Temporal changes of geochemistry and microbial community in low and intermediate level waste (LILW) repository, South Korea

Annals of Nuclear Energy 128 (2019) 309–317

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Annals of Nuclear Energy journal homepage: www.elsevier.com/locate/anucene

Temporal changes of geochemistry and microbial community in low and intermediate level waste (LILW) repository, South Korea Jinmo Ahn a, Won-Seok Kim a, Jin-Beak Park b, Arokiasamy J. Francis c, Wooyong Um a,d,⇑ a

Division of Advanced Nuclear Engineering (DANE), Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea Korea Radioactive Waste Agency (KORAD), Daejeon 305-353, Republic of Korea c Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA d Division of Environmental Science and Engineering (DESE), Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk 790-784, Republic of Korea b

a r t i c l e

i n f o

Article history: Received 4 September 2018 Received in revised form 11 January 2019 Accepted 16 January 2019

Keywords: Microorganisms Microbial activity Geochemistry Radioactive waste Wolsong disposal facility

a b s t r a c t Microbes play a major role in affecting the radioactive wastes disposed of in underground wastes repositories. In particular, low- and intermediate-level waste (LILW) contains biodegradable constituents that can affect stability and mobility of radionuclides by the changes of geochemical conditions due to microbial activity. Despite these potential impacts, the bacterial communities in underground repository remain largely unexplored and previous studies have focused on the traditional culture-dependent methods. We examined the bacterial community in a large-scale in situ container packed with simulated waste in LILW underground repository of South Korea using 454 pyrosequencing. Taxonomic analysis showed the presence of Curvibacter, Azonexus, Pseudomonas, Novosphingobium, Acidovorax, and Victivallis as the dominant bacterial genera. Based on the thermodynamic and geochemical results, the precipitation of calcite was estimated to occur in the presence of genus Curvibacter. Although the flow rate of groundwater input had an impact on the bacterial communities, the community structure was resilient to the change of groundwater’s velocity. In addition, total Fe concentration, [Fetotal] was positively correlated to [Cl
] in this system. The temporal changes of geochemical parameters and bacterial communities provide insight for understanding of the microbial activity inside the large scale container and additional biogeochemical information for long-term risk assessment of disposal facility. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Low and intermediate level waste (LILW) from nuclear fuel cycle in NPP operation, industries, and hospitals contain high proportion of organic materials such as cotton, cloths, paper, rubber gloves, resins, woods, etc. In South Korea, LILW in 200 L steel drums are packaged and disposed in disposal facility below surface at Wolsong. The Wolsong disposal facility consists of six silos to dispose of the initial 100,000 LILW drums, and ultimately, it is enlarged to handle 800,000 drums. More than 8057 drums were transferred to the LILW disposal facility by the middle of 2017. The safety of LILW disposal has been an important concern, because the disposed radionuclides could be released to the nearby

⇑ Corresponding author at: Division of Advanced Nuclear Engineering (DANE), Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea. E-mail address: [email protected] (W. Um). https://doi.org/10.1016/j.anucene.2019.01.029 0306-4549/Ó 2019 Elsevier Ltd. All rights reserved.

environments over a long period of time. Although the LILW disposal facility is properly operated to limit any potential release of radionuclides, unpredictable events such as gas generation and unanticipated natural (or manmade) disasters might occur during a long life-span of the disposal facility operation (e.g., 1000– 10,000 years). In fact, gas generation inside disposal facility has been an important issue because over-pressurized container resulting from gas generation may cause cracks in waste packages or container explosions (International Atomic Energy Agency, 2001). Furthermore, the expected gas compositions inside steel waste drums consist of carbon dioxide (CO2), methane (CH4) resulting from microbial degradation of organic materials (Molnár et al., 2010), as well as hydrogen (H2) which is flammable (Caldwell et al., 1988; Gillow and Francis, 2011). Recent fire accident at the waste isolation pilot plant (WIPP) near Carlsbad, New Mexico, USA, in 2014 was also related to use of organic sorbent that created a series of heat releasing reaction, created gasses within the drum, and released radioactive materials in the repository (Thakur, 2016; Thakur et al., 2016).

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Microbial activity both negatively and positively affects the long-term safety of disposal facility. In general, microorganisms inside the disposal facility pose a potential threat because it can generate the gas through biodegradation of organic compounds and microbiologically induced corrosion (MIC) of metal containers (Li et al., 2013; Little and Lee, 2007). However, bacterial communities, on the other hand, can also increase the long term safety by consuming the hydrogen (Bagnoud et al., 2016a) and by immobilizing radionuclides (Barescut et al., 2005; Kakiuchi et al., 2002). Although the repository safety and the radionuclides’ mobility are controlled by both indigenous microbes and geochemical conditions as well as their correlated biogeochemical reactions, the corresponding data and information are still lack and remained unexplored in details. Therefore, investigation of microbial communities and their surrounding geochemical environment in disposal facility is vital for the following reasons: First, microorganisms are ubiquitous and have been detected in low-level radioactive wastes, Transuranic (TRU) wastes, Pu-contaminated soils, backfill materials, natural analogue sites, and experimental waste-repository sites for highlevel radioactive wastes (Anderson et al., 2011; Barnhart et al., 1980; Brown et al., 1994; Caldwell et al., 1988; Francis et al., 1980; Fredrickson et al., 2004; Haldeman and Amy, 1993; Haveman et al., 1995; Horn et al., 2004; Kieft et al., 1997; Pedersen, 1996; Tate and Klein, 1985; Wang and Francis, 2005; West et al., 1985). Further, microbes are present even in harsh environments such as deep geological facility (Pedersen, 2000) and high radiation exposure environment (Ordonez et al., 2009; Rainey et al., 2005). Second, microbes can affect radionuclide’s mobility by working as sinks for sequestering radionuclides, or by moving as mobile colloids to facilitate transport of radionuclide in accelerating or decreasing the mobility of radionuclide in the biosphere (Jabbar and Wallner, 2015; Santschi et al., 2017; Shukla et al., 2017). Third, microbes can change the repository geochemical conditions (pH, Eh, alkalinity, and etc.) so that they also affect the stability of disposal facility. Recently, hydrogen gas (H2) produced by the anoxic corrosion of steel in geological repositories was consumed by autotrophic bacteria (Bagnoud et al., 2016a; Bagnoud et al., 2016b). Such microbial consumption of H2(g) is considered beneficial due to the presence of microbes for long-term safety of disposal facility. Therefore, it is necessary to investigate the composition of microbial communities present in underground environments in order to understand their impacts on the waste and to ensure the long-term safety and risk assessment of disposal facility. While microbial communities have become an important issue, reference data providing in situ microbial communities inside underground disposal facility are limited. Furthermore, most of published studies are based on traditional culture-dependent microbial isolation accounting for limited microbial communities (Choung et al., 2014; Unno et al., 2015). However, recently nextgeneration sequencing (NGS) method of the 16S ribosomal RNA (rRNA) gene has been applied to cover the shortage of traditional analysis, expediting quickly the quantitative and qualitative analysis of microbial communities. NGS is becoming an important tool for revealing microbial biodiversity (Sogin et al., 2006). In this study, we employed NGS-based analysis to monitor the changes in microbial communities inside a large-scale container filled with simulated LILW in drums at the Wolsong disposal facility in South Korea over 3 years. Besides, nonmetric multidimensional scaling (NMS) was used to visualize the temporal variation of microbial communities, and to provide a relative association among samples with geochemical variables. The results involved in this study provide better understanding of microbial communities under the real field condition at Wolsong repository.

2. Materials and methods 2.1. Site characteristics and sampling The Wolsong LILW disposal facility is located along with the coastal area of southern Korea (Fig. 1A). The Wolsong site comprises alluvium, rhyolite, granodiorite, and sedimentary rocks. The eastern part of the area is near the coastline, which mainly comprises alluvium and granodiorite, while the western area consisting of sedimentary rocks is close to mountains where the altitude is about 250 m. A large-scale concrete container was installed in the SILO at 130 m below sea level in Wolsong site (Choung et al., 2014; Park et al., 2012). In the large-scale container, 16 drums consisting of simulated dry activated waste (DAW) were placed for in situ study. DAW composed of combustible wastes such as rubber, cotton, wood, vinyl, plastic, paper, and activated carbon, and noncombustible wastes such as glass, steel, and concrete (Park et al., 2012). Indigenous groundwater at Wolsong repository was used to fill the 16 drums containing DAW and to saturate the gas-tight container (Fig. 1B). After being saturated with groundwater, the container was placed nearby the SILO without additional water flow and/or water mixing system. Sampling port lines were installed inside drums directly connected from top and bottom of the drums, and outside drums. B3X sampling port was chosen for microbial analysis, representing the center of container (Fig. 1C). As there are two drums filled with plastic, paper, steel, rubber, and vinyl, sampling commenced from one of the drums. Samples were collected nine times including input groundwater for up to three years for microbiological and water chemical analyses. The samples were numbered chronologically: S14-Jan (January 2014); S14-Sept (September 2014); S14-Oct (October 2014); S15Jan (January 2015); S15-Jul (July 2015); S15-Nov (November 2015); S16-May (May 2016); S16-Sept (September 2016). Leachate samples were collected from the sampling port lines directly connected to each drum and outside drums. Once sampling started, the samples were transferred into sterile polypropylene bottle with no headspace, and immediately stored in an ice box in order to minimize the contamination. 2.2. Chemical analysis All the collected samples were filtered using 0.45 mm PDVF filters before analysis. The pH, oxidation reduction potential (ORP), dissolved oxygen (DO), and electrical conductivity (EC) were directly measured right after sample collection using multiprobes (Orion 5-StarTM meters, Thermo Scientific Co.). Alkalinity was measured using potentiometric titration (888 Titrando, Methrom, Herisau, Switzerland). Solution samples were stored in a refrigerator and used for analysis in a laboratory within 1 week after sample collection. DX-60 Ion Chromatograph with AS-21 column and AG-21 column (DIONEX, USA) was used to determine anion concentrations (F
, 2

3
Br
, Cl
, NO
3 , SO4 , NO2 and PO4 ). The cation concentrations + + 2+ 2+ (K , Na , Ca , Mg , and Fetotal) were analyzed using Inductively Coupled Plasma-Optical Emission Spectroscopy, ICP-OES (JY Ultima2C, HORIVA Jobin Yvon Inc.). Every sample was treated with 2% nitric acid before cation analysis to prevent precipitation. 2.3. DNA extraction and PCR amplification Total bacterial DNA from each sample was extracted by a stool Power water ÒDNA Isolation Kit (MoBio laboratories Inc., Carlsbad, USA) according to the manufacturer’s instruction. After that, extracted DNA concentrations were analyzed by Nanodrop Spec-

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Fig. 1. (A) Location of the Wolsong LILW disposal repository in South Korea, (B) a silo-type disposal facility and unit disposal canister and (C) schematic diagram of 16 packed drums (sample name); the triangle points indicate individual sampling drum; the cross points indicate each sampling location outside drums; the grey-colored drums include noncombustible waste.

trophotometer ND-1000 (Thermo Fisher Scientific, USA) and stored at
20 °C until further analysis. For gene libraries construction, the DNA extracted from each sample was amplified by PCR using a primer set (27F, 50 -GAGTTT GATCMTGGCTCAG-30 , 518R, 50 -WTTACCGCGGCTGCTGG-30 ) (Ma et al., 2013) for V1-V3 region of the 16S rRNAs. The PCR protocol was conducted under the following conditions: 94 °C for 3 min, followed by 35 cycles of 94 °C for 15 s, 55 °C for 45 s, 72 °C for 1 min and a final elongation step at 72 °C for 8 min. The fusion primers were designed by Macrogen Ltd. (Seoul, Korea), which consisted of adapter A, B (454 Life Sciences), and 10-nucleotide barcode.

2.4. Pyrosequencing analysis After PCR amplification, an NGS run was conducted by the Macrogen Ltd. (Seoul, Korea) following the manufacture’s protocol (Genome Sequencer FLX plus, 454 Life Sciences). Low quality reads such as short (<170 bp), potentially-chimera, and homo-polymer sequences were trimmed after the adaptors, barcode, and primers sequence were removed using QIIME standard pipeline (Caporaso et al., 2010) and an ‘‘in-house” program (Macrogen Ltd., Korea). Subsequently, the remaining sequences for each sample were clustered using UCLUST, which had >97% sequence similarity as operational taxonomic units (OTUs). Taxonomic assignment of OTUs was carried out using the SILVA databases. The raw reads have been deposited at NCBI sequence read archive (PRJNA358462).

2.5. Data analysis Piper and activity diagrams were plotted using the Geochemist’s Workbench (GWB) version 11 and the thermodynamics database of ‘thermo.com’ (Bethke and Yeakel, 2016). Spearman correlation test was conducted using Minitab program (version 17) and non-metric multidimensional scaling (NMS) with Soresen distance (Bray-Curtis) was performed using the PC-ORD (version 5.0, MjM software, Gleneden, OR, USA) to visualize the similarities of individual bacterial communities (i.e., the relative abundance at genus level was used to plot the similarities). In this study, the joint NMS ordination was also employed using water chemistry variables (p < 0.1): pH, [Cl
], and [Fetotal]. The final NMS result met the statistical criteria such as the stress (<15) and the instability value (10
4). 3. Results and discussion 3.1. Physicochemical analysis Temporal variation of geochemical parameters inside the largescale container packed with 16 drums was investigated using nine leachates collected at different times over a period of 3 yr. The source of the leachate was groundwater that seeped into the large-scale container installed 130 m below sea level. The facts that (1) the geochemical conditions such as pH, temperature, and etc. seldom changed since the container was installed at 130 m below

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sea level and (2) the compositions of input groundwater remained unchanged even after 3 yr. Thus, the groundwater was sampled one time in January 2014. The properties of the leachate samples inside the container filled with 16 drums are shown in Table 1. The water pH varied from 7.23 to 7.74 and EC ranged from 1340 to 2080 ms cm
1. DOC decreased steadily from 154.3 to 19.04 mg L
1, indicating that microbes inside the container were utilizing DOC for assimilation (Robertson et al., 1982). Although the DOC value decreased due to biodegradation, pH was relatively stable. This might be associated with (1) inherently low DOC as compared to literature data (Small et al., 2017), which is not enough to change pHs and (2) the presence of concrete waste (B16 drum) which buffers to increase pH. Alkalinity significantly increased during the early stage, ranging from 582.4 to 929.5 mg L
1, and then deceased slowly until reaching 731.0 mg L
1. One possible explanation for this alkalinity variation is that the carbonate ion resulting from biodegradation might react with other cations (especially Ca2+ and Mg2+) to form carbonate precipitates, which was consistent with our experimental data that calcium concentration decreased from 50.3 to 15.3 mg L
1. Besides, as shown in piper diagram (Fig. 2), the leachate samples are sodium-bicarbonate water type, while input groundwater is calcium-bicarbonate water type. Although the major anion is bicarbonate resulting from microbial activity, the major cations are calcium and sodium in leachate samples. This difference can also be explained by the formation of the calcium carbonate precipitation. Furthermore, sulfate concentration was also decreased from 1.54 to 0.16 mg L
1 at early stage (S14-Jan to S14-Oct). Given that (1) experimental condition was still aerobic (DO > 6.5) and (2) sulfate reducing bacteria was not detected at early stage, the possible mechanism responsible for sulfate reduction in this system might be co-precipitation with respect to calcite and calcium sulfate minerals (Wynn et al., 2018; Zarga et al., 2013). The geochemical modeling calculations help identify potential formation of calcite in this system. Activity diagram supports the suggestion that the concentration of both calcium and bicarbonate inside the container contributed to the calcite precipitation (Fig. 3). During the decomposition of DOC, bicarbonate concentration was also increased. Under these conditions, the aqueous calcium concentration with respect to bicarbonate was reduced due to calcite precipitation. The descent trend of calcium and bicarbonate concentrations in leachates was continued until the solution was saturated with respect to calcite. The calcite formation was promoted again as calcium concentration was increased. The formation of

calcite is also associated with microbes, which will be discussed later in this article.

3.2. Diversity of bacterial communities in the container with 16 packed drums The 9 pyrosequencing libraries of bacterial 16S-rRNA gene generated rigid 102,504 reads (11,389 ± 8374 reads for each sample) after trimming the low quality chimeric sequences such as barcode and adaptor primers. The remaining reads were grouped into 581 operational taxonomic units (OTUs) for further study which shared above 97% similarity in sequence. In this study, target taxonomic level was selected above species level; the ratio of unclassified sequences to total sequence at the species level exceeds 0.80, indicating that species taxonomic level has lots of uncertainty when attempting to elucidate the microbial communities in this system. The number of OTUs ranged from 44 to 126 based on effective reads. The coverage of the 9 samples was higher than 95%, indicating that the sequence libraries could well reflect the bacterial communities in this study. In addition, Shannon indices inside the container ranged from 2.10 to 4.65, while Shannon index of input groundwater was 1.87. These results show that the biodiversity of the samples inside container were higher than that of the input groundwater sample. Across all 8 libraries except the input groundwater sample, a total 28 different bacterial classes were found in this study. The main classes (relative abundance > 1%) were classified in 10 classes: Betaproteobacteria (25.6–77.5%), Alphaproteobacteria (7.6– 44.0%), Gammaproteobacteria (4.1–37.8%), Bacilli (14.6%), Actinobacteria (7.7–12.9%), Lentisphaeria (3.6–15.6%), Clostridia (2.4–3.2%), Bacteroidia (1.1–4.8%), Spirochaetia (2.1%), and Deltaproteobacteria (1.0%) (Table 2). The percentages of other classes were lower than 1% in the 8 samples. In this study, 92 OTUs (18.3%) were classified into Betaproteobacteria and commonly shared by all the samples. Similarly, the dominating class for the input groundwater was also Betaproteobacteria, which accounted for 32 OTUs (44.4%), indicating that the bacterial communities inside container containing the simulated LILW drums were influenced by microbes from the input groundwater. Even though class Betaproteobacteria dominates inside the container packed with 16 drums, a more thorough exploration of microbial community is necessary to (1) compare with other studies which are based on traditional methods, and (2) provide a better understanding of microbial communities in this system. Moreover, as Betaproteobacteria highly varies, this

Table 1 The chemical and physicochemical properties of the water samples from location B3X (see Fig. 1). Parameters

Units

Input

S14-Jan

S14-Sept

S14-Oct

S15-Jan

S15-Jul

S15-Nov

S16-May

S16-Sept

pH EC DO DOC Alkalinity (CaCO3)

ms cm
1 mg L
1 mg L
1 mg L
1

7.71 1124 9.1 3.900 298.2

7.23 2019 6.52 154.3 582.4

7.57 2080 7.45 50.68 929.5

7.74 2040 <1 56.42 902.8

7.69 2010 1.2 45.10 890.4

7.70 2030 <1 40.50 910.4

7.38 1400 <1 30.72 679.9

7.45 1340 1.7 19.04 680.3

7.34 2000 <1 20.12 730.3

Anions

F
Br
Cl
NO
3 SO2
4 NO
2 PO3
4

mg mg mg mg mg mg mg

L
1 L
1 L
1 L
1 L
1 L
1 L
1

0.62 1.20 75.40 1.12 0.45 0.50 0.51

0.72 2.58 160.7 3.81 1.54 0.55 ND

0.76 1.77 132.2 1.53 0.72 1.27 0.22

0.59 0.93 124.1 0.90 0.16 0.93 0.12

0.53 0.50 120.1 0.68 0.20 0.82 ND

0.60 0.20 120.5 0.55 0.18 0.77 ND

0.84 0.51 135.6 2.27 0.26 0.77 0.02

0.65 1.69 121.3 ND 0.49 0.49 0.01

1.02 2.13 131.4 0.72 0.01 0.30 0.55

Cations

K+ Na+ Ca2+ Mg2+ Fetotal

mg mg mg mg mg

L
1 L
1 L
1 L
1 L
1

2.840 25.07 20.83 4.95 <0.05

290.2 192.8 50.30 5.30 0.323

346.8 242.5 29.30 4.97 0.285

396.7 263.4 25.70 4.55 0.105

380.0 240.1 23.30 4.40 0.075

350.1 230.2 20.10 4.50 0.079

318.3 220.8 15.33 4.88 0.280

257.6 175.7 17.28 4.17 0.090

298.1 158.2 19.78 5.10 0.160

Chemical

ND – Not determined.

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313

Fig. 2. Chemical facieses of input groundwater and the leachate samples in Piper diagram.

Fig. 3. Activity diagrams of log [Ca2+] versus log [HCO
3 ] at 20 °C in the leachate samples. In this model, the stability of Ca2+ and relevant calcium minerals (calcite and huntite) were calculated using GWB Act2 module and the database ‘thermo.com’.

(relative abundance > 3%) were considered as dominant orders: Burkholderiales (3.7–74.4%), Rhodocyclales (4.2–45.9%), Sphingomonadales (1.2–44.0%), Pseudomonadales (2.6–36.2%), Methylophilales (10.8%) Actinomycetales (7.7–12.9%), Chroococcales (9.9%), Bacillales (9.0%), Enterobacteriales (1.6–12.5%), Victivallales (1.0–15.6%), Neisseriales (6.1%), LactoBacillales (1.5–5.6%), Caulobacterales (2.2–4.7%) (Fig. 4A). In fact, only 3 orders were shared across all 8 samples inside the container: Burkholderiales, Clostridiales, and Syntrophobacterales. This suggests that bacterial community is gradually changing and adapting to the conditions in the waste container. Burkholderiales was the most dominant order present in the samples including in the input groundwater, whereas Rhodocyclales was second dominant in the leachates at the S14Jan, S15-Nov, and S16-May sampling times. At the family level, a total of 79 families were detected from the center of the container (B3X port, Fig. 1C), with 16 of which were dominant (>3% relative abundance at least in one sample) (Fig. 4B). The most dominating family was classified as Comamonadaceae that exists across all the samples including the input groundwater with relative abundance of 3.7–64.6%. The next most abundant families are Rhodocyclaceae, Pseudomonadaceae, and Sphingomonadaceae accounting for 11.1–45.9%, 2.5–36.2%, and 1.2– 43.7%, respectively. These four dominant families were dominant in all the samples except for S16-May. Enterobacteriaceae, Moraxellaceae, Aphanothecaceae, Staphylococcaceae, and Neisseriaceae were more dominant in only S16-May sample accounting for 49.3%. 3.3. Genera abundance analysis

makes it difficult to estimate the microbial effect based on the limited data. Taxonomic identification of filtered sequences showed a total 48 bacterial orders. Among these orders, thirteen bacterial orders

The 10 most abundant genera (a total of 136 genera across 8 samples excepting the input groundwater) were chosen to compare the abundances of each genera using heatmap (Fig. 5). The top 6 dominant genera included Curvibacter (3.7–39.4%), Azonexus

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Table 2 Relative abundances (%) of the bacterial classes in the 9 samples. Sample Bacteria

S14-Jan

S14-Sept

S14-Oct

S15-Jan

S15-Jul

S15-Nov

S16-May

S16-Sept

Betaproteobacteria Alphaproteobacteria Gammaproteobacteria Bacilli Actinobacteria Lentisphaeria Clostridia Bacteroidia Spirochaetia Deltaproteobacteria

57.9

40.1 37.1 17.0

47.7

67.4

49.0 44.0

77.5

25.6

37.8

16.3

69.0 7.6 4.1

27.0

25.7 14.6 7.7 3.6

3.2 2.2 2.1

2.4

12.9

15.6 2.7

1.1

4.8 1.0

chemical analysis (Fig. 5). This predominance of Curvibacter without correlation with individual parameters indicates that Curvibacter is not affected by the surrounding environments, which can be explained by previous study that Curvibacter genus are capable of surviving in the presence of antibiotics, CO2 (aq), heavy metals, and even UV radiation (Dekker et al., 2014; Falcone-Dias et al., 2012; Gulliver et al., 2014; Ordonez et al., 2009). The predominance of genus Curvibacter might affect the formation of calcium carbonate in this system. Although the calcite was not directly observed, the formation of calcite is expected based on the facts that (1) the steady decrease of both concentrations of calcium and bicarbonate ions from S14-Sept to S15-Nov, (2) the results of geochemical modeling (Fig. 3), and (3) the predominance of genus Curvibacter which is capable of forming calcite through biomineralization process (Zhang et al., 2017). Presumably, the formation of calcium carbonate is beneficial for the retardation of radionuclide. Because divalent radionuclides (e.g., Sr2+) have chemical similarity to Ca2+, co-precipitation of divalent radionuclides by substituting Ca2+ in calcite is well-known and previously reported (Curti, 1999; Lauchnor et al., 2013). In addition, various radionuclides can be immobilized by co-precipitation process with respect to other solid phases (Wang and Um, 2013; Kanematsu et al., 2014; Lee et al., 2016). In addition, denitrification is expected to occur inside the container, because the nitrate concentration steadily decreased from 3.81 to 0.55 mg L
1 in the presence of the dominant genus Acidovorax and Hydrogenophaga, which are known to be denitrifying bacteria (Hoshino et al., 2005). Moreover, the genus Hydrogenophaga was positively correlated with [NO
3 ] in this study, which is also associated with the denitrification. It has been reported that both [Mg2+] and [Ca2+] stimulated the genus Acidovorax (Miao et al., 2017). In this study, the genus Acidovorax was positively correlated with [K+], [Na+], and [Ca2+], indicating the monovalent cations can stimulate the genus Acidovorax as well. 3.4. Variations in microbial structure

Fig. 4. Bacterial communities at order (A) and family (B) levels inside the LILW container as revealed by pyrosequencing; minor groups consisting of <3% order and family levels.

(3.0–43.2%), Pseudomonas (2.5–36.2%), Novosphingobium (1.2– 42.0%), Acidovorax (2.1–23.5%), and Victivallis (1.0–15.6%) which are relative abundant (>1%) at least in four samples. The other 4 genera such as Pseudorhodoferax, Hydrogenophaga, Ferribacterium, and Acinetobacter come out from S15-Jul, S15-Nov, S16-Sept, and S16-May, respectively, with high relative abundance (>10%). In this study, Curvibacter was the most dominant genus and showed no correlation with explanatory variables obtained from

The joint NMS analysis revealed similarities between each bacterial community based on the relative abundance of OTU composition, assessing correlations with the environmental values: pH, [Cl-], and [Fetotal] (Fig. 6). The final stress value for the NMS was 9.52 and the cumulative R-square value was 88.3% as axes 1 and 2 were 53.6% and 34.7%, respectively. As the NMS ordination plot visualizes the shift of the microbial structures, bacterial structure was not dynamically changed over time; the trend of bacterial structures from S14-Sept to S15-Jan is consistent with one direction. Among other samples, bacterial structure of S16-May was distinct; this observation was related to operation history wherein the velocity of input groundwater was changed one week before the S16-May sampling. The velocity of the input groundwater was increased to simulate an unpredictable event such as flooding dur-

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315

Fig. 5. Heatmap of the 10 dominant bacterial genera of simulated LILW drums samples with spearman correlation coefficients (empty box, p  0.1) between explanatory variables obtained from chemical analysis and the relative abundance of the 10 dominant bacterial genera.

Fig. 6. Ordination plot of NMDS analysis of the bacterial community composition in leachate samples based on the SILVA rank taxonomy; [Cl
], [Fetotal], pH were added as vectors; the direction and length of red arrows denotes the correlation, i.e., if two vectors head to the same direction, they have positive correlation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

ing summer monsoon season. However, the bacterial structure of the S16-Sept sample turned back to previous bacterial structure after four months as collected between S15-Nov and S15-Jul. Although the velocity of input groundwater had an impact on the bacterial communities, the microbial community structure was resilient to the change of input groundwater’s velocity. In this study, [Fetotal] and [Cl
] (0.09–0.72 mg total Fe L
1; 100.3–160.7 mg Cl L-1) had significant impacts on bacterial communities (Fig. 6). For S14-Jan and S15-Nov samples, the bottom part of the NMS plot had high [Fetotal] and [Cl
] compared to the other samples; this agrees well with the results that (1) Dechloromonas (a genus that had 12.5% relative abundance at S15-Nov and could survive with toxic chloride) was detected in this study (Bender et al., 2002) and (2) Pseudomonas (a genus that occupied

19.6 ± 11.3% of average relative abundance and was capable of decomposing vinyl chloride through biodegradation) was dominant (Castro et al., 1992; Shim et al., 2001; Verce et al., 2001). The joint NMS plot shows that [Fetotal] was positively correlated with [Cl
], but negatively correlated with pH (Fig. 6). In general, the change of pH and Eh affects the iron solubility. Besides, the fact that (1) DAW in the buried container consisted vinyl waste (23.6 wt% of total DAW) which was composed of polyvinyl chloride, (2) Pseudomonas was the third dominant genus and could oxidize vinyl chloride while generating acetic acid, and (3) [Cl
] was high when Pseudomonas was dominant, suggest that vinyl waste might be degrading in this system. Overall, these findings regarding physicochemical properties, bacterial communities and their correlations can ensure the safety of repository management by discovering the uncertainty of temporal variation in physiochemical properties and microbial communities. Moreover, because microbially induced reactions are observed in this repository system, identifying bacterial communities needs to work as an indicator for predicting the geochemical reaction in underground environments. In summary, this study deals not only with the bacterial communities in a buried large-scale container packed with simulated wastes in 16 DAW drums via Pyrosequencing analysis, but also its correlation with geochemical variables. The simulated DAW is representative composition of the LILW in South Korea, and investigation of its correlations between indigenous microbes is worthwhile for long-term risk assessment of a disposal facility. 4. Conclusions This study describes the temporal variations of both geochemical parameters and bacterial communities in a large-scale in situ container packed with 16 LILW drums and stored 130 m below sea level via pyrosequencing for the first time. The bacterial communities in the container seemed to be influenced by microbes naturally present in groundwater. In the presence of Curvibacter, the precipitation of calcite was estimated based on the thermodynamic modeling results. The order Burkholderiales was dominant

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