Methane, Microorganisms, and Mine pollution:

The possible recovery and carbon cost in lake sediments after the Mount Polley Mine disaster

Supplementary Materials

Angus Ball

The University of Northern British Columbia

MSc Natural Resources and Environmental Studies

Dr. Michael Preston

05/2025

Abstract

A large fraction of all lakes on the earth is represented by the high latitude lakes, contributing up to 18.8 Tg methane emission (twice the emission from the oceans) into the atmosphere per year that has critical implications for global climate. Anaerobic oxidation of methane (AOM) is a major biogeochemical process that plays a pivotal role in mitigating methane emissions to the atmosphere in the absence of oxygen in various ecosystems. The high latitudes (boreal/sub-boreal zones) are where an estimated 80% of the mining in Canada occurs. The impacts of mine drainage generated from mining of massive sulfide ore bodies (mine effluent) on the quality and geochemistry of waters and sediments characterized by high concentrations of Fe^2+, SO₄^2-, heavy metals and other constituents have been well documented for lakes and pit lakes. Despite these lake sediments being one of the largest global sinks for methane (as a very potent greenhouse gas), anaerobic oxidation of methane and the impacts of mining activities on this important biogeochemical process (particularly AOM linked to iron) is still very poorly understood both in terms of geochemistry and characterization of the organisms and consortia involved. Notably, Canadian lakes are critically understudied, with CH₄ emissions measured from only six lakes to date. Mesocosm experiments were set up with sediments collected from mining impacted lakes and pristine lakes (as controls) located in BC. We are using a holistic system biology approach combining tools in genomics, geochemistry, and environmental microbiology to assess microbial communities and metabolic traits involved in AOM within the context of mining-impacted environments and the consequences for methane emission in high-latitude regions. In this poster, we will outline our experimental design and approach and discuss the implications of such knowledge for improvement of global biogeochemical and climate projection models.

Table of Contents

Abstract 2

1 Introduction. 5

2 Methods. 6

2.1 Site description. 6

2.1.1 Quesnel Lake. 6

2.2 Core collection. 8

2.2.1 Gravity cores. 8

2.2.2 Gravity core extrusion. 8

2.2.3 Methane Measurement 9

2.2.4 Sampling regime. 9

2.3 Geochemical analysis. 9

2.3.1 pH analysis. 9

2.3.2 Bioavailable Metal analysis. 10

2.3.3 Carbon and Sulfur measurements. 10

2.4 Metabarcoding analysis. 11

2.6 qPCR.. 11

2.7 Incubations. 16

2.8 Methane flux and rate modeling introduction. 17

2.8.1 Methane flux and modeling methods. 18

2.9 Statistical analysis. 19

3 Additional Results/Discussion. 19

3.1 pH.. 19

3.2 Methane modeling. 20

3.3 Incubations. 21

4 References. 21

4.1 Poster citations. 21

4.2 Supplementary materials citations. 21

1 Introduction

Methane (CH₄) is an important greenhouse gas with a global warming potential 28 times greater than carbon dioxide over a 100-year period (Jackson et al., 2021; Mao et al., 2022; Saunois et al., 2020). Methane is released from a variety of ecosystems but freshwater lakes are the largest natural methane source and contribute 9-27% of global methane emissions (D’Ambrosio and Harrison, 2022; Jackson et al., 2021). However, Canadian lakes are critically understudied with CH₄ emissions being measured from only 6 lakes (Byrne et al., 2018; Mandryk et al., 2021). Methane is biologically generated from anaerobic methanogenic archaea who degrade organic compounds within lake sediment depleted in oxygen and electron acceptors (Van Grinsven et al., 2022). Living sympatrically with methanogens are aerobic methanotrophic bacteria and anaerobic methanotrophic archaea and bacteria, who degrade methane using a variety of metabolic processes (Chadwick et al., 2022; Van Grinsven et al., 2022). Of particular interest is the metabolic process of the anaerobic oxidation of methane (AOM) which is only performed by anaerobic methanotrophic archaea (Chadwick et al., 2022; Van Grinsven et al., 2022). This process plays a pivotal role in mitigating methane emissions to the atmosphere in various ecosystems (Chadwick et al., 2022; Van Grinsven et al., 2022). While AOM is well documented in marine systems where sulfate (SO₄) plays a primary role as an electron acceptor, it is unclear how AOM proceeds in freshwater ecosystems where ammonium (NH₄), nitrite (NO₂), nitrate (NO₃), SO₄, iron (Fe), manganese (Mn), and heavy metals species are all used as possible electron acceptors through different mechanisms and consortiums of organisms (Zhao and Lu, 2023).

Furthermore, the Anthropocene has degraded many ecosystems and lake sediments are no exception (Walton et al., 2023). Often situated close to freshwater ecosystems, Canada has an approximate 2,100 km² of land used by mining operations (Maus et al., 2020). Mine drainage can alter the quality and geochemistry of lake water and sediments through increases in Fe, SO₄, and heavy metals (Maus et al., 2020). Therefore, this work proposes to study Quesnel Lake, a mine impacted lake in Canada. Quesnel Lake, located in central British Columbia, Canada, is the deepest fjord lake in the world (Byrne et al., 2018). Quesnel Lake is a large well studied lake, and is severely impacted after the Mount Polley mine disaster, the largest mining disaster in Canadian history (Baker and Thygesen, 2017; Byrne et al., 2018). In 2014, a tailings dam failure released approximately 18.6 Mm³ of tailings and supernatant water into the West basin of Quesnel Lake (Owens et al., 2023). These tailings settled in layers up to 1-5 meters thick on the lakebed, disturbing lake biogeochemical cycles and ecology (Byrne et al., 2018; Granger et al., 2022; Hatam et al., 2019). This disruption has provoked continued investigation from the scientific community in areas of lake geochemistry (Granger et al., 2022; Petticrew et al., 2015), water quality (Byrne et al., 2018; Owens et al., 2023), zoology (Pyle et al., 2022), and microbiology (Hatam et al., 2019). However, the lake sediment microbial community was measured shortly post disturbance, only 10 cm deep and without focus to methane associated organisms (Hatam et al., 2019). Now that there has been almost a decade of recovery time since the spill the microbial community needs to be reassessed to determine how it has responded to the acute disturbance.

Quesnel Lake, thus, unveils some unique questions, 1) How has methane cycling and emissions changed post disturbance, 2) how did the microbial community respond to tailings sedimentation, 3) can this disturbance elucidate how AOM functions within Quesnel Lake or possibly provide evidence for heavy metal AOM mechanisms, and finally 4) how has the geochemistry of Quesnel Lake’s sediments changed post disturbance?

2 Methods

2.1 Site description

2.1.1 Quesnel Lake

Quesnel Lake is a freshwater oligotrophic fjord-type lake situated within British Columbia, Canada (Petticrew et al., 2015). This lake has a west, north, and east arm which are narrow (2.7 km mean width), and long (east-west span ~100 km) (Figure 14) (Petticrew et al., 2015). With a maximum depth of 511 m and mean depth of 157 m, Quesnel Lake has an estimated volume of ~1 km³ and ~266 km² surface area (Petticrew et al., 2015). Quesnel Lake is home to Pacific and Sockeye (Oncorhynchus spp.) Salmon stocks and many resident fish populations such as Chars (Salvenlinus spp.) (Petticrew et al., 2015).

August 4^th 2014, a tailings damn failure within the Mount Polley mine released an estimated 25 Mm³ of material into the surrounding Polley lake, Hazeltine Creek and Quesnel Lake (Petticrew et al., 2015). The west basin in the west arm of Quesnel Lake received ca. 18.6 Mm³ of this material: 12.8 Mm³ of tailings and interstitial water, 4.6 Mm³ of supernatant water from the tailings storage facility and 1.2 Mm³ of native soil and eroded overburden from Hazeltine Creek (Owens et al., 2023). This material settled into a ca. 1-2 km wide, 5-10 m deep area, ~6 km across (Owens et al., 2023; Petticrew et al., 2015).

In July 2023, 14 gravity cores and 3 methane measurement cores were collected each from the north arm (reference site) and west arm (disturbed site) (Figure 1) (Hatam et al., 2019). All the cores in the west basin were collected between ca. 60 and 70 meters below the water line. All the cores in the north arm were collected between ca. 60 and 65 meters below the water line.

Figure 1. Overview of sampling locations within Quesnel Lake, British Columbia, Canada. Map created using QGIS (Creative Commons Attribution-ShareAlike 3.0 license (CC BY-SA)) using Atlas of Canada base data (Open Government License – Canada – Version 2.0).

2.2 Core collection

2.2.1 Gravity cores

To collect the gravity cores and methane measurement cores from Quesnel Lake, a piston type gravity corer with a core length of 50 cm and core internal diameter of 4.76 cm was used. Briefly, the gravity corer was lowered into the lake by hand until a depth of ca. 1 meter above the lake sediment. The corer was then released to fall into the sediment. A messenger was then released down the line to activate the piston, which created a suction to hold the sediment in the corer as it was hand pulled to the surface. At the surface, the core was removed from the corer, caps were added to the top and bottom of the core. The gravity cores collected via this method were stored at room temperature for up to 8 hours until sample collection was complete. The cores were then stored at 4°C until extrusion. The methane measurement cores were measured immediately after collection.

2.2.2 Gravity core extrusion

Gravity cores were extruded via a pneumatic core extruder at 1 cm intervals. Briefly, using a foot pedal the sediment was raised 1 cm, then a putty knife removed the sediment into a collection bag. For 4 cores, extrusion happened under aerobic conditions with the putty knife and parts of the extruder being disinfected with 10% bleach and 70% ethanol after each extrusion. For 9 cores, extrusion was performed anaerobically in a modified field anaerobic glove box designed specifically for core extrusion. No bleach or ethanol was used during anaerobic extrusion due to the accumulation of vapours within the glove box may contaminate the sediment and have systematic effects on incubations performed during later experiments. Instead, the putty knife was wiped down with paper towels between each extruded layer to minimize contamination between extrusions. Of these 9 cores, 5 were frozen at -20°C and 4 were refrigerated at 4°C. The 14^th core was left unextruded at 4°C.

2.2.3 Methane Measurement

Cores taken for methane measurements had holes predrilled at every centimeter which were plugged by tape. Immediately after the core was removed from corer, 1.5 ml of sediment/liquid slurry was extracted from predrilled holes and added to 3.5 ml of 10% NaOH to stop methane cycling within the sediment. The mixture was contained in a 10 ml test tube and capped with a butyl rubber stopper. Methane concentration in headspace was determined by gas chromatography flame ionized detection (GC-FID).

2.2.4 Sampling regime

Quesnel Lake sites have triplicate biological replication within each depth tested. Nine depths were measured throughout the Quesnel Lake sediments. Three anaerobically sectioned frozen cores were ground aerobically under liquid nitrogen. This material will be used for all geochemical and microbiological analyzes. Finally, three anaerobically sectioned refrigerated cores were be used for all incubations.

2.3 Geochemical analysis

2.3.1 pH analysis

Sediment pH was measured anaerobically using a pH meter with a 1:2 dilution of sediment and 0.01 M CaCl₂ solution. Briefly, 5 grams of wet sediment was diluted with 10 ml of 0.01 CaCl₂. The solution was stirred intermittently for 30 minutes, then stood for 1 hour to settle. The pH of supernatant solution was recoded on a ThermoFisher Orion 3.

2.3.2 Bioavailable Metal analysis

Bioavailable metals were extracted with a 1.0 M HCl digest (Yu et al., 2021). Briefly, 1 gram of ground air dried sediment was digested with 20 ml of 1.0 M HCl over 4 hours. The Northern Analytical Laboratory (NALs, UNBC) then measured the concentration of Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, O, Ob, S, Sb, Se, Sn, U, V, Zn using ICP-OES.

2.3.3 Carbon and Sulfur measurements

Total, organic and inorganic carbon aswell as total sulfur was measured at Natural Resources Canada, CanmetMINING using an ELTRA Elementrac CS-d. Briefly, ca. 150-200 milligrams of air-dried sediment was burnt in a Resistance furnace measuring CO₂ and SO₂ gas produced which is then calculated into % total carbon and % total sulfur. Another sediment sample was burnt at ca. 400-450 degrees C for 12 hours in a muffle furnace, removing organic carbon. This sample is then burnt in a resistance furnace to calculate % carbon in burnt sediment, or inorganic carbon. Inorganic carbon in unburnt sediment is determined by Equation 1, where %R_b is percent carbon in burnt samples, m_b is mass of burnt sample, and m_o is mass of unburnt sample. Organic carbon is then determined by Equation 2.

Equation 1

Equation 2

2.4 Metabarcoding analysis

DNA extractions were performed using a Qiagen Soil DNeasy PowerSoil Pro Kits (Cat. 47014) and then purified using Promega’s ProNex® Size-Selective Purification Magnetic beads (Cat. NG2001). Extractions were sent to the integrated microbiome resource, Dalhousie University, Halifax, Nova Scotia, Canada, where library preparation and long read sequencing was be performed using PacBio’s vega. The full length bacterial 16S sequence were amplified with 27F (AGRGTTYGATYMTGGCTCAG) and 1492R (RGYTACCTTGTTACGACTT) (Buetas et al., 2024; Paliy et al., 2009; Weisburg et al., 1991) and the full length archaeal 16S sequence were amplified with Arch21Ftrim (TCCGGTTGATCCYGCCGG) and A1401R (CRGTGWGTRCAAGGRGCA) (Comeau et al., 2011; Reysenbach et al., 2000).

The DADA2 pipeline was be used to process raw reads (Callahan et al., 2016) and a compositional data approach were be used for the relevant statistics (Gloor et al., 2017): Alpha diversity (Wereis and Martin, 2018), beta diversity (Martino et al., 2022), differential abundance (Nearing et al., 2022), and network analysis (Matchado et al., 2021).

2.6 qPCR

Bacterial and archaeal copy numbers of the 16S gene and methanogen and methanotroph copy numbers of the McrA gene were quantified on a BioRad CFX Opus 384 (Chen et al., 2017; Lever et al., 2015). Reaction volumes of 10 µl consisted of 1 µl of 1/10 diluted template, 0.5 µM of each primer, see Table 1., molecular-grade water and 1x of SSoAdvanced™ Universal inhibitor Tolerant SYBR® Green Supermix (Bio-Rad, USA-CA) (Bott et al., 2023). Thermocycling conditions consisted of an initial denaturation at 98°C for 120s, followed by 40 cycles of a denaturation of 98°C for 10s, annealing at each primer sets annealing temperature (Table 1) for 15s, and then extension at 72°C for 30s. After the final extension step a melt curve analysis was performed between 65 and 95°C at 0.5°C increments for 0.05s each to check for primer specificity. gBlocks (IDT, CA) of 16S and McrA genes were produced as standards for each assay based on if there were recommendations on which taxa to use in the original paper (All McrA Assays), otherwise model organisms were used (Methanococcus maripaludis, 16S total archaea assay; Escherichia coli, 16S total bacteria assay) (Table 2). A standard series of gene covered gene concentrations of 10¹ to 10⁹ was performed for each assay, however the linear range and efficiency of each assay differed slightly (Table 3). The NTC, and it’s C_q value are remarked in Table 4. Each sample was measured in triplicate or duplicate technical replicates if a single technical replicate had an abnormal amplification curve.

Each Assay’s optimal annealing temperature was determined by gradient qPCR, the possibility of multiple products was measured by melt curve analysis and gel electrophoresis. Thermocycling conditions consisted of an initial denaturation at 98°C for 120s, followed by 40 cycles of a denaturation of 98°C for 10s, annealing varied between 55 and 65°C for 15s, and then extension at 72°C for 30s. After the final extension step a melt curve analysis was performed between 65 and 95°C at 0.5°C increments for 0.05s each to check for primer specificity. Reaction conditions were the same as the assays above.

Table 1. 16S and McrA gene primers used for qPCR examinations of bacterial, archaeal and methanogen abundances

Region	Forward Primer	Forward primer sequence	Reverse primer	Reverse primer sequence	Annealing temperature (°C)	Citation
16S Total bacteria	Bac908F_mod	5′-AAC TCA AAKGAATTG ACG GG-3′	Bac1075R	5′CAC GAG CTG ACG ACA RCC-3′	60	(Chen et al., 2017; Lever et al., 2015)
16S Total Archaea	Arch915F_mod	5′-AAT TGG CGG GGG AGC AC-3′	Arch1059R	5′-GCC ATG CAC CWC CTC T-3′	NA	(Chen et al., 2017; Lever et al., 2015)
McrA ANME-1	Type a-b_F	5′-TGGTTCGG AACGTACATGTC-3′	Type a-b_R	5′-TCTYYT CCAGRAT GTCCATG-3′	NA	(Nunoura et al., 2006; Shi et al., 2020)
McrA ANME 2a,b,c	Type c-d_F	5′-GCTCTAC GACCAG AT MTGG CTTGG-3′	Type c-d_R	5′-CCGTAGTA CGTGAAGTCAT CCAGCA-3′	NA	(Nunoura et al., 2006; Shi et al., 2020)
McrA ANME-2d	159F	5′-AAAGTGCGG AGCAGCAATCACC-3	345R	5′-TCGTCCCATT CCTGCTGCATTGC-3′	NA	(Shi et al., 2020; Vaksmaa et al., 2017)
McrA ANME-3	Type e	5′-CHCTGGAA GATCACTTCGGTGGTTC-3′	Type e	5′-RTATCCGAAG AARCCSAGT CKRCC-3′	NA	(Nunoura et al., 2006; Shi et al., 2020)
McrA Total Methanogens and ANME-2d	mlas F	5′- GGT GTM GGD TTCACM CAR TA-3	mcrA-rev	5′-CGTTCATB GCGTAGTTVGGRTAGT-3	NA	(Meier et al., 2024; Steinberg and Regan, 2008)

Table 2. The taxa that the standards are based on and the associated qPCR assay. Citations are provided if the taxa has been explicitly used before as standard for the qPCR assay

qPCR Assay	Reference taxa	ncbi accession number	Citation (if available)
16S Total Archaea	Methanococcus maripaludis	AF005049	NA
16S Total Bacteria	Escherichia coli	MN900682	NA
McrA ANME-1	Uncultured archaeon clone	BX649197	(Nunoura et al., 2006)
McrA ANME-2a-c	Uncultured archaeon clone	AY324368	(Nunoura et al., 2006)
McrA ANME-2d	Candidatus Methanoperedens sp.	KX290067	(Vaksmaa et al., 2017)
McrA ANME-3	Uncultured archaeon clone	AY324364	(Nunoura et al., 2006)
McrA Methanogens and ANME-2d	Methanocorpusculum parvum	NZ_LMVO01000029	(Meier et al., 2024)

Table 3. qPCR Assay standard curve calculations

qPCR Assay	Linear Range	Efficiency (%)	R^2	Calibration curve slope	Calibration curve y intercept
16S Total Archaea
16S Total Bacteria	10^2-10^8	99.1	0.998	-3.344	33.442
McrA ANME-1
McrA ANME-2a-c
McrA ANME-2d
McrA ANME-3
McrA Methanogens and ANME-2d

Table 4. Results of the NTC in each qPCR assay

qPCR Assay	NTC Cq	Comment
16S Total Archaea
16S Total Bacteria	31.72	The standard curve suggests this is a concentration of 3.89 copies per reaction which is almost the theoretical limit to qPCR limit of detection (3) (Bustin et al., 2009)
McrA ANME-1
McrA ANME-2a-c
McrA ANME-2d
McrA ANME-3
McrA Methanogens and ANME-2d

2.7 Incubations

The determination of methanogenesis and methanotrophy rates can be measured via bag incubations of the sediment using ¹³C-labeled Methane and the isotope dilution model (Xiao et al., 2018, 2017). Briefly, sediment was incubated at environmentally relevant temperatures (ca. 4°C) for 1 month to allow for the stabilization of microbial community and conditions. Sample were subsampled and methane concentration and molar ratio were determined. Then ¹³C-labeled Methane was to be added to the incubations for a 2% increase in total methane and ¹³C concentration. Then a subsample would be removed for isotopic and concentration analysis via GC-C-IRMS and GC-FID respectively. Subsamples would’ve been tested 4 times for a total experiment length of 4 months. Methanogenesis and methanotrophy rates were to be calculated using the following equations derived from Blackburn (1979).

This model assumes that methane production, p, and consumption, r, are constant throughout experiment (Blackburn, 1979). The change in methane concentration, C, can be described based on Equation 3 and Equation 4.

Equation 3.

Equation 4.

Assuming that methanotrophy produces methane at a constant mole fraction (R_b) between ¹³C and ¹²C based on the natural conditions, and methanotrophy consumes methane at the current ¹³C and ¹²C ratio (R) the change in this ratio can be described by Equation 5 and Equation 6 respectively. This model assumes enzyme fractionalization is negligible. Mole fraction is described by Equation 7.

Equation 5.

Equation 6.

Equation 7.

Combining Equation 4 and Equation 7 creates Equation 8, and combining Equation 4 and Equation 8 creates Equation 9, initial conditions are represented by C₀, R₀. The production and consumption rates are assumed to be inequal for Equation 9.

Equation 8.

Equation 9.

By plotting Equation 4 the slope p-r can be determined, and by plotting Equation 9 the slop -p/p-r can be determined. By combining these slopes methanogenesis (p) and methanotrophy (r) can be calculated.

2.8 Methane flux and rate modeling introduction

There are three primary methods to model methane flux and rates from and within lake sediments (D’Ambrosio and Harrison, 2022). Sediment incubations, like that presented in section 2.7, water column models, which measure the methane profile in the water column, and sediment models (D’Ambrosio and Harrison, 2022). Sediment models allow the determination of methane flux from sediments into overlying waters and net methane cycling rates based on methane concentrations within the sediments, measured in section 2.2.3 (D’Ambrosio and Harrison, 2022). These models are limited by the often high error associated with measuring near sediment-surface methane concentrations, due sediment disturbance caused by gravity coring (D’Ambrosio and Harrison, 2022). These models also exclude some sediment surface processes, e.g. ebullition, and are unable to estimate sediment disturbance artifacts (D’Ambrosio and Harrison, 2022). Since these models are based on the current concentration of methane within sediments, they have a spatial resolution dependent of the sample area and a temporal resolution that can be projected as an average across months (D’Ambrosio and Harrison, 2022). However, care must be taken to over extrapolate these models as methane cycling rates, and thus methane concentrations, can fluctuate seasonally especially in thermally stratified lakes (D’Ambrosio and Harrison, 2022; Kang et al., 2024).

Briefly, these are diffusion-reaction models based on Fick’ first (Equation 3) and second law of diffusion with an added term for net methane cycling (Equation 4) (D’Ambrosio and Harrison, 2022).

Equation 3

Equation 4

Where J is flux, dC_sed/dt is the methane concentration gradient across time, t, dC_sed/dz is the methane concentration gradient across sediment depth, z, ϕ is porosity, D_s is the sediment diffusivity of methane in porewater, and R represents the net methane cycling rate.

2.8.1 Methane flux and modeling methods

The REC (v3.1) and PROFILE (v1.0) models were both run, the parameters for the models are presented below (Berg et al., 1998; Lettmann et al., 2012). Average methane concentration measured in section 2.2.3 was assumed to be the steady state methane concentration. While sedimentation data exists (see Gilbert and Desloges (2012)) this is from pre-disruption Quesnel lake and sites with limited applicability to this work’s sampling regime. Thus, sedimentation rates are excluded from modeling and assumed to be 0. Bioirrigation and bioturbation are often included in modeling calculations; however, there was no apparent burrows or animals within the sediments collected so these can be assumed to be 0 (Berg et al., 1998). A porosity of 0.9 was assumed based on D’Ambrosio and Harrison (2022). The depth dependent pore water diffusion coefficient was calculated based on the relationship D = D₀/(1+c(1- ϕ)) whereby D₀ is the diffusion coefficient of methane in pure water (D₀ = 0.66*10^-5 cm²s^-1) based on Lettmann et al. (2012). Specifically for the REC model, the smoothing parameter was chosen to be 100 based on Lettmann et al. (2012). Specifically for the PROFILE model, the number of unique zones is determined by the model itself (Berg et al., 1998).

2.9 Statistical analysis

Each geochemical test was tested for normality, and unimodality with a Shapiro-Wilks and Hartigans dip test respectively. All datasets were not normally distributed and displayed a log-normal distribution. Samples were grouped by depth and a Kruskal-Wallis rank sum test was used to determine significance. If significance was detected the Conover-Iman test was used as a post-hoc test.

3 Additional Results/Discussion

3.1 pH

pH within the tailings material on average is significantly higher by ca. 0.5 pH units than that within the natural sediments. Tailings deposition changes the pH of underlying natural sediments, such that there is only a nonsignificant difference in pH of the natural sediments between the disturbed and reference site at ca. 6 cm under the tailings material.

Figure 3. pH at different homogenized segments of depths in disturbed and reference sites at Quesnel Lake. Segments are represented by vertical lines between vertices and the tailings material is differentiated from the natural sediments by a dashed line within the disturbed site.

3.2 Methane modeling

Both models agreed that there is net methane loading into Quesnel Lake from the reference site; whereas, there is net methane loading into the disturbed site sediments from Quesnel Lake (Table 5).

Table 5. Calculated methane flux at the sediment water interface in both the disturbed and reference sites within Quesnel Lake based on the PROFILE and REC models. A negative flux is net methane flux out of the sediment into the overlying water, a positive flux is net methane flux into the sediment from the overlying water.

Model	Reference site methane flux (µmol/cm^2s)	Disturbed site methane flux (µmol/cm^2s)
PROFILE	-2.570×10^-4	1.074×10^-5
REC	-8.4385×10^-5	5.7962×10^-6

3.3 Incubations

After one month of stabilization there was no methane measured within the incubations and the incubation experiment was postponed indefinitely. With the added context of the methane modeling suggesting that there is net methane consumption throughout most of the disturbed core and only areas of net methane production within the reference core the lack of methane generation is explained. An initial spike would then be required for future measurements.

4 References

4.1 Poster citations

D’Ambrosio, S.L., Harrison, J.A., 2022. Measuring CH4 Fluxes From Lake and Reservoir Sediments: Methodologies and Needs. Front. Environ. Sci. 10, 850070. https://doi.org/10.3389/fenvs.2022.850070

Owens, P.N., Petticrew, E.L., Albers, S.J., French, T.D., Granger, B., Laval, B., Lindgren, J., Sussbauer, R., Vagle, S., 2023. Annual pulses of copper-enriched sediment in a North American river downstream of a large lake following the catastrophic failure of a mine tailings storage facility. Science of The Total Environment 856, 158927.

Petticrew, E.L., Albers, S.J., Baldwin, S.A., Carmack, E.C., Déry, S.J., Gantner, N., Graves, K.E., Laval, B., Morrison, J., Owens, P.N., Selbie, D.T., Vagle, S., 2015. The impact of a catastrophic mine tailings impoundment spill into one of North America’s largest fjord lakes: Quesnel Lake, British Columbia, Canada: Aquatic impacts of a mine tailings spill. Geophys. Res. Lett. 42, 3347–3355. https://doi.org/10.1002/2015GL063345

4.2 Supplementary materials citations

Anderson, J., Caron, F., Beckett, P., Spiers, G.A., Lévesque, N., Charbonneau, G.M., Halvorson, B., Dufour, H., Lock, A., 2022. Distribution of metals and radionuclides in the lichens Cladonia rangiferina and C. mitis from the past uranium mining region of Elliot Lake, Ontario, Canada. Heliyon 8, e11863. https://doi.org/10.1016/j.heliyon.2022.e11863

Baker, E., Thygesen, K., 2017. Mines tailings storage: Safety is no accident. UN Environment, GRID-Arendal, Nairobi, Kenya.

Berg, P., Risgaard‐Petersen, N., Rysgaard, S., 1998. Interpretation of measured concentration profiles in sediment pore water. Limnology & Oceanography 43, 1500–1510. https://doi.org/10.4319/lo.1998.43.7.1500

Blackburn, T.H., 1979. Method for Measuring Rates of NH ₄ ⁺ Turnover in Anoxic Marine Sediments, Using a ¹⁵ N-NH ₄ ⁺ Dilution Technique. Appl Environ Microbiol 37, 760–765. https://doi.org/10.1128/aem.37.4.760-765.1979

Bott, T., Shaw, G., Gregory, S., 2023. A simple method for testing and controlling inhibition in soil and sediment samples for qPCR. Journal of Microbiological Methods 212, 106795. https://doi.org/10.1016/j.mimet.2023.106795

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Bustin, S.A., Benes, V., Garson, J.A., Hellemans, J., Huggett, J., Kubista, M., Mueller, R., Nolan, T., Pfaffl, M.W., Shipley, G.L., Vandesompele, J., Wittwer, C.T., 2009. The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clinical Chemistry 55, 611–622. https://doi.org/10.1373/clinchem.2008.112797

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