Article pubs.acs.org/est
Carbon Dioxide and Hydrogen Sulfide Associations with Regional Bacterial Diversity Patterns in Microbially Induced Concrete Corrosion Alison L. Ling,† Charles E. Robertson,‡ J. Kirk Harris,§ Daniel N. Frank,∥ Cassandra V. Kotter,∥ Mark J. Stevens,§ Norman R. Pace,‡ and Mark T. Hernandez*,† †
Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States ‡ Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado 80309, United States § Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado 80045, United States ∥ Department of Infectious Diseases, University of Colorado School of Medicine, Aurora, Colorado 80045, United States S Supporting Information *
ABSTRACT: The microbial communities associated with deteriorating concrete corrosion fronts were characterized in 35 samples taken from wastewater collection and treatment systems in ten utilities. Bacterial communities were described using Illumina MiSeq sequencing of the V1V2 region of the small subunit ribosomal ribonucleic acid (SSU-rRNA) gene recovered from fresh corrosion products. Headspace gas concentrations (hydrogen sulfide, carbon dioxide, and methane), pore water pH, moisture content, and select mineralogy were tested for correlation to community outcomes and corrosion extent using pairwise linear regressions and canonical correspondence analysis. Corroding concrete was most commonly characterized by moisture contents greater than 10%, pore water pH below one, and limited richness (100 ppm) and carbon dioxide (>1%) gases, conditions which also were associated with low diversity biofilms dominated by members of the acidophilic sulfur-oxidizer genus Acidithiobacillus.
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INTRODUCTION
manholes, and other structures. It is also a weak acid, and its dissolution causes local pH to decrease. Additionally, H2S is oxidized biotically or abiotically through intermediate sulfur species such as elemental sulfur and thiosulfate and eventually to sulfuric acid.7 Biogenic sulfuric acid can react with calcium, silicon, and aluminum oxides and carbonates in cured cement to produce the expansive corrosion products gypsum and ettringite, which increase internal crystal pressure and compromise structural stability.8−12 Environmental factors that have been reported to contribute to sulfuric acid production and subsequent corrosion include headspace hydrogen sulfide concentrations, relative humidity, temperature, concrete mix design including admixtures used, and the types of sulfur compounds produced at concrete surfa-
The microbially induced corrosion of concrete (MICC) in wastewater infrastructure is a serious and widespread problem. The maintenance and replacement of damaged sewer pipes carry significant financial and energy costs for metropolitan areas all over the globe; their repair and replacement can substantially disrupt community activities and present a threat to environmental health. The U.S. EPA estimates that wastewater system maintenance in the United States alone costs $4.5 billion annually, $3.3 billion of which is used to rehabilitate or replace about 8000 miles of sewers.1,2 The Congressional Budget Office estimates that the cost of restoring wastewater infrastructure to proper function in the foreseeable future will exceed $12 billion per year.3,4 Corrosion is commonly reported in pipes and other structures that carry elevated levels of hydrogen sulfide gas and can destroy structural concrete at rates approaching 5 mm/ year.5,6 Hydrogen sulfide (H2S) is a redox-active gas that can dissolve in condensate on the crown or walls of pipes, © 2014 American Chemical Society
Received: Revised: Accepted: Published: 7357
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ces.5,7,8,12−14 Corroded surfaces are characterized by cement binder loss, which leads to protruding aggregate, the presence of yellow elemental sulfur deposits, and occasionally gray, brown, or green staining from the biofilms that can develop.15−17 Microbially induced concrete corrosion has been studied in the United States,18 Germany,19 Belgium,20 Denmark,6,21 Lebanon,22 Japan,5,16 and Australia.23 While MICC is a widely distributed phenomenon, the relatedness between geographically disparate microbial communities associated with corrosion is not well understood. Seminal culture-based studies found corrosion extent to be directly correlated to sulfur-oxidizer cell counts and H2S concentrations.19,24 Several recent studies have used small subunit ribosomal ribonucleic acid (SSU-rRNA)based molecular methods to describe community composition in this context. A Japanese study of concrete corrosion community development16 reported that the most abundant types of organisms were gamma-Proteobacteria, including Halothiobacillus spp. and Acidithiobacillus spp. Acidithiobacillus thiooxidans was one of the first bacteria to be implicated in this process and is often associated with corrosion-causing biofilms.25,26 A study of Cincinnati MICC biofilms found that beta-Proteobacteria such as Thiomonas spp. outnumber gammaProteobacteria in crown corrosion biofilms,18 and a study in Australia found that Acidithiobacillus spp. was frequently present but not always dominant.23 The microbial communities that develop in MICC biofilms appear to depend on a large number of factors, but the limited amount of taxonomic data and associated metadata published thus far limits understanding of how environmental factors and geography may manifest in selection pressures that correlate to community characteristics. To date, no DNA sequence-based studies have compared MICC communities recovered from different cities or regions. Additionally, there has been no standard for environmental metadata collected from these environments. Although local pH is likely the most important factor in determining MICC community composition and predicting corrosion severity, many studies do not report it. In this context, headspace hydrogen sulfide is frequently measured, but methane (CH4) and carbon dioxide (CO2) also can indicate corrosion risk and are not typically reported.27,28 Methane can be an indicator because methanogenesis in collection systems can only occur when anaerobic conditions, which also favor sulfide production, are present. Carbon dioxide supports acidogenic sulfur oxidation by serving as a carbon source. In the present study, samples of corrosion products were collected from wastewater systems in ten cities across the United States, and the compositions of associated microbial communities were determined by sequence analysis of rRNAencoding genes. Environmental conditions as described by pore water pH, corrosion matrix chemistry, and headspace gas concentrations were concomitantly documented and analyzed to identify factors correlated with corrosion extent and bacterial ecology. The goal of this work was to develop a better understanding of how chemical and geographic gradients may affect the microbial communities associating with this unique type of corrosion.
Mountain region; utilities E and F were in the Midwest region. Utilities G and H were in the Southeast region, and utilities J and K were in the Pacific West region. Sampling within cities was planned with the aid of local sanitation district technicians with the goal of collecting biofilms from a variety of structures, including manholes, lift stations, conveyance channels, and treatment basins. Sampling Methodology. Corroded concrete from each site was collected into two sterile, polycarbonate 15 mL tubes using sterilized metal spatulas. Samples for chemistry and phospholipids were collected in dry tubes. Samples for DNA analysis were collected in 3 mL of 1 M Tris Base/0.01 mM EDTA buffer adjusted to pH 7 to minimize DNA hydrolysis and to complex metal ions that could potentially inhibit DNA amplification. All samples were kept at 4 °C for a maximum of 48 h before analysis. Gas Measurements. Hydrogen sulfide, carbon dioxide, and methane concentrations were measured in structure headspaces immediately before samples were recovered. At sites E01-04, G01-05, and A31-45, CH4 measurements were taken using a MDU 420 dual-range methane monitor (Industrial Scientific Corp., Oakdale, PA), and CO2 and H2S measurements were taken using a Draeger CMS analyzer (Draeger Safety Inc. Gas Monitors, Sugarland, TX). At other sites, measurements for all three gases were taken using a GasAlert Micro5 IR (BW Technologies, Lincolnshire, IL). Physical and Chemical Parameters. Surfaces were categorized into one of three groups: coated concrete surfaces, mildly corroded concrete, and severely corroded concrete. Coated surfaces had been previously treated with a synthetic liner to mitigate corrosion. Surfaces with less than 15% moisture content were designated as mildly corroded, while surfaces with moisture content greater than 15% (and up to 65%) were designated as severely corroded. Dry samples were stored at −80 °C until processing. Pore water pH was measured by adding a known amount of ultrapure water (MilliQ, pH 6−8) to dry-collected samples. Samples were allowed to equilibrate for an hour, after which pH was measured with a calibrated probe. Original sample pH was calculated on the basis of the amount of sample, the amount of water added, and the original pH of the water added. Corrosion product was dried at 60 °C for at least 8 h and massed. Moisture content was determined by dividing water mass lost by the original (wet) mass. Oven-dried samples were sieved to remove silicabased aggregate particles larger than 300 μm and digested in nitric acid. Eluents were analyzed for total iron, calcium, and sulfur using inductively coupled plasma/optical emission spectroscopy (ICP/OES 3140+ from Applied Research Laboratory). Instrument standards were made by diluting certified standards; a blank and three standards were used for calibration. DNA Extraction. Buffered samples were vortexed and allowed to settle for 1 min before 500 mL of the liquid phase was transferred to DNA extraction tubes containing lysis buffer, phenol, and chloroform. Bulk genomic DNA was extracted as previously described29,30 using bead-beating and phenolchloroform extraction with ammonium acetate precipitation. DNA pellets were washed with 70% ethanol and resuspended in sterile DNase/RNase-free water (Fisher Scientific). Extracted DNA was stored at −80 °C. SSU-rRNA V1V2 Region Amplicon Sequencing. Bacterial profiles of extracted sample DNAs were determined by broad-range amplification and sequence analysis of the V1V2
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METHODS Sampling Sites. Between one and 14 samples of corroded concrete and/or surface biofilm were collected from each of ten cities in four distinctly different climate regions across the United States. Utilities A, B, C, and D were in the Rocky 7358
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Figure 1. Geographical summary juxtaposing bacterial taxa relative percent abundance (A), pore water pH and corrosion severity (B), and the onsite concentrations of hydrogen sulfide, carbon dioxide, and methane gas (C). In all cases, the black colors indicate higher levels of relative abundance (A), acidity (B), or gas concentrations (C). Site designations are presented on the horizontal axis and maintained throughout the three parts. In part B., “M” stands for mildly corroded, “S” stands for severely corroded, and “C” stands for coated surface. In part C., light gray, dark gray, and black highlighting indicate concentrations in the range of 0−10, 10−100, and >100 ppm H2S and 0−0.5, 0.5−1.0, and 1.0−2.0 percent CO2 by volume. For CH4, light gray highlighting indicates absence and dark gray highlighting indicates presence. For all parts, squares with “X” indicate no data.
than one ambiguity or shorter than 200 nt were discarded. Potential chimeras identified with Uchime (version usearch6.0.203_i86linux32)38 using the Schloss Silva reference sequences 39 were removed from subsequent analyses. Assembled sequences were aligned and classified with SINA (1.2.11),40 configured to yield the Silva taxonomy, using the 418 497 bacterial sequences in the Silva 115NR99 database as reference.41 Operational taxonomic units were produced by clustering sequences with identical taxonomic assignments. The software package Explicet v2.8.4 (www.explicet.org) was used for analysis (Chao1, Good’s, p-values via Two-Part Analysis42), display, and figure generation.43 This process generated 505 121 sequences from 35 samples (average size: 14 432 sequences/sample; min: 4478; max: 53 291). The median Goods coverage score for libraries, a measure of completeness of sequencing, was ≥99%.44,45 Alpha-diversity (sample richness) was calculated in Explicet at the rarefaction point of 4478 sequences with 1000 bootstrap resamplings. Chao1 was chosen to quantify alpha-diversity.46 Raw pairedend Illumina MiSeq reads were submitted to the NCBI Small Read Archive under accession number SRP022243. SSU-rRNA Universal Clone Sequencing. In order to achieve higher phylogenetic resolution, select samples were sequenced using Sanger sequencing of clones transformed with
region of the SSU-rRNA gene following previously described methods.31,32 In brief, amplicons were generated using primers that target approximately 300 bp of the V1V2 variable region, which was chosen on the basis of its ability to resolve taxonomic categories in the bacterial domain.33−35 PCR products were normalized on the basis of agarose gel densitometry, pooled, purified, and concentrated using a DNA Clean and Concentrator Kit (Zymo, Irvine, CA). Pooled amplicons were quantified using a Qubit Fluorometer 2.0 (Invitrogen, Carlsbad, CA). Each DNA pool was diluted to 4 nM and denatured with 0.2 N NaOH at room temperature. Denatured DNA was diluted to 15 pM and spiked with 25% of PhiX bacteriophage DNA (control sequences) prior to loading the sequencer. Illumina paired-end sequencing was performed on a MiSeq platform with version 2.2.0.2 of the MiSeq Control Software and version 2.2.29 of MiSeq Reporter, using a 500cycle version 2 reagent kit (Illumina, Inc., San Diego, CA). V1V2 Sequence Analysis. Illumina MiSeq paired-end sequences were sorted by sample via barcodes in the paired reads with a python script as previously described.32 The sorted paired reads were assembled using phrap.36,37 Pairs that did not assemble were discarded. Assembled sequence ends were trimmed over a moving window of 5 nucleotides until average quality met or exceeded 20. Trimmed sequences with more 7359
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atmosphere downstream of anaerobic digesters in Utility B and in manholes in Utility F. Effect of Environmental Parameters on Bacterial Community Composition. Environmental metadata were tested for potential correlations with community or environmental outcomes using pairwise linear regressions (Table 1).
515F and 1391R amplified SSU-sequences as previously described.47 Sequences were assembled, trimmed, checked for chimeras, and aligned using the same process described above in V1V2 sequence analysis. Taxonomy assignments were made by using parsimony insertion using ARB into the SILVA 115NR database.48 Statistical Analyses. Prior to statistical analyses, H2S concentrations were log-transformed to achieve a more normal distribution. The detection limit concentration (20 000 ppm) was used for sites with CO2 concentrations above the detection limit. Sites with incomplete gas data were omitted from statistical analysis. Linear regressions were performed using the R Statistics Program49 to relate environmental factors to community composition and corrosion severity. Multivariate analysis was conducted using canonical correspondence analysis (CCA) of OTU tables and headspace gas metadata.50 Calculations were performed using the cca function of the vegan package in R Statistics Program49,51 using headspace gas data as environmental parameters. Data were transformed and scaled50,52 prior to analyses as follows: taxa abundances were square-root transformed to dampen the contribution of abundant taxa, and each headspace gas data point was converted to its z-score. Individual microbial groups that differed in prevalence or abundance between treatment groups were identified through a two-part statistical test that accounts for zero-inflation and non-normal distributions.42 Because of the exploratory nature of this study, p-values were not corrected for multiple tests.53
Table 1. Significant Pairwise Linear Regressions for Regional Data outcome
log[H2S] log[H2S] log[H2S]
0.0090 0.0056 0.014
*** *** **
−0.54 −0.52 −0.47
Acidithiobacillus richness (Chao1)
pH pH
0.0405 0.023
** **
−0.40 0.44
Acidithiobacillus richness (Chao1) corrosion stage
CO2 CO2 CO2
0.10 0.0053 0.000 13
* *** ***
0.32 −0.53 0.67
richness (Chao1)
corrosion stage corrosion stage corrosion stage corrosion stage
0.056
*
−0.04
0.035
**
0.062
*
0.000 27
***
log[S/Ca] free calcium
RESULTS Bacterial Diversity of Corrosion Biofilms. The corrosion-associated bacterial communities studied had relatively limited bacterial richness (median of 41 taxa, interquartile range of 180 taxa as judged by Chao146) when compared to a broad range of other microbial habitats.54−57 The communities observed generally fall into three groups: communities dominated (>50%) by acidophilic sulfur oxidizer Acidithiobacillus spp. (18), communities dominated (>50%) by Acidiphilium spp. (three), and diverse communities with more than two major taxa (13) (Figure 1A). These two dominant genera are acidophilic bacteria typically capable of sulfur oxidation that have been previously associated with concrete corrosion and acid mine drainage environments.16,58 More diverse communities were observed in mildly corroded environments, which typically had pH values higher than two (Figure 1D) and relatively low levels of H2S (Figure 1C). Neutrophilic sulfur oxidizing genera observed may have contributed to corrosion in these mildly corroded sites and include Thiomonas, Thiobacillus, Sulfobacillus, and Thermothiobacillus. These groups were not observed in sites dominated by acidophilic genera Acidithiobacillus and Acidiphilium. Mycobacterium spp. also were observed in corrosion fronts, mostly in biofilms dominated by acidophilic sulfur oxidizers. On the basis of SSU-rRNA sequence parsimony insertion of Sanger sequences into the Silva 115NR database, sequences obtained in this study do not appear to be closely related to Mycobacterium spp. sequences previously observed in acidic soils.54 The fourth most abundant taxa observed in this study was Metallibacterium spp., an acidophilic member of the Xanthomonadaceae family that previously has been observed in a corrosive environment.23 This group was found on the corroded walls of wet-wells in contact with a methane-rich
p-value
Pearson’s correlation coefficient
pH Acidithiobacillus richness (Chao1)
moisture
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co-variate
significance thresholda
0.13 −0.25 0.68
a Significance of 90%, 95%, and 99% confidence is indicated by ∗, ∗∗, and ∗∗∗, respectively.
Any pairwise relationships not listed had a regression p-value greater than 0.1. In accordance with previous work,24 hydrogen sulfide gas concentrations were closely correlated to bacterial community composition. Figure 2 illustrates the two most significant environmental selection factors, H2S and CO2 gases, and their effect on community richness and composition. H2S concentration was strongly correlated with surface pH, the relative abundance of Acidithiobacillus spp., and richness (pvalues below 0.02 for all three). As judged by a nonparametric two-part test with p-values at 95% confidence level,42 the taxa emerging in significantly different relative abundances between sites with H2S concentrations below 20 ppm and those above were Comomonadaceae, Candidate Division TM7, Legionella spp., Sulfobacillus spp., and Acidithiobacillus spp. All these groups, except for Acidithiobacillus spp., were more abundant at sites with H2S below 20 ppm. The higher CO2 levels observed were correlated with lower bacterial community richness, increased corrosion severity, and increased relative abundance of Acidithiobacillus spp. (p-values of 0.005, 0.0001, and 0.1, respectively). Despite their parallel correlations to community parameters, CO2 and H2S were not significantly correlated to each other. Although methane (CH4) potentially could act as an alternate (heterotrophic) energy source which might otherwise compete with reduced sulfur compounds (autotrophic), methane did not appear to have a significant effect on community composition, as judged by linear regression analysis. One environment (Samples B06 and B07), with significant CH4 in its headspace, supported bacteria related to aerobic C1 metabolizers Methylophilus spp., suggesting that 7360
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Figure 2. Comparison of in situ H2S and CO2 concentrations with descriptive community parameters. Top: Estimated sample richness (as predicted by Chao1 at rarefaction). Bottom: Percent of the library composed of Acidithiobacillus. Black lines represent linear regression models. Lines indicate linear regression trendlines. Linear regression p-values and Pearson’s correlation coefficients (r-values) are listed on each plot.
correlation to environmental variables. On the basis of CCA, the concentrations of the three headspace gases H2S, CO2, and CH4 accounted for 29% of the community variation observed (Figure 3). The vectors for H2S and CO2 are orthogonal, supporting the univariate results that did not identify a specific relationship between these two gases. The relatively small
methane can serve as a competitive substrate in these environments. However, samples collected from the same site 15 months later (B01 and B02) had a different community devoid of methylotrophic genera, which may be a result of increased moisture content and associated anaerobic conditions. This indicates that, while concrete can corrode at surface dissolution rates of millimeters per year, corrosion communities are more dynamic than previously presented and may change in time frames on the order of months as dictated by environmental conditions. Previous work has implicated methylsulfides as an additional source of reduced sulfur in these environments,24 and these compounds may also support methylotrophic taxa. The extent of corrosion (mild or severe) was also correlated with richness, moisture content, calcium content, and sulfur to calcium ratio (p-values of 0.06, 0.04, 0.0003, and 0.06, respectively). The literature suggests that late-stage corrosion is typified by high surface moisture and low pH, which is expected to cause decreased community richness and increased acidophile abundance.16,25 Severe corrosion involves the oxidation of gaseous H2S and the subsequent incorporation of oxidized sulfur into a dissolving cement matrix as gypsum or ettringite. These high-moisture corrosion products may explain the trends of high sulfur to calcium ratios observed in the more severely corroded samples. The moisture content of the decaying cement was associated with significant community differences: thirty-two taxa exhibited significantly different relative abundance between samples with less than 10% moisture and samples with more than 10% moisture based on two-part analysis. Only two of these, Acidithiobacillus spp. and Mycobacterium spp., were more abundant in the high moisture environments (two-part p < 0.05). Canonical correspondence analysis (CCA) enables the visualization of multidimensional community data along with
Figure 3. Canonical correspondence analysis of regional sample community composition using the square-rooted value of relative OTU abundance. Samples are labeled in black. Blue vectors indicate the effect of scaled gas concentrations on sample community outcomes. Red taxa points indicate the average CCA location of the four most abundant bacterial taxa (i.e., a community composed of only that taxa). 7361
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magnitude of the CH4 vector indicates that methane is not as strongly correlated to community composition as either H2S or CO2. One unmeasured factor that may affect community composition is the service age of the concrete structure, as corrosion communities are likely to change in response to extended exposure to corrosive gases. The cluster at the upper left corner of the diagram contains samples dominated by Acidithiobacillus spp. These samples coincide with high levels of both H2S and CO2 and were collected from manholes in two different areas within the collection system of Utility A. In addition, one sample collected from a coated manhole in Utility F is included in the cluster. These nine samples and the other three samples from Utility F included both coated and uncoated concrete surfaces, so this result indicates that similar environments can harbor similar communities even if the construction materials are markedly different. A smaller cluster in the center of the CCA plot (G01 and G04) contains samples dominated by Acidiphilium spp., and the remaining samples have more diverse communities. When considered independently of environmental conditions, geography was not associated with bacterial community composition.
The high lipid content and extreme hydrophobicity of mycobacterial cell walls may account for their selection in acidic environments.64 Mycobacteria are typically heterotrophs that require organic material as an energy source, but one study isolated a chemolithotrophic organism related to Mycobacterium spp. by SSU-rRNA sequencing that oxidized sulfur compounds at pH 3.6.63 Although reduced sulfur is considered the primary energy source for autotrophs in MICC, heterotrophic organisms may use microbial byproducts from the autotrophs as substrates and so provide thermodynamic benefit to the autotrophic community (e.g., sulfur-oxidizing biofilm). Researchers have previously hypothesized that heterotrophs in this environment can contribute to corrosion by producing organic acids and by metabolizing microbial byproducts that would otherwise inhibit sulfur-oxidizing populations.16,65 The bacterial richness observed in late-stage corrosion communities (
