity (5). In the LEC approach, toxicity occurs when the organism’s exposure to bioavailable copper (net assimilation, net uptake) exceeds a threshold value (e.g., LEC50, exposure concentration that causes 50% lethality). The LEC50 of copper was the same for both wateronly and whole-sediment toxicity tests. To cause acute toxicity to M. plumulosa and T. deltoidalis, greater copper uptake rates were required in 4-d exposures than in 10-day exposures, while the LEC50 was the same for these exposure times (5). In a study of dietary zinc accumulation and toxicity to isopods, van Straalen et al. (12) found that the rate of zinc uptake was a superior predictor of toxicity than was the zinc body concentration, and they proposed that the initial uptake rate is a useful indicator of bioavailability. Rainbow (6) proposed that toxicity would occur when the rate of metal uptake into the body exceeds the combined rate of excretion and detoxification of metabolically available metal. Although the rate of metal excretion can be quantified, it is not yet possible to quantify the rate of metal detoxification within organism bodies (e.g., metal-metallothionein binding, metal granule formation). For this reason, it is not currently possible to determine or model the effects of the true metabolically available concentration of metals. In the LEC approach, the total metal assimilated during acute exposures was considered as metabolically available metal, regardless of whether it was subsequently rendered nonmetabolically available through excretion and detoxification processes. The study by Simpson and King (5) indicated that the uptake rates from all sources (exposure route dependent parameters) and the toxicity threshold (mode of toxicity dependent) need to be considered to predict toxicity of sediment-associated contaminants. The effect of Kd on the predicted exposure that M. plumulosa will receive from water and sediment exposure pathways is shown in Figure 1. For sediments with the same total copper concentrations, decreases in Kd from 1 × 105 L/kg (e.g., silty sediment, with high copper binding capacity) to 1 × 103 L/kg (e.g., sandy sediment, with low copper binding capacity) did not affect the copper uptake rate from sediment particulates; however, the copper uptake rate, and net exposure, the organisms received from the water (pore water and overlying water) increased as dissolved copper concentrations increased. Consequently, to achieve the same LEC, lower total copper concentrations were required in sediments VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. The predicted effect of Kd (1×105, 1×104, 1×103 L/kg) on copper exposure and effect concentrations (LC50) for M. plumulosa. The EC50 values are calculated as the sediment copper concentration that results in a lethal exposure concentration (LEC50) of 210 µg Cu accumulated/g organism. having lower Kd values (Figure 1). All effect concentrations (e.g., LC50s) determined from total sediment copper concentrations should therefore be considered as “conditional” EC50 values, as they vary with sediment properties (e.g., Kd and assimilation efficiency of ingested solids). According to the modeling, for sediments with Kd values of 1 × 105, 1 × 104, and 1 × 103 L/kg, the LEC50 for M. plumulosa would be achieved at LC50 concentrations of 1400, 825, and 163 mg/ kg Cu, respectively (Figure 1). These calculations provide a mechanistic understanding of why individual species of organisms show a large variability in their sensitivity to sediments that have the same total metal concentrations but differing sediment properties. The calculations also explain why poorly equilibrated metal-spiked sediments that have unrealistically high metal concentrations in the pore waters are generally more toxic than naturally contaminated sediments with the same total metal concentrations (13). The major assumptions of the exposure-effects model were that the copper uptake rate was a linear function of copper concentration in water and sediment and that the copper-induced toxic effects were independent of exposure pathway (i.e., copper assimilated from water caused the same effects as copper assimilated from ingested particles). Both of these assumptions may depend on organism behavior and physiology because (i) the copper uptake may decrease as organism function becomes impaired at higher copper exposures (e.g., decreased filtration and ingestion rates), and (ii) the toxic effects from water and sediment copper exposures may be different due to different detoxification mechanisms for each exposure route. In the relatively short exposure-effect studies undertaken by Simpson and King (5) (i.e., 4- and 10-d acute toxicity tests), these assumptions appeared appropriate and the LEC of copper was the same for both water-only and whole-sediment toxicity tests. Most current water quality guidelines have been derived from species sensitivity distributions (SSDs) of water-only effect concentrations for individual contaminants; for example, the Australian and New Zealand guidelines (14) use SSDs of no observable effect concentrations (NOECs) to calculate concentrations that are protective of 95% of species for slightly to moderately disturbed systems. In contrast, sediment quality guidelines (SQGs) are not based on clear cause-effect relationships (11, 14, 15). The use of the SSDbased approach for calculating SQGs is more difficult because of the influence of sediment properties on contaminant exposure pathways and effect concentrations (5). For naturally copper-contaminated estuarine/marine sediments, Kd values are typically between 1 × 103 and 5 × 105 L/kg, which covers the range from sandy to silty, organic-rich sediments (i.e., from low to high densities of metal binding sites). This paper extends the exposure-effect model described by Simpson and King (5) to a wider range of benthic organisms and investigates the application of the model for deriving SQGs for metals in sediments. Exposure-effect models are developed for nine benthic organisms and used to calculate effect concentrations for a range of sediments differing in 7090

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sediment-water copper partitioning and copper assimilation efficiency. The effect concentrations are used to calculate SSD-based guidelines for copper for these sediments. The exposure-effect models are used to determine how sediment-water partitioning and metal assimilation (from ingested solids) affect guideline concentrations for metals in sediments. The strengths and limitations of the modeling approach, and the importance of considering contaminant biodynamics (10) when predicting toxic effects of sedimentassociated contaminants, are discussed.

Methods Benthic Organisms and Effects Data. A benthic alga (Entomoneis cf punctulata), three amphipods (Melita plumulosa, Corophium colo, Corophium insidiosum), three bivalves (Tellina deltoidalis, Mysella anomala, Soletellina alba), and two polychaete worms (Australonereis ehlersi, Nephtys australiensis) were used in the study. Descriptions of these species are provided in the Supporting Information (S1). Sensitivity of these species to dissolved copper in seawater and whole-sediments copper is shown for adult organisms in Table 1 (16-19). Sensitivity of the species to copper in marine sediments was determined using copper-spiked sediments equilibrated for 20 d before use in experiments (8). The sediments were organic-rich, iron-rich, silty (mangrove) sediments, and, following equilibration of the added copper, measured dissolved copper concentrations in pore waters and overlying waters were below the LOEC of 10-d water-only exposures. As shown in Table 1, the species used in this study have a wide range of sensitivities to copper, both in water-only and in whole-sediment exposures. The sediment-water partitioning for copper, Kd, was ∼5 × 104 L/kg for the copper-spiked sediments. The accuracy of the effect concentrations calculated from whole-sediment toxicity tests is dependent on the partitioning of copper throughout the 10-d exposures. Only for M. plumulosa, T. deltoidalis, and Entomoneis cf punctulata have sufficient whole-sediment tests been undertaken to calculate reliable LC50 values. The effect concentrations for the other benthic organisms were calculated from single tests with copper-spiked sediments; however, the values were suitable for the model development presented in this study. Exposure-Effects Model. Copper bioaccumulation by the organisms from filtration of water or ingestion of sediments was described by the bioenergetic-based kinetic model (5, 20-22):

dCO/dt ) (ku-W‚Cw - ke-W ‚CO-W) + (AE‚IR‚CS - ke-S‚CO-S) (1) where CO is the amount of copper taken up by the organism (mg/kg dry weight) for an exposure time, t (days), ku-W is the uptake rate constant from the dissolved phase (L/g/d), CW is the copper concentration in the dissolved phase (µg/L), AE is the copper assimilation efficiency from the ingested

TABLE 1. Copper Effects Data for 4-d Water-Only and 10-d Whole-Sediment Toxicity Tests 4-d water-only testsa organism

10-d whole-sediment testsb

LC50 (95% CL) mg/L

LOEC mg/L

ref

LC50 (95% CL) mg/kg

ref

0.07 (0.05-0.10)

0.04

16

850

d

2.1 0.28 (0.10-0.40) 0.47 (0.39-0.62)

0.12 0.20 0.34

d 17 e

4100 1500 1300

d d 18

1.50 (1.30-1.80) 0.15 (0.11-0.20) 0.12 (0.10-0.14)

0.90 0.14 0.09

19 19 19

3700 1020 1000

d 18 d

0.16 (0.14-0.19) 0.21 (0.20-0.22)

0.14 0.18

17 18

1150 2000

d d

algaec

benthic Entomoneis cf punctulata amphipods Corophium colo Corophium insidiosum Melita plumulosa bivalves Mysella anomala Tellina deltoidalis Soletellina alba polychaetes Australonereis ehlersi Nephtys australiensis

a All effect concentrations were calculated using the mean of measured initial and final concentrations. b 10-d effect concentrations for sediment with Kd (Cu) ) 5 × 104 L/kg, and dissolved copper concentrations were below the LOEC of the 10-d water-only exposures. c Entomoneis cf punctulata, a 24-h EC50 value was used. d Estimated for this study using unpublished copper effects and copper accumulation data (Simpson, unpublished results). e Spadaro (unpublished results).

particles (%), IR is the ingestion rate of the organism (g/g/d), CS is the copper concentration in the ingested particle (CS, mg/kg), and ke-W and ke-S are efflux rates constants for copper taken up from the dissolved and particulate phase, respectively. The model assumed that uptake from dissolved and sediment sources was additive. Growth of the adult organisms during 10 d was negligible, and a growth parameter was not necessary in the model. Efflux rates for copper taken up from the dissolved and particulate phases are considered independently because the two copper accumulation routes have different mechanisms and accumulation may occur in different types of organs or tissues. For the water and sediment exposures, the model was used to predict the copper body burdens of the organisms (both uptake and efflux rates considered) and the total copper assimilated by the organisms (only uptake rates considered). Copper body burdens (mg/kg dry weight) were calculated as the sum of background copper initially present in the test organisms and the predicted copper accumulation by the organisms. The exposure-effects model was based on the net assimilation (net uptake) of copper, expressed as an exposure concentration that was independent of the postexposure copper efflux (5). The total copper assimilated by the organisms was used as a measure of the organisms’ exposure (E) to bioavailable copper and was calculated using the equation:

E ) ((ku-W‚Cw) + (AES‚IR‚CS))‚t

(2)

where E is the net assimilation (net uptake) of copper by the organism (mg/kg dry weight) over the time period, t (days). The organism’s copper exposure was considered as the cause of the toxic effects, and the lethal exposure concentration (LEC50) was calculated as E at the LC50. The LEC was dependent on the copper uptake rate from each exposure source (dissolved or particulate).

Results and Discussion Bioenergetic-Based Kinetic Models for Other Benthic Organisms. Radiotracer uptake experiments (64Cu, t1/2 ) 12.7 h) were used to develop bioenergetic-based kinetic models describing the copper exposure pathways and bioaccumulation of copper by M. plumulosa and T. deltoidalis (20). Because the radiotracer experiments were undertaken using controlled laboratory conditions, the models developed may

FIGURE 2. Comparison of measured and predicted copper bioaccumulation for (a) water-only and (b) whole-sediment exposures (Kd ) 5 × 104 L/kg). Standard errors for measured body burdens were generally (25% (n ) 15). be considered more qualitative than quantitative when applied to environments different from those used in their development. However, the models may be refined through the collection of additional bioaccumulation data following exposure of organisms to whole sediments with wellcharacterized (measured) metal partitioning. The models developed by King et al. (22) provided good predictions of the copper accumulated by M. plumulosa and T. deltoidalis following 10-d water-only and whole-sediment copper exposures (5). Copper radiotracer experiments have not been undertaken to develop bioenergetic-based kinetic models for other benthic organisms. Models for M. anomala, S. alba, A. ehlersi, and N. australiensis were developed using copper effects data and copper bioaccumulation data from water-only and whole-sediment toxicity tests. For C. colo and C. insidiosum, copper bioaccumulation data were not available, and model parameters were selected on the basis of the copper effects data alone. Bioenergetic-based kinetic models for copper developed using this approach are shown in Table 2, and comparisons of predicted and measured copper bioaccumulation are shown in Figure 2. For all organisms, large ranges are expected for each parameter; for example, feeding rate may be influenced by food availability, and assimilation efficiency may be affected by food (sediment) type. The cumulative error associated with the model parameters was estimated to be 30-50% (5), which is suitable for the exposure-effect model “synthesis” undertaken in this study. Intra-species variability in sensitivity to contaminants is commonly a factor of 2 (14, 16, 19). The LEC50 for S. alba was much smaller than those of the other benthic species and may be explained by the much larger mass of this bivalve as compared to the other organisms VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Bioenergetic-Based Kinetic Model Parameters for Copper Bioaccumulation by Australian Benthic Organisms biokinetic model parameters AE-sed ku-water, L/g/day kew-water, /day kef-sed, /day IR, g/g/day LOEC, µg/L LC50, µg/L LC50, mg/kg LEC50, mg/kg a

amphipod Melita plumulosa

amphipod Corophium colo

0.070 0.120 0.160 0.31 0.20

0.10 0.02 0.20 0.40 0.10

amphipod Corophium insidiosum

bivalve Tellina deltoidalis

0.10 0.10 0.20 0.40 0.15

bivalve Soletellina alba

0.28 0.20 0.10 0.20 0.08

bivalve Mysella anomala

0.05 0.09 0.20 0.40 0.05

polychaete Australoneris ehlersi

0.20 0.07 0.25 0.50 0.05

polychaete Nephtys australiensis

0.03 0.60 0.30 0.30 0.70

0.05 0.45 0.30 0.30 0.20

160 470

Copper Effect Concentrations for 4-d Water-Only Exposures 1800 200 110 90 900 2100 280 180 120 1500

140 160

180 210

1300

Copper Effect Concentrations for 10-d Whole-Sediment Exposuresa 4100 1500 1020 900 2700

1150

2000

210

Lethal Exposure Concentration of Copper 250 260 43

380

380

420

LC50 values for sediment with Kd (Cu) ) 5 ×

104

420

L/kg.

TABLE 3. Whole-Sediment LC50 Values Calculated Using Equilibrium-Partitioning and 4-d Water-Only Effect Concentrationsa LC50, mg/kg copper (4-d exposureb)

organism

silt high Fe/TOC (Kd, L/kg: 1 × 105)

silt med Fe/TOC (Kd, L/kg: 5 × 104)

silt-sand med Fe/TOC (Kd, L/kg: 1 × 104)

silt-sand low Fe/TOC (Kd, L/kg: 5 × 103)

sandy low Fe/TOC (Kd, L/kg: 1 × 103)

sand v. low Fe/TOC (Kd, L/kg: 5 × 102)

Entomoneis cf punctulata Melita plumulosa Corophium colo Corophium insidiosum Tellina deltoidalis Soletellina alba Mysella anomala Australoneris ehlersi Nephtys australiensis

7000 47 000 210 000 28 000 18 000 12 000 150 000 16 000 21 000

3500 23 500 105 000 14 000 9000 6000 75 000 8000 10 500

700 4700 21 000 2800 1800 1200 15 000 1600 2100

350 2350 10 500 1400 900 600 7500 800 1050

70 470 2100 280 180 120 1500 160 210

35 235 1050 140 90 60 750 80 105

a Equilibrium-partitioning model (eq 3) used 4-d water-only LC50 values from Table 1 and K ) 5 × 104 L/kg. d a 24-h EC50 value was used.

(e.g., wet weight of 700 mg as compared to ∼100 mg for other organisms). Effect of Sediment Properties on Water-Only Exposure and Toxicity. If only dissolved metals are considered bioavailable and cause toxic effects in benthic organisms, that is, the effects of ingested particulates are ignored, then the toxicity of sediment metals can be predicted on the basis of equilibrium partitioning (EqP):

b

For Entomoneis cf punctulata,

Effect of Sediment Properties on Exposure and Toxicity for Whole Sediments. The exposure-effect model (eq 2) can be transposed to allow the calculation of copper effect concentrations for sediments with a range of copperpartitioning properties:

whole-sediment LC50 (mg/kg) ) LEC50/(t × (AE × IR + ku-water/Kd × 1000)) (4)

whole-sediment LC50 (mg/kg) ) water-only LC50 (mg/L) × Kd (L/kg) (3)

where LEC50 is the organisms’ exposure to copper at the LC50 (eq 2).

where Kd ) [Sediment-Cu, mg/kg]/[Water-Cu, mg/L]. Table 3 shows LC50 values for sediments with a range of different copper-partitioning properties (Kd) calculated using eq 3. The calculations are expected to provide reasonable estimates of effect concentrations for sediments with low Kd values (e.g., Kd < 3 × 103 L/kg), as the copper uptake rate will be influenced mostly by dissolved copper (Figure 1). For sediments with high Kd values, the copper uptake rate from dissolved copper is low and contributes little to toxic effects; however, the copper uptake rate increases with increasing particulate copper concentration, through ingestion of copper-contaminated sediment particles, and toxicity occurs. The use of the EqP approach (eq 3) will overestimate the LC50 values for sediments with high Kd values. For silty sediments with Kd values of 5 × 104 L/kg, LC50 concentrations for copper have been found to range from 900 to 4100 mg/kg (Table 1).

Table 4 shows the LC50 values for sediments with a range of different copper-partitioning properties calculated using eq 4. The exposure-effect model includes both water and sediment exposure pathways, and the toxic effects due to these copper exposure routes are additive (5). For sediments with high Kd values, the contribution of the dissolved copper exposure pathway to the observed toxicity becomes negligible, and calculated LC50 values are mostly influenced by the sediment exposure route. For sediments with low Kd values, the uptake rate and LC50 values are influenced most by a water exposure route and were similar to those determined for water-only exposures (Table 3). As benthic algae do not have a sediment exposure pathway, the calculated LC50 values for E. cf punctulata were the same as those calculated for the water-only exposure (Table 3). This assumption was justified by studies that have shown that metals solubilized from metal-rich particles by algal exudates are not very bioavailable (23). Changes in sediment properties

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TABLE 4. Effect Concentrations for Sediments with a Range of Copper-Partitioning Properties (Kd) Calculated Using the Exposure-Effects Model with Both Water and Sediment Exposure-Effects Pathways (10-d Exposure)a LC50, mg/kg copper (10-d exposure)

organism

silt high Fe/TOC (Kd, L/kg: 1 × 105)

silt med Fe/TOC (Kd, L/kg: 5 × 104)

silt-sand med Fe/TOC (Kd, L/kg: 1 × 104)

silt-sand low Fe/TOC (Kd, L/kg: 5 × 103)

sandy low Fe/TOC (Kd, L/kg: 1 × 103)

sand v. low Fe/TOC (Kd, L/kg: 5 × 102)

Melita plumulosa Corophium colo Corophium insidiosum Tellina deltoidalis Soletellina alba Mysella anomala Australoneris ehlersi Nephtys australiensis

1400 4120 1570 1070 1270 3920 1410 2620

1300 4100 1500 1020 1000 3700 1150 2000

825 3500 1040 630 380 2470 470 690

570 3000 760 430 212 1750 270 385

163 1400 238 122 47 525 61 82

85 840 130 65 24 280 31 41

a

Exposure-effect model (eq 4) used the parameters in Table 2 and Kd ) 5 × 104 L/kg.

FIGURE 3. Effect of Kd on LC50 values for benthic organisms in 10-d whole-sediment exposure to copper. (copper partitioning) affect the sensitivity of each organism differently (Figure 3). The Kd-dependence of the calculated LC50 values was consistent with past studies of species sensitivity to metalcontaminated sediments. Besser et al. (2) showed that increasing the amount of organic matter in sediments increased the partitioning of cadmium and copper to the sediments and lowered the toxicity of the sediments to the freshwater amphipod, Hyalella azteca. Riba et al. (3) showed that the lethal toxicity of Cd, Cu, Pb, and Zn in sediments to the estuarine clam Ruditapes philippinarum increased as the overlying water pH decreased. The changes in sediment toxicity to these organisms may be explained by changes in the metal exposure occurring due to changes in metal partitioning between the pore water and sediment phases (exposure-effect models).

Because the whole-sediment exposure-effect model is time dependent (eq 4), it was possible to predict how species sensitivity may change with exposure time. Table S1 shows 4-d LC50 values calculated for the same sediments as those in Table 4 (i.e., LC50 values required to achieve toxicity in 4 d rather than 10 d). The LC50 values calculated for the 4-d exposure were greater than those calculated for the 10-d exposure because the toxic effects of copper were dependent on the rate of copper exposure. For long exposure times, assimilated copper may be detoxified by organisms, for example, metallothionein binding or storage as inert granules (4, 10). Although detoxification mechanisms are not considered by the present exposure-effect model, these processes could be incorporated in future models following estimation of detoxification rates. Species Sensitivity Distributions (SSD) for CopperContaminated Sediments. Species sensitivity distributions (SSDs) can be used to calculate PC 95 (50%) (95% protection concentration, 50% confidence limit) for these species (24). Limitations and misuses of this type of approach for metals have been discussed previously (25). SSDs calculated using (i) 4-d water-only LC50 values calculated using the EqP model (Table 3) and (ii) 10-d whole-sediment LC50 values calculated using the exposure-effect model (Table 4) are shown in Figure 4 for a sediment with Kd ) 5 × 104 L/kg. As the exposure-effects relationships are dependent on sediment properties, the SSDs will vary with Kd. Figure 5 (and Table S2) shows PC 95 (50%) calculated for sediments with a range of Kd values using the EqP model for water-only exposures (Table 3) and the exposure-effect model for whole-sediment exposures (Table 4 and Table S1). Predicted SSDs for 4-d water-only exposures (LOEC values, Figure S2) and 10-d

FIGURE 4. SSDs calculated using (i) 4-d water-only LC50 values (Table 3) and (ii) 10-d whole-sediment LC50 values (Table 4) for a sediment with Kd ) 5 × 104 L/kg. The solid line represents the cumulative frequency model. VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 5. Effect of Kd on PC 95 (50) calculated using species sensitivity distributions (SSDs) for nine Australian benthic organisms.

FIGURE 6. Effect of AE on PC 95 (50) calculated using species sensitivity distributions (SSDs) for nine Australian benthic organisms.

whole-sediment exposures (LC50 values, Figure S3) are shown in the Supporting Information for sediments with log Kd ranging from 2 to 5. For sediments with low Kd values, the SSDs were influenced most by the water exposure route, and the calculated PC 95 (50) values were similar for water-only (eq 3) and wholesediment (eq 4) exposure-effects models (Figure 5, Table S2). For sediments with high Kd values, the contribution of the particulate-ingestion exposure pathway became increasingly important and the PC 95 (50) values calculated using the whole-sediment exposure-effect model were lower than the values calculated using the water-only model; that is, toxic effects occurred due to ingested particles. As well as sediment properties influencing Kd, the speciation of the particulate phase will also influence the assimilation of metals from ingested particles. Both Kd and AE will be influenced by a variety of factors within one sediment type, including (i) the speciation of the metals in the sediments (e.g., sulfides, iron hydroxides, organic matter, detritus, algae), (ii) ions that compete for metal binding sites on particles and at organism receptor sites (e.g., H+ (pH), Ca2+, Mg2+ (hardness/salinity), Fe2+, Mn2+ (sub-oxic sediments)), and (iii) soluble metal-complexing ligands (e.g., dissolved organic carbon, chloride) in pore waters and gut fluids. With respect to sediment exposure pathways, the degree of assimilation of each metal from each sediment phase will depend on the organism’s physiology (e.g., gut passage time, gut chemistry) as well as the properties of the sediment phase (5, 7, 26). For the two bivalves Macoma balthica and Mytilus edulis, AEs of Ag, Cd, and Co associated with particles of differing geochemistry (e.g., sulfide, iron oxide, and organic matter) varied by greater than an order of magnitude (7). It is expected that the AE for copper associated with particles of differing geochemistry would vary similarly. As discussed previously, for sediments with the same total metal concentrations, relationships between effect concentrations and Kd and AE were predicted to be nonlinear (5). The sediment Kd affects the amount of metal in the pore water and therefore greatly influences the exposure to dissolved contaminants. The metal assimilation efficiency (AE) of the organism affects the exposure an organism receives from particulate contaminants that are accumulated through the digestive system. Consideration of both sediment Kd and AE will therefore be necessary to make quantitative estimations of the toxicity of different sediments to benthic organisms. Sediment ingestion rates (IR) will also vary with sediment nutrition value, and variations in IR will have the same influence on exposure-effects as do variations in AE (eq 4). The influence of the copper assimilation efficiency (AE) of an organism on copper exposure pathways and EC50 values was calculated for sediments with a range of metal parti-

tioning properties using eq 4 (Table S3, Supporting Information). For these calculations, the LEC50 was constant and the AE was (i) unaltered, (ii) 50% greater, or (iii) 50% less as compared to the value used in the initial models (Table 2). The model predicted that as the AE of copper from sediment particulates increased, the importance of the sediment exposure (ingestion) pathway increased; that is, sediments with lower particulate copper concentrations were required to reach the LEC50 (Table S3). Conversely, as the AE of copper from sediment particulates decreased, the importance of the sediment exposure (ingestion) pathway decreased; that is, sediments with greater particulate copper concentrations were required to reach the LEC (Table S3). Figure 6 (Table S4) shows PC 95 (50) calculated using the LC50 data shown in Table S3 (calculated using the exposureeffect model). For sediment with log Kd < 4, changes in AE had a minimal effect on the PC 95 (50); however, for sediments with higher Kd values (as is common for sediment copper) large changes in species sensitivity were predicted. Examples of particulates with low AE include natural copper minerals and copper contained deeply within sediment particles. Particulates with high AE would include recently adsorbed or precipitated copper, or copper present in biota such as algae. The sediments used to develop the models described represented particulates with medium to high AE (recently introduced copper). Model Extrapolation, Uncertainties, and Sediment Quality Guidelines. The predictions from the described exposureeffects model may be considered more qualitative than quantitative. The errors associated with the bioenergeticbased kinetic models were >30% (22). The 95% confidence limits for the water-only LC50 values were 10-40% of the LC50 value (Table 1). The whole-sediment LC50 values were determined for copper-spiked sediments of a single sediment type (Kd ) 104 L/kg), and, for seven of the nine organisms, were calculated using data from a single test. To refine the model and increase the accuracy of predictions, copper effect concentrations need to be determined for each species using sediments with a range of sediment properties (varying Kd and AE). The value of exposure-effects models is their ability to predict the influence of sediment properties and organism physiology on toxic effects and how these factors will influence derived SQGs. For SQGs to be generic, they need to be predictive of effects for a wide range of sediment types. The exposure-effect models indicate that “single value” guidelines such as the commonly used empirical SQGs (14) will be ineffective for predicting toxicity. For all sediment contaminants (not just metals), a better approach would be to have guideline concentrations, or ranges, that are applied to different sediment types. These guideline values should be based on contaminant partitioning relationships (Kd) and use the contaminant assimilation efficiency (AE) of the

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sediment to correct for the influence of particulate ingestion exposure pathways. Methods for the rapid estimation of the AE values of particulates require development. Sediments are heterogeneous, the speciation of particulate metals varies greatly over small spatial scales, and partitioning of contaminants is easily disrupted by animal activity (27, 28). Selective feeding behavior of benthic organisms (feeding rate and particle selection) has been shown to influence the assimilation and toxic effects of copper in sediments (22, 29). This was not considered in the current models; however, in natural sediments, many organisms may selectively ingest, for example, organic-rich fine particles that have higher contaminant concentrations than nonselected particles, for example, the sand component of the sediment. Understanding how these factors affect species sensitivity requires further study to determine whether assumptions of heterogeneity and contaminant dynamics can be ignored or require greater consideration (30). Metabolically Available Metal Concentrations and Predicting Chronic Effects. In the LEC-based exposure-effects model, the total assimilated copper was considered as metabolically available copper, regardless of whether it was subsequently rendered nonmetabolically available through excretion and detoxification processes, and was considered as the cause of the toxic effects. The use of a LEC, that was independent of the postexposure copper efflux, provided a better explanation for the observed toxicity than a LBC (5). In addition to Rainbow’s (6) proposal that toxicity occurs when the rate of metal uptake becomes excessive (discussed earlier), Luoma and Rainbow (10) showed that the dynamic multi-pathway bioaccumulation model (DYM-BAM) could provide a unified explanation of metal bioaccumulation by a wide range of benthic organisms in a range of sediment environments. While DYM-BAM can predict metal bioaccumulation for chronic exposures, its use for predicting toxic effects is more challenging. The difficulties arise because it is not yet possible to quantify the rate of metal detoxification within organism bodies; for example, rates of metallothionein production and sequestration of metals in nontoxic forms (metal-metallothionein complexes, solid granules). Furthermore, metal detoxification rates may be different for metals assimilated from different exposure routes (e.g., dissolved and particulate). Without knowledge of these, and other, metal detoxification reaction rates, it is not possible to know the metabolically available concentration (MAC) of metals. Consequently, it is not possible to determine when the MAC will rise to and beyond the toxicity threshold (e.g., the MAC that causes 50% effect, EMAC50). During chronic exposures, the MAC will fluctuate as uptake rates (exposure sources) and efflux rates (excretion and detoxification) vary over time. A chronic EMAC will depend on the specific chronic effects being assessed; for example, the EMAC for growth rate effects may be greater than the EMAC for reproductive effects. A chronic exposure-effects model should determine if the chronic EMAC is exceeded during any stage of the chronic exposure. As the organism’s exposure history will seldom be known for chronic exposures, some estimates will be needed. The current study indicated that exposure-effects models that use metal uptake rates from all sources (exposure route dependent parameters) and the toxicity threshold (mode of toxicity dependent) can provide useful predictions of acute metal toxicity in contaminated sediments. The development and application of exposure-effects models for other metals, metal mixtures, and other sediment contaminants is required. Further research is required to understand the relationships between metal uptake, regulation (e.g., subcellular partitioning) and toxic effects to benthic organisms. For some organisms, metals assimilated through dissolved and particulate exposure pathways will cause toxic effects of different

magnitudes. However, provided the toxic effects of each metal exposure pathway are characterized, exposure-effect models could be developed to mechanistically describe the observed toxicity and predict toxic effects for sediments with different metal-binding properties. Considerably greater information requirements will be needed to predict chronic effects of metals due to the need to predict, for all exposure sources, the uptake and detoxification rates and understand how these parameters influence the MAC and the various chronic effects being assessed.

Acknowledgments I would like to thank Graeme Batley, Jenny Stauber, and three anonymous reviewers for constructive comments on this manuscript. The project was supported, in part, by research grants received from Rio Tinto, BHP Billiton, Xstrata Copper, and the New South Wales Environmental Trust, Australia.

Supporting Information Available Descriptions of organisms under study, effect concentration ranges for sediments, PC 95 (50) values, effect of copper assimilation frequency on exposure effects and calculated PC 95 (50) values, and species sensitivity distributions. This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review April 20, 2005. Revised manuscript received June 28, 2005. Accepted July 1, 2005. ES050765C