Effect of pH and environmental ligands on ... - CSIRO Publishing

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Research Paper

Environ. Chem. 2012, 9, 547–557 http://dx.doi.org/10.1071/EN12078

Effect of pH and environmental ligands on accumulation and toxicity of Ni21 to Lemna minor Yamini Gopalapillai,A,B,D Bernard VigneaultC and Beverley HaleA A

School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada. B Mining and Mineral Sciences Laboratories, Natural Resources Canada, 555 Booth Street, Ottawa, ON, K1A 0G1, Canada. C Earth Sciences Sector, Natural Resources Canada, 601 Booth Street, Ottawa, ON, K1A 0E8, Canada. D Corresponding author. Present address: Vale, 2060 Flavelle Boulevard, Mississauga, ON, L5K 1Z9, Canada. Email: [email protected]

Environmental context. Predicting metal toxicity is an important tool for effective and efficient risk assessment and regulation of metal pollution in the environment. The present study aims to provide scientific support for the development of a predictive Ni toxicity model for aquatic plants that is particularly applicable to miningaffected natural waters. We show that the effects of pH and natural organic ligands on Ni accumulation and toxicity can be modelled, but further research is required to understand the effects of flotation ligands used in the mining industry. Abstract. Effects of water chemistry and metal speciation on metal uptake and toxicity to aquatic plants such as Lemna minor are not fully understood. The present study examined the effect of pH and environmental ligands (dissolved organic carbon (DOC) and mining related flotation ligands diethylenetriamine (DETA), triethylenetetramine (TETA), sodium isopropyl xanthate), on Ni toxicity to L. minor. Exposure and tissue residue toxicity thresholds were assessed to validate the use of a Biotic Ligand Model (BLM) or a Tissue Residue Approach (TRA) as a framework for predicting Ni toxicity. An increase in the activity of Hþ non-linearly decreased the toxicity of free Ni ion activity, whereas Ni accumulation kinetics indicated that the mechanism of Ni2þ and Hþ interaction was not competitive inhibition as expected by the BLM framework. The effect of DOC on the toxicity of total Ni concentration was relatively small (toxicity decreased by less than a factor of 2) and was explained solely by the complexation of Ni2þ by DOC. Alternatively, the protective effect of flotation ligands (DETA and TETA) was much less than expected based on estimated Ni complexation. Overall, a TRA model was directly applicable in the presence of organic ligands but not to varying pH, whereas a BLM-type model was applicable with changes in pH and DOC but not in the presence of the lesser studied flotation ligands. Such mechanistic information is essential for the development of reliable Ni toxicity models that would aid in risk assessment and regulation of Ni in the environment, particularly in mining-affected regions. Received 30 May 2012, accepted 8 November 2012, published online 20 December 2012

Introduction

Approach (TRA) has been recently explored as a possible tool for metal risk assessment[5,6] because of difficulties in estimating ‘bioavailable external dose’ in complex exposure solutions such as mining-affected freshwaters. However, TRA is considered more useful for organic compounds than metals because the effective internal dose at the sites of toxic action tend to be more complex for metals, often because of essentiality. Further research is necessary to fully understand the applicability of TRA for metal toxicity in plants. Free Ni ion activity ({Ni2þ}) can be predicted by a speciation model known as WHAM (Windermere Humic Aqueous Model), which is designed to calculate equilibrium speciation in aqueous, sediment and soil media, particularly where organic matter comes into play,[7] and was recently calibrated for low Ni concentrations.[8] One of the laboratory methods considered suitable for estimating {Ni2þ} is the ion exchange technique (IET). The IET involves a simple concept in which the ions in

The effect of Ni contamination on environmental health is a concern and efforts are underway to develop predictive models that estimate Ni toxicity to organisms. One of the available exposure-based models, the Biotic Ligand Model (BLM), predicts site-specific metal toxicity to an organism based on the consensus that the free ion activity is most predictive of toxicity,[1] and takes into account major cation interactions at the organism surface, including Hþ.[2] Of the few BLM studies on the aquatic plant Lemna minor, one attempt to predict Ni toxicity using cross-species extrapolation of existing Ni BLMs found that the observed toxicity was better explained by the Daphnia magna than the algal BLM.[3] L. minor is currently used in bioassays for regulating mining effluent release in Canada.[4] An alternative approach to predicting metal toxicity other than by using exposure chemistry is by using metal concentration in tissue (i.e. tissue residue-based toxicity). The Tissue Residue Journal compilation Ó CSIRO 2012

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Y. Gopalapillai et al.

the sample solution exchange with ion exchangers of a strong acid cation resin, by which a relationship between the concentrations of metal adsorbed to the resin and the free metal ion can be established.[9] Gopalapillai et al.[10] applied IET to the measurement of free Ni in mining and municipal effluents. However, the authors were unable to link measured free Ni to observed toxicity in algae as the effluents were complex and contained metal mixtures.[11] Currently, the only organic complexant included in the BLM is naturally occurring dissolved organic carbon (DOC). However, flotation reagents are often used in the mining industry for wastewater treatment and are strong metal complexants. Commonly used flotation ligands in Canada include diethylenetriamine (DETA) and triethylenetetramine (TETA), for which there are no published data describing their effect on metal toxicity. However, some studies have looked at the toxicity and bioavailability of metals in the presence of xanthates, which are commonly used as flotation ligands and are known to form hydrophobic complexes with metals.[12] The study by Boullemant et al.[12] showed that lipophilic complexes of cadmium and ethyl xanthate were taken up by algal species at a much greater rate than free cadmium ions. As a result, bioavailability may not be perfectly described by the BLM traditional framework, which estimates toxicity by assuming that the bioavailable Ni2þ (taken up by the organism) is the toxic species (i.e. the toxicity corresponds to bioaccumulated Ni). Predicting metal toxicity in plants is complex because they regulate nutrient uptake to maintain homeostasis which likely results in a kinetically controlled metal uptake environment rather than being at equilibrium. In addition, because Ni is a slow-reacting metal (i.e. it complexes slowly with ligands compared with other trace metals like copper), uptake is more prone to kinetic limitations and may result in increased bioavailability of Ni complexes and formation of ternary complexes on the organism surface.[13] A non-equilibrium condition means that the general BLM framework, which is based on equilibrium assumptions, cannot be applied directly but may be modified to take into account the observed relationship (e.g. for non-linear pH effect).[14] The BLM assumes competitive inhibition between cations, where the competing cation binds only to the site of toxic action or biotic ligand (BL), thereby competing with the metal of interest.[1] However, when more than one type of binding site is present, other types of competitive interactions such as anti-competitive, non-competitive and mixed-competitive interactions may exist.[15] Discrepancies to the general BLM framework may be incorporated into a toxicity model or be considered negligible for the purposes of risk assessment. The overall objective of the present paper is to characterise the relationship between exposure-based and tissue residuebased Ni toxicity at various pH or in the presence of various ligands, with a focus on mechanistic data analysis that provides insight into implications for toxicity modelling. A mechanistic predictive model for L. minor will be relevant as an environmental management tool for mining effluents and receiving waters, and to support decision making regarding changes in environmental monitoring programs to include flotation ligands.

system (Millipore, Billerica, MA, USA) that is designed to provide ultra-low dissolved organic carbon (DOC) levels (1–5 mg L1) by an integrated dual-wavelength UV lamp and multiple purification systems. A standard solution of NiII was prepared before each toxicity test using ultrapure Ni(NO3)2 powder (Puratronic 99.9985 %; Alfa Aesar, Ward Hill, MA). Reagents used for the culture and test media[16] were all ACS grade. The DOC used throughout this research was Suwannee River natural organic matter (International Humic Substances Society, St Paul, MN). The flotation reagents tested were DETA (Fluka, Zwijndrecht, the Netherlands), TETA (Fluka, Buchs, Switzerland) and sodium isopropyl xanthate (NaIX) (technical grade; Prospec Chemicals, Fort Saskatchewan, AB, Canada). All pH adjustments, including the pH effect test, used 0.1 M NaOH (99.998 % metals basis; Sigma–Aldrich, St Louis, MO) and 0.1 M HCl (OPTIMA; Fisher Scientific, Ottawa, ON). The pH meter used throughout was a Hach HQ Series Portable Meter (Loveland, CO). Ultrapure HNO3 (OPTIMA; Fisher Scientific) was used for spiking samples for preservation until metal analysis, for digestion of plants and for IET extractions. Additional solutions for IET included NaHCO3 (Puratronic 99.998 %; Alfa Aesar), NaNO3 (Suprapur; EMD, Gibbstown, NJ), KNO3 (Suprapur; EMD), Ca(NO3)2 4H2O (Suprapur; EMD), Mg(NO3)2 6H2O (Suprapur; EMD) and 0.1 M NaOH (same as above). The resin used for IET was Dowex 50W-X8 (mesh 50–100; Aldrich, Milwaukee, WI), and Chelex-100 (50–100 mesh, sodium form; Bio-Rad, Hercules, CA) was utilised to purify IET solutions. Additional buffers were used only for the pH effect tests and were as follows: 7.17-mM 2-(N-morpholino) ethanesulfonic acid (MES) (Biotechnology Performance Certified; Sigma–Aldrich), 7.17-mM 3-(N-morpholino)propanesulfonic acid (MOPS) (99.5 % Titration; Sigma–Aldrich) and 0.10-mM disodium tetraborate decahydrate (Borax) (ACS grade .99.5 %; Fluka, Buchs, Switzerland). L. minor culture, toxicity testing and digestion Lemna minor culture and toxicity testing followed an Environment Canada protocol.[16] Lemna minor L. #8434 (Canadian Phycological Culture Centre at University of Waterloo, Waterloo, ON) was cultured in modified Hoagland’s Eþ medium[16] sterilised by autoclaving and the pH adjusted to 4.6 using 0.1 M HCl or NaOH. An AirClean 600 (PCR Workstation; AirClean Systems, Raleigh, NC) was used to perform L. minor transfers. The plants were aseptically inoculated into four 50-mL tubes weekly and incubated under 64–90-mmol m2 s1 white fluorescent lights at 25 2 8C. The plants were acclimatised in test media 24 h before starting the tests. The test duration was 7 days. Each test (pH or a single ligand concentration) was set up in one of two concentration ranges, depending on the range required for an appropriate dose–response curve: (1) six replicates of 0 mg L1 Ni, four replicates each of 1.56, 3.13 and 6.25 mg L1 Ni and three replicates each of 12.5, 25, 50 and 100 mg L1 Ni test concentrations or (2) six replicates of 0 mg L1 Ni, four replicates each of 7.8, 15.6 and 31.3 mg L1 Ni and three replicates each of 62.5, 125, 250 and 500 mg L1 Ni. Each replicate was prepared in a 100-mL solution in 210-mL transparent disposable plastic cups (Polar Plastic Ltd, St-Laurent, QC). The pH test included nominals pHs of 5.5, 6.0, 6.5, 7.0, 7.5 and 8.3 (measured 5.50, 6.06, 6.51, 6.98, 7.50, 7.87 and 8.15), and all ligands were tested at environmentally relevant concentrations (0.5–40 mg L1 DOC, 10.3 mg L1 DETA (0.1 mM), 14.6 mg L1 TETA (0.1 mM) and 0.1 mg L1 NaIX (0.63 mM)) which were

Experimental Materials and reagents All solutions were prepared with ultrapure water (resistivity 18.2 MO cm) obtained from a Milli-Q Gradient A10 purification 548

Effect of pH and ligands on Ni2þ toxicity to duckweed

based on preliminary testing of ligand concentrations in mining effluents from the Ontario region. The average pH of the DOC, DETA and TETA tests were 8.20, 8.09 and 8.05. Note that each replicate is considered a ‘test solution’ and each manipulation from the control dose response test (media only) is considered a ‘treatment’ (e.g. addition of 40 mg L1 DOC). The toxicity tests used modified APHA (American Public Health Association) medium except for the pH tests which were conducted in SIS (Swedish Institute Standards) medium,[16] in order to minimise pH fluctuations by the test end which was observed in a preliminary pH test with APHA. Both modified APHA and SIS exclude ethylenediaminetetraacetic acid (EDTA), which is typically included in growth media as a buffer and prevents formation of metal precipitates resulting from exceeding the solubility limits. However, EDTA is a strong metal complexant which would influence the speciation of the test metal and thus affect its bioavailability. Alternatively, in the present study, 0.5 mg L1 of DOC was added as a weak buffer to ensure nutrient availability, particularly Fe. A preliminary study showed that there was no substantial effect of addition of 0.5 mg L1 DOC on L. minor root length growth in the dose–response control solution, and the effective Ni toxicity threshold for 0.5 mg L1 DOC added was not different from that with no DOC added.[17] Preliminary testing of Ni concentrations in the test media indicated the levels to be less than the detection limit (,1 mg L1). Because the required MOPS buffer in the SIS media[16] could not buffer in the wide pH range of the test, other buffers were used when outside of MOPS’ range. The buffers used were 7.17 mM MES for pH 5.5, 7.17 mM MOPS for pH 6.0–7.5 and 0.1 mM Borax for pH 8.0. At test end, the root length and frond count were measured as endpoints as they were the most sensitive endpoints; however, for the present study, root length was used for data analysis as it was the less variable of the two endpoints (data not shown). Plants were then blotted dry with KimWipes (Kimberley-Clark, Irving, TX) to remove excess test water and then oven-dried at 60 8C for 24 h before digestion. Dry weights were measured on a high precision balance (Sartorius, Data Weighing Systems Inc., Elk Grove, IL; 0.00006 g). Plant tissue (dry weight ranges from ,7.7–22 mg on average) was digested in 30-mL glass tubes using ultrapure concentrated HNO3 (0.5 mL) and heated in a Tecator Digestion System 40 (1016 Digester, Foss, Hillerod, Denmark) located inside a fume hood at 100–110 8C for 1–2 h. KIMAX Filling Funnels (Kimble Chase Life Science and Research Products LLC, Vineland, NJ; short stem, 25-mm top diameter) were used to cover the glass tubes in order to minimise evaporation of acid during digestion. After digestion, the sample volume was made up to 5 mL with ultrapure water, and stored at ,4 8C until chemical analysis. Note that ‘Ni accumulation’ consisted of surface-bound Ni plus internalised Ni, as plant tissues were not rinsed before drying. All containers used to prepare the test solutions were acid washed by soaking them in ,15 % HNO3 (trace metal grade; Fisher Scientific) for a minimum of 24 h and rinsing three times with deionised water, and once with ultrapure water. Treatment controls (i.e. a single Ni dose–response test in APHA media) are labelled ‘control’ unless otherwise specified as the dose–response control (i.e. the 0 mg L1 Ni treatment within the dose–response).

at test start and in the digested plant tissue were measured by inductively coupled plasma–mass spectrometry (ICP-MS) (XSeries 2, Thermo Fisher Scientific, West Palm Beach, FL) or inductively coupled plasma–atomic emission spectroscopy (ICP-AES) (Vista RL, Varian, Mississauga, ON) with an ultrasonic nebuliser to enhance the detection limit. Measured total concentrations were not different from dissolved (filtered ,0.45 mm) concentrations as found in preliminary testing (P , 0.05). Also, there was no change in measured dissolved cation concentrations on day 0 v. day 7 (P , 0.05). Test solutions for chemical analysis were preserved by spiking with concentrated ultrapure HNO3 to 0.2 % (v/v) and storing at ,4 8C until analysis. Ion exchange technique A column IET was used to determine {Ni2þ} in test solution for the ligand treatments. A mini high-performance liquid chromatography (HPLC) column of 30 4.6-mm internal diameter and made of PEEK material (#JR-66174; Western Analytical Products, Inc., Lake Elsinore, CA) connected to an automated Bio-Rad BioLogic system (Bio-Rad) was utilised. Approximately 8.0–8.2 mg of dry DOWEX resin, which was pretreated as indicated by Cantwell et al.[18] was weighed into the column. The step-by-step procedure of the IET has been described elsewhere.[10] The composition of the supporting electrolyte solution (matrix) used to equilibrate the column was: 1.79 103 M NaHCO3, 3.00 103 M NaNO3, 2.55 104 M KNO3, 3.00 104 M Ca(NO3)2 4H2O and 1.2 103 M Mg(NO3)2 6H2O, which amounts to an overall ionic strength of 6.54 mM. This matrix, which was also adjusted to pH 8.3, was created to match, as best as possible, the APHA medium[16] utilised in the toxicity testing. Each test solution (120 mL) was run through the column, followed by extraction using 3 mL of 1.5-M ultrapure HNO3. The standards used to calibrate the IET method were a mixture of the above described matrix spiked with the appropriate amount of nickel sulfate, to create Ni standards with concentrations of 5, 50 and 500 mg L1. The matrix solutions were pretreated by filtering through a 20-mL plastic column (Bio-Rad) containing Chelex-100 in order to remove any metal impurities. All containers used for preparing test solutions for IET were made of Teflon or high density polyethylene and were acid washed (method described earlier). At the beginning of each test, a blank solution (matrix only) was run through the system and the resulting total Ni concentration ([NiTot]) was subtracted from the subsequent samples when [NiTot] was greater than 15 mg L1, likely caused by sample Ni contamination within the IET system. Metal speciation calculation: WHAM 6 The WHAM (ver. 6, Centre for Ecology and Hydrology, Natural Environment Research Council, Lancaster, UK) computer program[7] was used to estimate {Ni2þ} in the exposure solutions, for the purposes of calculating toxicity thresholds. The WHAM 6 database was modified to include thermodynamic constants for DETA and TETA complexes from the NIST (National Institute of Standards and Technology) database; which included complexation constants for DETA and TETA with Hþ, Mg2þ, Ca2þ, Cr2þ, Mn2þ, Fe2þ, Co2þ, Ni2þ, Cu2þ, Cr3þ, Agþ, Pd2þ, Zn2þ, Cd2þ, Hg2þ, Pb2þ and Bi3þ (see http:// www.nist.gov/srd/nist46old.cfm, accessed 5 December 2012). Note that NaIX was not included in the NIST thermodynamic database, hence {Ni2þ} in the presence of NaIX could not be calculated.

Elemental analysis All Ni concentrations reported in the present paper were measured instead of nominal. The metal content in the test solutions 549


Table 1. Measured toxicity thresholds based on total Ni concentration in solution for two endpoints (root length and frond count) under various treatments IC25NiTot, 25 % toxicity threshold based on total [Ni]; IC50NiTot, 50 % toxicity threshold based on total [Ni]; DETA, diethylenetriamine; TETA, triethylenetriamine; NaIX, sodium isopropyl xanthate Treatment

pH 6.06 pH 6.51 pH 6.98 pH 7.50 pH 7.87 DETA TETA NaIX

Root length

Frond count

IC25NiTot (mg L1)

IC50NiTot (mg L1)

IC25NiTot (mg L1)

IC50NiTot (mg L1)

15.5 (9.8–22.8) 16.6 (10.7–24.6) 11.4 (7.2–16.6) 10.4 (7.0–14.4) 4.03 (2.78–5.57) 164 (80.8–269) 107 (53.4–353) 14.1 (10.7–18.1)

52.2 (36.9–73.6) 70.6 (43.4–116) 32.4 (23.8–44.0) 25.4 (19.4–33.3) 16.3 (12.8–20.8) 411 (244–695) .524 (n/a) 40 (33.1–49.0)

12.5 (5.73–22.7) 14.8 (7.96–26.0) 7.71 (3.86–13.7) 16.2 (9.13–26.1) 44.9 (25.7–71.3) 198 (86.9–368) .524 (n/a) 17.0 (10.8–26.7)

53.0 (28.9–97.0) 97.9 (39.0–258) 51.4 (28.2–93.7) 61.4 (36.1–105) 201 (68.9–584) 775 (212–2874) .524 (n/a) 120 (57.6–268)

control. Similarly, the predicted IC50NiTot (50 % toxicity threshold based on total [Ni]) was iterated using WHAM 6. The iteration was performed using a WHAM spreadsheet created with the appropriate chemical composition for the sample in question (e.g. ligand concentration) and covered a range of expected [NiTot]. The [NiTot] that corresponded with the average IC25Ni2þ in the WHAM 6 output is the predicted IC25NiTot (i.e. assuming the free ion activity model (FIAM) concept holds true). Toxicity thresholds (IC25NiTot and IC50NiTot) for various treatments are presented in Table 1 for the purpose of comparison to other studies.

The default parameters were optimised according to suggestions by Van Laer et al.[8] The distribution parameter (DLK2) for Ni–fulvic acid binding was increased to 2.35 from 1.57, and the Zn–fulvic acid binding constant (log KMA(Zn)) was increased from 1.6 to 1.8.[19] As in Deleebeeck et al.,[20] thermodynamic constants for inorganic complexes such as NiCO3 and NiHCO3 in the default database were replaced with updated constants from Martell and Smith (see http://www.nist.gov/srd/nist46old. cfm). Data entered into WHAM 6 as input included pH, [DOC], [DETA], [TETA] and [NiTot], as well as other cation (Naþ, Mg2þ, Kþ, Ca2þ, Mn2þ, Fe3þ, Co2þ, Cu2þ and Zn2þ) and 2 2 3 anion (Cl, NO 3 , SO4 , CO3 and PO4 ) concentrations in the APHA/SIS test media. Note that measured values at test start were utilised when available (e.g. all Ni concentrations and test start pH). A temperature of 298 K and the atmospheric partial pressure for CO2 of 103.5 atm were used. The DOC concentration was assumed to be 90 % colloidal fulvic acid and 10 % colloidal humic acid for WHAM input. However, preliminary results showed that changing the ratio of fulvic to humic acid content did not affect the speciation results for the present study.

Results and discussion Effect of pH: exposure-based Ni toxicity A change in pH is known to have a significant effect on a metal’s speciation and its toxicity to an organism. Most of the large changes in the effect of pH occur above pH 7, due to the formation of Ni carbonates and hydroxides. In the present study, the pH range of 5.5 to 8.3 (nominal) was studied. The relationship between {Hþ} and dissolved [NiTot] toxicity threashold was non-linear (Fig. 1a). In fact, as the average measured {Hþ} in solution increased, IC25NiTot, IC25Ni2þ and IC25NiTiss, (25 % toxicity threshold based on [Ni] in plant tissue) increased nonlinearly (Fig. 1b), i.e. as the pH increased, the toxicity of Ni2þ to L. minor increased. The factor difference between the minimum and maximum IC25NiTot as well as IC25NiTiss was approx. threefold; but was only two-fold for IC25Ni2þ. Note that at pH 5.5, the toxicity was too low to calculate a reliable threshold (IC25Ni2þ . 62 mg L1), and also corresponded to a much lower Ni accumulation rate (AR) (up to 2 mg g1 h1) compared with pH 6.0 (up to 6 mg g1 h1) (P , 0.05). The linear decrease in IC25Ni2þ as a function of pH (not plotted) suggests that in addition to the free ion activity being an indicator for a metal’s toxicity (i.e. the FIAM), interaction among ions at the surface of the organism also influence toxicity (i.e. the BLM framework). The general BLM framework considers cation competition at the biological surface using linear regression fitting of log K parameters of cation–BL binding.[21] In the present study, the proton effect on the toxicity threshold was curvilinear, i.e. IC25Ni2þ v. {Hþ} was best-fitted with polynomial regression (Fig. 1b). This non-linear effect of {Hþ} on IC25Ni2þ was similar to findings by Deleebeeck et al. in algae[14] and fish[20] and by Wang et al.[22] in barley in hydroponic solution, and suggests that Hþ does not have a pure single-site competitive effect on Ni

Data analysis Toxicity threshold values (e.g. the 25 % inhibition concentration, IC25) for test data were estimated by the Comprehensive Environment Toxicity Information System (CETIS) version 1.7, which uses Grubb’s test to check for outliers, using non-linear regression modelling when possible, and otherwise linear interpolation (see http://www.tidepool-scientific.com/Cetis/ Cetis.html, accessed 5 December 2012). Often, the dose response curves fit best with the ‘3P Log-Logistic EV’ nonlinear model, which is used in survival analysis as a parametric model for events whose rate increases initially and later decreases. Statistical comparisons between treated and control plants were performed by analysis of variance (ANOVA) using SAS ver. 9 and P , 0.05. SigmaPlot 9.0 (see http://www.sigmaplot.com/, accessed 5 December 2012) was used to determine the binding affinity constant (Kd) and the binding capacity (Bmax) for Ni accumulation in plant tissue, all curves were fitted with a one site saturation ligand binding curve (F ¼ Bmax abs (x)/(Kd þ abs(x))). The predicted IC25 based on total [Ni] (IC25NiTot) in the presence of DETA and TETA was iterated using WHAM 6 and the measured average IC25 based on {Ni2þ} (IC25Ni2þ) for the 550

Effect of pH and ligands on Ni2þ toxicity to duckweed 120

25

100

20

% (Ni species/Ni total)

IC25NiTot (µg L1)

(a) 30

15 y 16.2/(1e(x5.72)/104) R 2 0.555, P 0.30

10 5 0 0

200

400

800

600

y 12.7/(1e(x51.0)/91.4) R 2 0.853, P 0.057

5

400

200

0

IC25NiTiss (µg g1)

IC25Ni2 (µg L1)

600

400

600

800

Ni AR (µg g1 h1)

(c) 8

4

pH 5.50 pH 6.06 pH 6.51 pH 6.98 pH 7.50 pH 7.87 pH 8.15

2

0

0

20

40

60

6

10

8

12

14

increase in Ni toxicity was observed from pH 6.0 to 8.3. Consequently, a physiological effect is more likely the reason behind the increased toxicity rather than pure competition between Hþ and Ni2þ at the BL, as expected by BLM. The physiological effect may largely be due to the role of Hþ as symporters in plant nutrient uptake. In general, plants are proposed to utilise membrane proteins that function as metal–Hþ and anion–Hþ symporters[24]; hence, the reduction in Hþ in solution may increase the plant’s sensitivity to a toxicant by affecting its uptake of essential elements. In addition, the surface electrical potential at the plant membrane will be affected, as it will become more negative with increasing pH and result in dissociation of acidic amino acids on membrane proteins.[24] Also, the increase in pH causes conformational changes in metal transport proteins that may lead to increased toxicity.[25] Finally, it is also possible that multiple binding sites exist, where one site is more important at the lower pH and one is more important at the higher pH.

Average measured {H} (nM)

6

20

Fig. 2. The effect of pH on Ni speciation as predicted by Windermere Humic Aqueous Model 6, for a solution with a Swedish Institute Standards media matrix plus 0.5 mg L1 of natural organic matter, and a nominal total Ni concentration ([NiTot]) of 100 mg L1.

0 200

40

pH

15

0

60

4

800 y 570/(1e(x13.2)/14.2) R 2 0.768, P 0.112

10

80

0

Average measured {H} (nM) (b) 20

{Ni2} NiHCO3[] NiOH[] Ni(OH)2 NiCO3

80

{Ni2} in solution (µg L1)

Effect of pH: tissue residue-based Ni toxicity Tissue residue-based toxicity predictions have been demonstrated to be effective for organic environmental contaminants, but not always for metals which may have complex accumulation patterns in biota, particularly if they are essential.[5] The present study investigated the use of a tissue residue-based toxicity model to understand the effect of pH on L. minor’s response to Ni toxicity. A consistent IC25NiTiss value with varying pH would indicate a threshold value that can be utilised to model toxicity despite water chemistry. However, Fig. 1b illustrates that similar to the non-linear effect of {Hþ} on IC25Ni2þ, it has a non-linear effect on IC25NiTiss. This suggests that the lower IC25Ni2þ (higher toxicity) at lower {Hþ} (high pH) is not due to the reduced availability of Hþ as a competing cation (basis of the BLM framework). In such a case, IC25NiTiss is expected to be constant whereas IC25Ni2þ will decrease linearly. Hence, these findings are contrary to the BLM framework which assumes that Hþ exhibits competitive inhibition on Ni2þ accumulation at the biotic ligand. In addition, although IC25NiTiss is changing with {Hþ}, the toxicity is constant at the higher {Hþ} range (lower pH), and thus applicable for low pH conditions. But

Fig. 1. (a) Effect of average measured activity of Hþ ({Hþ}) on 25 % toxicity threshold based on total [Ni] (IC25NiTot); (b) Effect of average measured {Hþ} on 25 % toxicity threshold based on {Ni2þ} (IC25Ni2þ) (open symbol) and 25 % toxicity threshold based on [Ni] in plant tissue (IC25NiTiss) (closed symbol) and (c) effect of pH on the relationship between the activity of Ni2þ {Ni2þ} and Ni accumulation rate (NiAR). Error bars represent 5 and 95 % confidence intervals except for pH 8.15 which is the standard deviation for a minimum n ¼ 3.

uptake as in the classic BLM. However, a non-linear relationship can be added to a BLM as a linear relationship (i.e. a regression equation) to model the effect. The traditional BLM is based on conditional stability constants derived from metal binding to fish gill as the ligand.[23] Thus, alternative mechanisms of toxicity are likely being invoked by Hþ. Other Ni species causing toxicity are ruled out, as although Ni speciation also changed with higher pH, predictions by WHAM 6 showed that speciation was altered mostly at pH 7.5 or higher (Fig. 2). Thus, it is unlikely that toxicity was significantly affected by the presence of Ni hydroxide or carbonate species, as a linear 551


Table 2. Kinetic parameters, Kd (binding affinity constant), log KNi]BL (nickel to biotic ligand (BL) binding constant) and Bmax (binding capacity), of Ni accumulation in L. minor after 7 days of exposure at different pH or in the presence of various ligands [NiTot], total Ni concentration; APHA, American Public Health Association; DOC, dissolved organic carbon; DETA, diethylenetriamine; TETA, triethylenetriamine [NiTot] range (mg L1)

Treatment

0–100

pH 5.50 pH 6.06 pH 6.51 pH 6.98 pH 7.50 pH 7.87 pH 8.15 (APHA) Control 5.5 mg L1 DOC 10.5 mg L1 DOC 20.5 mg L1 DOC DETA TETA

0–500

Kd (mg L1)

log KNi–BL (mol)

Bmax (mg g1)

Bmax (nmol g1)

34.5 28.5 16.6 4.0 20.0 5.3 9.44 2.47 13.4 4.8 24.2 5.5 12.8 1.8 20.7 4.3 16.9 2.7 22.9 8.7 11.2 1.0 3.92 106 3.69 107 1.34 107 1.79 108

6.23 6.55 6.47 6.79 6.64 6.38 6.66 6.45 6.54 6.41 6.72 13.2 13.6

2.76 1.16 8.15 0.77 9.93 1.14 6.12 0.53 8.73 1.21 6.32 0.63 6.35 0.36 9.11 0.55 7.49 0.34 9.64 1.14 7.40 0.17 5.26 0.14 7.40 0.49

47 139 169 104 149 108 108 155 126 164 126 89.6 126

Table 3. The measured activity of Ni21 ({Ni21}) using the ion exchange technique (IET) The water chemistry of the solutions was as follows; ionic strength of 6.5 mM and pH 8.30, with a total Ni concentration ([NiTot]) gradient between 2 and 100 mg L1. DOC, dissolved organic carbon

the TRA approach may not necessarily be a better tool for assessing Ni toxicity in L. minor than the BLM, because the same plots with pH on the x-axis instead of {Hþ} (not plotted) provides a linear relationship that can be incorporated into a BLM framework or a TRA approach.

[DOC] (mg L1)

Effect of pH on Ni accumulation kinetics In the present study, the effects of Hþ on Ni2þ accumulation kinetics in L. minor over a 7-day exposure was investigated (Fig. 1c). The linear increase in tissue residue-based toxicity with decreasing {Hþ} (Fig. 1b) suggested that Hþ does not competitively inhibit Ni2þ accumulation. In competitive inhibition, Kd is expected to increase (reduction in binding affinity) whereas Bmax (the binding capacity) is expected to stay constant; however, in the present study, neither Bmax nor Kd showed a linear change between pH 6.0 and 8.3 (APHA) (Table 2). The inhibition may be mixed, in which case the protons may be competitively inhibiting Ni accumulation, binding to the BL–Ni complex (anti-competitive) or both; protons could also be noncompetitively inhibiting a second binding site. Non-competitive inhibition of metal accumulation by Hþ instead of competitive inhibition (as expected by the general BLM framework) was demonstrated by Franc¸ois et al.[25] in their study on the effects of pH on Mn and Cd uptake by algae. In non-competitive interactions, the added cation can bind to the free BL and the metal (M)–BL complex.[15] In anti-competitive interactions, the competing cation binds only to the M–BL complex, which is commonly referred to as a ternary complex.[26]

20

40

Measured [NiTot] (mg L1)

IET measured {Ni2þ} (mg L1)

1.99 6.25 25.2 99.8 1.99 6.09 22.6 86.9

0.24 1.36 3.96 33.8 0.21 1.14 4.56 27.3

predicted by 3.0, 2.5 and 2.2-fold. Either WHAM underestimated {Ni2þ} or IET overestimated {Ni2þ}. Because the availability of Ni2þ was very low in these samples, IET measurements are subject to greater variability to limitations of the instrument used for metal analysis (ICP-MS). In addition, at lower Ni concentrations, contamination becomes more important. Nevertheless, 20 mg L1 DOC is the more environmentally relevant concentration than 40 mg L1 with respect to miningaffected regions, for which WHAM 6 predicted {Ni2þ} values agree well with IET measured {Ni2þ} values, although a [DOC] of ,5–10 mg L1 is more common in natural waters.[27] In addition, the IET-measured {Ni2þ} for 40 mg L1 of DOC treatment was still within three-fold of predicted values. The modified WHAM 6 used in this study was also validated by Van Laer et al.[8] and used throughout the present study to predict {Ni2þ}, except when mentioned otherwise. The present study did not apply IET to real mining effluent samples which are composed of a complex mixture of ligands and multiple metals, in which case IET-measured v. WHAM-calculated {Ni2þ} was not found to be in agreement.[11] Further work is required to reliably apply IET for measuring {Ni2þ} in real effluent samples.

Validation of WHAM 6 estimated {Ni21} in solutions containing environmental ligands To test the viability of using modified WHAM 6 to predict {Ni2þ} in the presence of ligands, particularly DOC, IET was utilised to measure {Ni2þ} (Table 3). It is evident that {Ni2þ} is slightly lower in the presence of 40 v. 20 mg L1 DOC for a solution with the same total Ni concentration. WHAMpredicted {Ni2þ} in test solutions were within two-fold of the measured {Ni2þ} using the IET, except for the lower three [NiTot] (1.99, 6.09 and 22.6 mg L1) in the presence of 40.5 mg L1 of DOC (Fig. 3), which differed from the values 552

Effect of pH and ligands on Ni2þ toxicity to duckweed (a) 100 IC25Ni2

Toxicity thresold (µg L1)

WHAM predicted p{Ni2}

9

8

7 1 : 1 line 20.5 mg L1 DOC 40.5 mg L1 DOC

Average IC25Ni2 IC25NiTot Liniear regression IC25NiTot

80 60

y 0.860x 12.6 R 2 0.994, P 0.0001

40 20

0

6

0 6

7

8

10

9

20

30

40

50

[DOC] (mg L1)

IET measured p{Ni2} (b) 600

IC25NiTiss (µg g1)

Fig. 3. Comparison on a log scale of {Ni2þ} measured by the ion exchange technique (IET) and that predicted by Windermere Humic Aqueous Model 6 in American Public Health Association media containing 20 and 40-mg L1 Suwannee River natural organic matter. The solutions had an ionic strength of 6.5 mM and pH 8.30, with a total Ni concentration ([NiTot]) gradient between 2 and 100 mg L1. The horizontal error bars represent 1 standard deviation of the mean (minimum n ¼ 3).

Effect of ligands: exposure-based and tissue residue-based Ni toxicity WHAM 6 predicted significant complexation of Ni by DOC. In the presence of 0.5 (control), 20 or 40 mg L1 of DOC, ,42, 17 and 7 % of [NiTot] remained as {Ni2þ} when [NiTot] in the test solution was ,25–30 mg L1. As a result, the expected effect of DOC was a linear reduction in toxicity of total [Ni] (IC25NiTot). Although a reduction in toxicity was observed (Fig. 4a), the toxicity linear reduction was relatively small (less than factor of 2) but significant (IC25NiTot ¼ 0.860[DOC] þ 12.6, P , 0.0001). Most importantly, as expected by the BLM framework, IC25Ni2þ did not change with [DOC] (i.e. the regression slope was not different from zero), indicating that the influence on solution chemistry explains the effect of DOC on Ni toxicity to L. minor. Toxicity based on [NiTiss] (IC25NiTiss) increased from ,350 mg g1 in 0.5 mg L1 DOC to a maximum of ,400 mg g1 in 5–20 mg L1 DOC (Fig. 4b). However, when fit with linear regression, the slope was not different from zero (not plotted). Despite the initial increase in IC25NiTiss, it is likely that [DOC] is not affecting the plant’s physiological response to Ni accumulated in tissue, unlike other water chemistry factors have in the present study (e.g. {Hþ}). This result is in support of findings by Vigneault and Campbell[28] that showed no physiological effect of DOC on cadmium uptake in two different freshwater species of green algae. However, other studies in green algae have found that DOC adsorbed on the cell surface and increased membrane permeability.[29,30] This difference in DOC interaction between plants and algae may be due to the additional presence of a cell wall in plants, which has many of the same characteristics as DOC. Overall, we suggest that the effect of DOC on Ni toxicity to L. minor in typical Canadian surface waters can be modelled using the traditional BLM framework. Fig. 5a illustrates the effect of the environmental ligands on exposure-based toxicity, for [NiTot]. The flotation ligands, DETA and TETA, increased IC25NiTot (reduced NiTot toxicity)

500

400

300

y 334 103(1e(0.576x)) R 2 0.999, P 0.035

200 0

5

10

[DOC] (mg

15

20

25

L1)

Fig. 4. (a) Effect of dissolved organic carbon (DOC) on Ni toxicity to Lemna minor. Toxicity is represented as total [Ni] (25 % toxicity threshold based on total [Ni], IC25NiTot) or as free Ni (25 % toxicity threshold based on {Ni2þ} (IC25Ni2þ)) based on {Ni2þ} calculated by Windermere Humic Aqueous Model 6. When the slopes of regressions are not significant, an average line is presented. Error bars represent 5 and 95 % confidence intervals; (b) The effect of [DOC] on toxicity of Ni concentration in L. minor tissue (25 % toxicity threshold based on [Ni] in plant tissue, IC25NiTiss). When slopes of regressions are not significant, an average line is presented. Error bars represent 5 and 95 % confidence intervals.

by approximately one order of magnitude relative to the control, likely due to complexation of Ni2þ in solution, but NaIX did not have any effect. Interestingly, when toxicity was based on {Ni2þ}, DETA and TETA treatments were more toxic than the control or DOC treatment (Fig. 5b). This is largely because of the strong stability constants for Ni complexation by these ligands (log KNi–DETA is 10.5, log KNi–TETA is 14.0),[18] and the consequent negligible {Ni2þ} calculated by WHAM 6 in the presence of 0.1 mM DETA or TETA. However, it is also possible that Ni–DETA or Ni–TETA complexes are bioavailable. For example, Fig. 5c displays a decrease in IC25NiTiss in the presence of TETA (i.e. less Ni in tissue is needed to cause toxicity to the plant). A less likely, but probable, explanation may be that the available binding constants for Ni–DETA and Ni–TETA are not accurate. Because no binding constant for NaIX with Ni was available, the effect of NaIX on IC25Ni2þ was not calculable. Some flotation ligands such as NaIX are known to form hydrophobic complexes with some metals (e.g. Cd) which are taken up by aquatic organisms.[31] It’s possible that the same process is leading to greater Ni uptake by the plant than 553


(a) 1000

(a) 10

IC25NiTot (µg L1)

8 100

6

4 10

0.5 mg L1 DOC (APHA)

2

5.5 mg L1 DOC 10.5 mg L1 DOC 20.5 mg L1 DOC

0 1 Control

DOC

NaIX

DETA

TETA

0

(b) 1e3

100

150

200

250

(b) 6 DETA

1e2 5

Ni AR (µg g1 h1)

1e1

IC25Ni2 (µg L1)

50

1e0 1e1 1e4 1e5 1e6 1e7

4 3

1.5

2

1.0 0.5

1

0

1e8

0

0

1e9 Control

DOC

TETA

DETA

3e-7

6e-7

9e-7

IET DETA 0

2e-5

4e-5

6e-5

(c) 1000 (c) 6 IC25NiTiss (µg g1)

TETA 5 4 100

3 2 1 0

10 Control

NaIX

DOC

DETA

TETA

Ligands

0

1e-7

{Ni2}

Fig. 5. Effect of dissolved organic carbon (DOC) (10.5 mg L1) and ligands used in flotation reagents (10.3-mg L1 diethylenetriamine (DETA) (predicted by Windermere Humic Aqueous Model and measured by the ion exchange technique, when applicable), 14.6-mg L1 triethylenetriamine (TETA), or 0.1-mg L1 sodium isopropyl xanthate (NaIX)) on Ni toxicity threshold based on (a) total Ni concentration ([NiTot]), (b) the activity of Ni2þ ({Ni2þ}) and (c) the concentration of Ni in the tissue ([NiTiss]), in Lemna minor, presented on a log scale. Error bars represent 5 and 95 % confidence intervals.

2e-7

(µg

3e-7

L1)

Fig. 6. Effect of environmental ligands, (a) dissolved organic carbon (DOC), (b) diethylenetriamine (DETA) or (c) triethylenetriamine (TETA), on Ni accumulation rate (AR) in Lemna minor in American Public Health Association media. Note that error bars are not available for tissue concentrations as plant tissue of replicates in tests were pooled to obtain measurable amounts.

Effect of ligands on Ni accumulation kinetics For DOC (Fig. 6a), although small differences in Ni AR were observed, there was no effect of DOC on Ni accumulation kinetics (Kd or Bmax) (Table 2). This further indicates that [DOC] does not affect Ni2þ uptake, or its toxicity, and supports the conclusion that the effect of DOC on Ni toxicity to L. minor can be attributed solely to complexation of Ni in solution, as evident in Fig. 4a. In the presence of DETA (Fig. 6b), Ni AR is closer to

2þ

is expected from estimated {Ni }. Toxicity based on tissue Ni (Fig. 5c) is similar for NaIX, DETA, DOC and control, and likely also for TETA, which had a lower threshold but with a large confidence interval. Note that DETA and TETA controls (ligand only) showed no significant toxicity to L. minor (data not shown). Thus, in the presence of organic ligands, TRA may be a viable approach for assessing Ni toxicity to L. minor. 554


Table 4. Comparison of measured v. predicted 25 % toxicity threshold based on total [Ni] (IC25NiTot) for Lemna minor in the absence and presence of environmentally relevant ligands Predicted IC25NiTot was based on the average 25 % toxicity threshold based on [Ni2þ] (IC25Ni2þ) for the control (6.24 2.98, n ¼ 15) and iterations using Windermere Humic Aqueous Model 6. APHA, American Public Health Association; DOC, dissolved organic carbon; FIAM, free ion activity model Ligand Control (APHA) DOC Diethylenetriamine Triethylenetriamine

[Ligand] (mg L1)

Measured IC25NiTot (mg L1)

Predicted IC25NiTot using FIAM (mg L1)

Difference (predicted v. measured)

0.5 10.5 10.3 14.6

13.5 2.2A 22.1 (13.9–32.4)B 113 (70.0 426) 88 (n/a–214)

13.2 20.5 3.99 103 5.86 103

0.98 0.93 27 67

Standard deviation, n ¼ 8. 5–95 % confidence interval.

A B

reaching saturation than in the presence of TETA (Fig. 6c), although both ligands were at a concentration of 0.1 mM. Calculated binding strength (log KNi2þ) values for DETA (13.2) and TETA (13.6) were similar, but the binding capacity (Bmax) for TETA (7.40 0.49 mg g1) was greater than DETA (5.26 0.14 mg g1) (Table 2). With respect to the control solution, both TETA and DETA have twice the binding strength for Ni2þ, but a lower binding capacity than the control (log K ¼ 6.45, Bmax ¼ 9.11 0.55 mg g1) (Table 2). Similar calculations were not possible for NaIX, as {Ni2þ} was not calculable due the lack of availability of a binding constant for NaIX–Ni2þ in solution. However, the effect of [NiTot] on Ni AR in solutions amended with NaIX indicated a binding capacity for Ni of 7.15 0.41 mol, which is similar to that of TETA. Because only one flotation ligand concentration was tested for DETA, TETA and NaIX, it was not possible to determine the type of inhibition (e.g. competitive, non-competitive or anticompetitive) that might have influenced Ni accumulation. However, it is evident that in the presence of DETA and TETA, Ni AR is lower (i.e. maximum Ni AR ,5 mg g1 h1 for both) than in the control (maximum Ni AR ,9 mg g1 h1) (Fig. 6). This reduction could be due to some form of inhibition occurring in the presence of DETA or TETA, or due to the presence of multiple transport systems. Many species, including plants, have been shown to have low affinity–high capacity and high affinity–low capacity transport systems for trace metals.[32] In summary, the present study found that the addition of 10.3 mg L1 of DETA to a Ni dose–response test reduced Ni toxicity (IC25NiTot 113 mg L1) with respect to the control (IC25NiTot 19.1 0.6 mg L1). Similarly, the presence of 14.6 mg L1 TETA also reduced Ni toxicity (IC25NiTot 88.5 mg L1). The observed reduction in toxicity in the presence of DETA or TETA was likely due to a reduction in {Ni2þ} (i.e. by complexation), but the magnitude of toxicity reduction does not correspond with the calculated {Ni2þ}. In fact, WHAM calculated negligible {Ni2þ} in the presence of DETA (maximum 1013 mg L1 of Ni2þ) and TETA (maximum 1015 mg L1 of Ni2þ) based on complexation (log KNiDETA is 10.5, log KNiTETA is 14.0). Thus, the application of a BLM framework to these solutions (i.e. exposure-based toxicity for {Ni2þ}) would predict a much larger reduction in toxicity than was observed. Predictions of IC25NiTot in the presence of DETA and TETA were nearly two orders of magnitude higher than the measured values (Table 4). Thus, the effect of flotation ligands on the toxicity of Ni to L. minor presents an exception to the BLM-expected behaviour. Further research is necessary to

confirm these findings, such as a controlled titration experiment to confirm the stability constant of Ni–DETA and Ni–TETA under conditions similar to the present study. In addition, examination of the plausibility of adsorption of Ni-flotation ligands to the plant membrane surface and the subsequent effect on membrane permeability should also be useful. Free nickel ion activity as a predictor of toxicity in the presence of environmental ligands With respect to the FIAM principle (basis of the BLM framework), {Ni2þ} is considered the best predictor of toxicity. Thus, toxicity based on {Ni2þ} (IC25Ni2þ) should be constant. However, based on the results of the present study, it was concluded that FIAM works well for control and DOC treatments, but not for pH or flotation reagent treatments for estimating Ni toxicity in L. minor. Based on the assumption that WHAM 6 would estimate {Ni2þ} correctly using the available stability constants from NIST, IC25NiTot was predicted for the various ligands using an iteration technique. This technique uses the average measured IC25Ni2þ value for the control (6.24 2.98, n ¼ 15) which is assumed to be constant in the presence of flotation ligands as well, to determine the corresponding IC25NiTot. The results showed accurate (i.e. less than 1 standard deviation) prediction of toxicity for the control and DOC tests; however, DETA predictions were 27-fold higher than measured, whereas for TETA it was 67-fold higher (Table 4). This deviation was further investigated through the use of IET to measure {Ni2þ} in the toxicity test solutions for the DETA treatment. The toxicity threshold based on IET measured {Ni2þ} was then compared with the IC25Ni2þ based on WHAM predicted {Ni2þ} (Fig. 5b). Again, the results suggest that the observed toxicity of DETA is much greater than expected, as the IET measured significant amounts of {Ni2þ} whereas the stability constant indicates {Ni2þ} in solution should be negligible. It may be that DETA or TETA may not be complexing Ni as strongly as expected by the available stability constants, and should be confirmed by further testing. The difference in predicted v. measured IC25Ni2þ of approximately seven orders of magnitude indicates that further work is required to be able to better predict Ni toxicity in the presence of flotation ligands. Conclusions Exposure-based and tissue residue-based Ni toxicity to L. minor, as well as Ni2þ accumulation kinetics, showed that the pH effect can be modelled but the mechanism of Hþ interaction with Ni2þ 555


was likely not competitive inhibition as expected by the BLM framework but rather a physiological effect of pH on the plant. In addition, a TRA approach was not viable in this case as tissue residue-based Ni toxicity was not constant. In terms of organic ligands, the effect of DOC on Ni toxicity to L. minor, although small, can be explained by complexation alone, and can thereby be modelled using a BLM approach. However, the effect of flotation ligands could not be modelled using Ni2þ toxicity, possibly due to an unknown physiological effect or due to potentially inaccurate stability constants for complexation with Ni. Alternatively, a TRA approach may be viable in this case as the tissue residue-based toxicity stayed relatively constant between the control, DOC, DETA and TETA. Predicted and measured speciation indicated that the observed toxicity in the presence of DETA and TETA was much less than expected based on available stability constants for complexation with Ni in solution. Further work is necessary to understand the underlying mechanism of toxicity and confirm the stability constants of Ni-flotation ligand complexes.

[9] S. C. Apte, G. E. Batley, Trace metal speciation of labile chemical species in natural waters and sediments: non-electrochemical approaches, Metal Speciation and Bioavailability in Aquatic Systems (Eds A. Tessier and D. R. Turner) 1995, pp. 259–306 (Wiley: Chichester, UK). [10] Y. Gopalapillai, I. I. Fasfous, J. D. Murimboh, T. Yapici, P. Chakraborty, C. L. Chakrabarti, Determination of free nickel Ion concentrations using the ion exchange technique: application to aqueous mining and municipal effluents. Aquat. Geochem. 2008, 14, 99. doi:10.1007/ S10498-008-9027-2 [11] Y. Gopalapillai, C. L. Chakrabarti, D. R. S. Lean, Assessing toxicity of mining effluents: equilibrium- and kinetics-based metal speciation and algal bioassay. Environ. Chem. 2008, 5, 307. doi:10.1071/ EN08027 [12] A. Boullemant, M. Lavoie, C. Fortin, P. G. C. Campbell, Uptake of hydrophobic metal complexes by three freshwater algae: unexpected influence of pH. Environ. Sci. Technol. 2009, 43, 3308. doi:10.1021/ ES802832U [13] V. I. Slaveykova, K. J. Wilkinson, Predicting the bioavailability of metals and metal complexes: critical review of the biotic ligand model. Environ. Chem. 2005, 2, 9. doi:10.1071/EN04076 [14] N. M. E. Deleebeeck, K. A. C. de Schamphelaere, C. R. Janssen, Effects of Mg2þ and Hþ on the toxicity of Ni2þ to the unicellular green alga Pseudokirchneriella subcapitata: model development and validation with surface waters. Sci. Total Environ. 2009, 407, 1901. doi:10.1016/J.SCITOTENV.2008.11.052 [15] U. Borgmann, W. P. Norwood, D. G. Dixon, Modelling bioaccumulation and toxicity of metal mixtures. Hum. Ecol. Risk Assess. 2008, 14, 266. doi:10.1080/10807030801934929 [16] Biological test method: test for measuring the inhibition of growth using the freshwater macrophyte, Lemna minor. Environment Canada Environmental Protection Series, Report EPS 1/RM/37, 2nd edn 2007 (Ottawa, ON). [17] Y. Gopalapillai, B. Hale, B. Vigneault, Effect of major cations (Ca2þ, Mg2þ, Naþ, Kþ) and anions (SO2 4 , Cl , NO3 ) on Ni accumulation and toxicity in aquatic plant, Lemna minor L.: implications for Ni risk assessment. Environmental Toxicology and Chemistry, in press. [18] F. F. Cantwell, J. S. Nielsen, S. E. Hrudey, Free nickel ion concentration in sewage by an ion exchange column-equilibration method. Anal. Chem. 1982, 54, 1498. doi:10.1021/AC00246A012 [19] T. Cheng, K. de Schamphelaere, S. Lofts, C. Janssen, H. E. Allen, Measurement and computation of zinc binding to natural dissolved organic matter in European surface waters. Anal. Chim. Acta 2005, 542, 230. doi:10.1016/J.ACA.2005.03.053 [20] N. M. E. Deleebeeck, K. A. C. de Schamphelaere, C. R. Janssen, A bioavailability model predicting the toxicity of nickel to rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas) in synthetic and natural waters. Ecotoxicol. Environ. Saf. 2007, 67, 1. doi:10.1016/J.ECOENV.2006.10.001 [21] K. A. C. de Schamphelaere, C. R. Janssen, A biotic ligand model predicting acute copper toxicity for Daphnia magna: the effects of calcium, magnesium, sodium, potassium, and pH. Environ. Sci. Technol. 2002, 36, 48. doi:10.1021/ES000253S [22] X. Wang, Y. Ma, L. Hua, M. J. McLaughlin, Identification of hydroxyl copper toxicity to barley (Hordeum vulgare) root elongation in solution culture. Environ. Toxicol. Chem. 2009, 28, 662. doi:10.1897/07-641.1 [23] R. C. Playle, Modelling metal interactions at fish gills. Sci. Total Environ. 1998, 219, 147. doi:10.1016/S0048-9697(98)00232-0 [24] R. Reid, J. Hayes, Mechanisms and control of nutrient uptake in plants. Int. Rev. Cytol. 2003, 229, 73. doi:10.1016/S0074-7696(03) 29003-3 [25] L. Franc¸ois, C. Fortin, P. G. C. Campbell, pH modulates transport rates of manganese and cadmium in the green alga Chlamydomonas reinhardtii through non-competitive interactions: implications for an algal BLM. Aquat. Toxicol. 2007, 84, 123. doi:10.1016/J.AQUA TOX.2007.02.019 [26] K. S. Dodgson, B. Spencer, K. Williams, Examples of anti-competitive inhibition. Nature 1956, 177, 432. doi:10.1038/177432B0

Acknowledgements The authors thank Dr Paula Antunes, Dr Glen Van der Kraak and Dr P. Huntsman Mapila for their helpful comments and suggestions. They also thank the Analytical Services Group at Mineral and Mining Sciences Laboratories, Natural Resources Canada (MMSL NRCan) for all total metal analyses, particularly Duane Palmer. This research was supported by MMSL NRCan and the Natural Sciences and Engineering Research Council of Canada (NSERC), in the form of a Discovery Grant to B. Hale and a Canada Graduate Scholarship to Y. Gopalapillai.

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