Microbial and geochemical controls on waste rock

Science of the Total Environment 640–641 (2018) 1004–1014

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Microbial and geochemical controls on waste rock weathering and drainage quality Sharon Blackmore a,b, Bas Vriens a,⁎, Melanie Sorensen c,d, Ian M. Power e, Leslie Smith a, Steven J. Hallam c, K. Ulrich Mayer a, Roger D. Beckie a a

Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, 2020-2207 Main Mall, Vancouver V6T 1Z4, Canada BGC Engineering Inc., 1718 Argyle Street, Suite 630, Halifax B3J 3N6, Canada Department of Microbiology and Immunology, The University of British Columbia, 1365 - 2350 Health Sciences Mall, Vancouver V6T 1Z3, Canada d Department of Genome Sciences, University of Washington, 3720 15th Avenue NE, Seattle 98195-5065, United States e School of the Environment, Trent University, 1600 West Bank Drive, Peterborough K9L 0G2, Canada b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Integrated approach to study microbial and geochemical controls on drainage quality. • Characterization of microbial abundance and community structures in waste rock. • Waste-rock weathering under simulated field conditions using column experiments • Demonstration of microbial catalysis of waste-rock weathering • Discrete secondary minerals and adsorption affect waste-rock drainage quality.

a r t i c l e

i n f o

Article history: Received 19 April 2018 Received in revised form 30 May 2018 Accepted 30 May 2018 Available online xxxx Editor: Frederic Coulon Keywords: Waste rock Weathering Microbiology Geochemistry Secondary minerals

⁎ Corresponding author. E-mail address: [email protected] (B. Vriens).

https://doi.org/10.1016/j.scitotenv.2018.05.374 0048-9697/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t Bacteria can adversely affect the quality of drainage released from mine waste by catalyzing the oxidation of sulfide minerals and thereby accelerating the release of acidity and metals. However, the microbiological and geochemical controls on drainage quality from unsaturated and geochemically heterogeneous waste rock remain poorly understood. Here, we identified coexisting neutrophilic and acidophilic bacteria in different types of waste rock, indicating that robust endemic consortia are sustained within pore-scale microenvironments. Subsequently, natural weathering was simulated in laboratory column experiments with waste rock that contained either in-situ microbial consortia or suppressed populations with up to 1000 times smaller abundance and reduced phenotypic diversity after heating and drying. Drainage from waste rock with in-situ populations was up to two pH units lower and contained up to 16 times more sulfate and heavy metals compared to drainage from waste rock bearing treated populations, indicating significantly higher sulfide-oxidation rates. The drainage chemistry was further affected by sorption and formation of secondary-mineral phases (e.g., gypsum and hydroxycarbonates). This study provides direct evidence for the existence of diverse microbial communities in waste rock and their important catalytic role on weathering rates, and illustrates the mutual controls of microbiology and geochemistry on waste-rock drainage quality. © 2018 Elsevier B.V. All rights reserved.

S. Blackmore et al. / Science of the Total Environment 640–641 (2018) 1004–1014

1. Introduction

2. Materials and methods

Open-pit mining produces significant quantities of waste rock that are typically deposited in large dumps that can reach up to several 100 s of meters in height. These mostly unsaturated piles constitute complex systems that have highly heterogeneous physical, mineralogical, and geochemical properties (Amos et al., 2015) and consequently contain many potential niche environments for microbes (Nordstrom et al., 2015). The important role of lithoautotrophic prokaryotes on catalyzing the oxidative dissolution of minerals has been established for decades (Singer and Stumm, 1970; Baker and Banfield, 2003; Johnson and Hallberg, 2003) and the biotic oxidation rate of sulfidic minerals (e.g., pyrite, pyrrhotite) exceeds abiotic oxidation rates by orders of magnitude (Singer and Stumm, 1970; Elberling et al., 2000). This catalysis by acidophilic bacteria mainly stems from the microbially mediated regeneration of ferric iron (Fe3+) and the oxidation of ferrous iron (Fe2+) and reduced inorganic sulfur (S2−).(Johnson and Hallberg, 2003) Major Fe- and S-oxidizing bacteria include Acidithiobacillus ferrooxidans, A. thiooxidans, Leptospirillum ferriphilum, L. ferrooxidans, and Sulfobacillus and Thiobacillus species (Schippers et al., 2010; Aliaga Goltsman et al., 2009; Johnson et al., 2001; Korehi et al., 2014; Benner et al., 2000). The abovementioned Fe- and S-oxidizing bacteria are ubiquitous in acidic sulfide mineral-bearing environments, but have also been identified in more neutral and even alkaline pH environments (Nordstrom et al., 2015; Baker and Banfield, 2003; Dockrey et al., 2014). Microenvironments may exist in the pore spaces of unsaturated waste rock, which can protect microbes from unfavorable conditions and sustain biodiversity (Benner et al., 2000; Mesa et al., 2017; Blowes et al., 1995; Dockrey et al., 2014). For instance, acidophilic bacteria have been identified in biofilms in acid-neutralizing carbonate-rich waste rock that also harbored a robust consortium of neutrophilic bacteria, indicating acidic microenvironments protected these acidophiles (Dockrey et al., 2014). Generally, the microbial abundance and community diversity in (circum)neutral mine waste is less studied compared to that of acidic mine-waste environments (Lottermoser, 2010). Furthermore, the majority of microbial studies on mine wastes have focused on relatively homogeneous and saturated tailing materials (Schippers et al., 2010; Silver, 1987; Groudev and Groudeva, 1993) but acidic tailings have been shown to harbor a relatively low genetic variability (Méndez-García et al., 2015) and most open-pit mines produce significantly larger volumes of waste rock compared to tailings (Amos et al., 2015; Lottermoser, 2010). Since waste-rock piles are typically unsaturated and more heterogeneous than tailings, they also have the potential to host more diverse microbial communities; yet, there are to date only a few studies that have described the distributions, sizes, and diversity of microbial populations in waste rock (Schippers et al., 2010; Dockrey et al., 2014; Smith et al., 2013; Bailey et al., 2016; Bosecker et al., 2004; Sand et al., 2007; Schippers et al., 1995; SelenskaPobell et al., 2001). In addition, the role of microbes on accelerating weathering rates observed in waste rock under natural (field) conditions remains poorly constrained. This paucity of knowledge ultimately complicates the quantitative and timely prediction of the quality of drainage from waste-rock piles. Therefore, this study is aimed at: i) characterizing the in-situ microbial abundance and community structures in acid-generating and nonacid-generating waste-rock types, ii) discerning the role of microbes on accelerating waste-rock weathering under natural conditions, and iii) identifying a variety of major geochemical and microbial controls on the drainage quality originating from waste rock. To do so, laboratory column experiments and a combination of microbiological tools (i.e., cell counting and pyrosequencing) and geochemical analyses (i.e., XRD, XRF, and ICP-MS) were used to simultaneously study microbial populations, primary and secondary mineralogy, as well as the geochemical composition of the column drainage during waste-rock weathering.

2.1. Collection and characterization of waste rock

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Waste rock was collected from the Antamina Mine, one of the world's largest Cu-Zn mines, located in the Peruvian Andes, about 270 km north of Lima. The climate at Antamina shows a distinct monsoonal pattern with a wet season (October–April) accounting for ~80% of the mean-annual precipitation (~1200 mm) (Blackmore et al., 2014). Two end-member classes of waste rock were collected: class A, acid-generating monzogranitic intrusive exoskarn assemblages and class C, non-acid-generating carbonate-rich hornfels and marbles. Class A was subdivided into classes A1 and A2, which were mined from two distinct locations, but otherwise had comparable material properties (Table S1). The class A and C waste rock was originally excavated from the open pit as ~10 t blasts, from which representative 150 kg samples were collected, homogenized using the coning- and quartering method, and subsequently stored on-site in bags for 2 to 4 years prior to experiment initiation. This storage permitted minor ingress of oxygen and moisture, thereby allowing for some waste-rock weathering as well as survival of endemic microbial populations. For geochemical and mineralogical characterization of the waste rock, ~100 g subsamples were pulverized to b10 μm using a McCrone micronizing mill. The major chemical composition of the waste rock was analyzed with X-ray fluorescence (XRF; PW-2400 from PANalytical, Almelo, The Netherlands), carbon- and sulfur concentrations were determined using a LECO CNS-analyzer (NS-1000, LECO Corp., St. Joseph, USA) and trace-element concentrations were determined after aquaregia digestion using inductively coupled plasma–mass spectrometry (ICP-MS). The primary waste-rock mineralogy was characterized with X-ray diffraction (XRD; Siemens D8000 Diffractometer) using a corundum spike and Rietveld refinement. Spectra were qualitatively and quantitatively referenced with EVA software and Topas v.3.0 software, respectively, using the ICDD database (2016 version). Additional samples of secondary mineral precipitates were collected from the column outflows after the weathering experiments and similarly analyzed with XRD. Acid-base accounting was performed using the modified Sobek method, using sulfide-S to determine acid-generating potential and an HCl-digestion at ambient temperature to determine neutralizing potential (Price, 2009; Dold, 2017). The mineralogical and geochemical properties of the waste rock are given in Table S1. Particle size distributions (Fig. S1), soil-water characteristics curves (Fig. S2) and hydraulic parameters of the waste rock (Table S2) were previously determined (Blackmore et al., 2014). 2.2. Sample treatment and experimental design Each of the collected 150 kg waste-rock samples was thoroughly homogenized and split into equal halves. One half was heated and dried at 50 °C for 48 h in an oven to suppress the endemic microbial population, whereas the other half was left untreated. This heating and drying treatment was preferred over alternative and more effective sterilization methods because for example autoclaving or irradiation was impractical for N1 m columns with N150 kg of waste rock, and because chemical sterilization was deemed undesirable. The three different waste-rock classes were stacked in layers in two identical experimental columns that were different only in their microbiology: column 1 contained treated waste rock with altered (‘suppressed’) microbial populations whereas column 2 contained untreated waste rock with unaltered, insitu, populations (Fig. 1). The stacking sequence and relative quantities of the different waste-rock classes were similar in both columns; both columns contained a total of ~175 kg waste rock. After the waste rock had been placed into the columns, 10 L of a filter-sterilized (0.45 μm) solution was applied over 24 h to remove salts that had accumulated on mineral surfaces during storage and to promote bacterial growth at the start of the experiment. The solution was a salt solution reflecting

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Column 1

Column 2

treated waste rock suppressed microbial community d = 0.45 m

original waste rock unaltered (in-situ) microbial community d = 0.45 m Spray nozzle

Spray nozzle

Eaves

Eaves

Class A1

Class A1

51.9 kg

52.6 kg

Class A2 54.9 kg 5TE sensor

5TE sensor h = 0.81 m

h = 0.81 m

5TE sensor

Class A2 59.3 kg

5TE sensor

Class C

Class C

63.5 kg

64.9 kg 5TE sensor

5TE sensor

Silica sand

Silica sand

To tipping bucket

To tipping bucket

Fig. 1. Schematic of the experimental columns. Columns 1 and 2 were identical except the waste rock in column 1 was heated and dried (“suppressed” microbial population) whereas the same waste rock in column 2 was untreated (original in-situ microbial population).

the composition of 1:10-diluted growth media for A. ferrooxidans, A. thiooxidans, and Thiobacillus spp. (Unz and Lundgren, 1961; Tuovinen and Kelly, 1973) and contained 0.03 g (NH4)2SO4, 0.01 g KH2PO4, 0.04 g MgSO4·7H2O and 0.023 g CaCl2 per liter of deionized water. 2.3. Column specifics and applied weathering conditions The two identical experimental columns were constructed from 9 mm thick Plexiglas cylinders, measuring 0.45 m in diameter and 0.8 m in height. Each column was equipped with a basal lysimeter that was constructed at a low angle slope and topped with a 5 cm silica sand layer to ensure free drainage. Column drainage was conveyed via a basal half-inch (1/2″) diameter NPT port into a tipping-bucket system (Texas Instruments TR525-M with a CR1000 data logger) to record flow rate. The tipping-bucket systems for both columns were calibrated to at least a 0.1 mm precipitation per tip resolution and a maximum tip rate of 20 tips min−1. Decagon 5TE sensors were inserted into each layer of waste rock to measure temperature, electrical conductivity, and volumetric water content, which were recorded at 10-minute intervals with Decagon EM50 data loggers. The sensors were surrounded by a 2:1 mixture of silica sand and sieved (b5 mm diameter) waste rock of the corresponding layer to ensure pore-water contact with the sensor tips. The columns were open at the top (i.e., allowing for atmospheric gas exchange and evaporation). To mimic weathering under the field conditions at the Antamina mine, simulated rainfall was applied to the top of the columns using an air-atomizing full-cone spray nozzle (Lechler Airmist, Series 136.2) and filtered (b0.45 μm) tap water of pH 7.4, total dissolved solids b 15 mg·L−1. Simulated rainfall was applied in daily events for five days weekly at a constant rate of 0.6 mm·min−1 and the strongly

monsoonal or bimodal precipitation pattern at Antamina was replicated by applying ‘short’ 40 s precipitation events for the dry season (day 0 to 120) and ‘long’ 35 min events for the wet season (day 120 to 300). Eaves were placed along the inner perimeters of both columns to avoid channeling of precipitation along the column walls (Fig. 1). Eavecatchment water was funneled to a collection bottle for volumetric measurement to determine actual applied precipitation volumes. Evaporation ranged between 40%–45% of total precipitation, as calculated by the difference between applied precipitation volumes and measured discharge at the column base (Fig. S3). Laboratory air temperature was maintained at ~20 °C for the 400-day duration of the experiment. 2.4. Analysis of the column drainage aqueous chemistry Drainage samples were periodically collected from the column outflow and aliquoted in pre-cleaned 50 mL Falcon tubes and directly analyzed for pH using an Orion double junction electrode (model 9110DJWP). One unfiltered aliquot was used for alkalinity analysis using a Hach digital titrator and 0.16 N H2SO4. Additional aliquots were filtered with 0.45 μm cellulose-acetate filters, acidified to pH b 2 with trace-metal-grade HNO3 and stored at 4 °C in the dark until analysis for dissolved Al, As, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, S, Sb, Se, Si, Ti, and Zn using inductively coupled plasma-optical emission spectroscopy (ICP-OES) and -mass spectrometry (ICP-MS). Element loadings (i.e., mass discharged from the columns) were calculated from measured aqueous concentrations and flow rates. Geochemical equilibrium of the drainage samples was modeled with PHREEQC (Parkhurst and Appelo, 1999) and an adapted WATEQ4F database (Ball and Nordstrom, 1991) that included additional Mo phases (Conlan et al., 2012), schwertmannite (Sánchez-España et al., 2011;


Yu et al., 1999) and hydrozincite (Preis and Gamsjäger, 2001). All models assumed: i) prevailing oxic conditions (pO2 = 0.21 atm), ii) charge balance achievement through Cl, and iii) measured alkalinity reflecting dissolved carbonates only.

2.5. Quantification of microbial population sizes Microbial populations in the waste-rock samples (treated and untreated) were quantified prior to placement in the experimental columns, using enumeration with the Most Probable Number (MPN) method (Cochran, 1950). Three major microbial phenotypes were enumerated: acidophilic iron-oxidizing bacteria (AFeOB), acidophilic sulfur-oxidizing bacteria (ASOB), and neutrophilic thiosulfate-oxidizing bacteria (NSOB). Approximately 30 g of finer grained (b5 mm diameter) waste rock of each class was collected, of which ~2 g subsamples were placed in 178 mL of one of three phenotype-specific growth media (compositions given in Table S3). Serial dilutions were carried out by vortexing for 20 s and aseptically transferring 0.5 mL aliquots into 4.5 mL of bacterial growth medium. Maximum dilutions were [10 −1 –10 −4 ] and [10 −1 –10 −6 ] for columns 1 and 2, respectively. A five-tube MPN method was used, for which 500 μL of appropriate dilutions were inoculated into sterile borosilicate (13 × 100 mm; Fisherbrand) arrays and incubated for 6 weeks at 20 °C. Positive and negative tubes were counted for each dilution, which equated to the “most probable number”, i.e., the microbial population in bacteria g−1.

2.6. Characterization of microbial diversity Microbial diversity in the waste rock was assessed at the end of the weathering experiments using pyrosequencing. For that, multiple ~10 g samples were collected from different locations within each individual column layer (i.e., totaling up to 9 samples/~90 g waste rock per column) and stored at 4 °C in sterile 50 mL Nalgene tubes prior to analysis. DNA was isolated using PowerMax Soil DNA Isolation Kit (MoBio) and quantified using the Picrogreen assay (Invitrogen). DNA quality was checked using agarose gel electrophoresis by running 5 μL of each sample with 1 kb DNA ladder on 1% agarose gels in 0.5× TBE buffer (2 h at 100 V). Total DNA concentrations were quantified using the Picogreen® dsDNA quantitation assay. Following DNA isolation, the V6-V8 region of 16S ribosomal RNA (rRNA) was PCR-amplified by using the 16S rRNA gene-targeted primers 926F (5′-AAA CTY AAA KGA ATT GAC GG-3′) and 1392R (5′-ACG GGC GGT GTG TRC-3′). Reverse primer sequences were modified to include the Roche 454 adaptor sequence and a 5 base-pair barcode for multiplexing during sequencing. Reactions were run in duplicate under the following PCR conditions: initial denaturation cycle at 95 °C for 3 min; 25 cycles of 95 °C for 30 s, primer annealing at 55 °C for 45 s, and extension at 72 °C for 90 s; and a final extension cycle at 72 °C for 10 min. Each 50 μL reaction contained: 1–10 ng template DNA, 0.6 μL Taq polymerase (Bioshop Canada Inc., 5 U/μL), 5 μL 10× reaction buffer, 4 mM MgCl 2, 0.4 mM of each dNTP (Invitrogen), 200 nM each of forward and barcoded reverse primers, and 33.4 μL nuclease free water (Fisher). Duplicate reactions were pooled and purified using a QIAquick PCR Purification Kit (Qiagen) and quantified using the Picogreen assay (Invitrogen). Negative controls were included with each reaction to ensure that no contamination of DNA had occurred. Samples were diluted to 10 ng/μL and pooled in equal concentrations. Amplicon pools were 454 pyrosequenced using a Roche 454 GS FLX Titanium (454 Life Sciences, Branford, CT, USA) and data processed using the Quantitative Insights Into Microbial Ecology (QIIME, v.1.4.0) software (Caporaso et al., 2010). The pyrosequencing data processing is described in the Supporting Information.

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3. Results and discussion 3.1. Bacteria are abundant in all waste-rock types Phenotype-specific enumerations conducted at the beginning of the column experiments (Table 1) demonstrate that the original, untreated waste-rock materials (i.e., both the acid-generating as well as the carbonate-rich, non-acid-generating waste rock) contained both acidophilic and neutrophilic S-oxidizing bacteria. The observed total microbial abundances in the original waste rock (up to 108 bacteria g−1) are in the same order of magnitude as previously observed in Antamina waste rock (Dockrey et al., 2014), or in waste rock (Schippers et al., 2010; Bosecker et al., 2004; Sand et al., 2007; Schippers et al., 1995; Selenska-Pobell et al., 2001) and tailings materials (Korehi et al., 2014; Lindsay et al., 2009; Blowes et al., 1998; Korehi et al., 2013) from other mine sites. The enumeration estimates further show that, as expected, the acid-generating waste rock of class A2 contained considerably more acidophiles than neutrophiles (~10:1 in column 2), whereas the non-acid-generating carbonate-rich class C waste rock contained up to 5 orders of magnitude more neutrophiles than acidophiles. However, the enumeration estimates also suggest that the untreated class A1 material counterintuitively contained less acidophilic than neutrophilic bacteria (Table 1). The acidophilic S-oxidizer populations in the treated waste rock from column 1 were not significantly reduced compared to those in the untreated column 2 materials, but a significant reduction in the populations of acidophilic Fe-oxidizers and neutrophilic S-oxidizers (by over four orders of magnitude compared to column 2, Table 1) was observed upon waste-rock treatment. Various acidophilic Fe- and Soxidizing bacteria are known (moderate) thermophiles or at least thermo-tolerants (Baker and Banfield, 2003; Johnson and Hallberg, 2003), so that the efficacy of the applied treatment method to suppress microbial activity may have been related to desiccation rather than heating of the waste rock (in fact, temperatures of 50 °C may be naturally achieved from exothermic sulfide oxidation in waste-rock piles (Amos et al., 2015)). Overall, the enumeration results indicate that, at the start of the column experiments, the treated- versus untreated waste-rock materials with otherwise geochemically and mineralogically similar properties (Table S1) had clearly distinct microbial populations, both in terms of phenotypic composition and in terms of size (column 2 contained on average ~1000 times more bacteria g−1 waste rock than column 1).

3.2. Waste rock hosts highly diverse microbial communities Pyrosequencing analysis of the waste rock at the end of the column experiments revealed 2131 unique operational taxonomic units (OTUs). Diversity in the microbial populations was high, considering that out of all identified sequences, 48% mapped to 13 abundant OTUs (a frequency N 1% in at least one sample (Galand et al., 2009)), 26% mapped to 192 intermediate OTUs (a frequency of between 0.1% and 1% in at least one sample), while 27% mapped to 1926 rare OTUs (a frequency b 0.1% in all samples). Distribution analysis (Fig. 2) shows that samples from waste-rock classes A1 and C contained more unique OTUs than samples from waste-rock class A2. Carbonate-rich waste rock (class C), with the largest initial microbial population (Table 1), also hosted the highest biodiversity, with 53% of unique OTUs. The acid-generating waste rock of classes A1 and A2 together contained over 66% of the total identified OTUs, but shared b5%. In contrast, 15% of unique OTUs were shared between non-acid-generating class C and acid-generating class A1 waste rock. Approximately 0.8% of all OTUs was observed in all three wasterock classes. Column 1 materials contained up to 2.5 times less overall biodiversity than column 2 materials (Fig. 2), which supports the enumeration results conducted at the start of the column experiments (Table 1) that indicated that the suppression treatment (i.e., heating

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Table 1 Microbial population sizes at the beginning of the column experiments. Phenotype

Waste rock

Acidophilic sulfur-oxidizers (ASOB)

Acidophilic iron-oxidizers (AFeOB)

Neutrophilic thiosulfate-oxidizers (NSOB)

Class A1 Class A2 Class C Class A1 Class A2 Class C Class A1 Class A2 Class C

Column 2 untreated waste rock (in-situ microbial population)

Column 1 treated (heated and dried) waste rock (suppressed microbial population)

Bacteria g−1 waste rock

Bacteria g−1 waste rock

2

3.4 × 102 1.8 × 103 6.6 × 101 N.D. 4.6 × 100 4.7 × 101 2.6 × 102 N.D. 4.8 × 104

0.1 × 10 4.4 × 103 1.6 × 102 N.D. 2.2 × 105 3.6 × 101 1.1 × 106 2.2 × 104 4.8 × 108

N.D. = not detected.

Both columns shared evenly distributed OTUs for neutrophilic Feoxidizing bacteria (Hydrogenophilales, annotated as Thiobacillus and Ferritrophicum), but column 1 materials harbored slightly more OTUs associated with Nitrosomonas and Actinobacteria, whereas column 2 contained relatively more OTUs affiliated with Acidithiobacillales and Pseudomonadales (Fig. 3). The latter higher relative abundance of Acidithiobacilalles in column 2 relative to column 1 (both Acidithiobacillus and Acidiferrobacter associate within the genus Acidithiobacilalles) is consistent with the microbial enumerations done at the start of the column experiments, which indicated greater populations of AFeOB and ASOB in column 2 compared to column 1 (Table 1). Similarly corroborating the enumeration results is the observation that the microbial populations in class A1 and class C waste rock presented more similarities to each other than to class A2 at the microbial community level (compare Table 1 to Figs. 2 and 3). Considering that the pyrosequencing analyses, in contrast to the enumerations, were conducted at the end of the weathering experiments, the highly dissimilar microbial compositions of the adjacent acid-producing waste-rock classes A1 and A2 suggests that vertical translocation and mixing of bacterial communities due to downward percolation of water through the columns was negligible. The abundance of several bacterial taxa differed more between waste-rock classes (i.e., of different lithology and particle size) than between the two columns (i.e., different waste-rock treatment): for instance, Aciditihiobaccillales abundance varied up to 3 orders of magnitude between waste-rock classes but only by up to a factor of 5 between the two columns (Fig. 3). Furthermore, the acidproducing waste rock from classes A1 and A2 was geochemically and mineralogically similar (Table S1) but hosted different microbial populations, which suggests that strong alterations in their microbial

Class A1 600 305

31 17 Class C 708

Class A2 364 106

Chao1 rarefaction measure

and drying) not only reduced microbial abundance but also noticeably disrupted community structure by reducing (overall) variability. To better interpret microbial community structure, OTUs were annotated based on taxonomic affiliation using BLAST (see Supporting Information and Fig. 3). In total, the identified OTUs in the investigated waste rock affiliated with 36 phyla within the Bacterial (80%), Archaeal (0.5%) and Eukaryotic (19%) domains of life. Chemolithoautotrophic prokaryotes such as Acidiferrobacter, Ferritrophicum, Thiobacillus and Acidithiobacillus were very abundant species in all investigated wasterock classes (Fig. 3), corroborating previous studies that have implicated these bacterial species in the development of acidic pH conditions via Fe and S oxidation in mine tailings and drainage (Baker and Banfield, 2003; Benner et al., 2000; Blowes et al., 1998; Bernier and Warren, 2007; Fowler et al., 1999; Hallberg et al., 2011; Auld et al., 2013; Johnson, 1998). Class A2 material harbored the fewest taxa of all investigated waste rock and its species distribution was dominated by Acidithiobacillus and Bacillales (each ~30% of indicator OTUs). Class A1 and class C waste rock contained considerably more diversity and were dominated by Chromatiales (Acidiferrobacter), Thiobacillus, and Cytophagales (up to 19%, 16%, and 16% of indicator OTUs, respectively). The presence of strict heterotrophic microorganisms at low abundance levels (e.g., Acidiphilium; in Rhodospirillales) in all waste-rock samples agrees with the typically low organic carbon content of waste rock (Table S1) (Amos et al., 2015; Nordstrom et al., 2015), whereas phototrophic acidophiles (e.g., cyanobacteria or green nonsulfur bacteria) were slightly more abundant in the waste rock in the (transparent) columns. Despite a substantial variation in the microbial composition of the various waste-rock materials, no species specific to only one waste-rock class were observed at significant abundance levels.

1,000

Class A1 Class A2

500

Class C Column 1 (treated)

0

Column 2 (untreated)

0

1,000

2,000

3,000

4,000

5,000

6,000

Number of sequences per sample Fig. 2. Illustration of the microbial diversity in the investigated waste-rock classes. Left: Venn Diagram illustrating the distribution of unique OTUs, based on the combined pyrotags for both experimental columns enumerated at the order level, and right: rarefaction curves based on Chao1 richness estimates for the individual waste-rock classes and columns.


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1.0 0.9 Column 1 (treated waste rock)

Class A2 Class C

Column 2 (original waste rock)

Height

Class A1

0.8 0.7 0.6 0.5

Combined results (all samples from both columns)

0.4

Gamma-

Delta-

Beta-

Alpha-

0.3

70

50 30 15 7 3.5 1 0.1 Percent of total pyrotags

75 24 12 6 3 1 Number of Indicator OTUs

Fig. 3. Summary of the distribution of microbial diversity in the individual waste-rock classes and two columns (legend in the top left), plotted as a Bray-Curtis hierarchical cluster dendrogram with complete linkage method (top), as well as an indicator species analysis bubble plot (bottom). Values shown in the dendrogram are the unbiased boot-strapped pvalues to describe statistical significance between the microbial communities associated with the waste-rock classes with- and without heating and drying treatment for columns 1 and 2, respectively.

community structures may result from relatively small geochemical gradients (Dockrey et al., 2014). However, no direct relation between, for instance, average waste-rock particle sizes (Fig. S1) and microbial population sizes or community structures could be identified in the investigated samples. Therefore, the role of small-scale

lithogeochemical heterogeneities on sustaining microbial diversity should be further investigated, e.g., by coupling microbial diversity analyses to quantitative assessments of the pore-scale exposure or occlusion ratios of host minerals using mineral liberation analysis (Fandrich et al., 2007).

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The microbial populations in the waste rock showed a certain resistance to the waste-rock treatment method (i.e., heating and drying), considering that up to 5 × 104 bacteria g−1 waste rock could still be measured in the waste rock of column 1 after it was treated at the start of the column experiments (see enumeration results in Table 1). Yet, the different microbial populations observed in the waste rock in both columns at the end of the weathering experiments (Fig. 3), suggest that the microbial populations in the treated waste rock from column 1 did not recover to pre-treatment levels similar to those observed in column 2 within the experimental period (400 days). The effects of the applied heating and drying treatment on the microbial abundance and diversity in the waste rock may principally be temporary, but the microbial community structures in waste rock with otherwise similar physical and geochemical characteristics thus appeared to be remain different on significant timescales (N1 yr) through possible hysteresis patterns and despite a certain community resilience. In summary, the pyrosequencing results not only show that waste rock contains highly diverse microbial populations, but that their localization patterns depend on suppression treatment (i.e., heating and drying), as well as on the surrounding mineral assemblage. The weathering experiments with the two experimental columns were thus conducted with clearly distinct microbial populations in the waste rock. 3.3. Column hydraulics during simulated weathering The weathering experiments were run for 400 days, but applied precipitation reached the base of the columns only in the simulated wet season from day 120 to 300 and continued for ~2 months into the dry season (Fig. S3), similar to observations from field-barrel experiments at Antamina (Peterson, 2014). The drainage trends of both columns were highly similar temporally: both showed a steady increase in exfiltration rates during the simulated wet season, reaching up to 2 L·d−1 at the height of the wet season, followed by a gradual decline into the dry season. Approximately 1.0 m3·m−2 of outflow (≈4.5 pore volumes), equaling 58% and 52% of the precipitation applied to columns 1 and 2, respectively, was recorded during the experiments. The moisture content in the waste-rock layers was comparable between waste-rock types and between both columns (Fig. S4), with typical values between 0.05 and 0.10 m3·m−3 in the dry season and between 0.10 and 0.25 m3·m−3 in the wet season. Only waste rock class A2 exhibited slightly higher moisture content in column 2 compared to column 1. Average area-normalized fluxes (specific discharges) during the wet season were estimated at 0.6 cm·d−1 and 0.5 cm·d−1, for columns 1 and 2, respectively, resulting in calculated flow velocities of approximately 3 cm·d−1 for both columns, based on column-averaged moisture contents of 0.18 m3·m−3 for column 1 and 0.17 m3·m−3 for column 2 (Fig. S4) during the wet season. In addition to their similar physical and chemical characteristics (Table S1), both columns thus showed highly similar hydrological behavior and responded as capillary controlled porous media. Finally, all column thermistors recorded an internal average temperature of 21 ± 1.0 °C (data not shown), similar to the laboratory temperature of 20 °C. This suggests negligible net column heating due to exothermic reaction energy released from sulfide oxidation reactions and little effect of temperature fluctuations on, for instance, evaporation. 3.4. Temporal evolution of the column drainage chemistry The drainage pH, volume and solute release rates of a selection of elements are presented in Fig. 4 for the period between 120 d and 372 d, i.e., during the simulated wet season and approximately two months into the dry season when drain-down from the columns took place. Daily solute release rates for additional (trace) elements and cumulative element release plots are provided in Figs. S5 and S6, respectively. Over

the course of experiments, drainage pH from column 1 ranged from slightly acidic to neutral, whereas column 2 drainage pH was moderately acidic and consistently below 5. In both columns, drainage pH decreased slightly following the first month of drainage, corresponding to the piston flow releasing acidic pore waters previously held in capillary tension. The pH then stabilized for approximately 2 months and finally increased for most of the remaining wet season (by 1 pH unit in column 1 and by b0.5 pH units in column 2), likely due to continued addition of pH-neutral precipitation, depletion of the most reactive minerals, or their occlusion by secondary precipitates. The recorded element release rates (Fig. 4) were highly variable and different for each of the investigated elements. Generally, initial wash-out was followed by a gradual increase during the simulated wet season, after which sharply decreasing release rates were observed at the end of the wet season (N300 days), when column outflow began to diminish. The temporal trends of daily SO4 and Ca release rates were similar for both columns and appeared most strongly affected by the discharge rates from the columns (Fig. 4). This is illustrated, e.g., by the strong correlations between the observed Ca and SO4 release rates and drainage volumes (Fig. S7). However, whereas the cumulative amount of Ca released during the experiment was quantitatively similar for both columns, column 2 released about 50% more dissolved SO4 than column 1, indicative of a higher effective sulfide oxidation rate (Fig. S6). Daily alkalinity release rates were poorly correlated to column exfiltration rates and generally small compared with SO4 and Ca release rates (approximately 1%). The release of elements commonly associated with aluminosilicate dissolution (Si, Na, Mg, and K) was considerably smaller (in the μmol·kg−1·d−1 range) than that of SO4 and Ca and of heavy metals, but showed similar trends with high initial loads, a period of reduced release from 170 to 240 d and a gradual increase later in the wet season. Furthermore, the cumulative releases of Si, Na, Mg and K, like that of SO4 , were consistently higher in column 2 than in column 1 (Fig. S6). Dissolved-metal release rates of Cu and Zn were relatively small, considering the substantial concentrations of chalcopyrite (CuFeS2) and sphalerite ([Zn, Fe]S) in the waste rock (Table S1) (Dockrey et al., 2014). Up to 8 times more Cu and Zn was cumulatively released from column 2 than from column 1 (Figs. 4, S6), which is consistent with the different drainage pH ranges that affect Cu- and Znsolubility, and with the higher apparent sulfide-oxidation rates in column 2. The temporal evolution of Cu and Zn release appeared unrelated to column exfiltration or effective sulfide oxidation rates. Negligible dissolved Fe and Al was observed in the drainage of column 1, whereas their concentrations were slightly more elevated in column 2, again consistent with the prevailing pH conditions that limit Fe- and Al-solubility. Finally, trace-element concentrations in the column drainage were occasionally below their analytical detection limits (Fig. S5), obscuring clear trends. The temporal trends in the release of Cd, Co, Cr and Mn were similar to that of Zn, with a gradual decrease over the experimental period and a systematically higher (up to 16 times) cumulative load from column 2 than from column 1. In contrast, release patterns of oxyanion-forming trace elements (e.g., Sb, As, Se, Mo) were more erratic (Fig. S5) and usually showed higher cumulative loads from column 1 than from column 2, consistent with pH conditions in the columns. In summary, the experimental columns presented different element release trends, but the loading rates of acidity, SO4 and major primary ore metals like Fe, Cu, and Zn were substantially (i.e., up to an order of magnitude) and systematically (i.e., over the entire experimental period) higher in column 2 relative to column 1. This shows that the primary mineral weathering rates were clearly higher in the waste rock with in-situ microbial populations compared to the treated (i.e., heated and dried) waste rock with suppressed microbial populations.


8

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160 200 240 280 320 Experimental me (days)

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200 240 280 320 Experimental me (days)

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Fig. 4. Drainage pH (top left), daily drainage volume (top right) and daily solute release rates of SO4, alkalinity (dissolved carbonate), Zn, Cu, Na, K, Si, Ca, Al, and Mg from columns 1 (heated and dried waste rock) and 2 (untreated, original waste rock) during 120–370 days. No outflow occurred in the first 120 days of the experiment (simulated dry season). The end of the wet season is indicated by the shading in the top right frame. The legend in the top left frame applies to all frames.

3.5. Multiple geochemical controls on drainage chemistry With waste-rock oxidation, the release of acidity may be counteracted by a sequence of neutralizing reactions, namely through the dissolution of carbonates, Fe- and Al-hydroxides, and silicates (Amos et al., 2015). The occurrence of these neutralization reactions in the experimental columns is implicated only to a minor extent, i.e., by

slightly higher release rates of Si, Na, K and Al (aluminosilicate dissolution) in column 2 over column 1 (in fact, negligible dissolved Al could be measured in column 1 (Fig. 4), indicative of Al-precipitation under these neutral pH conditions). However, as the basal layer in both columns was composed of acid-neutralizing waste rock (Class C) with N70% carbonate content (Table S1), it may be expected that acidity is effectively neutralized primarily by calcite dissolution (Amos et al., 2015; Nordstrom

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et al., 2015; Jamieson et al., 2015; Lindsay et al., 2015). This would consequentially cause non-acidic pH and a higher Ca load in column 2 compared to column 1, but the observed low pH values in column 2 drainage clearly do not reflect a carbonate-buffered system and the cumulative Ca loads were surprisingly similar in both columns (Fig. S6). Instead, the comparable Ca release rates observed in both columns are probably the result of secondary minerals controlling Ca solubility (Al et al., 2000; Stockwell et al., 2006): gypsum formed in both columns upon calcite dissolution, as indicated by the identification of crystalline gypsum in secondary precipitates collected after the weathering experiments (Table S5, Fig. S8) and by the virtually constant and near 0 saturation index for gypsum over the entire experimental period (Fig. 5). Furthermore, gypsum has previously been identified as an important secondary mineral in waste rock from Antamina (Conlan et al., 2012). Dissolution of gypsum releases Ca into the pore water up to its solubility limit, which is relatively stable over a wide pH range and thus equal in both columns, explaining the comparable Ca loadings. Similar to the role of gypsum in affecting apparent Ca release, secondary hydroxy carbonate and hydroxy-sulfate minerals may have affected the apparent release rates of Cu, Zn, and alkalinity: various Cu/Znhydroxy-sulfates and -hydroxy-carbonates could be identified by XRD in precipitates collected at the outflow of column 1 at the end of the experiment (Table S5, Fig. S8), and the saturation indices for minerals such as Cu2CO3(OH)2 (malachite) or Zn5(CO3)2(OH)6 (hydrozincite) were consistently near 0 in column 1, and always higher in column 1 than in column 2 (Fig. 5). Various secondary Cu and Zn minerals (e.g., malachite) have also been previously identified in waste rock from Antamina (Conlan et al., 2012; Hirsche et al., 2017). The lower pH in column 2 appears to enforce a strong limitation on the precipitation of secondary Cu- and Zn- hydroxy carbonates, as no secondary carbonate minerals could be detected by XRD in the outflow of column 2 (Table S5): this agrees with the lower alkalinity measured in the drainage of column 2 compared to column 1, and the higher apparent Cu and Zn release rates in column 2 (Fig. 4). However, decreased alkalinity inputs in column 2 (i.e., through potential passivation of carbonate minerals by secondary oxides (Amos et al., 2015; Dockrey et al., 2014; Peterson, 2014)) may

have also affected the observed apparent alkalinity release rates and should be the focus of future investigation. Equilibrium modeling further indicated high oversaturation for (mixed) Cu-, Zn-, and Fe-(hydroxy)sulfates (e.g., jarosite, chalcanthite), Fe- and Al-hydroxides (e.g., lepidocrocite, gibbsite) and various molybdate phases in both columns (data not shown). A significant precipitation of secondary Fe-, Al-, and Mo-phases may explain their disproportionally small drainage concentrations relative to SO4 compared to the stoichiometry of the primary waste-rock mineralogy (Table S1). Indeed, the formation of secondary Mo-phases has been described in Antamina waste rock before (Hirsche et al., 2017; Skierszkan et al., 2017), and secondary Aland Fe-(oxy)hydroxide formation is relatively well-documented in waste rock from other mine sites (Lindsay et al., 2015; Stockwell et al., 2006). In addition to being a direct (temporary) sink for elements, secondary minerals may reduce metal release by preventing additional sulfide oxidation through passivation of primary minerals (Stott et al., 2000), and by providing sorption sites for trace metals, exacerbated by their amorphous nature. Indeed, XRD analyses of secondary mineral precipitates collected after the experiments indicated substantial amorphicity (Table S5). In the two experimental columns, the potential importance of sorption is exemplified by the differing temporal release trends of cationic metals versus those of oxyanion-forming elements. Elements like Cd, Ni, Co, and Mn showed higher release rates in column 2 over column 1 (Fig. S6), due to lower pH values that decrease their sorption and increase their mobility. In contrast, oxyanionic elements such as Sb, As, Se, and Mo all showed higher release rates in column 1 compared to column 2 (Fig. S6), as higher pH values decrease their sorption and increase their mobility, which likely outweighs the lesser extent of primary mineral oxidation in column 1 relative to column 2 and the potential formation of secondary Mo-phases (Hirsche et al., 2017; Skierszkan et al., 2017). Unfortunately, a quantitative assessment of the roles of sorption versus (co-)precipitation in secondary minerals is complicated by the amorphous character of secondary mineral phases that cannot be readily identified using conventional XRD and, in the case of highly soluble secondary phases (e.g., gypsum), the poor

Fig. 5. Saturation indices of Cu-, Zn-, and Ca-sulfate (top) and carbonate (bottom) minerals calculated from the drainage chemistry in column 1 (treated waste rock with suppressed microbial populations) and column 2 (untreated, original waste rock with in-situ microbial populations) over the course of experiments.


specificity of leaching tests. Future studies may therefore be specifically focused on investigating the composition of amorphous secondary minerals and on disentangling the quantitative contributions of sorption processes versus direct metal attenuation by incorporation into secondary minerals. 3.6. Integration of microbiology and geochemistry and practical relevance In the presented column experiments, metabolic activities of the microbial populations may have varied over the course of the experiments and led to variability in the column drainage chemistry: both primary mineral oxidation and the accompanying secondary mineral formation are typically microbially controlled (e.g., direct bacterial precipitation of secondary Si (Fortin and Beveridge, 1997) or Fe-(oxy)hydroxides (Konhauser, 1998)). Reciprocally, changes in column pore water chemistry may have caused shifts in the microbial community structures and caused competition effects (Zhang et al., 2017). For instance, the consistently neutral pH (6 b pH b 7) of drainage from column 1 suggests nonideal conditions for acidophilic Fe- and S-oxidizers, whereas the high Cu- and Zn-concentrations (~100 mg·L−1) in drainage from column 2 may be high enough to affect the bio-oxidation activity of selected acidophilic microorganisms through (competitive) inhibition (Watkin et al., 2008). It may thus be expected that in full-scale industrial waste-rock piles, the composition and activity of natural microbial consortia will change with progression of waste-rock weathering. This may be confirmed with additional, time-resolved monitoring of biodiversity in full-scale waste-rock piles. In addition, phenotypic abundance and metabolic activity levels may be more accurately linked to specific geochemical signals from column experiments as conducted in this study, by similarly monitoring the temporal evolution of drainage quality from individual waste-rock classes in conjunction with their microbial community structures (e.g., by additional and synchronous timeresolved MPN enumerations and pyrosequencing analyses). The role of microbes in governing waste-rock weathering rates, and ultimately drainage quality, is widely acknowledged but often neglected in the design of mine-waste treatment strategies. This study illustrates that suppression of microbial growth may decrease (and at least retard) the production of poor-quality waste-rock drainage, but the used heating and drying treatment will hardly be economically reasonable for industrial dimensions. Instead, an improved understanding of the microbiome in acid-generating and acid-neutralizing waste rock may lead to the development of practice-relevant approaches to limit microbial growth- and activity, e.g., by deliberately creating dry conditions, by inducing toxicity effects from chemical bactericides or from toxic metal (loid)s liberated from the waste rock, or by limiting substrate- and nutrient availability. Furthermore, an improved (mechanistic) understanding of the microbial controls on weathering rates may profit reactive-transport modeling efforts aimed at extrapolating ratelimitations from bacterial catalysis to macroscale systems. Finally, the presented results illustrate that drainage chemistry is not only dependent on microbial primary mineral weathering, but also on secondary mineral precipitation and sorption reactions, all of which are variably pH dependent. Therefore, intrinsic weathering rates in real-life, fullscale waste-rock systems, and ultimately drainage quality, may be better assessed by an integrative and quantitative review of the prevailing porewater conditions and in-situ microbial diversity and activity. 4. Conclusions The presented data demonstrates that a diverse range of robust microbial populations is sustained within various waste-rock types, with neutrophilic and acidophilic taxa occurring concurrently in heterogeneous sulfide-rich (acid-generating) or carbonate-rich (acid-neutralizing) waste rock. The presence and distribution of the microbial communities in the waste rock was related to the chemical composition and mineralogy of the host waste rock as well as to the applied waste-

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rock treatment (i.e., heating and drying). Although microbial resistance to waste-rock treatment and to temporal changes in the chemical composition of the waste-rock porewater was observed, further study of these resistive capabilities on a community level (e.g., through biofilm formation (Dockrey et al., 2014)) and of the temporal changes of microbial populations in relation to changing porewater chemistry is required. Under simulated natural weathering conditions, the oxidation rates of primary sulfide minerals were systematically and substantially higher with natural in-situ populations compared to suppressed microbial populations with reduced abundance and diversity. As a consequence, drainage from waste rock with natural populations had significantly poorer quality, i.e., lower pH and higher SO4 and heavy metal concentrations. Furthermore, the chemical composition of the column drainage was partially decoupled from the waste rock primary mineralogy through (temporary) retention of metals via sorption and (microbially mediated) secondary mineral formation. This study demonstrates that quantitative assessments of intrinsic waste-rock weathering rates and the consequential drainage quality can be improved when microbial dynamics are assessed in parallel with the prevailing geochemistry and attenuation mechanisms. This holds true not only for industrial mine waste-related settings, but also bears importance for improving the efficacy of commercial bio-mining operations (Rawlings and Johnson, 2007).

Acknowledgements We thank the staff at the Antamina mine for logistic and technical support with sample collection, Cameron Strachan for assistance with the pyrosequencing analyses and microbial enumerations, and Lan Kato and Jenny Lai for assistance with the XRD analyses. Financial support was provided by Compañia Minera Antamina S.A., the Natural Sciences and Engineering Research Council of Canada (NSERC; CRDPJ 3349092005), Teck Metals Limited's Applied Research and Technology Group, and an NSERC PGS-D scholarship awarded to S. Blackmore. Conflict of interest The authors declare no conflict of interest. Appendix A. Supplementary information Supplementary information to this article can be found online at https://doi.org/10.1016/j.scitotenv.2018.05.374.

References Al, T.A., Martin, C.J., Blowes, D.W., 2000. Carbonate-mineral/water interactions in sulfiderich mine tailings. Geochim. Cosmochim. Acta 64, 3933–3948. Aliaga Goltsman, D.S., Denef, V.J., Singer, S.W., Verberkmoes, N.C., Lefsrud, M., Mueller, R.S., et al., 2009. Community genomic and proteomic analyses of chemoautotrophic ironoxidizing “Leptospirillum rubarum” (group II) and “Leptospirillum ferrodiazotrophum” (group III) bacteria in acid mine drainage biofilms. Appl. Environ. Microbiol. 75, 4599–4615. Amos, R.T., Blowes, D.W., Bailey, B.L., Sego, D.C., Smith, L., Ritchie, A.I.M., 2015. Waste-rock hydrogeology and geochemistry. Appl. Geochem. 57, 140–156. Auld, R.R., Myre, M., Mykytczuk, N.C.S., Leduc, L.G., Merritt, T.J.S., 2013. Characterization of the microbial acid mine drainage microbial community using culturing and direct sequencing techniques. J. Microbiol. Methods 93, 108–115. Bailey, B.L., Blowes, D.W., Smith, L., Sego, D.C., 2016. The Diavik waste rock project: geochemical and microbiological characterization of low sulfide content large-scale waste rock test piles. Appl. Geochem. 65, 54–72. Baker, B.J., Banfield, J.F., 2003. Microbial communities in acid mine drainage. FEMS Microbiol. Ecol. 44 (2), 139–152. Ball, J.W., Nordstrom, D.K., 1991. User's Manual for WATEQ4F, With Revised Thermodynamic Database and Test Cases for Calculating Speciation of Major, Trace and Redox Elements in Natural Waters. Open-file Report 91-183. United States Geological Survey. Benner, S.G., Gould, W.D., Blowes, D.W., 2000. Microbial populations associated with the generation and treatment of acid mine drainage. Chem. Geol. 169, 435–448.

1014


Bernier, L., Warren, L.A., 2007. Geochemical diversity in S processes mediated by cultureadapted and environmental-enrichments of Acidithiobacillus spp. Geochim. Cosmochim. Acta 71, 5684–5697. Blackmore, S., Smith, L., Mayer, K.U., Beckie, R.D., 2014. Comparison of unsaturated flow and solute transport through waste rock at two experimental scales using temporal moments and numerical modeling. J. Contam. Hydrol. 171, 49–65. Blowes, D.W., Al, T., Lortie, L., Gould, W.D., Jambor, J.L., 1995. Microbiological, chemical, and mineralogical characterization of the Kidd Creek mine tailings impoundment, Timmins area, Ontario. Geomicrobiol J. 13, 13–31. Blowes, D.W., Jambor, J.L., Hanton-Fong, C.J., Lortie, L., Gould, W.D., 1998. Geochemical, mineralogical and microbiological characterization of a sulphide-bearing carbonaterich gold-mine tailings impoundment, Joutel, Quebec. Appl. Geochem. 13, 687–705. Bosecker, K., Mengel-Jung, G., Schippers, A., 2004. Geomicrobiological risk assessment of abandoned mining sites. In: Tsezos, M., Hatzikioseyian, A., Remoundaki, E. (Eds.), Biohydrometallurgy: A Sustainable Technology in Evolution, Part 1. University of Athens, Greece, pp. 585–593. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., et al., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. Cochran, W.G., 1950. Estimation of bacterial densities by means of the “most probable number”. Biometrics 6, 105–116. Conlan, M.J.W., Mayer, K.U., Blaskovich, R., Beckie, R.D., 2012. Solubility controls for molybdenum in neutral rock drainage. Geochem.: Explor., Environ., Anal. 12, 21–32. Dockrey, W.J., Lindsay, B.M., Mayer, K.U., Beckie, D.R., Norlund, L.K., Warren, A.L., et al., 2014. Acidic microenvironments in waste rock characterized by neutral drainage: bacteria–mineral interactions at sulfide surfaces. Fortschr. Mineral. 4, 179–190. Dold, B., 2017. Acid rock drainage prediction: a critical review. J. Geochem. Explor. 172, 120–132. Elberling, B., Schippers, A., Sand, W., 2000. Bacterial and chemical oxidation of pyritic mine tailings at low temperatures. J. Contam. Hydrol. 41, 225–238. Fandrich, R., Gu, Y., Burrows, D., Moeller, K., 2007. Modern SEM-based mineral liberation analysis. Int. J. Miner. Process. 84, 310–320. Fortin, D., Beveridge, T.J., 1997. Role of the bacterium Thiobacillus in the formation of silicates in acidic mine tailings. Chem. Geol. 141, 235–250. Fowler, T.A., Holmes, P.R., Crundwell, F.K., 1999. Mechanism of pyrite dissolution in the presence of Thiobacillus ferrooxidans. Appl. Environ. Microbiol. 65, 2987–2993. Galand, P.E., Casamayor, E.O., Kirchman, D.L., Lovejoy, C., 2009. Ecology of the rare microbial biosphere of the Arctic Ocean. Proc. Natl. Acad. Sci. 106, 22427–22432. Groudev, S.N., Groudeva, V.I., 1993. Microbial communities in four industrial copper dump leaching operations in Bulgaria. FEMS Microbiol. Rev. 11, 261–267. Hallberg, K.B., Hedrich, S., Johnson, D.B., 2011. Acidiferrobacter thiooxydans, gen. nov. sp. nov.; an acidophilic, thermo-tolerant, facultatively anaerobic iron- and sulfuroxidizer of the family Ectothiorhodospiraceae. Extremophiles 15, 271–279. Hirsche, D.T., Blaskovich, R., Mayer, K.U., Beckie, R.D., 2017. A study of Zn and Mo attenuation by waste-rock mixing in neutral mine drainage using mixed-material field barrels and humidity cells. Appl. Geochem. 84, 114–125. Jamieson, H.E., Walker, S.R., Parsons, M.B., 2015. Mineralogical characterization of mine waste. Appl. Geochem. 57, 85–105. Johnson, D.B., 1998. Biodiversity and ecology of acidophilic microorganisms. FEMS Microbiol. Ecol. 27, 307–317. Johnson, D.B., Hallberg, K.B., 2003. The microbiology of acidic mine waters. Res. Microbiol. 154, 466–473. Johnson, D.B., Rolfe, S., Hallberg, K.B., Iversen, E., 2001. Isolation and phylogenetic characterization of acidophilic microorganisms indigenous to acidic drainage waters at an abandoned Norwegian copper mine. Environ. Microbiol. 3, 630–637. Konhauser, K.O., 1998. Diversity of bacterial iron mineralization. Earth Sci. Rev. 43, 91–121. Korehi, H., Blöthe, M., Sitnikova, M.A., Dold, B., Schippers, A., 2013. Metal mobilization by iron- and sulfur-oxidizing bacteria in a multiple extreme mine tailings in the Atacama Desert, Chile. Environ. Sci. Technol. 47, 2189–2196. Korehi, H., Blöthe, M., Schippers, A., 2014. Microbial diversity at the moderate acidic stage in three different sulfidic mine tailings dumps generating acid mine drainage. Res. Microbiol. 165, 713–718. Lindsay, M.B.J., Condon, P.D., Jambor, J.L., Lear, K.G., Blowes, D.W., Ptacek, C.J., 2009. Mineralogical, geochemical, and microbial investigation of a sulfide-rich tailings deposit characterized by neutral drainage. Appl. Geochem. 24, 2212–2221. Lindsay, M.B.J., Moncur, M.C., Bain, J.G., Jambor, J.L., Ptacek, C.J., Blowes, D.W., 2015. Geochemical and mineralogical aspects of sulfide mine tailings. Appl. Geochem. 57, 157–177. Lottermoser, B.G., 2010. Sulfidic mine wastes. Mine Wastes – Characterisation, Treatment, Environmental Impacts, Second edition Springer Berlin Heidelberg, pp. 33–89 (Chapter 2).

Méndez-García, C., Peláez, A.I., Mesa, V., Sánchez, J., Golyshina, O.V., Ferrer, M., 2015. Microbial diversity and metabolic networks in acid mine drainage habitats. Front. Microbiol. 6, 475. Mesa, V., Gallego, J.L.R., González-Gil, R., Lauga, B., Sánchez, J., Méndez-García, C., et al., 2017. Bacterial, archaeal, and eukaryotic diversity across distinct microhabitats in an acid mine drainage. Front. Microbiol. 8, 1756. Nordstrom, D.K., Blowes, D.W., Ptacek, C.J., Nordstrom, D.K., Blowes, D.W., Ptacek, C.J., 2015. Hydrogeochemistry and microbiology of mine drainage: an update. Appl. Geochem. 57, 3–16. Parkhurst, D.L., Appelo, C.A.J., 1999. User's Guide to PHREEQC (Version 2): A Computer Program for Speciation, Batch-reaction, One-dimensional Transport, and Inverse Geochemical Calculations. Water-resources Investigations Report 99-4259. United States Geological Survey. Peterson, H.E., 2014. Unsaturated Hydrology, Evaporation, and Geochemistry of Neutral and Acid Rock Drainage in Highly Heterogeneous Mine Waste Rock at the Antamina Mine, Peru. The University of British Columbia (PhD Thesis, Available online at). https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0167404. Preis, W., Gamsjäger, H., 2001. (Solid + solute) phase equilibria in aqueous solution. XIII. Thermodynamic properties of hydrozincite and predominance diagrams for (Zn2+ + H2O + CO2). J. Chem. Thermodyn. 33, 803–819. Price, W.A., 2009. Prediction Manual for Drainage Chemistry From Sulphidic Geologic Materials. MEND Report 1.20.1. Mine Environment Neutral Drainage (MEND) Program. Rawlings, D.E., Johnson, D.B., 2007. The microbiology of biomining: development and optimization of mineral-oxidizing microbial consortia. Microbiology 153, 315–324. Sánchez-España, J., Yusta, I., Diez-Ercilla, M., 2011. Schwertmannite and hydrobasaluminite: a re-evaluation of their solubility and control on the iron and aluminium concentration in acidic pit lakes. Appl. Geochem. 26, 1752–1774. Sand, W., Jozsa, P.-G., Kovacs, Zs.-M., Săsăran, N., Schippers, A., 2007. Long-term evaluation of acid rock drainage mitigation measures in large lysimeters. J. Geochem. Explor. 92, 205–211. Schippers, A., Hallmann, R., Wentzien, S., Sand, W., 1995. Microbial diversity in uranium mine waste heaps. Appl. Environ. Microbiol. 61, 2930–2935. Schippers, A., Breuker, A., Blazejak, A., Bosecker, K., Kock, D., Wright, T.L., 2010. The biogeochemistry and microbiology of sulfidic mine waste and bioleaching dumps and heaps, and novel Fe(II)-oxidizing bacteria. Hydrometallurgy 104, 342–350. Selenska-Pobell, S., Kampf, G., Flemming, K., Radevan, G., Satchanska, G., 2001. Bacterial diversity in soil samples from two uranium waste piles as determined by rep-APD, RISA and 16S rDNA retrieval. Antonie Van Leeuwenhoek 79, 149–161. Silver, M., 1987. Distribution of iron-oxidizing bacteria in the Nordic uranium tailings deposit, Elliot Lake, Ontario, Canada. Appl. Environ. Microbiol. 53, 846–852. Singer, P.C., Stumm, W., 1970. Acidic mine drainage: the rate-determining step. Science 167, 1121–1123. Skierszkan, E.K., Stockwell, J.S., Dockrey, J.W., Weis, D., Beckie, R.D., Mayer, K.U., 2017. Molybdenum (Mo) stable isotopic variations as indicators of Mo attenuation in mine waste-rock drainage. Appl. Geochem. 87, 71–83. Smith, L.J.D., Bailey, B.L., Blowes, D.W., Jambor, J.L., Smith, L., Sego, D.C., 2013. The Diavik waste rock project: initial geochemical response from a low sulfide waste rock pile. Appl. Geochem. 36, 210–221. Stockwell, J., Smith, L., Jambor, J.L., Beckie, R., 2006. The relationship between fluid flow and mineral weathering in heterogeneous unsaturated porous media: a physical and geochemical characterization of a waste-rock pile. Appl. Geochem. 21, 1347–1361. Stott, M.B., Watling, H.R., Franzmann, P.D., Sutton, D., 2000. The role of iron-hydroxy precipitates in the passivation of chalcopyrite during bioleaching. Miner. Eng. 13, 1117–1127. Tuovinen, O.H., Kelly, D.P., 1973. Studies on the growth of Thiobacillus ferrooxidans. Arch. Mikrobiol. 88, 285–298. Unz, R.F., Lundgren, D.G., 1961. A comparative nutritional study of three chemoautotrophic bacteria: Ferrobacillus ferrooxidans, Thiobacillus ferrooxidans and Thiobacillus thiooxidans. Soil Sci. 92, 302–313. Watkin, E.L.J., Keeling, S.E., Perrot, F.A., Shiers, D.W., Palmer, M.-L., Watling, H.R., 2008. Metals tolerance in moderately thermophilic isolates from a spent copper sulfide heap, closely related to Acidithiobacillus caldus, Acidimicrobium ferrooxidans and Sulfobacillus thermosulfidooxidans. J. Ind. Microbiol. Biotechnol. 36, 461. Yu, J.-Y., Heo, B., Choi, I.-K., Cho, J.-P., Chang, H.-W., 1999. Apparent solubilities of schwertmannite and ferrihydrite in natural stream waters polluted by mine drainage. Geochim. Cosmochim. Acta 63, 3407–3416. Zhang, M., Liu, X., Li, Y., Wang, G., Wang, Z., Wen, J., 2017. Microbial community and metabolic pathway succession driven by changed nutrient inputs in tailings: effects of different nutrients on tailing remediation. Sci. Rep. 7, 474.