Development of a Biotic Ligand Model and a Regression Model

Jun 24, 2005 - A Terrestrial Biotic Ligand Model. 1. Development and Application to Cu and Ni Toxicities to Barley Root Elongation in Soils. Sagar Tha...
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Environ. Sci. Technol. 2005, 39, 5694-5702

Development of a Biotic Ligand Model and a Regression Model Predicting Acute Copper Toxicity to the Earthworm Aporrectodea caliginosa N A T H A N A E¨ L T . T . M . S T E E N B E R G E N , † , ‡ FEDERICA IACCINO,† MAAIKE DE WINKEL,† LUCAS REIJNDERS,‡ AND W I L L I E J . G . M . P E I J N E N B U R G * ,† Laboratory for Ecological Risk Assessment, National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands, and Faculty of Natural Sciences, Open University, Heerlen, The Netherlands

The purpose of this study was to develop a terrestrial biotic ligand model (BLM) for predicting acute copper toxicity to the earthworm Aporrectodea caliginosa. To overcome the basic problems hampering development of BLMs for terrestrial organisms, an artificial flowthrough exposure system was developed consisting of an inert quartz sand matrix and a nutrient solution, of which the composition was univariately modified. A. caliginosa was exposed for 7 days under varying concentrations of copper and the major cations modifying toxicity: H+, Ca2+, Mg2+, and Na+. In addition copper speciation was modulated by means of EDTA or dissolved organic carbon (DOC). An increase in pH or pNa resulted in a linear decrease of 7-days median lethal concentrations. Increasing Ca2+ and Mg2+ activities had inconsistent effects. EDTA addition decreased toxicity when the total copper concentration in the pore water was kept the same. This is attributed to the strong complexation capacity of EDTA and shows that total copper is not the toxic species. DOC was more protective than could be explained by its metal complexing properties. The BLM developed incorporates the effects of H+ and Na+. This BLM was validated with the results of a set of bioassays with artificial pore water in quartz sand and by a set of bioassays in spiked field soils. Prediction error was within a factor of 2, but some predictions were not within the 95% confidence interval. Therefore a more widely applicable regression type model was developed that was able to explain >95% of the (lack of) toxicity observed. To our knowledge this is the first report of the successful development of a terrestrial BLM.

Introduction The subject of this study is the ecotoxicity of copper in soil ecosystems. The widespread use of this metal, together with * Corresponding author phone: +31 30 2743129; fax: +31 30 2744413; e-mail: [email protected]. † National Institute for Public Health and the Environment. ‡ Open University. 5694

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its toxic potential, underlines the need for a correct risk assessment and appropriate soil quality criteria. To date, soil quality criteria and risk assessment of heavy metals are still predominantly based on total concentrations. However, total concentrations or even dissolved concentrations in pore water are not good predictors of their potential effects on ecosystems (1). In surface waters, several physicochemical parameters such as dissolved organic carbon (DOC), pH, and hardness influence the bioavailability of copper and can modify copper toxicity by several orders of magnitude (2). The same is true for soil. Consequently, soil quality criteria based on total or dissolved concentrations may be too lenient or too stringent dependent on the circumstances involved if bioavailability is not taken into account. Too stringent criteria may lead to increased societal costs for emission reduction and environmental sanitation measures, whereas criteria that are too lenient may result in harm to soil life and biodiversity. In this context the biotic ligand model (BLM) has gained increased interest, as recently overviewed by Gorsuch et al. (3). In the BLM both metal speciation and interactions of the metal at the site of toxic action are taken into account. BLMs have been developed for application in the aquatic environment and are designed to predict metal toxicity by integrating the most important determinants of toxicity. These determinants can be divided into two categories: (1) determinants that modulate metal speciation in solution and (2) determinants that interact directly with the metal ion at the site of toxicity. The main assumption is that metal toxicity occurs as the result of free metal ions reacting with binding sites at the organism-water interface. The concentration of this metal-biotic ligand complex determines the magnitude of the toxic effect, independent of the physical-chemical characteristics of the medium. Because of assumed similarity of mechanisms of toxicity between aquatic and terrestrial organisms, it is likely that the BLM approach as developed for the aquatic compartment is also applicable to the terrestrial environment. Until now, however, the BLM concept has not been applied to predict toxicity to soil organisms. There may be two reasons for this. The routes of metal uptake in soils are in general more complex than those in water, since exposure via the pore water and exposure via ingestion of soil particles may in principle both be of substantial importance. Besides, it remains highly problematic to univariately manipulate the composition of the soil pore water and to control the metal concentrations in the pore water, due to reequilibration of the system following modification of any of the soil properties (including addition of metal salts). In this study a BLM is developed to predict acute copper toxicity to the earthworm Aporrectodea caliginosa. In principle, the earthworm is exposed to copper in soils via two different routes of uptake: copper absorption through the skin and oral ingestion of contaminated soil particles (4, 5). In the bioassays that have been used in this study in constructing the BLM, copper uptake by ingestion of soil particles is excluded a priori. The bioassays were performed in an aqueous solution or in a flow-through system consisting of artificial pore water (Steiner nutrient solution (6)) and an inert solid matrix (quartz sand) that does not bind any metal, thus excluding uptake via the oral route while restraining the system from reequilibration. The main objective of this study was to examine the effects of Ca2+, Mg2+, Na+, and H+ on copper toxicity to A. caliginosa. In a first approximation a decreasing effect on the toxicity of the free copper ion is hypothesized at increasing con10.1021/es0501971 CCC: $30.25

 2005 American Chemical Society Published on Web 06/24/2005

centrations of any of these competing ions, due to competition at the binding site (at the same total copper concentration) (2, 7). As a second objective we examined the effects of increasing amounts of DOC or EDTA on the toxicity of copper to A. caliginosa. A decreased toxicity is hypothesized at the same total copper concentration when the LC50 is expressed as copper activity, as a result of complexation of copper with increasing amounts of DOC and EDTA. However, the presence of EDTA or DOC is expected not to change copper toxicity (when expressed as Cu2+ activity), when the activity of the free copper ion is kept constant. The BLM accounts for the expected effects of EDTA and DOC on copper speciation as it starts from the measured copper activities in the pore water. If proven successful, the BLM approach as advocated here for the earthworm A. caliginosa may initiate a fundamentally new approach toward risk assessment of metals in soil.

Experimental Section Test Organisms. The organism used for the toxicity bioassays is the earthworm Aporrectodea caliginosa L. This earthworm has as a pragmatic advantage that it is a geographically widespread species, common in many soils in temperate climate areas and available throughout the year. It can be easily identified, sampled, and stored. Moreover, A. caliginosa can survive in an aquatic environment, an essential requirement given the use of a flow-through system. The earthworms were sampled in a sandy, uncontaminated soil. Four A. caliginosa were used in each bioassay, the wet weight of individual worms was on average 381 mg (s.e. 7 mg). The worms were kept in Petri dishes on wetted filter paper for 48 h before exposure to allow the organisms to void their guts. The organisms, solutions, and all other materials were kept at 15 ( 2 °C during all bioassays. Continuous illumination (1500 Lux) was used to avoid a possible escape of worms from their containers. Exposure in water only was carried out in the dark. The animals were not fed during the experiments. Experimental Design. First the earthworms were exposed in 1 L of an aqueous Steiner nutrient solution (6) to a range of Cu2+ concentrations. Details on the Steiner nutrient solution are given in the Supporting Information. These bioassays were performed in a range from pH 4-8; each pH was fixed at a preset value. A set of tests is defined as one series of toxicity bioassays in which only the copper concentration was varied, leaving all other constituents of the test medium unchanged. A set consisted of 5-10 bioassays, allowing the calculation of the LC50 for each set. The mortality of the four earthworms incubated in each bioassay after 1 week of exposure was examined. Every day the Steiner nutrient solution was refreshed. In total six test sets in water, performed at different pHs, were available. Earthworms perform best in a solid medium and although they survive during at least 28 days in water, a life in solely water obviously causes an extra stress factor. To compare the difference between true water exposure and exposure in a soil environment, the experiments were continued in inert quartz sand percolated for at least 1 day with demineralized water. To reach equilibrium, the sand was subsequently kept in the test solution for 1 day, and the test solution was circulated through the equipment for another day before the start of the bioassays. The percolated test solution was recirculated into the jars during equilibration. The bioassays in quartz sand were performed with varying concentrations of H+, Ca2+, Mg2+, Na+, EDTA, and DOC. To distinguish the effects of the different parameters, each one was changed univariately, keeping all other concentrations (except copper) constant. Three hundred grams of quartz sand was used in each bioassay, and the flow rate of the modified nutrient solution

was 1 L per day. The hydraulic residence time was about 1 h, and the nutrient solution was discarded after percolation. Water characteristics were determined before and after percolation. The containers were covered with a lid to reduce light intensity and to avoid escape of the animals; the bottom consisted of cloth with a mesh of 100 µm. Mortality was recorded after 7 days of exposure. Twenty-one sets of copper bioassays were performed in inert quartz sand: eight sets with different pH, four sets with different pCa (added as CaCl2), four sets with different pMg (added as MgCl2), three sets with different pNa (added as NaCl), one set with 0.1 mmol L-1 EDTA, and one set with added DOC. The DOC used was Aldrich humic acid. The humic acids were dissolved in the Steiner nutrient solution until saturation. The solution was filtered trough a 0.45 µm acetate filter by suction; pH was adjusted after Cu addition. A maximum variation of 0.3 pH units was allowed within each set. An outline of the characteristics of the modified Steiner nutrient solution of the bioassays in quartz sand is given in Table 1. The bioassays in quartz sand were designed to verify the BLM in nonaquatic systems. Manipulation and Measurements of the Solutions. The free copper ion activity (mol L-1) and pH of the Steiner nutrient solution were checked daily and adjusted if necessary, allowing a maximum pH variation of 0.3 units and a maximum pCu variation of 0.1 units. Ion-selective electrodes (ISEs) were used to measure the free copper ion activity, in combination with a voltmeter of 0.1 mV resolution (ColePalmer Copper Electrode). The ISEs were calibrated in the relevant pCu range by means of stock solutions of Cu(NO3)2 of known activities at pH 2, using NaNO3 to adjust the ionic strength. The maximum deviation of the slope of the calibration curve that was considered acceptable was 3% of the theoretical value calculated with the Nernst equation. A reading was considered stable when deviations were less than 0.1 mV for at least 5 min. The copper activity was adjusted by adding copper nitrate Cu(NO3)2 to the Steiner nutrient solution. The pH was adjusted using either KOH or HNO3; pH was measured with a Metrohm 691 pH Meter equipped with a combination pH glass electrode (Metrohm AG, Herisau, Switzerland). Representative samples of the (percolated) Steiner nutrient solution were taken for all test sets (but not for each individual bioassay) to measure the total copper concentration and concentrations of Ca2+, Mg2+, Na+, and DOC (e.g. Ca2+ concentration was measured for each bioassay in the Ca test set but not always in the pH test set). DOC was measured with a TOC/DOC analyzer (Model DC-190, Rosemount Analytical, Dohrmann Division, Santa Clara, CA). Cation concentrations were determined by ICP-AES (Spectro Analytical Instruments, Kleve, Germany). WHAM (8) was used to calculate the activities of the competing cations considered in this study. Validation Experiments in Field Soils. The applicability of the BLM in nonaquatic systems was further validated by means of bioassays in artificially contaminated field soils taken from different locations in The Netherlands. With the results of these bioassays it can also be verified whether copper uptake via soil particles is of minor importance as the earthworms will now be exposed also via this route (as opposed to the experiments in quartz sand). The validation experiments were performed with five different soils, spiked in the laboratory with different concentrations of CuCl2 and allowed to age for at least 1 month at 15 °C, and one soil contaminated in situ with CuSO4 and aged for 20 years to reach an equilibrium, thus mimicking contaminations as they occur in practice. This was a sandy soil from Wageningen (NL) in which two pH ranges were created, a low pH with sulfur (about 4.5) and a higher one with calcium carbonate (about 6.5). The other five soil types are characterized as VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Outline of the Sets of Tests in Quartz Sand and Steiner Nutrient Solution (6)a no. of bioassays

pCu

pH

Ca mmol‚L-1

Mg mmol‚L-1

Na mmol‚L-1

DOC mg‚L-1

added EDTA mmol/L

LC50 (pCu)

8 7 10 10 10 10 10 6 10 10 5 5 10 6 10 10 5 8 1 1

6.42-5.22 6.23-4.97 6.24-4.94 6.49-5.09 6.72-5.36 7.10-5.30 7.57-6.35 9.81-8.22 6.81-5.96 6.82-5.78 5.37-4.74 5.09-4.87 6.94-6.10 7.39-5.44 6.51-5.28 5.86-4.96 8.08-6.16 6.88-5.41 saturation: ( 9.83 saturation: ( 8.53

4.07 4.16 4.56 5.04 5.17 5.50 6.04 (8 5.11 5.07 4.48 4.48 5.13 5.94 5.23 5.21 5.68 5.06 7.00 8.00

3.57 3.57 3.57 3.57 3.57 3.57 3.57 3.57 6.56 8.55 3.57 3.57 3.57 3.57 3.57 3.57 3.57 3.57 3.57 3.57

2.22 2.22 2.22 2.22 2.22 2.22 2.22 2.22 2.22 2.22 3.41 5.55 5.55 2.22 2.22 2.22 2.22 2.22 2.22 2.22

1.13 1.13 1.13 1.13 1.13 1.13 1.13 1.13 1.13 1.13 1.13 1.13 1.13 3.65 4.57 7.31 1.13 1.74 1.13 1.13

6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 25 6 6

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0 0 0

5.77 5.72 5.87 6.14 6.25 6.14 6.48 NM 6.72 6.46 4.88 4.89 6.75 6.12 5.72 5.67 6.42 5.64 NM NM

95 CI 5.62-5.92 5.33-6.11 5.79-5.96 6.06-6.44 6.06-6.44 5.90-6.39 6.33-6.64 5.85-6.57 6.28-6.64 4.85-4.93 4.74-4.98 6.60-6.85 5.94-6.29 5.62-5.82 5.53-5.80 6.12-6.73 5.35-5.94

a The concentrations given reflect total concentrations (except for pCu). LC50 values are expressed in units of pCu, as are the 95% confidence intervals (95 CI), NM ) no mortality observed.

TABLE 2. Outline of the Results of the Validation Tests in Field Soilsa % survival description

pH

pNa

pCu measd

Wageningen1 Wageningen2 Wageningen3 Wageningen4 Wageningen5 Wageningen6 Wageningen7 Wageningen8 VU-100.1 VU-100.2 VU-100.3 VU-100.4 VU-100.5 Boxtel1 Boxtel2 Boxtel3 Boxtel4 Boxtel5 Epen1 Epen2 Epen3 Epen4 Epen5 Epen6 Lepelstraat1 Lepelstraat2 Lepelstraat3 Lepelstraat4 Lepelstraat5 Lepelstraat6 Oost1 Oost2 Oost3 Oost4 Oost5 Oost6 Oost7

6.99 6.03 6.45 6.20 4.47 4.31 4.29 4.10 6.66 7.19 7.30 7.00 6.84 4.35 4.38 4.45 4.37 4.49 6.36 6.63 6.16 6.33 5.73 5.32 4.95 5.03 4.49 4.64 4.55 4.54 8.11 7.44 7.40 7.18 7.59 7.63 7.34

3.13 3.10 3.17 3.11 3.06 3.05 3.03 3.01 2.69 2.69 2.69 2.69 2.69 2.39 2.39 2.39 2.39 2.39 2.57 2.61 2.56 2.58 2.58 2.58 2.77 2.66 2.60 2.67 2.67 2.67 2.69 2.78 2.80 2.60 2.71 2.71 2.71

11.60 10.60 10.43 10.33 8.65 7.81 7.23 6.69 9.03 6.14 6.11 5.99 5.74 5.86 5.63 5.16 4.53 4.53 9.77 9.72 8.07 5.71 5.60 5.38 8.84 8.34 6.66 5.12 4.94 4.54 12.16 10.68 10.44 10.05 6.61 6.51 6.43

a

LC50 in pCu predicted

t)7 days

t ) 16 days

t ) 28 days

DOC mg L-1

6.95 6.57 6.77 6.64 5.94 5.88 5.85 5.77 6.54 6.74 6.78 6.67 6.61 5.46 5.47 5.50 5.47 5.52 6.34 6.48 6.26 6.34 6.11 5.95 5.94 5.90 5.65 5.75 5.72 5.72 7.09 6.89 6.89 6.68 6.91 6.92 6.81

100 75 100 100 75 100 75 75 100 0 50 50 0 100 100 75 0 0 100 100 100 0 0 0 100 100 100 50 0 0 100 100 100 100 25 50 0

100 75 75 100 75 100 75 100 75 0 75 0 0 75 100 25 0 0 75 100 50 0 0 0 100 100 75 25 0 0 100 100 75 75 0 25 0

100 100 100 100 100 100 100 75 25

26.04 17.20 24.45 22.44 14.63 13.81 15.01 13.47 18.52 52.72 52.83 64.07 34.22 33.26 35.36 39.89 39.47 58.63 34.71 108.41 32.24 178.00 54.81 102.80 42.71 43.56 46.10 64.10 102.90 61.33 59.95 19.40 19.50 19.66 77.00 79.02 67.58

75 25 100 50 75 100 50

100 100 100 0 100 50 50 75

Over 50% survival is expected when the measured pCu exceeds the predicted LC50.

follows: one sandy soil with a high pH, generated artificially by addition of grinded shells (Vrije Universiteit Amsterdam, 5696

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NL), two clay-rich soils with higher pH from Oosterhout (NL) and Epen (NL), one acidic loam from Boxtel (NL), and one

FIGURE 1. Relationship between p(LC50Cu2+) and pH in water. The dashed lines indicate the 95% confidence interval. The vertical lines indicate the 95% confidence intervals of each p(LC50Cu2+). The triangle point is a maximum p(LC50Cu2+) estimated from the tests. acidic sandy soil from Lepelstraat (NL). About 300 g of spiked soil was used, and tap water was added to adjust the moisture content to field capacity prior to the start of the bioassays. The bioassays were carried out in glass jars under continuous illumination at 15 ( 2 °C. Mortality was assessed after 7, 16, and 28 days of exposure. Four worms were used in each bioassay, totaling N ) 12 for each soil. Apart from duplication at the three exposure times, no further duplication of the bioassays was performed. Pore water was sampled by centrifugation (10 000g) and subsequent filtering (0.45 µm acetate filter) by suction. The same analytical procedures as described above were used for the pore waters collected from the spiked soils. Calculation of the LC50. LC50 values for 7 days of exposure were expressed as both the measured free copper ion activity (LC50Cu2+) and the total copper concentration in the soil solution (LC50Cutot). This was done for each test set, using the trimmed Spearman-Karber method (9). This method also gives the 95% confidence intervals. Excel Analysis ToolPak was used for regression analysis.

Results and Discussion Experimental Results. Especially in the water-only experiments, the response of the organisms was highly reproducible, and in general the same response was observed upon duplication of the bioassays. This is reflected in the small confidence intervals (on average less than 0.1 pCu units) that were calculated (Figure 1). Broader confidence intervals were obtained for the experiments carried out in artificial sand (Figures 2-4), but in these experiments too it was possible to deduce statistically significant estimates of the LC50Cu2+. Five values of LC50Cu2+ could be calculated from the six pH sets in the water-only bioassays. At pH ( 7 there was too little mortality, even at complete Cu saturation, to reliably calculate the LC50Cu2+. Still, a minimum LC50Cu2+ value equal to the measured Cu2+ activity at Cu saturation could be determined, as the real LC50Cu2+ will be higher than the Cu2+ activity at saturation. At pH ( 8 the 95% confidence limits were not available as in this set only four bioassays were performed. The results of the water bioassays are plotted in Figure 1. From Figure 1 it may be deduced that the LC50Cu2+ (expressed as pCu) increases with increasing pH. This means that copper is less toxic at low pH: H+ ions have a protective effect, which can be attributed to proton competition at the biotic ligand. The same protective effect of H+ was even more evident in the sand bioassays. The activities and concentrations at which mortality was observed were now higher, which exemplifies the additional stress encountered by the worms in water. The LC50Cu2+ values expressed as pCu are plotted in Figure 2 (filled squares) as a function of pH. All measured

FIGURE 2. Relationship between p(LC50Cu2+) and pH in sand. Dashed lines indicate the 99% confidence interval for the pH tests. The vertical lines indicate the 95% confidence intervals of each p(LC50Cu2+). The filled square points indicate the pH tests, the open square the EDTA tests, the triangles the Na tests, the circle the DOC tests, the crosses the Ca tests, and the asterisk the Mg tests.

FIGURE 3. Relationship between p(LC50Cutot) and pH in sand. Dashed lines indicate the 99% confidence interval for the pH tests. The vertical lines indicate the 95% confidence intervals of each LC50Cutot. The filled square points indicate the pH tests, the open square the EDTA tests, the triangles the Na tests, the circle the DOC tests, the crosses the Ca tests, and the asterisks the Mg tests.

FIGURE 4. Relationship between p(LC50Cu2+) and Na+ activity (pNa) in sand at pH 5.22. The vertical lines indicate the 95% confidence limits of each p(LC50Cu2+). points are within the 99% confidence intervals of the best fitting linear regression:

p(LC50Cu2+) ) 0.39‚pH + 4.15

(R2 ) 0.93, p ) 1.1‚10-4, F ) 79) (1)

When the LC50Cu2+ in mol L-1 is considered as a function of (H+) in mol L-1, linear regression gives the following expression for LC50Cu2+: VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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LC50Cu2+ ) 1.70‚10-2‚(H+) + 5.22‚10-7 2

-4

(R ) 0.87, p ) 7.4‚10 , F ) 40) (2) No mortality was observed at pH 7 and 8, although a saturated Cu solution was used at these pH values. Apparently, the activity of all potentially toxic copper species is too low at pH exceeding 7 to induce copper toxicity to earthworms via dermal exposure. Indeed, calculated Cu activities confirm that at pH > 7, the predicted LC50Cu2+ (eq 2) exceeds the Cu solubility. For the bioassays in quartz sand also the LC50Cutot values were calculated. As the total copper concentrations were not always measured in the pH bioassays, the missing concentrations were predicted on the basis of a linear correlation (regression) between the measured copper activities and the measured total copper concentrations (mol L-1) in the pH bioassays. This correlation holds for copper in the Steiner nutrient solution after percolation through the quartz sand:

pCutot ) 0.48‚pCu2+ + 1.15 (R2 ) 0.84, p ) 6.6‚10-13, F ) 148) (3) The LC50Cutot values exceed the LC50Cu2+ values by a factor of 1.75. The results of the linear regression analysis of LC50Cutot against pH are as follows (Figure 3):

p(LC50Cutot) ) 0.13‚pH + 3.49 (R2 ) 0.71, p ) 1.7‚10-2, F ) 12) (4) Whereas a protective effect is expected from the addition of calcium or magnesium due to competition at the biotic ligand, the measured effects are inconsistent. From Figure 2 it can be seen that the LC50, expressed as pCu2+, increased upon addition of Ca (crosses in Figure 2) and Mg (asterisk in Figure 2). Nevertheless this picture is inconsistent as addition of 3 mmol L-1 of Ca (highest cross in Figure 2) increased toxicity to a higher extent than addition of 5 mmol L-1 of Ca. The effect on the LC50Cutot was also inconsistent. The effect of calcium was still similar, but as shown in Figure 3 magnesium seemed to be protective in two test sets (with 1 and 3 mmol L-1 of Mg at pH 4.48; two coinciding asterisks in Figure 3) and inducing increased toxicity in the third set (also with 3 mmol L-1 of Mg, at pH 5.10). Actually, effects of Ca and Mg in an aquatic environment were until now often described as being either protective for Cu toxicity (7, 10, 11) or unimportant (12-15). Figure 2 shows that Na+ (open triangles) does protect A. caliginosa against Cu2+ toxicity. The effect of the addition of two different amounts of Na was measured at pH 5.22. In addition, mortality was assessed in the experiments in which the protective effect of H+ was determined. This leaves three data points to quantify the protective effect of Na. The lethal concentrations p(LC50Cu2+ ) are plotted as a function of pNa in Figure 4 and appear to increase with pNa. Linear regression analysis gives

p(LC50Cu2+ ) ) 0.64‚pNa + 4.27

(R2 ) 0.98, p ) 8.8‚10-2, F ) 52) (5)

or when the LC50Cu2+ (mol L-1) is expressed as a function of (Na+) (mol L-1):

LC50Cu2+ ) 2.43‚10-4(Na+) + 5.18‚10-7 (R2 ) 0.92, p ) 0.18, F ) 11) (6) The presence of 1 mmol L-1 EDTA did not change copper toxicity when expressed as Cu2+ activity; the LC50Cu2+ following addition of EDTA falls nicely within the 99% 5698

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confidence intervals of the linear regression line of the pH test sets (Figure 2). However, when copper toxicity was expressed in terms of total Cu concentrations in solution, then addition of EDTA had a markedly suppressive effect on toxicity, as shown in Figure 3. Comparison of Figures 2 and 3 shows that toxicity is related to the free Cu species and not to the total copper pool in solution. From Figure 3 it can be deduced that DOC addition also has a statistically significant protective effect on A. caliginosa when expressed as the total copper concentration. Curiously, the addition of DOC has, contrary to EDTA, also a protective effect when the activity is kept constant by adding copper (Figure 2). This finding may be attributed to the sodium content of the humic acid, used to prepare the DOC. The measured pNa was somewhat lower than the pNa in the pH tests. But it is not possible to compare the LC50Cu2+ found in the DOC bioassays to the LC50Cu2+ in the Na bioassays as the pH in the DOC tests was different from the pH in the Na bioassays. Therefore the DOC tests will be discussed after the development of the BLM, as it will be possible then to consider both the pH and the pNa effect. BLM Development. The bioassays in artificial sand were used to develop the BLM. The effects of pH and pNa will be incorporated in the BLM, as these effects were unambiguous (protective). Activities of Ca and Mg will not be accounted for in the development of the BLM as no consistent protecting effect of Ca and Mg on copper toxicity to A. caliginosa was observed. Because the presence of EDTA did not change the toxicity when expressed as copper activity, it is not needed to account for EDTA in the BLM, as the BLM starts from measured copper activities. Hence it will account for the effect of EDTA on copper speciation in solution. In the same way the BLM will account for the effect of DOC on copper speciation. Method Involving the Derivation of Cation-Biotic Ligand Equilibrium Constants. The method developed by De Schamphelaere and Janssen (11) is used here. It involves the derivation of cation-biotic ligand equilibrium constants and the calculation of the fraction of all binding sites on the biotic ligand occupied by copper when a toxic effect is observed in 50% of the organisms (11). The final outcome of the method of De Schamphelaere and Janssen is eq 7:

LC50Cu2+ )

50%L f CuBL 50%L (1 - f CuBL )‚KCuBL

‚{1 + KNaBL‚(Na+) + KHBL‚(H+)} (7)

In this equation, LC50Cu2+ is the free copper activity (mol L-1) resulting in 50% mortality of A. caliginosa after 7 days 50%L of exposure, f CuBL is the fraction of binding sites occupied by copper at 50% lethality, KCuBL, KNaBL, and KHBL are the stability constants (L mol-1) for binding to the biotic ligand of Cu2+, Na+, and H+, respectively. With eq 7, LC50Cu2+ can be predicted when (Na+) and (H+) are known, provided that one can estimate the values of three constants: KNaBL, KHBL, 50%L 50%L and at least the ratio f CuBL /(1 - f CuBL )‚KCuBL in the righthand term entirely. Equation 7 shows that a linear relationship exists between LC50Cu2+ and the activity of one cation, when the activity of the other cation is kept constant. From this equation we can obtain the expressions for intercept and slope corresponding to this linear relationship. In the following expressions they are called interceptH and slopeH for the pH bioassays when (Na+)H (i.e. the Na activity at a specified pH) is constant; they are called interceptNa and slopeNa for the Na tests when (H+) is constant. The linear regression parameters were obtained from eqs 2 and 6.

FIGURE 5. Relationship between the predicted (x-axis) and observed (y-axis) 7 days p(LC50Cu2+). The dashed lines indicate a deviation of a factor of 2 from the predicted p(LC50Cu2+), and eq 12 was used to calculate p(LC50Cu2+). For the pH tests holds

interceptH )

50%L f CuBL

(1 - f

50%L CuBL)‚KCuBL

‚{1 + KNaBL‚(Na+)H} ) 5.22‚10-7 (8)

and

slopeH )

50%L f CuBL 50%L (1 - f CuBL )‚KCuBL

‚KHBL ) 1.70‚10-2

(9)

For the Na tests

interceptNa )

50%L f CuBL 50%L (1 - f CuBL )‚KCuBL

‚{1 + KHBL‚(H+)Na} ) 5.18‚10-7 (10)

and

slopeNa )

50%L f CuBL 50%L (1 - f CuBL )‚KCuBL

‚KNaBL ) 2.43‚10-4 (11)

From these four equations KHBL and KNaBL can be calculated unambiguously: KHBL ) 4.10‚104 w log KHBL ) 4.61 (95% confidence limits: 4.5-5.0) and KNaBL ) 9.23‚102 w log KNaBL ) 2.97 (95% confidence limits: 2.7-3.5). The confidence intervals were derived directly from the regression analysis. The K values obtained may be compared to the values for logKHBL and logKNaBL found for aquatic organisms, like logKHBL ) 5.4 and logKNaBL ) 3.0 for freshwater fish and daphnids (16, 10) and KNaBL ) 3.2 for daphnids (11). The value found for KNaBL is almost equal to these values and also the KHBL value lies well within the frequently reported cell/membrane/ gill surface log KHBL ranging from 4.0 to 5.4, as determined by surface binding analysis or estimated from low pH toxicity test results (16). This suggests an empirical similarity of binding sites among aquatic organisms and earthworms for H+ and Na+. 50%L The values for f CuBL and KCuBL could not be calculated unambiguously since these parameters are coupled through 50%L eqs 8-11. Also direct measurements of either f CuBL or KCuBL in earthworms was not possible since the fraction of copper bound to the biotic ligand is only a minor fraction of the total amount of copper present in the earthworms. Therefore, for

every test the fraction of the biotic ligand occupied by copper was calculated with eq 8 using the stability constants just 50%L and obtained, by varying log KCuBL. To find values for f CuBL KCuBL, it is assumed that the best approximation of KCuBL would result in the best correlation between the calculated fCuBL and the logit of the percentage mortality after 7 days of exposure (11). As a matter of course the values for logit (%mortality) can only then be obtained when the mortality * 0% or 100%. 50%L Values for f CuBL ) 0.2 and the associated log KCuBL ) 5.9 resulted in the best fit (R2 ) 0.36) and were retained. These values are lower than the values obtained in other studies 50%L for freshwater fish and daphnia (for example: f CuBL ) 0.39 50%L and logKCuBL ) 8.0 (11), f CuBL ) 0.33 and logKCuBL ) 7.4 (10)). Incorporation of the BLM constants thus derived in eq 9 results in the expression for the LC50Cu2+ after all:

LC50Cu2+ ) 3.02‚10-7 + 1.24‚10-2(H+) + 2.79‚10-4(Na+) (12) The acquired BLM can be validated with a test set in which both the pH and Na activity differ from the pH and Na activity in the pH and pNa bioassays, respectively (the test set in Table 1 with pH ) 5.94 and [Na] ) 84 mg L-1). The predicted p(LC50Cu2+) values are plotted against the calculated values in Figure 5, together with the 1:1 line indicating a perfect match between the predicted and observed LC50s. Also the lines indicating a deviation of a factor of 2 between the predicted and observed LC50s were plotted. A factor of 2 is an accuracy that is not only commonly used in EU risk assessments, but this is also the accuracy that is typically obtained due to biological variability when an ecotoxicity experiment is repeated. From Figure 5 it can be deduced that the LC50Cu2+ predicted with the BLM never differs by more than a factor of 2 from the observed LC50Cu2+. However, the predicted LC50Cu2+ is in three cases not within the 95% confidence intervals of the observed LC50Cu2+. These include two LC50Cu2+ of the pH tests and also the LC50Cu2+ of the validation test set: toxicity was underestimated in all three cases. Looking for a BLM that can predict the LC50Cu2+ with 95% confidence, as is one of our aims, it can be concluded that the model given in eq 12 is not fully satisfactory. Apparently, the method followed to calculate KCuBL and 50%L f CuBL , as designed by De Schamphelaere and Janssen (11) for aquatic organisms, was inappropriate to this study. The incapacity of the BLM using the discussed constants may be due to the inappropriateness to this study of the assumption VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 6. Relationship between the predicted (x-axis) and observed (y-axis) 7 days p(LC50Cu2+). The dashed lines indicate a deviation of a factor of 2 from the predicted p(LC50Cu2+), and eq 13 was used to calculate p(LC50Cu2+). that the best approximation of KCuBL would result in the best correlation between the calculated fCuBL and the logit of the percentage mortality. Also, the prerequisite of BLMs in requiring linear relations is not met, as clearly shown in Figures 1-3. The approach of De Schamphelaere and Janssen (11) nevertheless allowed deducing the values of KNaBL and KHBL. Modeling Approach Starting from a Linear p(LC50Cu2+) - pH/pNa Regression. As it was not possible to derive a BLM that can predict the LC50Cu2+ in all cases with 95% confidence using the method developed by De Schamphelaere and Janssen (11), measured log-transformed LC50Cu2+ values were correlated directly to the measured pH and pNa. Multiple linear regression analysis of p(LC50Cu2+) on one hand and pH and pNa on the other hand resulted in an expression for p(LC50Cu2+) that accounts for the protective effects of both pH and pNa:

p(LC50Cu2+) ) 0.39pH + 0.64pNa + 2.24

(R2 ) 0.95, p ) 7.1‚10-6, F ) 73, SE ) 0.07) (13)

With the model represented by eq 13, the LC50Cu2+ was predicted for all test sets of this study and compared to the observed LC50Cu2+. Besides the model was validated with the validation test set, in which the pH and Na+ activity differed from the pH and Na+ activity in the pH and Na tests, respectively. Predicted LC50Cu2+ values are plotted against calculated values in Figure 6, together with the 1:1 line. Again the lines indicating a maximum deviation of a factor of 2 between the predicted and observed LC50s are plotted. From Figure 6 it can be seen that the LC50Cu2+ values predicted with the regression model match rather well with the observed ones. The observed LC50Cu2+ of the validation test set was not statistically different from the predicted LC50Cu2+, falling within the 95% confidence interval for predicted values. Besides, the observed LC50Cu2+ were all within the 95% confidence intervals. The prediction error of LC50Cu2+ in case of the model given in eq 13 was about half of the prediction error of the model given in eq 12: 13% versus 23%, with ranges of the prediction error for individual sets varying between 3 and 45% (eq 13) and 0 and 60% (eq 12), respectively. Investigation of the Results of the DOC Tests. Above it was stated that addition of DOC to the test solutions had a protective effect when the copper activity was kept constant. This finding might be attributed to the sodium content of the humic acid, used to prepare the DOC, as Na was found to have a protective effect on toxicity. The LC50Cu2+ found in 5700

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the DOC tests can be compared to the LC50Cu2+ predicted with the model represented by eq 13. The LC50Cu2+ found in the DOC tests and expressed as pCu was 5.6 (95% confidence interval 5.5-5.9). The predicted LC50Cu2+ ) 6.0. This value is higher than the observed LC50Cu2+ and is not within the 95% confidence interval. Therefore it is likely that DOC plays an independent protective role, in addition to decreasing copper toxicity by complexation of the metal and thus reducing the free copper ion activity at the same total copper concentration. This may be related to direct metabolic and physiological influences of DOC on the organisms involved. It has been reported that humic acids may associate with cell surfaces thereby changing its permeability (17) and that DOC has a shielding effect on metal toxicity, by accumulation of natural organic matter on the surfaces of living cells in an aquatic environment (18). The special shielding effects of DOC that were found were not incorporated in the BLM although it appeared to have a considerable effect on toxicity. Additional experiments to quantify these effects are required to achieve this. Validation of the Final Model with the Results of Bioassays in Field Soils. pH and pNa in the pore water were measured in all soils used for the bioassays. With these values the LC50Cu2+ could be predicted using the model represented by eq 13. This model was preferred over the model given in eq 12 since the prediction error is lowest, and all predictions for the previous test and validation sets fall within the 95% confidence intervals of the observed LC50Cu2+. The predicted LC50Cu2+ values were compared to the measured copper activities in the pore water. When the predicted p(LC50Cu2+) values were lower than the measured pCu, less than 50% mortality was expected. If the measured pCu was higher there should be significant mortality. The field soils all contained dissolved organic carbon. Because the model does not account for protective effects of DOC on toxicity, the predicted LC50Cu2+ may be an underestimate of the actual value, as was shown in the preceding paragraph. In the soils originating from Wageningen, which were aged for over 20 years, the copper activity in the pore water was rather low, and the model predicted for all tests was less than 50% mortality after 7 days. Indeed, in the experiments at least 3 of the 4 worms survived after 7 days of exposure. In the soils contaminated in the laboratory it was possible to get also higher mortality rates, for now increased amounts of copper chloride could be added. For 28 out of 29 bioassays (97%) the results were supporting the predictions of the model. In these tests the mortality rate was below 50% when

the predicted p(LC50Cu2+) was lower than the measured pCu and 50% or more when the predicted p(LC50Cu2+) was higher than the measured pCu. In one test the predicted p(LC50Cu2+) exceeded the measured pCu in the pore water, whereas the mortality was below 50% (namely 25%). It is not probable that this difference is accounted for by an increased DOC content as the DOC concentration in this bioassay was similar to the concentrations in the other 28 tests. It can be concluded from these results that the model represented by eq 13 can predict the LC50Cu2+ rather well in soils spiked with copper salts in the laboratory and aged for a relatively short period of time. This also provides additional evidence that the dermal route of uptake is actually the most important route for copper for earthworms. Otherwise deviations from the model would be expected following an exposure in field soils that contain appreciable amounts of copper bound to the solid phase. It can be concluded that the model shows a remarkable consistency between predicted and observed LC50Cu2+ in several spiked soils. This indicates among others that possible impacts of Ca and Mg (and of all other cations except H and Na interacting with the sorption sites at the biotic ligand of A. caliginosa) on copper toxicity are sufficiently taken into account in the BLM and the regression model derived in this study. This is opposed to observations made by various authors (7, 11, 19-21) who for instance showed that increases in Ca and Mg concentrations result in decreased copper toxicity for fish and crustacean species. The physiological mechanisms of copper toxicity are related to interference of the metal with the ability of the organism to regulate both ion uptake and efflux across the membrane (22). More specifically, a first-order effect of copper is the inhibition of the active uptake of Na, and at sufficiently high concentrations, copper affects the efflux of Na (23). The overall effects of copper on ion regulation result in decreased levels of plasma sodium. Hence, it is not surprising that the protective effect of Na exceeds the effect of the other cations present in pore waters. On the other hand, the finding of lack of impact of most cations on copper toxicity to A. caliginosa may in part be due to the correlation between cation concentrations in the pore water and pH. It cannot be ruled out that in soils with lack of covariance among cation concentrations and pH or in soils with pore waters in which the ratios of Na/Ca/Mg differ significantly from the ratios selected in this study the toxicity of copper is grossly overor underestimated. The wide range of soil and pore water characteristics of the field soils used for validation of the regression model given in eq 13 warrants that the model is broadly applicable for soils. Nevertheless, model results should be used with care following extrapolation outside of the domain of the soil and pore water characteristics used in this study. Also considering the similarity between eqs 12 and 13, it may finally be concluded that terrestrial BLMs may provide a new tool for environmental risk analysis. As a matter of course it is to be noted that the endpoint of the toxicity bioassays as selected here and the consequently low values of pCu needed to induce mortality are not representative of typical field conditions. As such, the experiments carried out within the framework of this study are representative for highly copper contaminated soils. Additional experiments aimed at assessing more sensitive endpoints are required to broaden the relevance of the approach taken here for assessing adverse impacts of copper in realistic field settings.

Acknowledgments Johannes Lijzen, Martina Vijver, and Christian Mulder are kindly acknowledged for critically evaluating earlier versions of the manuscript.

Supporting Information Available Details of the preparation of the Steiner solution. This material is available free of charge via the Internet at http:// pubs.acs.org.

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Received for review January 28, 2005. Revised manuscript received May 26, 2005. Accepted May 27, 2005. ES0501971