Phospholipid Fatty Acid Profiles and Biodensity Estimates for Water, Rock and Air Samples Recovered from Witwatersrand Basin Mines SUSAN M.PFIFFNER* JAMES M. CANTU AMANDA SMITHGALL AARON D. PEACOCK DAVID C. WHITE Center for Biomarker Analysis University of Tennessee Knoxville, TN USA DUANE P. MOSER Environmental Microbiology Group Pacific Northwest National Laboratory Richland, WA, USA T.C. ONSTOTT Department of Geosciences Princeton University Princeton, NJ USA E. VANHEERDEN Department of Microbial, Biochemical and Food Biotechnology University of the Free State Bloemfontein, Free State, Republic of South Africa
*Address correspondence to S. M. Pfiffner Department of Microbiology Center for Biomarker Analysis University of Tennessee 10515 Research Drive, Suite 300 Knoxville, TN 37932-2575USA Telephone: (865) 974-8031 Fax: (865) 974-8027 E-mail address: [email protected]
Running title: Deep Mine Microbial Communities Key words: Extreme environment, community composition, phospholipid fatty acids, deep subsurface, fracture water
Abstract The extreme environments of deep South Africa mines were investigated to gain a better understanding of the microbial community composition and biodensity present in these environments. These community parameters were determined using phospholipid fatty acid methyl esters (PLFA). Samples were collected from several levels and shafts in eight different mines. Water samples included water emanating from boreholes intersecting fluid filled fractures (indigenous) and water from the mine circulation system or from tunnel gutters referred to here as service water. Mine air sampling occurred within 3 m of the water sampling sites. Rock samples included hand samples and drill core collected from mine stopes. Air and service water samples were used to monitor the potential microbial contamination of fracture water and rock samples. Service water exhibited the highest biodensity with quantities ranging from 2.2x102 to 6.6x106 pmol L-1. Fracture water biodensity estimates ranged from 4x100 to 5.8x105 pmol L-1. Carbon leader and quartzite rock samples had biodensity values from 8.5x103 to 4.8x106 pmol L-1. The lowest biodensity was seen in the air samples, which ranged from 1x10-2 to 1.5x100 pmol L-1. Flow cytometry derived cell counts for some of the same water samples ranged from 5x105 to 2x109 cells L-1. When community composition was examined, the air samples were dominated by normal saturates and polyunsaturates, whereas the service water was dominated by monounsaturates. These PLFA indicate that the air samples were dominated by eukaryotic microorganisms and the service water had a predominance of Gram-negative bacteria. Fracture water samples contained diverse microbial community profiles. Fracture water from Driefontein shaft 5 had significantly more terminally branched saturates and branched monounsaturates compared to the other fracture water samples which indicated influences from Gram-positive bacteria and some anaerobic bacteria. Fracture water from Beatrix, Evander shaft
9, and Masimong contained predominately Gram-negative communities. All other fracture water had PLFA profiles with both Gram-negative and Gram-positive bacterial populations. Differences in PLFA relative abundances of fracture water and the presence of PLFA which were not found in the service water and air samples provided evidence that some fracture water was not contaminated by mining processes. A redundancy analysis that used environmental chemical data and PLFA profiles as species showed the influence of temperature, pH, Eh, and concentrations of chloride, sulfate and nitrate distinguished changes in the microbial community composition between service and fracture water samples, as well as between mines.
Extreme environments are under intense investigation by multidisciplinary and international teams of scientists, students, and educators, with the hope that a better understanding of life in these places may provide insights into the origin of life on Earth and contribute to the search for life on other planets. Extreme environments of interest on Earth include nuclear waste depositories, perennially ice-covered lakes in the Antarctic, interiors of rocks, oceanic hydrothermal vents, thermal springs, and deep subsurface aquifers (Onstott et al. 1997; Sassen et al. 1998; Pederson 1999; Zierenberg et al. 2000; Rothschild and Mancinelli, 2001). South African mines provide ready access to some of the world’s deepest terrestrial extreme environments, and allow investigators to collect water and air samples that can be analyzed for microbial and geochemical properties. Measurements of microbial community composition and function, relative to geochemical parameters in extreme environments in which
they occur, permit investigators to determine the similarity of the communities to those in other terrestrial environments. Analysis of microbial communities that occur in extreme environments also allows investigators to identify novel enzymes that may be of use to science and industry, and can provide information about issues that may be encountered during the search for extraterrestrial life. Phospholipid fatty acid methyl esters (PLFA) have been used as biomarkers of microbial communities for decades and have many desirable properties, one of which is they degrade rapidly upon cell death (White et al. 1979) rendering PLFA analysis as a direct real time monitoring analysis for viable biodensity of microbial communities. As constituents of all eukaryote and bacterial cell membranes, PLFA provide a non-selective means to assay changes in microbial communities. The profiles of the PLFA can be subdivided into several prokaryotic and eukaryotic components (Federle et al. 1986; Tunlid and White 1992; Findlay and Dobbs 1993) and provide information on the overall metabolic status and stress of a community (Kieft et al. 1994; Frostagård et al. 1996). Numerous studies have shown how PLFA analysis can aid in determining microbial community composition and the impact of environmental factors like contamination by hydrocarbons (Pfiffner et al. 1997; Stephens et al., 1998) or metals (Brandt et al. 1999; Frostegård et al. 1996; Bååth et al. 1998). Herein, we describe microbial communities found in air, service water and fracture water samples collected from within six mines in South Africa. We emphasize the use of PLFA analyses to characterize microbial communities and to explain how PLFA analyses can be used both to estimate microbial community biodensity and to determine community composition in relation to the physical and chemical conditions in the subsurface.
Materials and Methods
Site Information and Sample Collection The mines sampled are located in the Witwatersrand Basin and the Bushveld Igneous Complex, South Africa (Figure 1). Herein, the mines are designated as Beatrix (BE), Driefontein (DR), Evander (EV), Kloof (KL), Merrispruit (MS), Mponeng (MP), Masimong (MM), Tau Tona (TT) and Northam (NO). With the exception of the Northam mine which is platinum mine; all other mines listed are gold mines. Sampling depths in the mines ranged from 720 to 3300 m below land surface. Detailed information on these mines is provided by Onstott et al. (this volume) and Gihring et al. (this volume). A total of 162 samples were collected using aseptic technique (Table 1). Those samples consisted of 69 fracture water samples (F#), 24 service water samples (S#), 21 biofilms (B#), 14 air samples (A#), 10 Au-bearing carbon leader samples (C#), 2 quartzite samples (Q#) and 22 enrichment matrices (E#). Two other samples were collected: EV9F1, which is a cement containment dam water sample and EV2F4, which is acidic mining water from an upper level emanating from a borehole at a lower level. Service water, which is used in mine operations, eg., cooling the air supply system, lubrication of drilling rods and suppression of dust in the stopes, was collected in sterile, N2 flushed 1-L containers. Fracture water samples for lipid analysis were collected by two methods. One method used a sterile N2-flushed 12-L container. The second method used 0.2-μm pore size Anodisc filters (Whatman) in sterile filter casings, which were directly attached by quick-connects to the sampling apparatus, allowing in situ filtering.
Enrichment matrices (sand, elemental sulfur, elemental iron, listed in Table 1 sample # 135 to 156) were attached to the sampling apparatus for long-term exposure to enrich for microbial biodensity recovery. Air samples were collected in situ using the same filtering system attached to a hand- or foot-powered mattress pump. Air filters were removed from the casing and stored in muffled aluminum foil placed into Whirlpak bag prior to leaving the sampling stope. In the field laboratory, water samples collected in the containers were filtered through the Anodisc filters. Onstott et al. (2003) described sampling procedures for carbon leader and quartzite. All samples for lipid analysis were stored frozen at -40oC until analyzed. Fracture water (21 samples) was collected in sterile 50-mL disposable centrifuge tubes for flow cytometry measurements (Onstott et al. this volume). Physical and geochemical measurements and sample collection procedures are also reported in Onstott et al. (this volume).
Phospholipid Fatty Acid Analysis Lipids were extracted using a modified Bligh and Dyer extraction (Bligh and Dyer 1959; White et. al. 1979) and subsequently fractionated with the polar-lipids then subjected to a sequential saponification/acid hydrolysis/esterification. The PLFA methyl esters were separated, quantified and identified by gas chromatography-mass spectrometry (White and Ringelberg 1998). The identity of PLFA was verified using GC/MS in comparison with standards (Matreya Inc. Pleasant Gap, PA, USA). Fatty acid nomenclature is based on the fatty acid abbreviated by the number of carbon atoms (a), a colon, the number of unsaturated C-C bonds (b) followed by ‘ω’ followed by the number of carbons (c) from the methyl end of the molecule to the position of the unsaturation (e.g., a:bωc). For monoenoic fatty acids, the a:bωc molecule is followed by the suffix “c” for the
cis- or “t” for trans-configuration. Branched fatty acids are described by iso (i) or anteiso (a), if the methyl branch is one or two carbons from the methyl end or by the position of the methyl group from the carboxylic end of the molecule. The PLFA results are presented as biodensity in pmol g-1 or L-1 or as cells g-1 or L-1 using the conversion factor of 2.5 x 105 cells pmol-1 (Balkwill et. al. 1988). Community compositions are represented as mole percentage for individual PLFA or for PLFA structural groups. Structural groups are indicated as normal saturates (Nsat), terminally branched saturates (Tbsat), mid-chain branched saturates (Mbsat), monounsaturates (Mono), branched monounsaturates (Bmono), cyclopropyl fatty acids (Cyclo), and polyunsaturates plus monounsaturates with 20 or more carbons (Eukary).
Data Analysis Data were compiled in Microsoft Excel. The redundancy analysis (RDA) was calculated and graphed using CANOCO for Windows, version 4.02 (ter Braak 1998). The Redundancy analysis was preformed on fracture and service water samples. In this analysis, zero replaced missing values for environmental variables. Redundancy analysis, a linear canonical community ordination method, was used to visualize the relationships between the response variable values (PLFA), the environmental variable gradients (water chemistry) and the samples. RDA is similar to canonical correspondence analysis (CCA), except that RDA is a linear model, whereas CCA is unimodal (the PLFA data in this study did not conform to a unimodal distribution). In RDA, the ordination axes are constrained to be linear combinations of the environmental variables allowing the relationships between the environmental variables and the response variables to be directly compared. Points in the ordination space represented the samples (a
distance diagram), and the PLFA response variables (called ‘species’ in ordination models) and environmental variables are represented by arrows projecting from the origin (ter Braak and Prentice 1988). For the RDA in this study, PLFA were treated as ‘species’. The arrows in the resulting ordination diagrams point in the direction of maximum variation in the PLFA, and the arrow length is proportional to the rate of change. PLFA arrow heads near the edge of the plot are most important in explaining sample differences, whereas PLFA arrow heads near the center of the plot are of less importance. PLFA arrows pointing in the same general direction as environmental arrows can be interpreted as correlating well with that variable, and the longer the arrows, the greater the confidence in that correlation (ter Braak 1994). Environmental gradient arrows that are the longest allow more confidence in the inferred correlations, indicate a larger effect of that variable on the total species variation (ter Braak 1998), and point in the direction in which the species location would move if the value of that environmental variable increased.
Results General physical/chemical patterns Fracture water temperature ranged from 25 to >60°C, whereas the service water temperature ranged from 17 to 33°C (Table 2). Fracture water pH ranged from 5.5 to 10, whereas service water tended to either be circumneutral (6.4 to 7.9) or highly acidic (2.6 to 2.8). Differences in anion concentrations between fracture water within a mine and between mines were observed to range over 3 to 5 orders of magnitude. DR5F1-3 from 1998 to 2001 showed the highest Cl- concentrations, whereas DR9-11 and -13 had the lowest Cl- concentrations. Other high values for Cl- were seen in KL4F2, 3, and 6, KL7F1 and EV8F1-6 samples. The lowest sulfate concentrations were found in fracture water from BE, EV and KL mines. Higher sulfate
and nitrate concentrations were observed in DR5 service water as compared to the fracture water. The sulfate concentration of DR5 service water was low when the shaft was first opened in 1998, but over the subsequent 4 years of continuous mining activity, both the sulfate and nitrate concentrations increased by a factor of 50 and 2, respectively. The service water sulfate concentration for DR9 shaft, which has never been opened to mining, was quite low (Table 2). The service water from EV2 (EV2F4 and EV2S1), which has been an operating mine for many years, exhibit high concentrations of sulfate and nitrate than the fracture water (EV2F1-3 and 57). The service water for EV8 (EV8S1 and 2), which represents an exploration tunnel where no mining was occurring, had sulfate concentrations comparable to that of the fracture water EV8F1-6, but much higher nitrate levels; whereas EV8S3 comes from an older part of EV8 and both its sulfate and nitrate concentrations are higher than the fracture water. Both the sulfate and nitrate concentrations of KL4 (KL4S1 and 2) were higher than the fracture water and this mine had been active for several years prior to sampling. In general, service water has elevated nitrate concentrations compared to that of fracture water, but only service water from areas of the mine which have been in operation for more than a year or two exhibit sulfate concentrations that are elevated with respect to the fracture water.
Biodensity Estimates Estimates of microbial biodensity from the PLFA profiles ranged over 9 orders of magnitude variation from 1 x10-2 to 1 x107 pmol L-1 (Figure 2A, 2B). Biofilm samples had the largest biodensities ranging from 1x103 to 1 x107 pmol g-1. Service water tended to have higher biodensity than most fracture water, yet both types of samples had biodensity ranging over 5 orders of magnitude. Carbon leader and quartzite rock samples had PLFA biodensity estimates
of 8 x103 to 5 x106 pmol g-1. Air samples had the lowest biodensity at 0.01 to 1.5 pmol L-1. PLFA biodensity estimates for fracture water using enrichment matrices showed 5 to 7400 pmol g-1. The EV enrichment biodensity ranged from 20 to 80 pmol g-1 for treatments of sand mended to stimulate sulfate- and iron- reduction and methanol utilization. In DR5 enrichment series, the Fe(III) sand and spotted dyke had biodensity values of 7400 and 2550 pmol g-1 and the remaining series had values from 5 to 800 pmol g-1 (data not shown). The DR9 enrichment series showed the highest biodensity with sand at 2730 pmol g-1 followed by elemental Fe and Fe(III) sand at ~ 1100 pmol g-1 and the remaining at 40 to 700 pmol g-1. The enrichment matrices, therefore, did not show similar responses by treatment matrix for different fracture water samples (data not shown). When using a conversion factor of 2.5 x 104 cells pmol-1 (Balkwill et. al. 1988), the full range of biodensity represents 2.2 x 10-2 to 2.7x1011 cells g-1 or L-1. Flow cytometry provided cell count estimates for nine fracture water samples that ranged from 2-to 500-fold higher than PLFA estimates (Table 3). Ten other samples showed similar comparisons between the two assays, whereas seven samples had higher PLFA cell count estimates. Figure 3 shows the relationship between flow cytometry cells counts and PLFA cell estimates. Samples with cell estimates of > 1000 or >5000 have associated arrows, and are located outside the trend line for a 1:1 relationship. Other samples located outside the trend line were DR4F1, DR9F7, DR9F8, MS1F9 and EV8F2, which contain Archaea and have an “A” designation on the graph. The locations of these samples pull the sample trend line lower that the 1:1 relationship.
Microbial Community Composition The four major PLFA observed in these samples were 16:0, 18:0, 18:1ω7c and 16:1ω7c. Other prominent PLFA were 18:1ω9c, cy19:0, 18:2ω6, and 18:1ω7t. The combination of these prominent PLFA with minor PLFA provided differences in community composition between the different types of mine samples. Within the fracture water samples, four samples contained only normal saturates and four samples were below detection. The normal saturates and monounsaturates dominated in the fracture water at most sites (Table 4). Fracture water containing 80-90 mole % normal saturates included BE2F1, EV8F4 and 7, KL4F2 and 6, KL7F1, MS1F1-3 and 8, TT1F1-3, and DR9F1. Within the fracture water samples, EV8F9 had the lowest value at 11 mole % whereas, the remaining fracture water had normal saturates from 22-79 mole %. Monounstaturate PLFA were highest at 84 mole % for EV8F1 followed by ~75 mole % for BE1F1 and EV9F1. Some fracture water, especially DR9F1-8 and EV2F1, contained 20 to 40 mole% terminally branched saturates. In fact, three of these fracture water samples had no monounsaturated detected. Of the fracture water, DR6F4 had the highest proportion of mid-chain branched unsaturates (8 mole%) and DR5F1and 2 had the highest proportion of branched monounsaturates (13-18mole%). Samples EV8F9, KL4F3, EV2F3, 5, and 7, DR6F1, DR9F9, and BE2F2 had cyclopropyl fatty acids ranging form 10-30 mole %. Eukaryotic PLFA at proportions of 10 to 24 mole % were seen in ten fracture water samples with DR9F12 and MS1F9 containing 21 and 24 mole % respectively. In comparison to the fracture water, the service water community profiles contained much larger percentages of monounsaturates, where 72% of the samples had > 40 mole % of monounsaturates (Table 5). Exceptions were DR5S7 and DR6S1 service water which contained < 10 mole % monounsaturates with 70 % normal saturates. DR5S7 had the highest mid-chain
branched unsaturates at 20 mole %, while DR6S1 had 18 mole % of terminally and mid-chain branched saturates. In addition DR6S1, EV2S4, DR5S1, 2 and 5, and NO2S1, had profiles containing 10 to 17% mole % terminally branched saturates. Six samples had cyclopropyl fatty acids at >5 mole %, whereas six other samples had eukaryotic PLFA at >5 mole %. Biofilm sample profiles (Table 6) were similar to service water profiles in the relative proportions of normal saturates (20 to 49 mol%) and monounsaturates (26 to 68 mole %) However, 29 % of the PLFA profile for the biofilm samples, especially those from DR5B1-3, EV8B1, MS1B2 and DR6B1, had profiles comprised of 10 to 30 mole % as cyclopropyl fatty acids. Carbon leader and quartzite samples (Table 7) varied in the profiles composition with the major PLFA class for eight samples was normal saturates at 48 to 79 mole %, whereas three samples had 40 to 78% monounsaturates, and another sample had 43 mole % eukaryotic PLFA. Other samples, containing relatively large proportion of eukaryotic PLFA, were DR5C2, 6, 7, and 8, and DR5Q2. The rock samples had 1000 or >5000 have associated arrows, and are located outside the 1:1 relationship line with two samples located above this line, two samples near the line and four samples located below the line. These potentially overestimated samples also pulled the sample assay trend line below the 1:1 relationship. The second explanation is that the flow cytometry data may have overestimated cell counts because up to four morphological types were used to estimate total cells. Some of these morphologies may have represented particulate matter other than bacterial cells. Another consideration is that the amount of lipid membrane per cell may be altered under the temperature and pressure constraints in these deep environs as has been shown for cell size variation and lipid content between surface and subsurface microorganisms (Balkwill et al.1988). When the total cell counts and PLFA derived biodensities for the fracture water samples are plotted versus depth and compared with the results of previously published subsurface microbial studies (Fig. 7), the densities are consistent with a steady decline in microbial abundance with depth. The fracture water from BE overlap in depth with fracture water from the
ASPO Hard Rock Laboratory and yield values comparable to those reported by Kotelnikova and Pedersen (1997). If we consider the fracture water samples from boreholes only a few days to weeks old (large squares in Fig. 7) then the biodensity of the planktonic community at 3 km depth is 103 to 104 cell ml-1, whereas that of the planktonic community at 1 km depth is 105 to 106 cells ml-1. Boreholes that have been sampled multiple times over a period of years exhibit variations in biodensity by one order of magnitude, but no obvious trends that might record an impact from mining activities. In the case of DR9H3, the borehole exhibits a vertical variation in biodensity that ranges over an order of magnitude and may reflect accumulation of biomass is colloidal suspensions at depth. These results provide for the first time an approximate assessment of the distribution of microorganisms between solid and fluid phases. The planktonic biodensity is less dense that that of the sessile community for environments less than 1 km in depth, comparable to the sessile biodensity at 2 km depth and possibly greater than the sessile biodensity at 3 km depth, although the last comparison is based on only one study (Onstott et al. 2002). These estimates of sessile community densities are from the examination of the rock or sediment matrix. Wanger et al. (this volume) reported a biodensity of ~5x104 cm-2 for a fresh fracture surface at 2.8 kilometers depth where the planktonic density within the fracture was ~3 x104 cm-3. For a fracture aperture of 100 μm, this corresponds to a sessile to planktonic ratio of 100:1. At these depths in South Africa the rock strata may have 1 to 10 such fractures per 100 meters. A 1 m2 by 100 meter section of rock would be comprised of 103 to 104 planktonic cells, 109 to 1010 cells adhering to fracture surfaces and 1010 to 1011 cells in the rock matrix. This simple calculation illustrates that the sessile microorganisms probably still dominate the deep subsurface, but until significant improvements are made in the analyses of microbial content of rocks and fracture surfaces, we
will not know for certain how representative of the much easier to characterize planktonic community is.
Microbial Community Composition When considering the PLFA community compositions of the South African mine samples, the most prominent PLFA were 16:0, 18:0, 18:1ω7c, 16:1ω7c, 18:1ω9c, cy19:0, c18:2ω6, and 18:1ω7t. Monounsaturates (16:1 and 18:1) and cyclopropyl PLFA (cy19:0) represent Gram-negative bacteria (Wilkinson 1988), thus there appear to be a predominance of Gram-negative communities in these samples. Nonetheless, differences were observed in the proportions of the PLFA comprising the community composition between the different types of mine samples and among samples of the same type (Table 4-8). Differences in the relative proportion of the PLFA, when grouped by structural class, shows that many samples were not dominated by Gram-negative cells. Fracture water and air samples with low biodensity estimates (BE2F1, DR9F10, EV84F4 and 7, BE3A1and 2) contained large relative proportions (80-100%) of normal saturates (Table 4 and 8), which are ubiquitous in nature (Table 4 and 8). However, other low-biodensity samples (Figure 1B) for fracture water, air carbon leader and quartzite (EV5F1, EV8A4, DR5C1, DR5Q2) had PLFA profiles showing diverse microbial communities (Tables 4, 7 and 8). The diverse community profile contained biomarker lipids indicating the presence of Gram-positive (terminally branched saturates), sulfate and metal reducers (mid-branched saturates and branched monounsaturates), and eukaryotes (18:2ω6, polyunsaturates) (Weete, 1974; O’Leary and Wilkinson 1988; Kaneda 1991; Kohring et al. 1994; Stahl and Klug 1996; Zhang et al. 2003).
Fracture water samples (i.e., DR9F1-8 and EV2F1) contained large percentages of terminally branched saturates, suggesting a strong Gram-positive component, a finding that is supported by phylogenetic analysis of DNA extract from fracture water that showed the presence of sulfate reducing bacteria, including Desulfotomaculum-like organisms (Baker et al. 2003; Moser et al. 2003). These PLFA profiles were also similar to PLFA profiles for Desulfotomaculum putei which was isolated from the Taylorsville Triassic Basin in Virginia (Liu et al. 1997). Compared to the fracture water PLFA profiles, service water community profiles appeared to be dominated by Gram-negative populations based on the large proportion of monounsaturates (Table 5). DR5S7 showed indications of metal reducers with the presence of mid-chain branched unsaturates, while DR6S1, DR6S1, EV2S4, DR5S1, 2 and 5, and NO2S1, showed the influence of Gram-positive bacteria with the presence of terminally branched saturates. Biofilm sample profiles (Table 6) were similar to service water profiles in the predominance of Gram-negative communities. Carbon leader and quartzite samples (Table 7) varied in the profiles composition dominated by Gram-negative communities based on the proportion of monounsaturates (DR6C1) to profiles dominated by metal reducer (DR5C1) to profiles dominated by eukaryotes (DR5C8). Air samples were dominated by normal saturates, which reveal little regarding community makeup (Table 8). EV8A4 showed a Gram-positive community based on the proportion of terminally branched saturates, while EV8A3 showed a Gram-negative community based on the proportion of monounsaturates. Eukaryotic influences were found in BE3A2 and MP1A2 based on the presence of polyunsaturates. Phylogenetic 16S rDNA analysis by Onstott et al. (2003) on service water, air, and carbon leader samples showed that clones from service water belonged to
the α-, β- and γ-Proteobacteria division and that the carbon leader clones were more closely related to those from service water than air samples. The PLFA community profiles varied for the in situ enrichment cartridges (Table 9). The DR5 where elemental Fe was used had a PLFA profile containing a large proportion of branched monounsaturates indicative of metal reducers, whereas sand and Columbia River Basalt chips with higher proportions of cyclopropyl fatty acids were predominated by Gram-negative bacteria. The CRB-square filter with mid-chain branched saturates indicates metal or sulfate reducers while the spotted dyke and Fe (III) with terminally branched saturates indicate Grampositive bacteria. From these enrichments, Baker et al. (2003) found meso- and thermophilicsulfate reducers as well as Desulfotomaculum spp by DNA phylogenetic analysis on DR5E10 and DR6EF1. The DR9 enrichment series showed a mixed community of Gram-negative and Gram-positive bacteria based on the proportions of monounsaturates and terminally branched saturates. This mixed community appeared regardless of the matrix, with the exception of DR9E1 that contained magnetite and had a PLFA profile indicative of Gram-negative bacteria. The detection of eukaryotic PLFA, polyunsaturates such as 18:2ω6, generally indicates the presence of fungi and cyanobacteria (Weete, 1974; Stahl and Klug 1996). In addition to 18:2ω6, other eukaryotic PLFA detected in the South African samples were 18:2, 18:3ω3, 20:1’s, 20:5ω3, 22:4ω6, and 22:6ω3. When considering the chemistry of fracture water, which were generally anoxic (Onstott et al. 2003; Moser et al. 2003; Onstott et al. this volume), the presence of eukaryotic PLFA does not logically fit. It was initially thought that the eukaryotic influence could be due to the fact that some boreholes with low flow rates would have experienced oxygen penetration into the boreholes and the opportunity for aerobic biofilm formation and the influx of fungi from mine air. Only two of the ten air samples, however, had
the 18:2ω6 fungal PLFA. Cyanobacteria were ruled out because of the lack of light for photosynthesis and their general absence from the 16S rDNA clone libraries (Lin et al. this volume). Recently, Luo et al. (2005) documented the predominance of microeukaryotes in anaerobic sulfide and sulfur-rich springs using 18S rRNA gene-based analysis. A few South African fracture water samples were examined by 18S rDNA resulting in no detection of microeukaryotes (Kieft, personal communication). Alternative explanations for these eukaryotic PLFA come from reports of bacteria that produce PLFA of 20 or more carbons in length, with one or more double bonds. Takai et al. (2001b) demonstrated that a thermophilic heterotroph isolated from deep subsurface geothermal water produced 20:1ω9 as 50% of the total PLFA profile. In addition, some peizophiles or barophiles have been shown to contain polyunsaturates, 20:5ω3 and 22:6ω3 (DeLong and Yayanos 1985; Fang et al. 2000b). Bartlett (2002) suggested that polyunsaturates maintain membrane stability and fluidity. Furthermore, Nordström and Laakso (1992) showed that the proportions of i15:0 and i17:0 increased with temperature while the proportion of a15:0, a17:0 and a17:1 decreased when Thermus strains were grown at 40 to 70ºC. In 33% of the South African samples, i15:0 and i17:0 were more abundant than a15:0 and a17:0. At the South African mines, we were not able to assess the potential pressures or the combination of temperature and pressure experienced by microorganisms in the fracture water. However, one could speculate that the deep subsurface communities may have altered lipid compositions to maintain membrane integrity under these extreme environmental conditions.
Community Physiological Stress
Microorganisms change their fatty acid compositions to increase membrane ordering and decrease fluidity (Sikkema et al. 1995) in response to changes in environmental factors such as nutrient limitation (Kieft et al. 1994; Macnaughton et al. 1999) or exposure to a toxic or inhibitor compound, e.g., hydrocarbons (Pfiffner et al. 1997). These changes result in the formation of either trans-monounsaturates or cyclopropyl fatty acids within the Gram-negative community. For example, cyclopropyl fatty acids (cy17:0 and cy19:0) are formed preferentially over the monounsaturates 16:1ω7c and 18:1ω7c as bacteria switch from log to stationary growth phase, thus signaling a nutritional stress (Guckert et al., 1986; Findlay et al., 1993; Kieft et al. 1994; White et al. 1996,). Although varying between organisms, ratios of cyclopropyl-tomonounsaturate fatty acids generally fall within the range of 0.05 (log phase) to 2.5 or greater (stationary phase; Guckert et al. 1986; White et al. 1996). In the South African samples a ratio as high as 2.16 was observed for KL4F3. Of the 132 samples analyzed 17% had cyclopropyl-tomonounsaturate fatty acid ratios > 0.5. With exposure to toxic compounds, trans monounsaturates are produces and a ratio of trans to cis monounsaturate fatty acids above 0.1 indicates stress in the microbial communities (White el al. 1996). In the SA samples (i.e., MS1B14, DR5C1-3) the trans to cis ratio was elevated to 0.55 - 2.64, thus the community is under physiological stress. Of the 132 samples analyzed, 30% have trans-to-cis monounsaturate ratios >0.1. Another mechanism for membrane stability under potentially toxic conditions is the formation of oxirane. Oxiranes are formed when membrane are exposed to radical compounds such as hypochlorous anions (bleach, Smith et al. 2000) used by the mining companies to disinfect the service water or perhaps radical formation from the decay of natural radioactive elements (Lin et al. 2005). Oxiranes were detected in 19% of the South African samples, with the highest mole % 21 occurring in DR5S5.
Redundancy analysis The RDA analysis allowed us to examine the patterns in the PLFA data in terms of both the sample types and the measured environmental parameters (Fig. 4-6). In the RDA shown in figure 4 the sample types as centroids were distinguished and associated with specific PLFA. The PLFA 18:1ω7c, 16:1ω7c, 10Me16, 18:1, and cy19:0 were associated with biofilm, indicating Gram-negative and metal reducer influences. The 18:2, a eukaryotic indicator, and Gram-negative bacterial stress indicators, 18:1ω7t and, 16:1ω7t, were associated with the service water which is not surprising given the fact that service water is treated with hypochlorite. PLFA important to distinguishing rock samples included 18:2ω6, 17:0, 18:1ω9c, br17:1s, and br18:1s, which corresponds to a eukaryotic influence and metal reducers. Normal saturates of 18:0 and 20:0 were linked most closely to the air and fracture water samples and denote low diversity. The fracture water centroid was close to the center of the plot so other PLFA for Gram-negative bacteria and metal reducers played a less important role than the normal saturates in overall ordination. When the RDA was focused on fracture and service water, different mine samples were related to specific fatty acids and correlated well with environmental parameters (Figure 5 and 6). When the PLFA and environmental parameters were analyzed together, the combined explained variance was 88% for the x and y axes. This high variance explained is similar to the 80% variance explained seen by McKinley et al. (2005), who compared physical and chemical soil parameters with microbial community PLFA profiles. In Figure 5 the environmental gradients are shown with mine centroids and the species (PLFA). The service water centroid (regardless of mine location) is associated with 18:1ω7c,
16:1ω7c, 16:1ω5c, 16:1ω7t and 18:2, which indicates a Gram-negative and eukaryotic influence whereas increases in 18:0 to the opposite side of the plot are associated with fracture water of TT, MS and KL mines. This observation corresponds with the high amounts of normal saturates detected in these mines and again probably denotes a more homogeneous community. Both MM and BE facture water centroids were located near the service water centroid. This result is consistent with composition of the 16S rDNA clone libraries from the Beatrix mine fracture water, which bear a strong resemblance to that of the service water in that both are dominated by Proteobacteria (Lin et al. 2005). Both MM and BE have similar PLFA profiles to the service water profiles, with BE having a larger proportion of normal saturates, which aligned it with the TT, MS and KL centroids (Table 4 and 5). EV and DR centroids were associated with several terminally branched and mid-chain branched saturates and branched monounsaturates which indicated an influence of sulfate and metal reducers. When the environmental gradients are considered, chloride aligned with DR fracture; higher water temperature and pH were associated with TT, MS and KL fracture water; and sulfate, nitrate and Eh were correlated with service water and MM fracture water. BE and EV fracture water did not exhibit any strong correlation with these environmental parameters. Figure 6 shows the environmental gradients as vectors and the species as the fracture (open circles) and service water (closed circles) samples. This graph is a closer look at the individual water samples. The service water samples are aligned with the Eh, sulfate and nitrate vectors with a bit more spread as compared to the location of the service water centroid in figure 5. However the separation of fracture water is more understandable. For example, DR5 fracture water samples (#1-3) were most closely associated with the higher concentrations of chloride (0.6 M), whereas most other fracture water samples had much lower chloride concentration and
were located lower in the graph. KL fracture water was associated with both high temperature (55-61ºC) and high pH (8.2-9.0). Many fracture water samples from TT, MS, EV and DR associated more loosely with pH. Some DR and EV samples aligned with temperature at lower values than KL. The RDA analysis provided a robust method to understand the relatedness of environmental parameters to the PLFA profiles.
The mines provide access to the deep subsurface and an environment to assess at life in extreme environments. PLFA profiles provided a sensitive and meaningful measure of microbial community compositions. Service water samples had the most lipid biodensity, followed by fracture water, and air samples. PLFA profiles revealed a diverse microbial community structure for most samples. Relative proportions of PLFA biomarkers indicate the presence of thermophiles, sulfate reducers, and metal reducers. Air samples had more eukaryotic influences, yet the presence of eukaroytic PLFA may be explained by the production of polyunsaturates by extremophiles. Community structures could vary between mines and were related to chemistry and physical parameters. Further investigations into the deep terrestrial subsurface microbial communities will provide a better understanding of the microbial ecology at depth.
We would like to thank South African mines, the University of the Free State and Princeton University for site access and sampling assistance. We would also like to thank The University of Tennessee Center for Environmental Biotechnology for support and the other participants for
their cooperation. This research was supported by grants from the National Science Foundation/NASA LExEn program (EAR-9714214 and EAR-9978267) to Princeton University (T.C. Onstott) and by an REU supplement grant EAR-0228968 to Univ. of Tennessee (S.M. Pfiffner).
References Bååth E, Diaz-Ravina M, Frostegård Å, Campbell CD. 1998. Effect of metal-rich sludge amendments on the soil microbial community. Appl Environ Microbiol 64:238-245.
Baker BJ, Moser DP, MacGregor BJ, Fishbain S, Wagner M, Fry NK, Jackson B, Speolstra N, Loos S, Takai K, Sherwood Lollar B, Fredrickson J, Balkwill D, Onstott TC, Wimpee CF, Stahl DA. 2003. Related assemblages of sulphate-reducing bacteria associated with ultradeep gold mines of South Africa and deep basalt aquifers of Washington State. Environ Microbiol 5:267277.
Balkwill DL, Leach FR, Wilson JT, McNabb JF, White DC. 1988. Equivalence of microbial biodensity measures based on membrane lipid and cell wall components, adenosine triphosphate, and direct counts in subsurface sediments. Microbial Ecol 16:73-84.
Bartlett DH. 2002. Pressure effects on in vivo microbial processes. Biochimica et Biophysica Acta 1595:367-381.
Bligh EG, Dyer WJ. 1959. A rapid method of total lipid extraction and purification. Can J Biochem Phys 37:911-917.
Brandt CC, Schryver JC, Pfiffner SM, Palumbo AV, Macnaughton S. 1999. Using artificial neural networks to assess changes in microbial communities. In: Leeson A, Alleman BC, editors. Bioremediation of Metals and Inorganic Compounds. Columbus: Battelle Press. pp. 1-6.
DeFlaun, MF, Fredrickson JK, Dong H, Pfiffner SM, Onstott TC, Balkwill DL, Streger SH, E. Stackebrandt, S. Knoessen, and E. Van Heerden. Isolation and characterization of A novel Geobacillus thermoleovorans species from an ultra-deep goldmine in South Africa. International J Syst Appl Microbiol (submitted). DeLong EF, Yayanos AA. 1985. Adaptation of membranes to temperature, pressure and exogenous lipids. Science 228:1101-1103.
Fang J, Barcelona, MJ, Nogi, Y, Kato C. 2000. Biochemical implications and geochemical significance of novel phospholipids of the extremely barophilic bacteria from the Marianas Trench at 11,000m. Deep-Sea Research I 47:1173-1182.
Federle TW, Dobbins DC, Thornton-Manning JR, Jones DD. 1986. Microbial biomass, activity, and community structure in subsurface soils. Ground Water 24: 365-374.
Findlay RH, Dobbs FC. 1993. Quantitative description of microbial communities using lipid analysis. In: Kemp PF, Sherr BF, Sherr EB, Cole JJ editors. Handbook of Methods in Aquatic Microbial Ecology. Boca Raton: Lewis Publishers. p 271-284.
Frostegård, Å, Tunlid, A, Bååth, E. 1996. Changes in microbial community structure during long-term incubation in two soils experimentally contaminated with metals. Soil Biol Biochem 28:55-63.
Guckert JB, Hood MA, White, DC. 1986. Phospholipid, ester-linked fatty acid profile changes during nutrient depletion of Vibrio cholerae: increases in the trans/cis and proportions of cyclopropyl fatty acids. Appl Environ Microbiol 52:794-801.
Kaneda, T. 1991. Iso and anteiso fatty acids in bacteria: biosynthesis, function, and taxonomic significance. Microbiol Rev 55:288-302.
Kieft, TL, Ringelberg, DB, White, DC. 1994. Changes in ester-linked phospholipid fatty acid profiles of subsurface bacteria during starvation and desiccation in a porous medium. Appl Environ Microbiol 60:3292-3299.
Kieft, TL, Fredrickson JK, McKinley JP, Bjornstad BN, Rawson SA, Phelps TJ, Brockman FJ, Pfiffner SM. 1995. Microbiological comparisons within and across contiguous lacustrine, paleosol, and fluvial subsurface sediments. Appl Environ Microbiol 61: 749-757.
Kohring LL, Ringelberg, DB, Devereux R, Stahl M, Mittleman M, White DC. 1994. Comparison of phylogenetic relationships based on phospholipids fatty acid profiles and ribosomal RNA sequence similarities among dissimilatory sulfate-reducing bacteria. FEMS Microbiol Letters 119:303-308.
Kotelnikova S, Pedersen K. 1997. Evidence for methanogenic Archea and homoacetogenic Bacteria in deep granitic rock aquifers. FEMS Microbiology Reviews 20:339-349.
Lin L-H, Gihring T, Sherwood Lollar B, Boice E, Pratt LM, Lippmann-Pipke J, Bellamy RES, Hall J, Onstott TC. Heterogeneous microbial communities associated with a 0.7 to 1.4 kmbls section of the continental crust. Geomicrobiology Journal this volume.
Liu Y, Karnauchow TM, Jarrell KF, Balkwill DL, Drake GR, Ringelberg D, Clarno R, Boone DR. 1997. Description of two new thermophilic Desulfotomaculum spp., Desulfotomaculum putei sp. nov., from a deep terrestrial subsurface, and Desulfotomaculum luciae sp. nov., from a hot spring. International J Systematic Bacteriology 47:615-621.
Luo Q, Krumholz LR, Najar FZ, Peacock AD, Roe BA, White, DC, Elshahed MS. 2005. Diversity of the microeukaryotic community in sulfide-rich Zodletone Spring (Oklahoma). Appl Environ Microbiol 71:6175-6184.
Macnaughton S, Stephen JR, Chang Y-J, Peacock AD, Flemming CT, Leung KT, White DC. 1999. Characterization of metal resistant soil eubacteria by polymerase chain reaction-during gradient gel electrophoresis. Can J Microbiol 45:116-124.
McKinley VL, Peacock AD, White DC. 2005. Microbial community PLFA and PHB responses to ecosystem restoration in tall grass prairie soils. Soil Biology Biochem 37:1946-1958.
Moser DP, Onstott TC, Fredrickson JK, Brockman FJ, Balkwill DL, Drake GR, Pfiffner SM, White DC, Takai K, Pratt LM, Fong J, Sherwood-Lollar B, Slater G, Phelps TJ, Spoelstra N, DeFlaun M, Southam G, Welty AT, Baker BJ, Hoek J. 2003. Temporal shifts in microbial
community structure and geochemistry of an ultradeep South African gold mine borehole. Geomicrobiol J, 20:517-548.
Nordström, KM, Laakso SM. 1992. Effect of growth temperature on fatty acid composition of ten Thermus strains. Appl Environ Microbiol 58:1656-1660.
O’Leary, WM, Wilkinson, SG. 1988. Gram-positive bacteria. In: Ratledge C, Wilkinson SG, editors. Microbial Lipids, vol. 1. New York:Academic Press, Inc. pp.117-201.
Onstott TC, Tobin K, Dong H, DeFlaun MF, Fredrickson JF, Bailey T, Brockman F, Kieft T,. Peacock A, White DC, Balkwill D, Phelps TJ, Boone DR. 1997. The deep gold mines of South Africa: Windows into the subsurface biosphere. In: Hoover RB, editor. Instruments, Methods, and Missions for the Investigation of Extraterrestrial Microorganisms, v. 3111, p. 344-357.
Onstott TC, Phelps TJ, Colwell F, Ringelberg, D, White DC, Boone D. 1998a. Observations pertaining to the origin and ecology of microorganisms recovered from the deep subsurface of Taylorsville Basin VA. Geomicrobiol J 15: 353-385. Onstott TC, Phelps TJ, Kieft TL, Colwell FS, Balkwill DL, Fredrickson JK, Brockman FJ. 1998b. A global perspective on the microbial abundance and activity in the deep subsurface. Microorganisms and Life in Extreme Environments 487-500.
Onstott, TC, Moser DP, Pfiffner SM, Fredrickson JK, Brockman FJ, Phelps TJ, White DC, Peacock A, Balkwill D, Hoover R, Krumholz LR, Borscik M, Kieft TL, Wilson R. 2003. Indigenous and contaminant microbes in ultradeep mines. Environ Microbiol 5:1168-91.
Pederson K. 1999. Subterranean microorganisms and radioactive waste disposal in Sweden. Engineering Geology 52:163-176.
Pfiffner SM, Palumbo, AV, Gibon T, Ringelberg DB, McCarthy JF. 1997. Relating groundwater and sediment chemistry to microbial characterization at BTEX-contaminated site. Appl Biochem Biotech 63-65:775-788.
Rothschild LJ, Mancinelli RL. 2001. Life in extreme environments. Nature 409:1092-1101.
Sassen R, MacDonald IR, Guinasso NL, Joye S, Requejo AG, Sweet ST, Alcala-Herrera J, DeFreitas D, Schink DR. 1998. Bacterial methane osication in sea-floor gas hydrate: significance to life in extreme environments. Geology 26:851-854.
Sikkema J, deBont JAM, Poolman B. 1995. Mechanisms of membrane toxicity of hydrocarbons. Microbiol Rev 59: 201-222
Smith CA, Phiefer CB, MacNaughton SJ, Peacock AD, Burkhalter, RS, Kirkegaard RD, White DC. 2000. Quantitative lipid biomarker detection of unculturable microbes and chlorine exposure in water distribution system biofilms. Water Research. 34:2683-2688.
Stahl PD, Klug MJ. 1996. Characterization and differentiation of filamentous fungi based on fatty acid composition. Appl Environ Microbiol 62:4136-4146.
Stephen JR, Chang Y-J, Gan YD, Peacock A, Pfiffner SM, Barcelona MJ, White DC, Macnaughton SJ. 1998. Microbial characterization of PJ-4 fuel contaminated site using a combined lipid biomarker/PCR-DGGE based approach. Enivron Microbiol 1:231-241.
Takai K, Moser, DP, DeFlaun, MF, Onstott, TC, Fredrickson, JK. 2001a. Archaeal diversity in waters from deep South African gold mine. Appl Environ Microbiol 67:5750-5760.
Takai K, Komatsu T, Horikoshi K. 2001b. Hydrogenobacter subterraneus sp.nov., an extremely thermophilic, heterotrophic bacterium unable to grow on hydrogen gas, from deep subsurface geothermal water. International J Systematic Evol Microbiol 51:1425-1435.
ter Braak CJF, Prentice IC. 1988. A theory of gradient analysis. Advances in Ecological Research 18:271–317.
ter Braak CJF. 1994. Canonical community ordination. Part I. Basic theory and linear methods. Ecoscience 1:127–140.
ter Braak CJF. 1998. CANOCO Reference Manual and User’s Guide to CANOCO for Windows: Software for Canonical Community Ordination (version 4), Microcomputer Power, Ithica, NY.
Tunlid, A, White, DC. 1992. Biochemical analysis of biomass, community structure, nutritional status and metabolic activity of the microbial community in soil. In: Bollag J-M, Stotzky G, editiors. Soil Biochemistry Vol. 7. –. New York: Marcel Dekker Inc., pp. 229-262.
Wanger G, Onstott TC, Southam G. 2005. Structural and chemical characterization of a natural fracture surface from 2.8 kilometers below land surface: biofilms in the deep subsurface. Geomicrobiology Journal, this volume.
Weete JD. 1974. Fungal Lipid Biochemistry: Distribution and Metabolism. New York: Plenum Press.
White DC, Davis WM, Nickels JS, King JD, Bobbie RJ. 1979. Determination of the sedimentary microbial biodensity by extractable lipid phosphate. Oecologia 40:51-62.
White DC, Pinkart HC, Ringelberg DB. 1996. Biomass measurements: biochemical approaches. In: Hurst CJ, Knudson GR, McInerney MJ, Stetzenbach LD, Walter MV, editors. Manual of Environmental Microbiology. Washington: American Society for Microbiology Press. P 91-101.
White DC, Ringelberg DB. 1998. Signature Lipid Biomarker Analysis. In Burlage RS, Atlas R, Stahl D, Geesey G, Sayler G, editors. Techniques in Microbial Ecology. New York: Oxford University Press. pp. 255-272.
Wilkinson, SG. 1988. Gram-negative bacteria. In: Ratledge C,Wilkinson SG, editors. Microbial Lipids, vol. 1. New York: Academic Press, Inc. pp.299-488.
Zhang CL, Ye Q, Reysenbach AL, Götz D, Peacock A, White DC, Horita J, Cole DR, Fong J, Pratt L, Fang J, Yongsong H. 2002. Carbon isotope fractionations associated with thermophilic bacteria Thermotoga maritime and Persephonella marina. Environ Microbiol 4:59-64.
Zierenberg RA, Adams MWW, Arp AJ. 2000. Life in extreme environments: hydrothermal vents. Proc Nat Acad Sci USA 97:12961-12962.
Figure 1. Location of South African gold and platinum mines are indicated on the map with the abbreviation as NO=Northam platinum mine, and gold mines as BE=Beatrix, DR=Dreifontein, EV=Evander, KL=Klook, MP=Mponeng, MS=Merrispruit, TT=TauTona.
Figure 2. Box and Wisker plots of PLFA biodensity distribution (log pmol/L or g) for biofilm, fracture water and service water samples are shown in plot A, with carbon leader, quartzite and air samples shown in plot B.
Figure 3. Plots the PLFA cells count estimates verses the flow cytometry cell count estimates. Arrows indicate samples that were less than a detection value (1000 or 5000 cells). The upper line represents a 1:1 relationship of PLFA to flow cytometry counts, whereas the lower line depicts the actual relationship. Since the PLFA analysis does not include estimates for Archael, samples with detectable Archael DNA signatures were designated with an “A”. These samples were found below the 1:1 reference line, thus lowering the relationship line between PLFA and flow cytometry.
Figure 4. Redundancy analysis of the PLFA data set for 139 samples, using 90 PLFA as species. The arrows point in the direction of maximum variation in the species’ abundance, and the arrow length is proportional to the rate of change. For ease of viewing PLFA with longer arrows were displayed. The centroids (triangles) designated the sample type as fracture water (F), service water (S), biofilm (B), carbon leader (C), quartzite (Q), and air (A).
Figure 5. Redundancy analysis of the PLFA data set for 52 fracture water and 15 service water samples, using 62 PLFA as species. Environmental and PLFA vectors are plotted. The arrows point in the direction of maximum variation in the environmental and species’ abundance, with the arrow lengths being proportional to the rate of change of these parameters. For ease of viewing PLFA with longer arrows were displayed. The centroids (triangles) designated the mine as: BE=Beatrix, DR=Dreifontein, EV=Evander, KL=Klook, MP=Mponeng, MS=Merrispruit, TT=TauTona, and service water as SW.
Figure 6. Redundancy analysis of the PLFA data set for 52 fracture water and 15 service water samples, using 62 PLFA species and 6 environmental variables. Arrows representing environmental variables are shown with fracture water (open circle) and service water (closed circle). The arrows point in the direction of maximum variation in the variables’ abundance, and the arrow length is proportional to the rate of change.
Fig. 7. Total cell counts by flow cytometry (open symbols) and PLFA (solid symbols) biodensity versus depth for water from freshly intersected fractures (large squares), water from boreholes ranging in age from 6 months to 20 years, and mining water (diamonds). Squares with crosses are the total cell counts for the ASPO Hard Rock Laboratory (Kotelnikova and Pedersen 1997). Small plus signs are the biodensity (total cell counts or PLFA) for sediment and rock (Onstott et al. 1998). The small squares with plus signs are the biodensity estimates for Witwatersrand quartzite and carbon leader (Onstott et al. 2003). Solid horizontal lines connect total count and PLFA analyses for the same sample or multiple samples for the same borehole
over time. The dashed subvertical lines connect samples from a depth profile of the Dr938 H3 borehole (D8A in (Moser et al. 2005)).
Table 1. Sample listing with code designation. sample # Code Name 1 BE1F1 BE116 FW 121801 CXN 2 BE2F1 BE223 FW 031301 A4RD 3 BE2F2 BE224 062003 FW 4 BE2B1 BE224 062003 West 17A 5 BE2B2 BE224 062003 West 25 6 BE2F3 BE225 FW1 121598 7 BE3A1 Be324 FW 121801 ND AS20x 8 BE3A2 Be324 FW 121801 ND AS40x 9 DR2S1 E2-0-FD1 021099 – Fan Drift 10 DR4F1 E4 IPC DW2 111698 11 DR5F1 DR546 BH1 103001 12 DR5S1 DR546 SW 103001 13 DR5F2 DR546 BH1 110098 14 DR5F3 E546 BH1 120798 15 DR5B1 E5 4648 Bio 16 DR5C1 E5 4648 C-CL 17 DR5C2 E5 4648 H53 18 DR5C3 E5 4648 H5-0.7 19 DR5C4 E5 4648 H5-0.7m 20 DR5C5 E5 4648 H5-0.8M 21 DR5Q1 DR546 HS2 120198 22 DR5C6 DR546 HS3 120198 23 DR5C7 DR546 HS3 120199 24 DR5F3 DR546 BH1 020199 25 DR5S3 E546 sump 020299 26 DR5B2 E548 B Bio 27 DR5B3 E548 Biofilm drip 28 DR5S4 DR548 SW1 090198 29 DR5C8 EDCL-1 30 DR5S5 EDS1 31 DR5Q2 Quartz 5FE 32 DR5S6 DR550 30 SW-1 101502 33 DR5S7 DR550 30 SW-2 101502 34 DR5S8 DR550 30 SW-3 101502 35 DR9S1 DR938 SW 110201 37 DR9F1 DR938 H3 110701 38 DR9F2 DR938 H3 0M 102401 39 DR9F3 DR938 H3 125M 102401 40 DR9F4 DR938 H3 125M 102501 41 DR9F5 DR938 H3 250M 102401 42 DR9F6 DR938 H3 390M 102401 43 DR9F7 DR938 H3 550M 102501 44 DR9F8 DR938 H3 648M 102401 45 DR9S2 DR938 SW 46 DR9F9 DR938 CH 110701
sample # 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124
Code EV8S3 EV9F1 DRD1 DRD2 DRD3 KL4F1 KL4F2 KL4S1 KL4F3 KL4F4 KL4F5 KL4S2 KL4F6 KL7F1 MM5S1 MM5A1 MS1F9 MS1A1 MS1A2 MS1F1 MS1F2 MS1F3 MS1F4 MS1F5 MS1F6 MS1F7 MS1F8 MS1B1 MS1B2 MS1B3 MS1B4 MS1B5 MS1B6 MS1B7 MS1B8 MS1B9 MS1B10 MS1B11 MS1B12 MS1B13 MS1B14 NO2S1 NO2F1 TT1F1 TT1F2
Name EV821 SW 101601 EV914 FW 022801 ED F-Drill W First DW U-Drill W KL441 HWDS H1 120198 KL441 HWDN FW 020199 KL441 SW 120198 KL441 BH1 022801 KL441 HWDS H2 050201 KL441 HWDS H2 100201 KL441 HWDN SW 022801 KL443 HWDN FW 050801 KL739 FW 062901 MM51870 SW 030402 MM51870 FW 030402 A20x MPI51 FW 022202 XCT8EAST MPI51 FW 022202 XCT8EAST 1 20x MPI51 FW 022202 XCT8EAST 2 20x MS149 B53 062003 MS149 BH1 200603 MS149 BH2 200603 MS149 BH1 062503 MS149 BH2 062503 MS149 B53A 110703 MS149 B53B 110703 MS151 FW 230703 MS149 B53 062003 BF1 MS149 B54 062003 BF1A MS149 B54 062003 BF2 MS149 BH1 062003 BF1 MS149 BH1 062003 BF2 MS149 BH2 062003 BF1 MS149 BH2 062003 BF3 MS149 BH2 062003 BF4 MS149 BH2 062003 BF6 MS149 062503 BF1 MS149 062503 BF2A MS149 062503 BF2B MS149 B52 110703 BF MS149 B54 110703 BF1 NO27 RW100501 SW1 NO27 RWFWDE100501 H1 TT100 X/CS 070402 P2 TT100 FW 082702
47 DR9F10 DR938 CH 102102 48 DR9F11 DR938 H1 082001 Table 1. Sample listing with code designation continued. sample # Code Name 49 DR9F12 DR938 H2 083001 50 DR9F13 DR938 CH 071102 51 EV2F1 EV219 H5 030901 ED 52 EV2F2 EV219 H3 081601 53 EV2F3 EV221 H1 111601 54 EV2S4 EV221 H2 111601 55 EV2F5 EV221 H3 111601
TT104 FW 080703 W6-38 CL-1
sample # 127 128 129 130 131 132 133
Code DR6B1 DR6F1 DR6S1 DR6F2 DR6F3 DR9F14 DR6C2 DR6S2 EV2E1 EV2E2 EV2E3 EV2E4 EV2E5 DR9E1 DR9E2 DR9E3 DR9E4 DR9E5 DR9E6 DR5E1 DR5E2 DR5E3 DR5E4 DR5E5 DR5E6 DR5E7 DR5E8 DR5E9 DR5E10 EV2E6
Name DR638 BH1 020199 W638 FW3 111598 W638 SW 120899 DR638 BH2a 111598 DR638 BH2b 110198 WD938 6PK 071102 – DR938CH WDCL-1 WDF1 – acid service water SW2 120198 EV219 082902 MeOH EV219 082902 IRB EV219 082902 SRB EV219 082902 Control EV219 082902 Sand control DR938 CH1-121502 magnetite DR938 CH1-121502 elemental sulfur DR938 CH1-121502 crb DR938 CH1-121502 sand DR938 CH1-121502 elemental iron DR938 CH1-121502 Fe(III) sand DR546 BH1 spotted dyke DR546 BH1 elemental iron DR546 BH1 Fe(III) sand DR546 BH1 sand DR546 BH1 elemental sulfur DR546 BH1 Fe304 sand DR546 BH1 CRB-II no filter DR546 BH1 spotted dyke filter DR546 BH1 CRB-square filter DR546-SC1 – quartzite chips DR638-SC1 – quartzite chips
56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
EV2F6 EV2F7 EV2S1 EV5F1 EV5F2 EV8F1 EV8S1 EV8F2 EV8F3 EV8F4 EV8F5 EV8B1 EV8F6 EV8A1 EV8A2 EV8S2 EV8F7 EV8F8 EV8F9 EV8A3 EV8A4 EV8A5 EV8A6
EV221 H3 121702 EV221 H4 121702 EV221 SW EV522 FW 030801HWD EV522 H1 041801CTS EV818 H5 102502 EV818 H5 SW2 102502 EV818 FW 062101 EV818 H5 102702 EV818 H6 102702 EV818 H6 111502 EV818 H6 121902 EV818 H6A 102702 EV818 NE AS-4 121302 EV818 NE AS-5 121302 EV818 SW1 102502 EV820 FW 061802 EV820 BF 121401NWD EV820 FW 121401 EV820 FW 121401 NWD AS20x-A EV820 FW 121401 NWD AS20x-B EV820 FW 121401 NWD AS20x-B EV820 FW 121401 NWD AS60x-B
134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
EV821 FW 101601
157 158 159 160 161 162 163
DR9F9 MS1A3 MS1A4 TT1A1 MS1A5 MP1F2 DR9F15
DR938 H1 110201 MS149 200603 AS 40x MS149 062503 AS 100x TT104 RAW070803 AS7x MS151 072303 AS MP104 64XCBH1 110902 DR9 IPC H1 101602
Table 2. Geochemical parameters for fissure and service water samples Name pH Eh (mv) ToC Cl SO42BE116 FW 121801 N.A. N.A. N.A. 3.40E-02 2.16E-05 BE224 FW-2 200603 6.8 N.A. 34.0 2.60E-02 1.00E-05 BE225 FW1 121598 9.5 N.A. 35 3.66E-02 7.12E-06 E4 IPC DW2 111698 7.0 398 25.0 4.37e-04 1.69e-03 DR546 BH1 120798 5.5 N.A. 37.0 6.34E-01 4.81E-04 DR546 BH1 020199 7.4 N.A. 37.2 6.37E-01 9.64E-04 DR546 BH1 103001 7.9 26 32.0 6.76E-01 1.11E-03 DR546 SW 103001 N.A. N.A. N.A. 7.97E-03 9.11E-03 Dr546 sump 11.6 337 34.2 5.01e-02 9.47e-04 DR548 SW1 090198 7.9 N.A. 17.6 6.25E-03 4.16E-04 DR550 30 SW-1 101502 7.0 349 24.8 1.05E-02 1.52E-02 DR550 30 SW-3 101502 2.6 665 35.0 2.80E-02 1.94E-02 W638 SW 120899 7.5 617 20.0 9.18E-04 1.16E-03 WDF1 acid service water SW2 121098 3.0 N.A. 30.0 2.89e-03 8.01e-03 DR6S2 W638 FW3 111598 9.0 N.A. 45.0 1.19e-02 9.49e-04 DR6F1 DR638 BH2a 111598 10.0 N.A. 45.0 1.72e-02 1.17e-03 DR6F2 DR938 CH 110701 7.5 N.A. 43.0 3.13E-03 1.75E-04 DR9F9 DR938 CH 102101 9.2 -20 42.0 3.00E-03 1.76E-04 DR9F10 9.2 -40 42.0 3.00E-03 1.35E-04 DR9F13&14 DR938 CH 071102 DR938 H1 082001 9.7 N.A. 42.6 3.00E-03 1.38E-04 DR9F11 DR938 H2 083001 9.4 N.A. 44.0 2.17E-02 4.82E-05 DR9F12 DR938 H3 110701 9.0 -90 60.5 3.16E-02 1.35E-04 DR9F1 DR938 H3 125M 102401 8.2 N.A. 46.0 2.63E-02 3.27E-05 DR9F3 DR938 H3 125M 102501 8.2 N.A. 46.0 2.58E-02 3.62E-05 DR9F4 DR938 H3 250M 102401 7.8 N.A. 54.0 2.64E-02 3.49E-05 DR9F5 DR938 H3 390M 102401 7.7 N.A. 54.0 2.61E-02 2.63E-05 DR9F6 DR938 H3 550M 102501 7.4 N.A. 54.0 2.62E-02 1.58E-05 DR9F7 DR938 H3 648M 102501 7.3 N.A. 48.0 2.67E-02 4.02E-05 DR9F8 DR938 SW 110201 N.A. N.A. 30.0 2.01E-03 2.08E-04 DR9S1 DR938 SW 091202 N.A. N.A. 30.0 1.38E-03 1.74E-03 DR9S2 EV219 H1 030901 ED 7.4 -50 30.9 1.65E-02 2.74E-03 EV2F1 EV219 H3 081601 7.6 N.A. 25.0 9.04E-03 2.57E-04 EV2F2 EV221 H1 111601 6.9 N.A. N.A. 1.56E-02 3.30E-04 EV2F3 EV221 H2 111601 2.8 678 32.0 2.98E-02 7.88E-03 EV2F4 EV221 H3 111601 8.7 -62 36.0 2.66E-02 9.14E-04 EV2F5 EV221 H3 121702 8.9 N.A. 35.5 2.95E-02 7.85E-04 EV2F6 EV221 H4 121702 9.3 28 33.0 2.86E-02 6.74E-04 EV2F7 EV221 SW 103002 N.A. N.A. N.A. 2.66E-02 3.89E-03 EV2S1 EV522 FW 030801HWD 7.2 190 36.7 3.68E-02 1.32E-03 EV5F1 EV522 H1 041801CTS 7.0 N.A. 32.0 6.50E-02 7.38E-06 EV5F2 EV818 FW 062101 7.8 -52 45.0 1.47E-01 5.46E-04 EV8F2 EV818 H5 102502 7.3 -112 40.5 1.71E-01 2.62E-03 EV8F1 EV818 H5 102702 8.8 -48 42.0 1.73E-01 2.97E-03 EV8F3 EV818 H5 SW2 102502 8.0 N.A. 37.8 4.00E-02 3.08E-03 EV8S1 EV818 H6 102702 N.A. -94 47.0 1.61E-01 2.89E-03 EV8F4 Code BE1F1 BE2F2 BE2F3 DR4F1 DR5F2 DR5F3 DR5F1 DR5S1 DR5S3 DR5S4 DR5S6 DR5S8 DR6S1