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Environmental Science & Technology
Dynamic coupling of iron, manganese and phosphorus behavior in water and sediment of shallow ice-covered eutrophic lakes (submitted ES&T as a research article)
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Andrew W. Schroth*1,2, Courtney D. Giles2, Peter D.F. Isles2,3, Yaoyang Xu2, Zachary Perzan4, Gregory K. Druschel5
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1 Department of Geology, University of Vermont, Delehanty Hall, 180 Colchester Ave. Burlington, Vermont 05405
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2. Vermont EPSCoR, University of Vermont, Cook Physical Science Building, 82 University Place, Burlington, Vermont 05405
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3 Rubenstein School of Environment and Natural Resources, University of Vermont, Aiken Center, 81 Carrigan Drive, Burlington, Vermont 05405
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4 Department of Geology, Middlebury College, McCardell Bicentennial Hall, 276 Bicentennial Way , Middlebury College, Middlebury, VT 05753
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5. Department of Earth Sciences, Indiana University Purdue University, Indianapolis, 723 W. Michigan Street, SL118, Indianapolis, Indiana 46202
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* Corresponding author: Phone: (603) 252 6551 email:
[email protected] Fax: (802) 656 3131
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Abstract: Decreasing duration and occurrence of northern hemisphere ice cover due to recent
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climate warming is well-documented; however, biogeochemical dynamics underneath the ice are
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poorly-understood. We couple time-series analyses of water column and sediment water
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interface (SWI) geochemistry with hydrodynamic data to develop a holistic model of iron (Fe),
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manganese (Mn), and phosphorus (P) behavior underneath the ice of a shallow eutrophic
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freshwater bay. During periods of persistent subfreezing temperatures, a highly reactive pool of
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dissolved and colloidal Fe, Mn and P develops over time in surface sediments and bottom waters
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due to reductive dissolution of Fe/Mn(oxy)hydroxides below the SWI. Redox dynamics are
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driven by benthic O2 consumption, limited air-water exchange of oxygen due to ice cover, and
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minimal circulation. During thaw events, the concentration, distribution and size partitioning of
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all species changes, with the highest concentrations of P and ‘truly dissolved’ Fe near the water
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column surface, and a relatively well-mixed ‘truly dissolved’ Mn and ‘colloidal’ Fe profile due
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to the influx of geochemically distinct river water and increased circulation. The partitioning
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and flux of trace metals and phosphorus beneath the ice is dynamic, and heavily influenced by
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climate-dependent physical processes that vary in both time and space.
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Introduction: One of the most clear and consistent harbingers of climate change in the
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Anthropocene has been a gradual reduction in the frequency and duration of lake wintertime ice
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cover in the northern hemisphere 1-3. In the great lakes, increases in water temperature above
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those predicted by observed changes in air temperature have confirmed this process and its
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physical impact4. Using a space-for-time substitution approach that compares otherwise similar
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Canadian (consistent winter freeze) and Danish (lacking ice cover) lake systems, Jackson et al.
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2007 suggested that the duration of ice cover has profound impacts on nutrient loading, food web
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structure, and water column anoxia5. Sediment water interface (SWI) redox chemistry, especially 2 ACS Paragon Plus Environment
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in shallow systems, is linked to overlying water column anoxia, through competitive processes of
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oxygenation (via O2 entrainment and photosynthesis) v. reductive processes (including
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O2/alternate electron acceptor respiration)6. Ice cover adds a physical barrier to the rate of O2
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entrainment, both due to a change in the diffusivity of O2 through ice as compared to water, and
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due to effectively eliminating wave action as a driver of enhanced O2 entrainment rates 7, 8, both
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of which should have a profound impact on the position and structure of the redoxcline and
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related biogeochemical cycling. However, the role of ice cover in lake ecosystem productivity or
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biogeochemical dynamics remains poorly understood, particularly in shallow systems where
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impacts on SWI geochemistry are likely most pronounced9. Given that the duration and extent
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of regional ice cover is predicted to decrease over time as climate continues to warm, it is critical
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to understand biogeochemical dynamics underneath the ice, and how under ice processes supply
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or sequester nutrients and pollutants in lake ecosystems.
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In shallow lakes or bays, an important driver of P dynamics is thought to be the behavior of
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secondary amorphous Fe and Mn (oxy)hydroxides that are concentrated as nanoparticles at the
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SWI6, 10, 11. These nanoparticle phases are the product of dissolved Fe2+ and Mn2+ oxidation; Fe2+
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oxidation by O2 at circumneutral pH is very fast whereas Mn2+ oxidation is slower and involves
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important Mn3+ intermediates12. The high surface area and positive surface charge of these Fe
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and Mn (oxy)hydroxide phases (pHzpc ~9) allow them to be highly effective scavengers of
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orthophosphate and other oxyanions under oxidizing conditions at circumneutral pH typical of
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many lake systems13. However, these phases are highly susceptible to reductive dissolution under
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hypoxic and anoxic conditions 14, 15, which are largely driven by biotic processes such as
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heterotrophic respiration by iron or manganese reducing bacteria, which couple organic molecule
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oxidation with the reduction of Fe and Mn (oxy)hydroxides16 upon consumption of oxygen and 3 ACS Paragon Plus Environment
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nitrate17. As such, when strong redox gradients are established at the SWI, reduction of
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nanoparticulate Fe and Mn (oxy)hydroxides bearing significant sorbed P species can result a
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significant flux of this macronutrient from the sediment into the water column 6, 18. In the
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summertime, when water column temperatures are sufficient to support plankton blooms,
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reductive dissolution of these phases is thought to initiate harmful algal bloom development and
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propagation in many shallow P-limited systems19 20. While this process has been documented in
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a few shallow systems during periods of high productivity, it is unclear to what extent it also
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occurs under ice cover, when benthic microbial communities remain active, but oxygen diffusion
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and hydrodynamic mixing may be highly limited. Thus, the continued occurrence of oxygen-
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demanding processes, coupled with relatively stable hydrodynamic conditions, would be
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expected to maintain, and possibly enhance, anoxia at the SWI. Indeed, Gammons et al. 2014
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observed the build-up of anoxia and a related suite of reduced dissolved species (Mn+2, Fe+2,
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NH+4, S-2) in under ice bottom waters of a Montana reservoir21. However, no studies have
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examined the size partitioning or speciation of soluble metal phases in the under ice water
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column. Speciation often controls the role of metals, particularly Fe, in various freshwater
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biogeochemical cycles15. The size of metal phases (and thus their surface area) is controlled by
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external factors influencing particle coarsening rates, such as temperature, salinity, and specific
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ligands , as well as the timing between formation and any subsequent reductive dissolution22.
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Furthermore, the response of the under ice biogeochemical system to intermittent external
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disturbances such as winter rain or thaw periods typical of the northern hemisphere winter is
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completely unknown, although such events are forecast to increase in frequency and severity in
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the northern hemisphere due to climate change23, 24. Conceptually, it is intuitive that such events
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should influence under ice biogeochemistry by impacting redox status and related processes at
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the SWI, under ice hydrodynamics in the lake, and the input of metal and P loads with
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potentially unique geochemical composition associated with biogeochemical processes internal
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to the watershed rather than the lake. As such, comprehensive studies based on empirical data
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are needed to understand the interaction between the under-ice lake water column, sediment
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water interface (SWI) and the lake watershed during the northern winter. Such studies can
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provide a basis for understanding and predicting how overall lake nutrient-metal systems may
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evolve as ice cover extent and duration decrease, as well as the occurrence, nature and severity of
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extreme weather events change.
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Here we took advantage of the relatively severe winter of 2013-14 in northern Vermont to study
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under ice biogeochemistry in Missisquoi Bay, a shallow eutrophic arm of Lake Champlain. We
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couple under ice sediment, water column metals and nutrient time-series geochemical data with
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hydrodynamic data from a continuously-deployed Acoustic Doppler Current Profiler (ADCP)
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array to develop a comprehensive understanding of the behavior of metals and phosphorous in
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the under ice water column and SWI across the variable hydrodynamic conditions typical of the
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northern winter climate. For the first time, the biogeochemical and hydrodynamic behavior of an
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under-ice water column is linked to the evolution of sediment nutrient and metal pools, and the
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response of both to climate disturbance.
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Site Description: The study was conducted in the Missisquoi Bay of Lake Champlain, which
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spans the border between Vermont, USA and Quebec, Canada (Figure 1A). Missisquoi Bay is
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eutrophic and has experienced regular blooms of toxic cyanobacteria over the past two decades
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due to persistent primarily nonpoint source-derived P and N loading from its agriculturalized
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watershed25, 26 . Missisquoi Bay is uniformly shallow (max depth 5m, mean depth 2.8m) and
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largely isolated from the main body of Lake Champlain. As such, Missisquoi Bay is similar in 5 ACS Paragon Plus Environment
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configuration to many eutrophic systems where cyanobacteria can access sediment nutrient
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reserves (shallow, with a high agriculturalized watershed to lake ratio)27, but quite different from
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most of the rest of the relatively deep and oligo/mesotrophic Lake Champlain28. During a typical
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winter, the Bay freezes completely during the month of December and thaws sometime during
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the month of April, although significant interannual variability of the duration of ice cover has
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been observed, and the ice-over date and decadal frequency of ice-over of Lake Champlain has
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changed significantly over the last 150 years due to climate warming29. During the winter of
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2013-14, ice thickness approach meter in thickness by the end of the winter (Figure 1B). The
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main tributary of the bay is the Missisquoi River, which drains a large (~1000 km2) forested
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(62%) and agricultural (25%) watershed, is heavily impacted by nonpoint source agricultural
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pollution26, 30. Our biogeochemical monitoring location is located in the southwest corner of the
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bay (N 44˚ 59.503’, W 73˚ 06.798’), while our hydrodynamic array was spatially distributed across
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the system at various depths (Figure 1A).
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Materials and Methods:
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Hydrodynamics: Water velocity measurements within the Bay were made using six upward-
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looking 1,200-KHz acoustic Doppler current profilers (Workhorse Montior ADCPs by Teledyne
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RDI) mounted to concrete platforms on the bottom of the Bay(Figure 1A). The hydrodynamic
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sensor array was deployed during May of 2013 in a configuration optimized to study baywide
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circulation, with one unit proximal to our biogeochemical monitoring site (Figure 1A). The
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ADCPs were configured to measure vertical current profiles from 1.25 m above bottom to ~0.67
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m below the water surface in 25-cm intervals. The instrument recorded mean velocity profiles
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every 30 minutes, using an ensemble of 190 pings. Statistical correlation between individual
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pings provided an indication of the overall reliability of the calculated velocity; weakly
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correlated measurements were removed from the dataset.
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Water Sampling and Analyses: Biweekly field sampling trips were conducted throughout the
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winter period while ice was thick enough to support researchers and equipment, which was
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assessed based on ice fisherman activity and distribution. We used a Garmin GPS unit to locate
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the same monitoring site upon each visit (Figure 1A). The depth below the ice surface ranged
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from 3.4m to 3.1m during the winter, and the ice thickness ranged from 0.45m to 0.8m. Upon
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reaching the site, 6 randomly distributed holes were drilled using a propane-powered ice auger
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(8” diam.). Two of the holes were used for depth measurements (by weighted measuring tape)
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followed by the deployment of water column sensors. A YSI EXO2 sonde was manually
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deployed into each of two holes to collect measurements of T (oC), pH, DO (ppm), conductivity
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(uS cm-1), and ChlA/phycocyanin (RFU) at predetermined depths(all data shown in Supporting
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Information-Figure S1) . The sensors on the sonde were calibrated as specified by the
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manufacturer (YSI Xylem, Yellow Springs, OH, USA). Unfortunately, our sonde was not
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equipped with an ORP electrode to further quantify and describe redox dynamics near the SWI.
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Sensor measurements were taken immediately below the ice, then at 0.5m increments, and
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finally as close to the SWI as possible without disturbing the lake sediment. The position of the
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sonde and sampling intake tubing for the ‘bottom water’ sample likely varied slightly relative to
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each sampling event or sonde cast, which is nontrivial since dramatic concentration and redox
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front gradients have been observed in those environments6, but this was unavoidable. Duplicate
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sonde casts and water samples did reveal that differences between bottom water samples were
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generally less than 10%, and, more importantly, those differences do not impact our 7 ACS Paragon Plus Environment
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interpretations. Another unused hole was accessed for the collection of water samples, which
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were collected using a Masterflex peristaltic pump at the same depths that the sonde
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measurements were taken. Acid-cleaned (7.5% hydrochloric acid) tubing was used to collect all
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water samples. The tubing was flushed with at least 10 full volumes of water prior to sample
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collection at each depth. Samples to be analyzed for SRP were collected in duplicate at each
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depth in acid-cleaned 1 L polyethylene bottles. All samples were stored in a cooler and brought
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back to the laboratory for immediate filtration within a clean laminar flow hood (within 4 hours
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of sample collection). Trace metals were collected separately and filtered following the
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published protocols and then analyzed by ICP-MS at the Woods Hole Plasma Facility31. The
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‘truly dissolved’ size fraction is operationally defined by the
