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Metal oxides in surface sediment control nickel bioavailability to benthic macroinvertebrates Raissa Marques Mendonca, Jennifer M Daley, Michelle L Hudson, Christian Schlekat, G. Allen Burton, and David Costello Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03718 • Publication Date (Web): 18 Oct 2017 Downloaded from http://pubs.acs.org on October 18, 2017

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Environmental Science & Technology

TOC (Table of Contents Art)

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ACS Paragon Plus Environment


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Metal oxides in surface sediment control nickel bioavailability to benthic

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macroinvertebrates

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Raissa Marques Mendonca1*, Jennifer Marie Daley2, Michelle Lynn Hudson2, Christian Eduard

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Schlekat3, Glenn Allen Burton, Jr.2, David Matthew Costello1

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1

Department of Biological Sciences, Kent State University, 1275 University Esplanade, Kent,

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Ohio 44242, United States

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2

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Arbor, Michigan 48109, United States

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3

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Parkway, Suite 240, Durham, North Carolina 27713, United States

School for Environment and Sustainability, University of Michigan, 440 Church St., Ann

Nickel Producers Environmental Research Association (NiPERA, Inc.), 2525 Meridian

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Corresponding author: Raissa M Mendonca Department of Biological Sciences, Kent State University 1275 University Esplanade, Kent, OH 44242 USA Tel: (330) 554-6244 Email address: [email protected]


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Abstract

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In aquatic ecosystems, the cycling and toxicity of nickel (Ni) are coupled to other elemental

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cycles that can limit its bioavailability. Current sediment risk assessment approaches consider

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acid-volatile sulfide (AVS) as the major binding phase for Ni, but have not yet incorporated

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ligands that are present in oxic sediments. Our study aimed to assess how metal oxides play a

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role in Ni bioavailability in surficial sediments exposed to effluent from two mine sites. We

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coupled spatially explicit sediment geochemistry (i.e., separate oxic and suboxic) to the

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indigenous macroinvertebrate community structure. Effluent-exposed sites contained high

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concentrations of sediment Ni and AVS, though roughly 80% less AVS was observed in surface

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sediments. Iron (Fe) oxide mineral concentrations were elevated in surface sediments and bound

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a substantial proportion of Ni. Redundancy analysis of the invertebrate community showed

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surface sediment geochemistry significantly explained shifts in community abundances. Relative

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abundance of the dominant mayfly (Ephemeridae) was reduced in sites with greater bioavailable

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Ni, but accounting for Fe oxide-bound Ni greatly decreased variation in effect thresholds

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between the two mine sites. Our results provide field-based evidence that solid-phase ligands in

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oxic sediment, most notably Fe oxides, may have a critical role in controlling nickel

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bioavailability.

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Introduction

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Sediment metals can impair biodiversity and ecological functions in aquatic

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environments.1-4 However, the cycling and toxicity of metals, such as nickel (Ni), are coupled to

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other elemental cycles that determine toxic metal speciation and sorption behavior onto aqueous

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and solid phase ligands. In sediment, metal mobilization is determined by binding (i.e., sorption,



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precipitation, chelation) to reduced and oxidized ligands, which controls metal bioavailability

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and toxicity.5,6 The distribution of solid-phase ligands in natural sediments is complex and often

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characterized by spatial stratification and temporal variation related to dynamic physicochemical

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conditions. In oxic sediment, oxidized species (i.e., iron, manganese, and aluminum oxide

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minerals) represent the major binding phases, whereas sulfide minerals are the predominant

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metal ligand in anoxic sediment layers.5 Lotic sediments are often vertically stratified with thin

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(mm to cm) aerobic layers overlying anaerobic horizons, and the distinct physicochemical

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conditions in these sediment layers modify their metal binding capacity. Unlike for sulfide,7

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binding constants for oxide minerals and the partitioning behavior of nickel to these solid phases

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in situ have not yet been applied to sediment metal risk assessment efforts.5

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Bioavailability models based on acid-volatile sulfide (AVS) and simultaneously extracted

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metals (SEM) have been extensively applied in metal toxicity guidelines and assume sulfide as

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the main factor controlling metal partitioning.8,9 The SEM-AVS models provide important

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information for predicting conditions when sediment metals are present but non-toxic for several

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aquatic species, but in some cases these models fail to predict metal toxicity due to unique

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sediment redox chemistry in surface sediment and benthic fauna ecology.10-13 In oxic surface

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sediment, AVS concentrations are generally low and highly variable owing to the rapid oxidation

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of sulfide in the presence of oxygen.12,14-16 In addition, oxic microenvironments can occur at

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deeper sediment depths that are oxygenated through diffusion via bioirrigation and bioturbation

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by benthic organisms.17,18 These variable sediment redox gradients can render SEM-AVS models

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less suitable for predicting metal bioavailability in naturally stratified sediment, and for deriving

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toxicity thresholds for taxa with unique bioturbation behavior.19 This limitation generally results


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in conservative criteria that do not account for metal binding and co-precipitating with oxide

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minerals.12,20

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Incorporating concentrations of oxidized ligands into metal toxicity assessments may

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provide benchmarks more compatible with environmental conditions, especially for lotic

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ecosystems.12,14,21,22 In addition to including metal oxide minerals (i.e., Fe, Mn, and Al oxides) as

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potential binding phases, accounting for the distinct geochemistry in surface sediments should

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also provide more realistic estimates of geochemical conditions under which sensitive taxa are

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exposed to sediment metals. While the SEM-AVS models can be effective at distinguishing

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between non-toxic and potentially toxic conditions in some sediments, the model’s limited

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applicability to only anoxic conditions constrains its scope in metal bioavailability assessment.

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To address this limitation, research on the behavior of metal partitioning onto metal oxide

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minerals in natural sediments is needed to derive information on binding coefficients. Analyses

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of toxic metals binding to metal oxide minerals have been widely performed on pure synthetic

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mineral substrates,23-25 but the studies did not examine metal binding within the context of the

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mixture of competing ligands found in natural sediments. With the potential importance of oxic

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sediment layers in modifying metal bioavailability, there is a need to explicitly test whether

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including oxidized species in models of bioavailability more accurately predicts sediment metal

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toxicity.

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In this study, we considered whether spatially resolved sediment geochemistry (i.e.,

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separate oxic and suboxic sediment) and water physicochemistry increased our ability to predict

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the effects of nickel exposure to macroinvertebrates compared to current methods (i.e.,

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homogenized sediment and SEM-AVS bioavailability models). Furthermore, we used in situ

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natural gradient of total Ni from two mine sites to determine if concentration–response



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thresholds could be determined from the field using indigenous benthic macroinvertebrates. We

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hypothesized that sediment geochemistry would be vertically stratified between oxic and anoxic

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horizons and characterized by a greater proportion of metal oxide minerals and low AVS in the

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surface layer due to oxidizing conditions. Additionally, we predicted sediment characteristics in

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surface sediments (e.g., total nickel concentration, nickel bound to solid-phase ligands) would

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significantly affect benthic macroinvertebrate composition and explain community variation

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between sites. Within effluent-exposed tributaries, we predicted the natural nickel gradient

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would reveal spatial patterns of invertebrate abundance, with sites farther from the effluent

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discharge more similar to reference conditions. Finally, we hypothesized that nickel-

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contaminated lotic sediments would be less toxic to the native invertebrate community than

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expected by current benchmarks due to the partitioning of nickel to metal oxide minerals in the

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oxic sediment layer.

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Methods

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Site description

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Sampling was conducted in tributaries of the Burntwood River near Thompson,

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Manitoba, Canada in August 2014. Two pairs of reference and effluent-exposed tributaries were

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selected near the Birchtree (55° 41’ N, 97° 57’ W) and Thompson (55° 49’ N, 97° 41’ W) nickel

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mines (Fig. S1). The tributary in the Birchtree mining area received effluent from the mine

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treatment plant, whereas effluent from the Thompson mine passed through a wetland complex

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(approximately 9 km) prior to reaching the sampling area (Fig. S1). Paired reference tributaries

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were within 8 km of effluent-exposed tributaries and had similar sediment characteristics but Ni

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at background concentrations. Within tributaries, ten sampling sites were spatially distributed


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from upstream to downstream, which established a gradient of nickel contamination in effluent-

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exposed sites (Fig. S2-S4).

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Water, sediment and invertebrate sampling and analysis

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Surface water physicochemical characteristics were assessed in situ with hand-held

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meters and a logging sonde (YSI 6290). Unfiltered surface water samples were collected for

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analysis of water hardness (as mg CaCO3 L-1) by EDTA titration. Additional surface water

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samples were filtered (0.45 µm) and acidified with 1% nitric acid for analysis of Ni and major

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anions (nitrate, sulfate, and chloride) via inductively-coupled plasma mass spectrometry (ICP-

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MS) and ion chromatography, respectively. Intact sediment cores were collected using 5-cm

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diameter plastic tubes and caps. The tubes were manually pressed into the sediment, plastic caps

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were placed at the ends of the core tube, and the intact core was carefully removed. Sediment

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cores with visible disturbance of surface sediment were discarded and new cores were collected

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within a 50-cm radius of the original core. At each reference tributary, sediment cores were

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collected at four sites (Ref 1–4), whereas sediment samples were collected at 10 sites within both

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exposure tributaries (n total = 28 sediment cores). The intact sediment cores were processed

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within 6 h by removing the overlying water, extruding the sediment using a plunger,

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and sectioning the core into surface sediment samples (0–2 cm) and deep sediment samples (2–4

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cm). Surface and deep sediment samples were sealed in polyethylene bags and frozen (-18° C)

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prior to analysis.

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Prior to geochemical analysis, sediment samples were defrosted and manually

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homogenized without opening the bags. Analysis of AVS and SEM was performed according to

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Allen and colleagues,26 which used 1M hydrochloric acid extraction (50 min) to obtain dilute



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acid-extractable metal concentrations (i.e., SEM). Total metal concentrations (FeTOT, MnTOT, and

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NiTOT) were obtained through sediment digestion in concentrated nitric acid at reflux

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temperatures using a digestion block. For oxidized Fe and Mn and associated metals, selective

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extractions27 were used to differentiate between the amorphous (MeHFO), crystalline (MeCFO), and

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total oxidized minerals (MeOXID). Metal concentrations (Fe, Mn, and Ni) for SEM metals, total

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metals, and selective extractions were determined by inductively coupled plasma optical

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emission spectrometry (ICP-OES). An elemental analyzer was used to determine carbon content

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of sediment samples. All analyses were completed with appropriate quality assurance/quality

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control checks (Table S1), which included blanks, duplicates, and a standard reference material

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(total metals only, Domestic Sludge, NIST SRM 2781).

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Benthic macroinvertebrate community samples were taken using a 152 x 152 mm petite

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ponar grab (surface area 231 cm2, depth 5–7 cm). Samples were collected at each of the ten

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sites within the reference and exposure tributaries (total n = 40 invertebrate samples). The

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sediment collected with the ponar was sieved through a 500-µm bucket sieve, washed, and

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manually transferred into polystyrene specimen vials filled with 90% ethanol. All invertebrates

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within each sample were sorted and identified to family28 using a dissecting microscope. All

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pelagic taxa identified were excluded from statistical analyses.

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Statistical analysis

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Sediment geochemical characteristics were analyzed with three-way analysis of variance

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(ANOVA) that used location (Birtchtree or Thompson), tributary (Ref or Exp), and sediment

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depth (surface or deep) as predictor variables. When necessary, data were log transformed to

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meet assumptions of ANOVA. Analyses for NiTOT required an additional statistical block for site


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to account for the variation related to the effluent dilution gradient. Concentrations of select

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extractions of metal (i.e., SEM, oxidized metal extractions) were expressed as a percentage of the

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total concentration of metal. When significant interactions were identified, post-hoc Tukey’s

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multiple comparison tests were used to detect significant pairwise differences. Water

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physicochemical properties were analyzed by analysis of covariance (ANCOVA) with location

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(Birchtree or Thompson) as a main effect and sediment total nickel (mean of surface and deep)

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as a covariate to account for the gradient in effluent exposure.

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Invertebrate community composition was analyzed via transformation-based redundancy

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analysis (RDA) to assess the proportion of variance in the invertebrate community explained by

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the environmental variables.29 The objective is to obtain components of the invertebrate

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community matrix that can be linearly explained by the predictor matrix (i.e., environmental

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variables) and that represent most of the variance in the community. In reference tributaries,

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invertebrate community samples that lacked paired sediment geochemistry measures were

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assigned mean values from sampled reference sites in the respective tributaries (n = 4).

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Invertebrate taxa abundances were transformed using Hellinger distance, which avoids the use of

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dual absences as an indication of similarity between sites. All benthic taxa identified (n = 38

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families) were included in the statistical analysis (Table S2). Four RDA models comprising

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different sets of explanatory variables were considered: water only, surface sediment only, deep

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sediment only, and all environmental variables. The statistical significance of each RDA model

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was tested by permutation with a type I error rate (α) of 0.05. Forward selection on variables

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within significant models was used to determine the most parsimonious model based on adjusted

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R2 and variable significance (α = 0.05) as selection criteria.30 Final models are depicted in



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ordination plots, and taxa with a significant proportion of variation explained by any RDA axis

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(coefficient of determination > 0.20), referred to as best-fit families, are identified.

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The most abundant and responsive mayfly taxon (i.e., Ephemeridae) was further analyzed

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using log-linear regression to assess responses to Ni exposure. Ephemeridae relative abundance

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(% of total invertebrate abundance) was regressed against three potential measures of Ni

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bioavailability: simultaneously extracted nickel (NiSEM), SEM Ni in excess of AVS corrected for

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organic carbon ((NiSEM-AVS)/ƒOC), and SEM Ni in excess of oxide-bound Ni corrected for

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organic carbon ((NiSEM-NiHFO)/ƒOC). The latter fraction represents the concentration of NiSEM that

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is not sorbed to amorphous metal oxides, which is similar to how NiSEM-AVS represents the Ni

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that is not precipitated as NiS. These measures of sediment Ni were selected for comparison due

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to their use in current sediment risk assessment (i.e., (SEM-AVS)/ƒOC)7 or their identification as

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important variables in this study (i.e., FeHFO explained significant variation in the community

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matrix). All measures of Ni and binding ligands were from measurement in surface sediments. A

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sigmoid log-logistic model, which is the conventional model for laboratory concentration–

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response, could not be fit for each mine location due to greater inherent variability and a limited

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sample size. Thus, for each mine location, a simpler log-linear regression was used to derive

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concentration–response relationships. Log-linear regression requires positive concentrations,

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therefore sites with negative values for Ni bioavailability (e.g., (SEM-AVS)/fOC < 0) were

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substituted with the minimum positive value measured in the corresponding mine location and

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tributary. We chose this approach because we wanted to use all the invertebrate data available,

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and if negative values were excluded from the regression, the dataset would be biased towards

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only those sites with greater concentrations of bioavailable Ni. From each concentration–

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response curve, we determined the concentration that reduced the abundance of Ephemeridae by


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20% (EC20) relative to the abundance of Ephemeridae in downstream effluent-exposed sites

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with minimal measurable exposure to the effluent (i.e., NiTOT ≤ reference mean NiTOT + 1

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standard deviation, n = 2 sites). The downstream Ephemeridae relative abundance was used as a

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baseline in lieu of the mean abundance at reference sites because in general the reference

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tributaries had greater abundance of taxa not found at effluent-exposed sites (e.g., Gammaridae,

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Polycentropodidae; Table S2), which lead to lower mean relative abundance of Ephemeridae in

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reference sites. For each bioavailability measure we calculated a relative difference in EC20

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between each mine site (RD = |EC20BIRCHTREE-EC20THOMPSON|/mean EC20) to assess how well

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the bioavailability model normalized the concentration–response relationship (i.e., lower RD

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indicates better agreement between locations). For two mine sites AVS was in excess of NiSEM;

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for comparative purposes, we derived thresholds for concentration-response relationships that

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both included corrected concentrations (i.e., set negative (SEM-AVS)/fOC to lowest positive

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value) and excluded these negative values. Concentration-response relationships were attempted

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for additional sensitive taxa (e.g., Talitridae, Caenidae) but the absence of organisms in over

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25% of the sampled sites prevented the detection of nickel-related effects.

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Reported values are either means ± standard deviation or for log-normally distributed

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variables geometric means ± geometric standard deviation. All statistical analyses were

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performed using RStudio31 with appropriate packages for generalized linear regressions (lme4

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version 1.1-11)32 and community ordinations (vegan version 2.3-2).33

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Results

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Sediment geochemistry

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Acid volatile sulfide concentrations did not differ between Thompson and Birchtree

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locations (p = 0.30) but varied significantly with sediment depth (p < 0.001) and between

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reference and effluent-exposed tributaries (p = 0.017; Fig. 1A). All surface sediment (geometric

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mean = 0.08 µmol g-1 dw, geometric SD = 5.4) had on average 82% less AVS than deep

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sediment (geometric mean = 0.48 µmol g-1 dw, geometric SD = 4.9). Effluent-exposed sites had

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greater AVS concentrations (maximum = 7.6 µmol g-1 dw) than reference tributaries. In effluent-

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exposed tributaries, elevated sulfate concentrations in mine effluent provided abundant substrate

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for microbial reduction of sulfur and precipitation of metal sulfide minerals. Sediment carbon

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content was consistent across mine locations and between surface and deep horizons (1.5–9.2%),

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but differed marginally between reference and effluent-exposed tributaries in Birchtree sediment

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(2% difference, p = 0.03).

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The concentration of total Fe (FeTOT) ranged from 2.1–3.9% in all sediment samples and

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was consistent among sediment depths and mine locations, only differing slightly between

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Thompson reference and effluent-exposed tributaries. The proportion of dilute acid-extracted

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iron (FeSEM) was significantly greater in Birchtree sediments and reference tributaries (p
0.20), and aligned near reference sites, which suggests

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that these taxa were most abundant in sites with lower total NiSURF concentrations and/or greater

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concentrations of FeHFO. The RDA identified a single tolerant invertebrate family (i.e.,

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Sphaeriidae (SPH)), whose abundance was significantly explained by the RDA but aligned

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closer to effluent-exposed sites with higher total NiSURF. Both the simplified models for

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RDAWATER and RDASURF explained a significant proportion of the variation in the

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macroinvertebrate community (p < 0.001).

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Ephemeridae concentration–response

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The relative abundance of Ephemeridae (Ephemeridae abundance/total invertebrate

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abundance at each site) declined with increasing concentrations of NiSEM in the surface sediment,

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but the concentration–response relationships differed among mine locations (Figure 3A).

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Ephemeridae relative abundance in Birchtree declined at a slightly lower NiSEM concentration

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(0.6 µmol g-1) than those in Thompson (0.8 µmol g-1, EC20 relative difference (RD) = 0.28). Ni

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concentrations in excess of AVS corrected for organic carbon also demonstrated concentration–

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response relationships with Ephemeridae relative abundance, with increasing concentrations of

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(NiSEM-AVS)/ƒOC reducing the abundance of mayflies (Figure 3B). However, a large difference

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in concentration–response thresholds was observed between Birchtree (EC20 = 4.9 µmol g-1) and

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Thompson (EC20 = 0.4 µmol g-1) sites, and the difference between EC20s expressed as (NiSEM-

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AVS)/ƒOC (RD = 1.70) was much greater than the difference between locations for NiSEM. The

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increased relative difference in the (NiSEM-AVS)/ƒOC model is partly due to the fact that sites

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with high concentrations of AVS relative to NiSEM, which should be indicative of no toxicity, had


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much lower Ephemeridae relative abundances (14% and 3%) than the baseline relative

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abundance (43%).

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Considering the critical role of Fe identified in the multivariate community analysis and

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the differences in NiHFO between sites, we related Ephemeridae relative abundance to an index of

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sediment Ni that accounts for binding by metal oxide minerals and that can be directly compared

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to the AVS-based model. We found that the relative abundance of Ephemeridae declined with

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increasing concentrations of Ni not bound to amorphous Fe (Figure 3C). Considering the relative

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difference between concentration–response thresholds, the Fe-corrected Ni model did not

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decrease inter-site variation substantially more than the already low NiSEM model. However,

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while (NiSEM-NiHFO)/ƒOC is a coarse measure of Fe binding, the difference in EC20 thresholds

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between Thompson (EC20 = 6.3 µmol g-1) and Birchtree (EC20 = 4.8 µmol g-1) locations

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expressed as (NiSEM-NiHFO)/ƒOC was much smaller (RD = 0.27) when compared to (NiSEM-

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AVS)/ƒOC (RD = 1.70).

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Discussion

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In this study, we assessed if the spatially stratified geochemistry of field-contaminated

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sediments improved our ability to predict the effects of nickel exposure to benthic

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macroinvertebrates compared to current methods that partly overlook the distinct

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physicochemistry of oxic and suboxic sediments. Moreover, we used the natural contamination

357

gradient of nickel to determine field-based concentration–response thresholds using indigenous

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benthic macroinvertebrates. Though our field-based assessment involved organism exposure to

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nickel in both overlying water and sediment, the metal gradient at the study sites along with our



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multivariate analysis allowed us to distinguish whether macroinvertebrate taxa were responding

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to either sediment or overlying water impairment.

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As expected, we observed a substantial effect of mine effluent discharge on water quality,

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largely related to the input of ions in effluent-exposed tributaries. We observed a significantly

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higher dissolved nickel concentration in Thompson effluent-exposed tributaries, which was

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unexpected considering that the effluent-exposed tributary at the Thompson mine had slightly

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lower NiTOT than at the Birchtree mine. The difference in dissolved nickel in the tributaries can

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likely be attributed to differences in the effluent discharge at the two mines. First, the Birchtree

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mine effluent was inactive (i.e., summer shutdown) during the sampling period. Second, the

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Thompson mine effluent flows through a wetland complex (approximately 9 km) before reaching

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the sampled tributary, whereas the Birchtree effluent discharges directly upstream of the sampled

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tributary. The long flow path between outflow and sampling location at the Thompson tributary

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likely results in more consistent dissolved Ni concentrations, while the Birchtree site would be

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more responsive to mine operating conditions. Finally, the Birchtree mine effluent treatment

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plant was updated in 2008 which substantially decreased metal loading due to 87% reduction in

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dissolved nickel concentration and 40% increase in discharge capacity.34 Previous environmental

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monitoring of the Birchtree effluent-exposed tributary during regular mine activity reported

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dissolved nickel concentrations on average 2.5× higher than the 9 ± 8 µg L-1 observed in this

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study.34 This difference in overlying water Ni concentration between mine sites likely explains

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why dissolved Ni did not improve RDAWATER model fit, and also indicates that water–sediment

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partitioning of Ni may be more stable in Thompson sediment over both long and short

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timescales.


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As hypothesized, we observed distinct vertical variation in sediment chemistry and metal

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speciation in both Birchtree and Thompson sediment. AVS concentrations were substantially

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lower in surface sediment when compared to deep sediment. This distinct difference is despite

385

the fact that surface samples (i.e. top 2 cm) were likely not discrete samples containing only

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oxidized sediment. The oxic layer of fine-grained sediments is often 0.2).



Ephemeridae abundance (%)

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A

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C

B

Birchtree Thompson

40 30 20 10 0 0.1

736

1.0

10.0

NiSEM (µmol g-1)

10

100

1000

1

10

100

-1 (NiSEM-AVS)/ƒOC (µmol g-1) (NiSEM-NiHFO)/ƒOC (µmol g )

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Figure 2. Concentration–response relationships between Ephemeridae relative abundance and

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select sediment nickel variables: bioavailable sediment nickel (A), bioavailable nickel as a

739

function of AVS corrected for organic carbon (B), bioavailable Ni not associated with

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amorphous metal oxides corrected for organic carbon (C). Solid (Thompson mine, open

741

symbols) and dashed (Birchtree mine, closed symbols) lines are best-fit lines from least-squares

742

regression for effluent-exposed sites at each mine location. Crosses (+) on panel B indicate sites

743

with AVS concentration in excess of NiSEM (negative NiSEM-AVS) for which the minimum

744

positive value measured in the corresponding mine and tributary was assigned to meet

745

requirements of log-linear regression. The latter sites are also identified by crosses (+) on panels

746

A and C. Concentration–response relationships and relative differences in EC20s show that the

747

amorphous iron-corrected bioavailability model was more effective in normalizing the

748

concentration–response relationship between Birchtree and Thompson mines than the AVS-

749

based model. Note the log scale on the x-axis of each panel.


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