understanding microbial production of sulphide in deep … › files › 4411 › wr_2016-48.pdf ·...

Understanding Microbial Production of Sulphidein Deep Olkiluoto Groundwater

Compilation and Evaluation of Three Consecutive Sulphate

Reduction Experiments (SURE) Performed in 2010 − 2014

POSIVA OY

Olki luoto

FI-27160 EURAJOKI, F INLAND

Phone (02) 8372 31 (nat. ) , (+358-2-) 8372 31 ( int. )

Fax (02) 8372 3809 (nat. ) , (+358-2-) 8372 3809 ( int. )

October 2016

Working Report 2016-48

Johanna Edlund, L isa Rabe, Andreas Bengtsson,

Björn Hal lbeck, Lena Eriksson, Jessica Johansson,

Linda Johansson, Karsten Pedersen

October 2016

Working Reports contain information on work in progress

or pending completion.

Johanna Edlund, L isa Rabe, Andreas Bengtsson,

Björn Hal lbeck, Lena Eriksson, Jessica Johansson,

Linda Johansson, Karsten Pedersen

Microbial Analyt ics Sweden AB

Working Report 2016-48

Understanding Microbial Production of Sulphidein Deep Olkiluoto Groundwater

Compilation and Evaluation of Three Consecutive Sulphate

Reduction Experiments (SURE) Performed in 2010 − 2014

UNDERSTANDING MICROBIAL PRODUCTION OF SULPHIDE IN DEEP OLKILUOTO GROUNDWATER COMPILATION AND EVALUATION OF THREE CONSECUTIVE SULPHATE REDUCTION EXPERIMENTS (SURE) PERFORMED IN 2010−2014 ABSTRACT

High-level radioactive waste in the form of spent fuel from Finnish nuclear power plants will be encapsulated in copper canisters. A possible microbial deterioration process for the safety case is the corrosion of the canister by sulphide produced from sulphate by sulphate-reducing bacteria (SRB). Microbial activity could thus impact the safety case by compromising the isolation and containment functions of the canister. While the presence, numbers and diversity of SRB in deep groundwater have been well documented, their activity has been less well studied. Hence, the remaining key issue for the safety case was to identify the factors controlling the rate of sulphide production in the geosphere, including man-made artefacts. Therefore, a sulphate reduction experiment (SURE) programme was initiated to seek a better understanding of the processes underlying sulphide production, i.e., sulphate reduction. SURE involved the chemical characterization and detailed microbiological investigation of groundwater from two depths in the ONKALO tunnel and the investigation of how the availability of sulphate as an electron acceptor and H2 and CH4 as electron donors can serve as controlling factors for sulphide production. Two groundwater types were selected for SURE, one from ONK-PVA6 that was sulphate-rich (≈1 mM SO4) and contained methane (1.6–4.5 mM CH4) and one from ONK-KR15 that was sulphate-poor but rich in methane (≈6 mM CH4). A total of 9 experiments were performed with the two different groundwater types and varying treatments with additions of sulphate, H2 and methane. Over the 3 year period of SURE, the chemistry of ONK-PVA6 drifted towards more dilute groundwater with decreasing concentrations of methane and chloride and an increasing concentration of sulphate. Only small changes were seen in the chemistry of ONK-KR15 during the experimental period. The experiments consequently evaluated the effect of sulphate, H2 and methane on microbial activity and diversity. An extensive analysis program was applied to observe how these variables influenced mainly sulphate-reducing activity by SRB. However, many other parameters were analysed as well to ensure that our understanding of sulphate reduction in deep Olkiluoto groundwater is significantly deepened and increased. The microbial diversities of cultures were analysed using cultivation and cloning analysed with 16S rDNA Sanger sequencing, while the attached and planktonic microbial diversities were examined using microarrays for Bacteria or 454 pyrotag and Illumina HiSeq sequencing of the bacterial and archaeal v4v6 and v6 regions of 16S rDNA, respectively. The concentrations of H2, CH4, sulphate, sulphide, ferrous iron, organic acids, and carbon as well as pH and Eh were analysed. The cultivable numbers of aerobic heterotrophic bacteria, sulphate- nitrate-, iron- and manganese-reducing bacteria, acetogenic bacteria, methanogens and virus-like particles as well as the total number of cells and amounts of planktonic and attached biomass were measured and the amounts of ATP were also determined. The groundwater chemistry of ONK-PVA6 and ONK-KR15 was analysed repeatedly in 2010 through

2014. Analyses of the δ34SV-CDT values of sulphate in the experimental groundwater and also of the δ13CV-PDB in dissolved inorganic carbon were carried out as well. There were large numbers of sequence 16S rDNA reads in ONK-PVA6 that are related to a sulphate-reducing community including representatives of the genera Desulfobacula, Desulfovibrio, Desulfobacterium, Desulfosporosinus and Desulfotignum. There was also a large read representation related to sulphur-reducing bacteria Desulfurivibrio and Desulfuromonas, sulphur-reducing archaeal class Thermoplasmata and to sulphide and sulphur-oxidizing genus Thiobacillus. The microbial diversity in ONK-KR15 was very different from ONK-PVA6 and comprised representatives of the genera Hydrogenophaga, Pseudomonas, Hoeflea, Thiobacillus, Fusibacter, and Lutibacter; SRB-related genera were not found. The SURE results corroborated field observations from Olkiluoto where microbial abundance and diversity correlated positively with increasing concentrations of sulphate and methane. The field work showed the zone with mixing sulphate- and methane-rich groundwater in Olkiluoto to be inhabited by active microbial communities. Similar results were recently published for microcosms in bottles containing deep groundwater from Outokumpu where the combined addition of sulphate and methane activated microorganisms. Several independent investigations including SURE reported here have, consequently, shown that a combination of methane and sulphate in deep hard rock aquifer groundwater stimulates microbial sulphide producing activity. However, there are several possible, alternative metabolic and geochemical pathways that may operate the sulphur transformations observed in SURE and in groundwater from the deep aquifers of Olkiluoto and Outokumpu. Because infiltration of organic matter from the surface is very slow, as probably is the transport of H2 from deep layers, sulphide production by SRB with H2 and organic matter as reducing agents will also be very slow in deep Olkiluoto groundwater. The anaerobic methanotrophs (ANME) of type 1 oxidize methane with SRB partners to carbon dioxide. Members of ANME type 1 with its SRB partners were not found, or were found in low relative abundance in SURE and deep Olkiluoto groundwater, indicating that they do not contribute to an anaerobic oxidation of methane (AOM) process in Olkiluoto. A new type 2 of sulphate dependent AOM process was published in 2012. This type might not be an obligate syntrophic process between ANME and SRB, but may be carried out by ANME alone. This ANME type 2 deposits elemental sulphur from sulphate and expels disulphide (HS2

−). There was a large representation of sequence reads related to ANME type 2 in all SURE samples. The produced sulphur will be deposited on surfaces where sulphur-reducing bacteria can produce sulphide from the sulphur. A large relative abundance of the sulphur-reducing genera Desulfuromonas was found in SURE, almost exclusively in the biofilms. The disulphide produced by ANME 2 is expelled to the water phase were members of Desulfobulbaceae such as Desulfurivibrio can disproportionate it to sulphate and sulphide. Desulfurivibrio was found mainly in the groundwater from ONK-PVA6. Dissimilatory sulphate reduction can be coupled to ferric iron reduction without the involvement of iron-reducing bacteria in high-sulphate systems. Sulphur (re)cycling was recently suggested to be a dominant process in iron cycling even in low-sulphate systems. A cryptic sulphur cycle may consequently have contributed to the precipitation

of iron sulphide in SURE. Such a cycle would be fed with sulphide from the AOM process of ANME type 2. Half of the produced sulphide ion would efficiently be precipitated as iron sulphide while the other half would be oxidized to sulphur which, in the presence of organic matter or H2, would be reduced to sulphide by sulphur-reducing bacteria and so on in a cycle. Eventually most of the sulphide produced would end up as iron sulphide, as long as ferric iron is available to react with the biogenic sulphide. In conclusion, the SURE results, supported by results from field investigations in Olkiluoto, imply an AOM process that reduces sulphate to disulphide and sulphur coupled to a process that disproportionates sulphur to sulphate and sulphide and a cryptic sulphur cycle that reduces ferric iron to ferrous iron which eventually precipitates sulphide as iron sulphide. However, unambiguous arguments for this hypothesis were not achieved and there are still some unresolved issues regarding AOM in Olkiluoto. Metagenomic studies of the DNA obtained in this study could possibly show if the metabolic pathways for AOM were present in the experiment or not. Keywords: Sulphate reduction, sulphide production, microbial, corrosion, geosphere, hydrogen, methane, groundwater, sulphate reducing bacteria, anaerobic oxidation of methane.

MIKROBIOLOGINEN SULFIDIN TUOTANTO OLKILUODON SYVISSÄ KALLIOPOHJAVESISSÄ

VUOSINA 2010−2014 TEHTYJEN KOLMEN SULFAATIN PELKISTYS-KOKEEN (SURE) TULOSTEN KOKOAMINEN JA ARVIOINTI

TIIVISTELMÄ

Korkea-aktiivinen käytetty ydinpolttoaine suomalaisista ydinvoimalaitoksista tullaan loppusijoittamaan kallioperään kuparikapseleissa. Sulfaattia pelkistävien bakteerien (SRB) aikaansaama sulfaatinpelkistys on mikrobiologinen prosessi, jossa muodostuu kuparin korroosiota aiheuttavaa vetysulfidia. Mikrobiologinen aktiivisuus voi näin ollen vaikuttaa kapselin turvallisuuteen heikentäen kapselin ominaisuuksia eristää radio-nuklidit ympäristöstä ja hillitä niiden kulkeutumista. SRB -määristä ja diversiteetistä syvissä pohjavesissä on olemassa julkaistua tietoa, mutta niiden aktiivisuutta on tutkittu vain vähän. Turvallisuusanalyysia varten tärkeintä on ollut määrittää sulfidin muodos-tumisnopeutta kontrolloivat tekijät geosfäärissä, mukaan lukien rakentamisen aikana aiheutetut häiriötilat. SURE-koesarja (Sulphate Reduction) käynnistettiin sulfidin-muodostusprosessin (sulfaatin pelkistys) ymmärtämiseksi. SURE-kokeet sisälsivät pohjaveden kemiallisen karakterisoinnin sekä mikrobiologiset määritykset kahdesta ONKALO-tunnelin eri syvyydellä olevasta kairareiästä. Koesarjassa selvitettiin miten sulfaatti elektronien vastaanottajana ja vety sekä metaani elektronien luovuttajina kontrolloivat sulfidin muodostusta. SURE-kokeisiin valittiin kaksi pohjavesityyppiä: toinen pohjavesiasemasta ONK-PVA6, jossa oli sulfaattirikasta (∼1 mM SO4) metaania (1,6–4,5 mM CH4) sisältävää pohjavettä ja toinen karakterisointireiästä ONK-KR15, jossa oli runsaasti metaania (∼6 mM CH4), mutta ei sulfaattia. Näillä kahdella pohjavesityypillä suoritettiin yhteensä yhdeksän koetta, joissa lisättiin vaihtuvia määriä sulfaattia, vetyä ja metaania. Kokeelliset vaiheet toteutettiin kolmen vuoden kuluessa, jona aikana pohjavesi ONK-PVA6:ssa laimentui lievästi, jolloin metaani- sekä kloridipitoisuudet alenivat kun taas sulfaattipitoisuus kasvoi. Vesi ONK-KR15:ssa pysyi lähes muuttumattomana. Koesarjassa seurattiin sulfaatin, vedyn ja metaanin vaikutusta mikrobien aktiivisuuteen ja diversiteettiin. Analyysiohjelman avulla seurattiin näiden muuttujien vaikutusta erityisesti SRB aktiivisuuteen, mutta myös muita parametreja analysoitiin, jotta Olkiluodon syvien pohjavesien sulfaatinpelkistysprosessit opittaisiin ymmärtämään tarkemmin. Mikrobidiversiteettiä tutkittiin maljaviljelymenetelmällä ja kloonaamalla. Klooneista sekvensoitiin 16S rDNA Sangerin menetelmällä. Kiinnittyneiden ja vapaiden mikrobien diversiteettiä tutkittiin DNA-sirun (microarray) avulla, jossa siru sisältää bakteereiden pääluokkaan kuuluvien mikrobien tunnisteita, sekä 454 pyrotag- tai Illumina HiSeq sekvensointimenetelmällä. Pyorotaq -menetelmällä tutkittiin bakteereiden 16S rDNA:n v4v6 aluetta ja Illumina HiSeq -menetelmällä arkkien vastaavan geenin v6 aluetta. Kokeiden aikana analysoitiin vety-, metaani-, sulfaatti-, sulfidi-, ferrorauta- ja hiilipitoisuudet sekä orgaanisten happojen määrät. Lisäksi seurattiin pH- ja Eh-arvoja. Viljeltyjen mikrobien määrät (MPN, most probable number) määritettiin heterotrofisille bakteereille, sulfaatin-, nitraatin-, raudan- ja mangaaninpelkistäjäbakteereille, asetogeeneille, metanogeeneille sekä virusten kaltaisille partikkeleille. Myös kokonaissolumäärät määritettiin (TNC, total number of

cells). Lisäksi määritettiin mikrobien ja ATP:n määrät sekä kiinnittyneestä biomassasta että vedessä olevista mikrobeista. ONK-PVA6 ja ONK-KR15 pohjaveden kemia analysoitiin säännöllisesti 2010−2014. Pohjavesistä analysoitiin myös sulfaatin δ34SV-

CDT ja liuenneen epäorgaanisen hiilen δ13CV-PDB. ONK-PVA6 pohjavesi sisälsi laajan joukon 16S rDNA sekvenssejä, jotka kuuluvat sulfaattia pelkistäviin eliöyhteisöihin, ml. suvut Desulfobacula, Desulfovibrio, Desulfobacterium, Desulfosporosinus ja Desulfotignum. Runsaasti edustettuna olivat myös sulfaatinpelkistäjäbakteerit Desulfurivibrio ja Desulfuromonas, sulfaatin-pelkistäjiin kuuluva arkeoniluokka Thermoplasmata sekä sulfidia- ja rikkiähapettava suku Thiobacilius. ONK-KR15 pohjaveden mikrobiologinen diversiteetti poikkesi huomattavasti ONK-PVA6:n diversiteetistä ja edustettuina olivat suvut Hydrogenophaga, Pseudomonas, Hoeflea, Thiobacillus, Fusibacter ja Lutibacter. Sulfaatinpelkistäjäbakteereihin liittyviä sukuja ei löydetty. SURE-kokeiden tulokset tukivat Olkiluodossa tehtyjä kenttähavaintoja, joissa mikrobien määrä ja diversiteetti korreloivat positiivisesti kasvavan sulfaatin ja metaanin määrän kanssa. Kenttämittauksissa on havaittu aktiivisia mikrobikantoja sekoit-tumisvyöhykkeellä, jossa sulfaatti- ja metaanirikas pohjavesi sekoittuvat keskenään. Vastaavia tuloksia on julkaistu myös Outokummun syväpohjavedellä tehdyissä pullokokeissa, joissa sulfaatin ja metaanin samanaikainen lisääminen aktivoi mikro-organismeja. Useat riippumattomat tutkimukset, SURE mukaan lukien, ovat osoittaneet, että metaanin ja sulfaatin yhdistelmä syvissä kalliopohjavesisysteemeissä kiihdyttää mikrobiologista sulfidinmuodostusaktiivisuutta. Tutkimustuloksista huolimatta on edelleen avoinna useita vaihtoehtoisia aineenvaihdunta- ja geokemiallisia reittejä, joiden kautta rikin kierto voi tapahtua. Koska orgaanisen aineen suotautuminen pinnalta on hidasta kuten mahdollisesti myös vedyn kulkeutuminen syvemmältä kalliosta, on todennäköistä, että Olkiluodon syvissä pohjavesissä myöskin sulfaatinpelkistäjäbakteerien sulfidinmuodostus käyttäen vetyä tai orgaanista ainetta on hidasta. Tyypin 1 anaerobiset metanotrofit (ANME1) hapettavat metaania yhdessä sulfaatinpelkistäjäbakteerien kanssa hiilidioksidiksi. Tyypin 1 ANME-jäseniä yhdessä sulfaatinpelkistäjäbakteerien kanssa ei kuitenkin SURE-kokeiden aikana havaittu. Myöskään muissa Olkiluodon pohjavesinäytteissä ei niitä ole havaittu, tai niitä on ollut vain hyvin pieniä määriä, joten voidaan päätellä, että ne eivät ota osaa metaanin anaerobiseen hapettumiseen (AOM-prosessi) Olkiluodossa. Vuonna 2012 julkaistiin uusi sulfaattiin perustuva tyypin 2 AOM-prosessi. Tämä prosessi ei välttämättä vaadi sekä ANMEn että SRB:n läsnäoloa, vaan voi tapahtua pelkästään tyypin 2 ANME avulla. ANME2 erottaa sulfaatista elementaarista rikkiä ja tuottaa veteen disulfidia (HS2

-). ANME2 sekvenssejä löytyi kaikista SURE-näytteistä. Tuotettu rikki jää pinnoille, joista sulfaatinpelkistäjäbakteerit voivat käyttää sitä sulfidinmuodostukseen. Rikkiä pelkistävän suvun Desulfuromonas suhteellinen määrä oli korkea SURE-kokeissa, mutta lähinnä ainoastaan biofilmeissä. ANME2:n tuottama disulfidi jää vesifaasiin, jossa Desulfobulbaceaet kuten Desulfurivibrio voivat hajottaa sen sulfaatiksi ja sulfidiksi. Desulfurivibrio löydettiin pääsääntöisesti vain ONK-PVA6 pohjavedestä.

Korkean sulfaattipitoisuuden systeemeissä dissimilatorinen sulfaatin pelkistyminen vetysulfidiksi voi olla samanaikaista ferriraudan pelkistymisen kanssa ilman, että siihen tarvitaan raudanpelkistäjäbakteereita. Rikin (uudelleen)kiertoa on viime aikoina esitetty hallitsevaksi prosessiksi raudankierrossa, jopa alhaisen sulfaattipitoisuuden systeemeissä. Tällainen monitahoinen rikkikierto on voinut aiheuttaa SURE-kokeiden rautasulfidin saostumisen. Kyseisen rikkikierron voi käynnistää AOM-prosessin seurauksena ANME2:n muodostama sulfidi. Puolet tuotetuista sulfidi-ioneista voivat tehokkaasti saostua rautasulfidina, kun taas puolet sulfidi-ioneista hapettuvat rikiksi, joka vedyn tai orgaanisen aineen läsnäollessa pelkistyvät sulfaatinpelkistäjäbakteerien vaikutuksesta sulfidiksi, ja kierto jatkuu. Lopulta, niin kauan kun saatavilla on ferrirautaa, joka voi reagoida biogeenisesti muodostuneen sulfidin kanssa, suurin osa muodostuneesta sulfidista saostuu rautasulfidiksi. SURE-kokeiden tulokset, joita tukevat Olkiluodon muut kenttähavainnot, voidaan selittää anaerobisella metaanin hapetusprosessilla, jossa sulfaatti pelkistetään disulfidiksi ja rikiksi, yhdistettynä prosessiin, jossa elementaarinen rikki edelleen hajotetaan sulfaatiksi ja sulfidiksi. Lisäksi rikin monitahoiseen kiertoon liittyy ferriraudan pelkistäminen ferroraudaksi, joka taas lopulta saostaa sulfidin rautasulfidina. Tätä oletusta tukemaan ei kuitenkaan saatu yksiselitteisiä todisteita ja edelleen on avoimena kysymyksiä liittyen Olkiluodossa tapahtuvaan AOM-prosessiin. Metagenomiset tutkimukset SURE-kokeissa saaduista DNA-näytteistä voisivat mahdollisesti osoittaa, onko kokeessa ollut aktiivisena AOM-aineenvaihduntareittejä vai ei. Avainsanat: Sulfaatin pelkistys, sulfidin tuotanto, mikrobiologinen, korroosio, geosfääri, vety, metaani, pohjavesi, sulfaatinpelkistäjäbakteeri, anaerobinen metaanin hapettuminen.

1

TABLE OF CONTENTS ABSTRACT TIIVISTELMÄ 1 INTRODUCTION............................................................................................................ 7

1.1 Radioactive waste, KBS-3 and microbially induced corrosion ..................................... 7 1.1.1 Microbial sulphide production from organic matter, H2 and methane .................. 7 1.1.2 H2S from microbially induced anaerobic corrosion of metals ............................... 9

1.2 Cultivable numbers and diversity of microorganisms in Olkiluoto groundwater ......... 11 1.2.1 Cultivable numbers and diversity ...................................................................... 11 1.2.2 Microbial cultivable numbers and their correlation with concentrations of of methane, hydrogen and sulphate ................................................................................. 12 1.2.3 Genomic diversity of sulphate-reducing bacteria ............................................... 14 1.2.4 Distribution of sulphate-reducing bacteria over depth ....................................... 14

1.3 ONKALO tunnel ........................................................................................................ 16 1.4 Outline and objectives of sulphate reduction experiment programme ....................... 17 1.5 Experimental approach during Phase III ................................................................... 18

1.5.1 SURE 1 ............................................................................................................ 19 1.5.2 SURE 2 ............................................................................................................ 19 1.5.3 SURE 3 ............................................................................................................ 20

1.6 This report ................................................................................................................ 21 2 DRILLING, INSTRUMENTATION AND CHARACTERISATION OF ONK-PVA6 AND ONK-KR15 .................................................................................................................. 23

2.1 Drilling and rock analysis .......................................................................................... 23 2.2 Drillhole instrumentation ........................................................................................... 24 2.3 Characterization scheme .......................................................................................... 25 2.4 Groundwater chemistry ............................................................................................ 27 2.5 Sampling and analysis of dissolved gas in groundwater ........................................... 27 2.6 Sampling and analysis of microorganisms in groundwater ....................................... 28

2.6.1 ATP analysis .................................................................................................... 28 2.6.2 Total number of cells ........................................................................................ 28 2.6.3 Analysis of cultivable heterotrophic aerobic bacteria ......................................... 28 2.6.4 Analysis of most probable numbers of culturable anaerobic microorganisms ............................................................................................................ 29

2.7 Nucleic acids analysis .............................................................................................. 29 2.7.1 DNA extraction from groundwater ..................................................................... 29 2.7.2 Amplicon 16S rDNA sequencing ....................................................................... 29 2.7.3 Statistical analyses and data visualization ........................................................ 32

3 SULPHATE REDUCTION EXPERIMENTS – MATERIALS, CONFIGURATIONS AND METHODS .................................................................................................................. 33

3.1 Field phase with flow cell field circulation systems.................................................... 33 3.1.1 Flow cell field circulation system configuration for SURE 1 ............................... 33 3.1.2 Flow cell field circulation system configuration for SURE 2 ............................... 36 3.1.3 Flow cell field circulation system configuration for SURE 3 ............................... 36 3.1.4 Water collection and transportation................................................................... 36

3.2 Flow cell circulation systems for laboratory experiments .......................................... 36 3.3 Laboratory phase experiments with flow cell circulation systems.............................. 38 3.4 Configuration and sampling of growth experiments, SURE 1 .................................... 40

3.4.1 Sampling procedures ........................................................................................ 40 3.5 Configuration and sampling of growth experiments, SURE 2 .................................... 41

3.5.1 Sampling procedures ........................................................................................ 41 3.6 Configuration and sampling of growth experiments, SURE 3 .................................... 42

2

3.6.1 Sampling procedures ........................................................................................ 42 3.7 Methods for measurements and analyses ................................................................ 44

3.7.1 Total number of virus-like particles ................................................................... 44 3.7.2 Ph and Eh analysis using a portable meter ....................................................... 44 3.7.3 Acetate, lactate, organic carbon, ferrous iron, and sulphide analysis ................ 44 3.7.4 Analysis of sulphate and δ34SV-CDT values of sulphate ....................................... 44 3.7.5 Analysis of δ13CV-PDB in dissolved inorganic carbon ........................................... 45 3.7.6 DNA extraction from biofilms and MPN cultures ............................................... 45 3.7.7 16S rDNA sequence analysis on biofilms and MPN cultures ............................ 46

4 GEOCHEMICAL AND MICROBIAL CHARACTERISTICS OF ONK-PVA6 AND ONK-KR15 OVER TIME ...................................................................................................... 49

4.1 Geochemistry and dissolved gases .......................................................................... 49 4.2 Microbiology ............................................................................................................. 51

4.2.1 Total number of cells ........................................................................................ 51 4.2.2 ATP .................................................................................................................. 52 4.2.3 CHAB ............................................................................................................... 52 4.2.4 Nitrate-reducing bacteria .................................................................................. 52 4.2.5 Iron-reducing bacteria ....................................................................................... 52 4.2.6 Manganese-reducing bacteria .......................................................................... 52 4.2.7 Sulphate-reducing bacteria ............................................................................... 54 4.2.8 Acetogens ........................................................................................................ 54 4.2.9 Methanogens .................................................................................................... 54 4.2.10 Overall evaluation of microbiological observations ........................................ 54

4.3 Groundwater 16S rDNA sequence diversity ............................................................. 54 4.3.1 DNA recovery in groundwater extractions ......................................................... 54 4.3.2 Groundwater 16S Bacteria rDNA v4v6 sequence diversity ............................... 55 4.3.3 Groundwater 16S Archaea rDNA v6 sequence diversity ................................... 57 4.3.4 16S Bacteria and Archaea rDNA sequence diversity in groundwater over time and by drillhole ..................................................................................................... 57

5 RESULTS FROM SULPHATE REDUCTION EXPERIMENTS 1, 2 AND 3 ................... 65 5.1 TNC ......................................................................................................................... 65 5.2 Virus-like particles .................................................................................................... 66 5.3 ATP .......................................................................................................................... 68

5.3.1 Circulating groundwater .................................................................................... 68 5.4 Cultivable heterotrophic aerobic bacteria .................................................................. 71 5.5 Most probable number of bacteria ............................................................................ 72

5.5.1 Nitrate-reducing bacteria .................................................................................. 72 5.5.2 Iron-reducing bacteria ....................................................................................... 74 5.5.3 Manganese-reducing bacteria .......................................................................... 75 5.5.4 Sulphate-reducing bacteria ............................................................................... 76 5.5.5 Autotrophic acetogens ...................................................................................... 76 5.5.6 Methanogens .................................................................................................... 77

5.6 Gases and chemistry ................................................................................................ 77 5.6.1 H2 ..................................................................................................................... 77 5.6.2 Methane ........................................................................................................... 78 5.6.3 pH..................................................................................................................... 79 5.6.4 Carbon dioxide ................................................................................................. 79 5.6.5 13CV-PDB in dissolved inorganic carbon ............................................................... 81 5.6.6 Dissolved organic carbon ................................................................................. 82 5.6.7 Acetate ............................................................................................................. 82 5.6.8 Lactate ............................................................................................................. 84 5.6.9 Redox potential................................................................................................. 84

3

5.6.10 Sulphate ....................................................................................................... 87 5.6.11 34SV-CDT in SO4 .............................................................................................. 88 5.6.12 Ferrous iron .................................................................................................. 90 5.6.13 Sulphide ....................................................................................................... 91

5.7 16S rDNA sequence diversity ................................................................................... 92 5.7.1 SURE 1: Phylochip 16S rDNA diversity of biofilms ........................................... 93 5.7.2 SURE 2: Cloning and 16S rDNA Sanger sequencing of MPN cultures ............. 95 5.7.3 SURE 2: 16S rDNA bacterial diversity analysis of biofilms ................................ 95 5.7.4 SURE 2: 16S rDNA archaeal diversity analysis of biofilms ............................... 96 5.7.5 SURE 3: 16S rDNA diversity analysis of biofilms ............................................ 105

6 UNDERSTANDING MICROBIAL REDUCTION OF SULPHATE TO SULPHIDE IN DEEP OLKILUOTO GROUNDWATER .............................................................................. 115

6.1 Experimental approach – choice of methodology ................................................... 115 6.1.1 The choice of material for flow cell circulation systems ................................... 115 6.1.2 Ratios between attached and planktonic biomass .......................................... 116 6.1.3 Cultivation of planktonic microorganisms and 16S rDNA sequence analysis of attached microorganisms in circulation systems ..................................................... 118

6.2 Effect of total 3-year duration of three consequtive experiments using ONKALO groundwater ................................................................................................................... 118 6.3 16S rDNA diversity in sequence libraries – interpretations and implications for results and conclusions .................................................................................................. 119

6.3.1 Bacteria .......................................................................................................... 119 6.3.2 Archaea .......................................................................................................... 121

6.4 Groundwater types and effect of sulphate on microbial diversity ............................ 123 6.5 Observed microbial sulphate reduction and reported isotopic sulphur fractionation factor ε (‰) ................................................................................................ 126 6.6 Effects of H2 on microbial activity ........................................................................... 126 6.7 Effect of CH4 on microbial activity ........................................................................... 128 6.8 A model of possible metabolic and geochemical pathways of sulphur in Olkiluoto ......................................................................................................................... 129

6.8.1 Sulphate-reducing bacteria produce sulphide from sulphate with H2 and/or organic matter ............................................................................................................ 130 6.8.2 Anaerobic methanotrophs (ANME) type 1 and sulphate-reducing bacteria (SRB) oxidize methane with sulphate to carbon dioxide and sulphide ........................ 130 6.8.3 Anaerobic methanotrophs (ANME) type 2 oxidize methane with sulphate to carbon dioxide and sulphur and disulphide................................................................. 130 6.8.4 Sulphur-reducing bacteria produce sulphide from sulphur .............................. 131 6.8.5 Sulphate-reducing bacteria disproportionate disulphide to sulphate and sulphide in a sulphide-sulphate loop .......................................................................... 131 6.8.6 A cryptic sulphur cycle produces ferrous iron and sulphur from ferric iron and sulphide ............................................................................................................... 131

6.9 Proposed reduction-oxidation pathways for sulphur in Olkiluoto groundwater ........ 134 7 REFERENCES .......................................................................................................... 135 A. APPENDIX 1 .............................................................................................................. 141 B. APPENDIX SURE 1 ................................................................................................... 149 C. APPENDIX SURE 3 ................................................................................................... 159 D. APPENDIX SURE 1-3 ................................................................................................ 163

5

Key to abbreviations used frequently in the text Abbreviation Meaning Brief description AA Autotrophic acetogens Microbes able to produce acetate from carbon dioxide and hydrogen

AGW Analytical-grade water Filter purified tap water

AM Autotrophic methanogens Microbes able to produce methane from carbon dioxide and hydrogen

ANME Anaerobic methanotroph Microbes able to oxidize methane under O2-free conditions

AOM Anaerobic oxidation of methane

A process where microbes oxidize methane to hydrogen and carbon dioxide, and use hydrogen to reduce sulphate to sulphide

AODC Acridine orange direct count Method based on nucleic acid staining for determining cell numbers

apsA Adenosine-5’-phosphosulphate reductase alpha subunit gene

Functional gene for a key enzyme in the reduction of sulphate by sulphate-reducing bacteria

ATP Adenosine triphosphate Energy carrier in living organisms

CFU Colony-forming unit A cell that has divided repeatedly, e.g., on an agar plate, forming a dense colony of many identical cells

CHAB Cultivable heterotrophic aerobic bacteria

Microbes that are able to live on oxygen and organic carbon and grow in the laboratory

DNA Deoxyribonucleic acid The genetic code, which builds the genome unique to each organism

δ13CV-PDP Delta 13 carbon value relative the Vienna- Pee Dee Belemnite

A ratio of carbon isotopes13C/12C expressed in relation to an international standard: the Vienna- Pee Dee Belemnite

δ34SV-CDT Delta 34 sulphur value relative the Vienna-Canon Diablo Troilite

A ratio of sulphur isotopes 34S/32S expressed in relation to an international standard: the Vienna-Canon Diablo Troilite value.

FC Flow cell Pressure resistant container with solid support for biofilms

FCCS Flow cell cabinet system A circulation system in a temperature controlled cabinet with 4 flow cells

FCFCS Flow Cell Field Circulation Systems

A circulation system with 4 flow cells used in the field, i.e., the Onkalo tunnel

HA Heterotrophic acetogens Microbes able to produce acetate from organic carbon

MM Multi-methanogens Medium for microorganisms able to produce methane from organic carbon or from H2 and CO2

IRB Iron-reducing bacteria Microbes able to reduce iron(III) to Fe2+ in their respiration

MPN Most probable number Method for enumerating microbes with cultivation

MRB Manganese-reducing bacteria Microbes able to reduce manganese(IV) to Mn2+ in their respiration

NRB Nitrate-reducing bacteria Microbes able to reduce nitrate in their respiration

OTU Operational Taxonomic Unit Taxonomic level of sampling selected by the user to be used in a

study, such as individuals, populations, species, genera, or bacterial strains

PCR Polymerase chain reaction Technique used to exponentially amplify DNA

RNA Ribonucleic acid Part of the ribosome, which constructs all the proteins in an organism

rDNA Ribosomal DNA DNA encoding for the ribosome

RT Room temperature 20 °C

SRB Sulphate-reducing bacteria Microbes able to reduce sulphate to sulphide in their respiration

TNC Total number of cells The number of cells in a water sample or on a solid phase, usually determined by means of microscopy using the AODC method

7

1 INTRODUCTION

1.1 Radioactive waste, KBS-3 and microbially induced corrosion

High-level radioactive waste in the form of spent nuclear fuel (SNF) from Finnish nuclear power plants will be encapsulated in copper canisters according to the KBS-3 concept (SKB 2010). The canisters will be surrounded by an engineered barrier consisting of swelling clay. The metal and the clay barriers are commonly denoted engineered barrier systems (EBS) and are susceptible to deterioration processes. A possible microbial deterioration process for the safety case is the corrosion of the canister by sulphide produced from sulphate by sulphate-reducing bacteria (SRB) (King et al. 2011). Corrosion may eventually cause the canister to breach, leading to radionuclide release. Microbial activity could thus impact the safety case by compromising the isolation and containment functions of the canisters.

The presence and activity of SRB in the host rock groundwater and bentonite clays is well documented (Masurat et al. 2010; Pedersen et al. 2015; Pedersen et al. 2012; Svensson et al. 2011). The sulphide they produce is not expected to reach the canisters because of slow diffusion through the compacted bentonite buffers (Wersin et al. 2014). However, in the case of a clay buffer failing, e.g. due to erosion, the probability of sulphide coming into contact with the canisters and generating corrosion is higher. It is, therefore, important to understand the conditions that foster sulphide production in the geosphere (often called the far-field) and its migration towards the canisters.

While the presence, numbers and diversity of SRB in deep groundwater have been well documented, their activity is less well studied. There are Scandinavian cases when sulphide concentrations in groundwater exceed the safety case value of 9.4 × 10-5 M sulphide used for Olkiluoto, Finland (Wersin et al. 2014). Values of 500 − 1400 × 10-5 M sulphide have been observed in drillholes and the underlying reasons for this accumulation are presently under investigation. Hence, the remaining key issue for the safety case is to identify the factors controlling the rate of sulphide production in the geosphere, including man-made artefacts. The availability of electron donors such as H2 and CH4 from deep geological sources, and electron acceptors such as sulphate and ferric iron is hypothesized to be one of several controlling factors.

The action of competing iron reducing bacteria (IRB) may mitigate sulphide production by SRB due to the precipitation of iron sulphide phases (Wersin et al. 2014). Alternatively, the presence of a cryptic sulphur cycle may mitigate the amount of sulphide that could reach the canisters. A cryptic sulphur cycle involves the consumption of sulphide via a reaction with iron oxides and the microbial respiration of the subsequent sulphur products (sulphate, sulphur, thiosulphate) (Hansel et al. 2015). This cycle leads to low sulphate and aqueous sulphide levels. There is growing evidence in the scientific literature that sulphur recycling via a cryptic sulphur cycle is an essential catalytic engine driving the Fe cycle (Kwon et al. 2014).

1.1.1 Microbial sulphide production from organic matter, H2 and methane

Sulphate reduction at the temperatures and pressures prevailing in the deep groundwater environment is a microbiological process. The chemical reduction of sulphate to

8

sulphide is very slow under these conditions, as revealed by the calculated half-life of thermo-chemical sulphate reduction in the presence of acetate and elemental sulphur at 100°C, i.e., 372,000 years (Cross et al. 2004). The SRB use the sulphur atom in the sulphate molecule as an electron-acceptor and the reduced product is sulphide. The energy and electron donor for SRB can be either organic compounds or H2. In the case of a general organic compound, the reaction with sulphate can be written:

2(CH2O) + SO42−

+ 2H+→ 2HCO3− + HS− + 3H+ Eq. 1

Organic compounds that can be used by various SRB are fermentation products such as short-chain organic acids, fatty acids, and higher-molecular-weight hydrocarbons (Widdel and Bak 1992). Many SRB, especially of the genus Desulfovibrio, can grow on lactate. The lactate molecule is incompletely oxidized to acetate and carbon dioxide, and the electrons are transported to electron transport enzymes in the cell membrane and then further to the sulphate reduction enzymes in the cytoplasm. The overall reaction is written:

2 lactate + SO42− + 2H+ → 2 acetate + 2CO2 + HS− + H+ + 2H2O Eq. 2

In this metabolism, lactate is used as an energy and electron source as well as a carbon source for biomass production. Other SRB use H2 as an electron donor and energy source during lithoautotrophic growth (Brysch et al. 1987). The reaction for the reduction of sulphate with H2 is written:

4H2 + SO42− + 2H+ → H2S + 4H2O Eq. 3

Note that no carbon is involved in this energy-transforming reaction and that protons are consumed (Eq. 3). The carbon sources for H2-utilizing SRB are either short-chain organic compounds, such as acetate, or carbon dioxide. The carbon is used for biomass production and the biosynthesis consumes chemical energy. Acetate and carbon dioxide are incorporated into the cell metabolism via the molecule coenzyme A (CoA) to produce pyruvate in the following way:

CH3COO−–CoA + CO2 + H2 → CH3COCOO− + CoA + H2O Eq. 4

The pyruvate then enters the cell metabolism and is incorporated into new biomolecules.

Some microbial consortia can use methane as a source of energy and produce sulphide (Knittel and Boetius 2009):

CH4 + SO42– HCO3

– + HS– + H2O Eq. 5

For methane to act as an energy source, the SRB must have a symbiotic relationship with methanogenic Archaea, a situation hitherto found only in sea bed sediments (Losekann et al. 2007).

9

Sulphide can react with ferrous iron according to equation 6:

Fe2+ + HS− FeS + H+ Eq. 6

The produced sulphide may also react with ferric iron in minerals according to the following reactions (Hansel et al. 2015; Kwon et al. 2014):

HS− + 2Fe(OH)3 + 5 H+ 2Fe2+ + S0 + 6H2O Eq. 7

HS− + 2Fe(OOH) + 5 H+ 2Fe2+ + S0 + 4H2O Eq. 8

The produced elemental sulphur can be used as an electron acceptor by sulphur reducing bacteria such as Desulfurivibrio and Desulfuromonas:

2(CH2O) + 4S0 + 4H2O → 4HS− + 2HCO3− + 6H+ Eq. 9

1.1.2 H2S from microbially induced anaerobic corrosion of metals

Underground repositories for SNF will contain large amounts of iron, i.e., rock steel reinforcing material such as rock bolts and wire mesh, that will corrode with the concomitant production of H2 (Reardon 1995). The oxidation of metallic iron with sulphate and biodegradable organic matter by microorganisms is regarded as the principal reaction in the anaerobic chemical microbially induced corrosion (CMIC) of iron (Figure 1-1, Eqs 10 and 11,). The suggested reaction mechanism of corrosion is that the negative redox potential (i.e., Fe2+/Fe, E0’ = −0.44 V) of iron can liberate H2 (i.e., 2H+/H2, E0’ = −0.41 V) and may in this way indirectly act as an electron donor for SRB (Cord-Ruwisch and Widdel 1986). Another possible mechanism in anaerobic corrosion has been proposed: the direct microbial utilization of the electrons liberated during the oxidation of iron (Eq 12) (Dihn et al. 2004, Enning and Garrelfs 2014). This mechanism is kinetically much faster and more favourable than is the consumption of electrochemically formed H2.

10

Figure 1-1. Schematic illustration of different types of iron corrosion by sulphate-reducing bacteria (SRB) at circum-neutral pH (Simplified from Enning and Garrelfs 2014). Biotic reactions are shown. SRB attack iron via chemical microbially influenced corrosion (CMIC) or electrical microbially influenced corrosion (EMIC). Stoichiometry of the illustrated reactions is given in the text. Please note that all depicted processes may occur simultaneously on corroding metal surfaces but differ in rates and relative contributions to corrosion. Organotrophic SRB produce hydrogen sulphide which reacts with metallic iron during CMIC. Note that CMIC quantitatively depends on the availability of biodegradable organic matter (here schematically shown as carbon with the oxidation state of zero, CH2O). Specially adapted lithotrophic SRB withdraw electrons from iron via electro-conductive iron sulphides during EMIC. Excess of accepted electrons may be released as H2 (via hydrogenase enzyme). Biogenic, dissolved hydrogen sulphide reacts with metallic iron.

H2S + Fe0 → H2 + FeS Eq. 10

3(CH2O) + 2Fe0 +2SO42− + H+ → 3HCO3

− + 2FeS + 2 H2O Eq. 11

4Fe0 + SO42− + 3HCO3

− + 5H+ → FeS + 3FeCO3 + 4H2O Eq. 12

Sulphide is a compound that can mediate anaerobic corrosion not only of iron but also of copper. The principal mechanism of anaerobic copper corrosion is thought to be the same as for the anaerobic corrosion of metallic iron. The complete mechanism of

2H+

H2e-

Fe0

FeS

IronH2S

SO42-

(CH2O)

H2

SRB

CO2

FeS,

FeC

O3SR

B

Fe2+ Fe0

e-

SO42-

H2S

HCO3-

H2

Anode

Cathode

IronCMIC EMIC

11

anaerobic copper corrosion is not yet fully understood, but the theory is that electrons from copper reduce protons to H2 in the presence of sulphide produced by SRB (Eq 13). The produced H2 can be used by SRB that produce more sulphide, which may react with the copper and produce copper sulphides.

2Cu + HS− + H+→ Cu2S + H2 Eq. 13

1.2 Cultivable numbers and diversity of microorganisms in Olkiluoto groundwater

1.2.1 Cultivable numbers and diversity

Microbial processes comprise many biochemical oxidation and reduction reactions that in various ways influence the environment in which microorganisms are active. The successful and conclusive study of microorganisms and their processes at depth requires a range of methodologies. The microbiology programmes examining Olkiluoto and ONKALO groundwater have included methods for quantifying microorganisms determined as the total number of cells (TNC), the amount of the ubiquitous cell constituent adenosine triphosphate (ATP), the numbers of cultivable heterotrophic aerobic bacteria (CHAB), and the most probable numbers (MPN) of nine metabolic, with respect to electron acceptors and donors, groups. These nine groups includes nitrate-, iron-, manganese-, and sulphate-reducing bacteria (NRB, IRB, MRB, and SRB, respectively), aerobic methane-oxidizing bacteria (MOB), autotrophic and heterotrophic acetate-producing bacteria (AA and HA, respectively), and autotrophic and heterotrophic methane-producing microorganisms (AM and HM, respectively). Details about the methods can be found elsewhere (Pedersen et al. 2008; Pedersen et al. 2012).

From 1997 up to the end of 2013, 153 samples were analysed for microbial numbers and cultivable diversity in Olkiluoto. All data from these investigations have recently been summarized by Pedersen et al. (2015) in comparison with similarly obtained data from the Swedish site investigations for a future SNF repository (Hallbeck and Pedersen 2012). The average TNC were approximately similar for all the deep sites but significantly higher for the shallow Olkiluoto groundwater. As the TNC method does not distinguish dead or inactive bacteria from active and metabolizing bacteria, an analysis of ATP was introduced (Eydal and Pedersen 2007). ATP is an energy transporting molecule that is present in all living and active cells. In other words, the presence of ATP attests to the presence of active and metabolizing cells. It was found that ATP generally correlated well with TNC in deep granitic groundwater suggesting that the investigated microbial communities were metabolically active.

Logarithms with the base 10 were calculated for the MPN values for each metabolic group and stacked in bar graphs. The stacked profile of MPN values for all deep groundwater samples analysed from 2005 to 2012 is shown in Figure 1-2. The obtained stacked numbers then represent both the diversity, i.e., how many metabolic groups could be cultivated, and the numbers of cultivable microorganisms within each metabolic group in a groundwater sample. A large stack will be obtained if both the diversity and the values of cultivated metabolic groups are large. A value for each cultivated group can be appreciated from the bar length for the respective metabolic

12

group. The stack heights remained rather homogenous over depth for the first 0 to −200 masl, but deeper the frequency of samples with a large stack height increased. For the deepest samples, the stack height generally decreased. The metabolic groups cultivated for were found in most but not all samples.

1.2.2 Microbial cultivable numbers and their correlation with concentrations of of methane, hydrogen and sulphate

The depth profiles of anaerobic aquatic sediments where sulphate from sea-water and methane from deep layers meet with the concomitant occurrence of sulphide have been interpreted as evidence of anaerobic methane oxidation (AOM), with sulphate as the final electron acceptor (Knittel and Boetius 2009; Thomsen et al. 2001; Zehnder and Brock 1980). A typical such sulphate–methane transition zone (SMTZ), albeit on a much broader scale than in aquatic sediments, is indicated by an analysis of methane, sulphate, and sulphide over depth in groundwater below the island of Olkiluoto, Finland (Wersin et al. 2014). In the −250 to –350 masl depth zone, sulphate concentrations decrease with depth from 5 mM to <0.1 mM, while methane concentrations increase with depth from 40 µM to 4 mM. In addition, sulphide concentration reaches in this zone approximately 300 µM on several sampled sites, which is 10–15 times higher than that found in shallower or deeper groundwater. Similarly, the total number of cells (TNC), the ATP concentration, and the numbers of cultivable SRB were greater in groundwater samples from this zone than in shallower or deeper samples (Pedersen et al. 2015). A correlation was found when the combined concentrations of methane and sulphate where pair-wise compared with the numbers of cultivable microorganisms (Figure 1-3). A distance-weighted least squares model confirmed that more microorganisms could be cultivated from groundwater with significant concentrations of methane and sulphate. Because H2 is often nested with the presence of methane, it cannot be excluded that the increase in microbial numbers where methane and sulphate occur is triggered by H2 and not methane. It is also possible that both methane and H2 explain the observed increase in microbial numbers in groundwater with methane and sulphate. But because SRB consume H2 much faster than methane, the concentration of this gas will not rise above approximately 1 µM if sulphate is available as indicated by the results presented by Pedersen et al. (2015) and Pedersen (2012a). The addition of sulphate and methane to deep groundwater from Outokumpu activated the microorganisms markedly (Rajala et al. 2015). H2 was not added which shows that only sulphate and methane were needed to activate microbial populations in groundwater from deep hard rock aquifers.

13

Figure 1-2. Stacked 10Log(MPN) values of most probable numbers of metabolic (with respect to electron donors and acceptors) groups of microorganisms analysed for in deep Olkiluoto groundwater (top figure) and ONKALO groundwater (bottom figure), 2005–2012. Only complete analyses with 8 MPN results are shown. The drillholes are listed from left to right in order of increasing depth. NRB = nitrate-reducing bacteria, IRB = iron-reducing bacteria, MRB = manganese-reducing bacteria, SRB = sulphate-reducing bacteria, AA = autotrophic acetogens, HA = heterotrophic acetogens, AM = autotrophic methanogens, and HM = heterotrophic methanogens. The height of each stack represents both diversity i.e., how many metabolic groups could be cultivated, and the values of cultivable organisms in each metabolic group in a drillhole. The values for each cultivated group can be gauged from the bar length for each metabolic group. Each scale step indicated by a tick represents a 10log10 unit = 1; two steps thus equal 100 cells mL–1 and three steps 1000 cells mL–1 (Figure from Pedersen et al. 2015).

HM AM HA AA SRB MRB IRB NRB

OL-

KR32

_50_

1

OL-

KR30

_50_

2

OL-

KR30

_50.

6

OL-

KR53

_65.

5

OL-

KR8_

77_1

OL-

KR47

_77_

1

OL-

KR43

_96_

1

OL-

KR33

_95_

1

OL-

KR46

_82_

1

OL-

KR6_

98_8

OL-

KR6_

98.5

OL-

KR39

_108

_1

OL-

KR6_

125_

6

OL-

KR44

_116

OL-

KR45

_117

_1

OL-

KR6_

125

OL-

KR6_

135_

8

OL-

KR6_

135

OL-

KR47

_131

_1

OL-

KR37

_166

_1

OL-

KR47

_145

_1

OL-

KR46

_131

_1

OL-

KR31

_143

_1

OL-

KR42

_175

_1

OL-

KR46

_175

_1

OL-

KR43

_214

_1

OL-

KR47

_217

_1

OL-

KR41

_213

_1

OL-

KR53

_265

OL-

KR41

_257

_2

OL-

KR51

_292

OL-

KR45

_295

_1

OL-

KR40

_282

_1

OL-

KR8_

302_

2

OL-

KR13

_362

_3

OL-

KR6_

393_

1

OL-

KR10

_326

_2

OL-

KR6_

422_

5

OL-

KR47

_413

_1

OL-

KR51

_422

OL-

KR39

_403

_1

OL-

KR39

_400

OL-

KR50

_363

OL-

KR11

_415

OL-

KR50

_424

OL-

KR49

_614

_1

OL-

KR44

_651

OL-

KR47

_708

_1

OL-

KR40

_786

_1

OL-

KR40

_788

.5

OL-

KR50

_751

Drillhole

Sta

cked

10lo

g(M

PN

) val

ues

HM AM HA AA SRB MRB IRB NRB

PVA1

PVA1

PVA1

PVA1

ON

K-PV

A 2

ON

K-PV

A 3

ON

K-PV

A8

ON

K-PV

A6

ON

K-KR

15

ON

K-PV

A9

Sta

cked

10lo

g(M

PN

) val

ues

14

Figure 1-3. The relationship between MPN numbers in a dataset consisting of 528 data triplets (CH4, SO4 and MPN), and the concentrations of sulphate and methane represented in a three-dimensional graph according to a distance-weighted least squares model (Figure from Pedersen et al. 2015).

1.2.3 Genomic diversity of sulphate-reducing bacteria

The diversity of microorganisms in Olkiluoto groundwater was initially determined from MPN analyses. During 2012, analyses of genomic DNA using high throughput sequencing were added in the diversity analysis programme. The genomic diversity, represented by 16S rDNA sequence libraries, correlates with the cultivation results (Pedersen et al. 2015). Although a direct comparison is difficult, some general conclusions can be drawn. Sequences related to sulphate-reducing genera Desulfobacula, Desulfobulbus, and Desulfovibrio and to sulphur-reducing genera Desulfurivibrio and Desulfuromonas, were found in ONKALO groundwater, mainly between −200 and −366 masl. Similar genetic diversity over depth was previously observed in sequence libraries from Olkiluoto drillholes using 454 pyrosequencing targeting functional gene dsrB (dissimilatory sulphite reductase β-subunit) (Nyyssönen et al. 2012).

1.2.4 Distribution of sulphate-reducing bacteria over depth

The distribution of 10Log numbers of SRB over depth as shown in Figure 1-2 indicates that there was a maximum in cultivable SRB from −200 m down to approximately −400 masl with 13 observations above 100 cells mL−1. If the absolute number of cultivable SRB is plotted versus depth, this maximum appears more obvious than in a plot of log SRB numbers (Figure 1-4). The MPN cultivation method returns the value of cultivable SRB in the groundwater samples. However, there may be other groups of SRB present that escape the cultivation methodology. The analysis of genomic DNA is

> 3 < 2.7 < 2.2 < 1.7 < 1.2 < 0.7 < 0.2

-3.0-2.5

-2.0-1.5

-1.0-0.5

0.00.5

1.01.5

2.0

10 Log(CH4) (mM)

01

23

45

67

8

SO4 (mM)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10Log(MPN) (cells mL -1)

15

complementary to cultivation and returns a more complete diversity estimate than does MPN cultivation, but the method is not quantitative although a relative abundance of different groups of microorganisms is indicated. When the percentages of SRB related Operational Taxonomic Units (OTUs) in the sample libraries were plotted versus depth, a profile similar to the one obtained with MPN cultivation was found (Figure 1-5). Two independent lines of evidence consequently report a maximum in SRB abundance between −200 and −400 masl depth in Olkiluoto.

Figure 1-4. The distribution of MPNs of sulphate-reducing bacteria (SRB) versus depth in Olkiluoto groundwater (Data from Pedersen et al. 2015).

0 200 400 600 800 1000 1200 1400

SRB (cells mL-1)

-1200

-1000

-800

-600

-400

-200

0

Dep

th (m

asl)

16

Figure 1-5. Distribution of the percentage of SRB related OTUs obtained with v4v6 pyrotag sequencing over depth in Olkiluoto groundwater samples (confer Figure 1-4). _1 and _2 refer to different sampling dates (Data from Pedersen et al. 2015).

1.3 ONKALO tunnel

On the island of Olkiluoto, selected for the construction of a deep repository for SNF, a tunnel denoted ONKALO has been excavated in the future repository area at a depth of 420 m. The current vertical variation in hydrogeochemical groundwater parameters in Olkiluoto comprises shallow and partly oxygenic freshwater from 0 to approximately 25 m followed by intermediate-depth brackish, sulphate-rich, methane-poor groundwater with a salinity of less than 1% to a depth of approximately 300 m. In the depth range from 300 m to more than 1000 m, salinity increases with depth from 1% to >10%, sulphate concentration is very low or below detection, and the concentrations of hydrogen and methane increase from 1 to >20 µM and 5 mM to >50 mM, respectively. At depths of 250–350 m, there is a layer in which sulphate-rich and methane-rich groundwaters mix. The construction of the ONKALO tunnel intersects groundwater-conducting aquifers; this generates drawdown of sulphate-rich groundwater that mixes with deep methane-rich groundwater. This mixing layer is expected to slowly move deeper in the water-conducting fracture system due to the drawdown effect (Aalto et al. 2011). In other words, the construction of ONKALO can be regarded as a large-scale experiment investigating how slow and continuous mixing of sulphate-rich groundwater with methane-rich groundwater influences microbial diversity and activity.

0 10 20 30 40 50SRB abundance (%)

-1200

-1000

-800

-600

-400

-200

0D

epth

(mas

l)

ONK-KR15_1ONK-KR15_2

ONK-PVA10

ONK-PVA6_1ONK-PVA6_2ONK-PVA5

ONK-PVA3

ONK-PVA1

17

1.4 Outline and objectives of sulphate reduction experiment programme

In the ReRoc programme for characterizing repository host rock in the ONKALO tunnel (Aalto et al. 2009), three issues were identified to be connected to the process of microbial sulphate reduction:

• Issue I-11: Evolution of groundwater composition – Flow paths to/from the host rock, especially at the near-surface interface: Our understanding of the connection between the near-surface water and groundwater types in bedrock has advanced, due to the development of near-surface hydrological modelling, though some questions remain. The infiltration experiment, which was in progress recently (2014-2015), will provide data relevant to this issue.

• Issue I-12: Evolution of groundwater composition – Impact of surficial water intrusion (pH, redox, and buffering capacity): The process of sulphate reduction is well understood, and at groundwater temperatures it is only possible through microbial activity, but the quantification of the associated microbial activity, particularly the rate of sulphate reduction, is uncertain. The infiltration experiment (see issue I-11) will provide data relevant to this issue. Detailed characterization and monitoring below the fracture zone denoted HZ20 is important to evaluate sulphate reduction due to groundwater mixing.

• Issue I-13: Evolution of groundwater composition – Formation of gas phase/dissolved gases in groundwater: The origin of the detected dissolved methane is uncertain and the current production rate and source of methane production are unclear. It is essential to obtain more gas content, microbial, and isotopic data from deep saline groundwater.

This issue list was based on site descriptions in 2008, which was the initial stage of the ReRoc-programme. However, new information has later been gathered outside ReRoc and the site description has been updated in 2011: There is presently a good understanding of the origin of methane, which is composed of microbial and thermal end-members. The current accumulation rate and source are, however, still uncertain, although the information obtained and the model calculations indicate very slow migration from a deep crustal source (Trinchero et al. 2014).

The ONKALO tunnel was deemed well suited for research into issues I-12 and I-13.

The sulphate reduction experiment (SURE) programme seeks a better understanding of the processes underlying sulphide production (i.e., sulphate reduction) and involves chemical characterization and detailed microbiological investigation of groundwater from several depths in the ONKALO tunnel.

The objectives of the SURE programme, as defined by Aalto et al. (2009), were:

• To demonstrate microbial reduction of sulphate via anaerobic oxidation of methane,

• to determine case-specific reduction rates (i.e., variable concentrations), and

18

• to determine the renewal rates of energy sources for sulphate reduction (Trinchero et al. 2014).

The experimental plan for sulphate reduction investigations reported here was divided into three different phases:

• Phase I: Drilling and baseline characterization of the investigation drillholes. • Phase II: Identification, characterization, and selection of groundwater. • Phase III: Microbiological in situ tunnel-based research using sulphate- and

methane-rich water types.

During Phase I, two drillholes in ONKALO were selected and characterized. They were ONK-PVA6 (Toropainen 2009) and ONK-KR15 (Toropainen 2011). ONK-PVA6 represented groundwater that was originally poor in sulphate and rich in methane. Over time, however, the situation changed and the concentration of sulphate increased significantly in this drillhole water over the years (2010 – 2014) when SURE 1, 2 and 3 were executed. ONK-KR15 represented deep, sulphate-free and methane-rich groundwater. Phases II and III were executed concomitantly. Groundwater from ONK-PVA6 and ONK-KR15 was analysed repeatedly for chemistry, microbial numbers and diversity as well as gas compositions.

The main objective of the SURE experiments was to investigate the influences of various combinations in concentration of H2, methane and sulphate on microbial sulphate reducing and sulphide producing activity.

1.5 Experimental approach during Phase III

Initial research into microorganisms in Scandinavian deep granitic aquifers strongly suggested that the vast majority of these microorganisms live attached to surfaces and that they are more metabolically active than are planktonic microorganisms (Ekendahl and Pedersen 1994; Pedersen and Ekendahl 1992a, b). This poses a sampling challenge, because core drilling is required to collect attached microorganisms (Jägevall et al. 2011). Due to the obvious risk of contamination and washout of attached microorganisms by drilling water, an alternative investigation approach is to use in situ experimental installations in underground tunnels. Flow cells (FCs) incorporating rock surfaces or other solid materials can be installed in contact with deep aquifers under in situ conditions and later be sampled after the attachment of, and biofilm formation by, groundwater microorganisms (Pedersen 2012a, b).

SURE utilised FCs that were exposed to flowing groundwater in an open circuit connected to a water conductive fracture for a couple of months. After that, the FCs with biofilms were moved to a laboratory circulating system in a closed mode. This approach was expected to combine the advantages of working with an open continuous system during the establishment of natural biofilms that subsequently could be studied for a period of time in the laboratory isolated from its environment. This made it possible to investigate the effect of the addition of H2, methane and sulphate on the growth and activity of microbial populations that had developed under in situ conditions in two different groundwater types of Olkiluoto. The first type contained natural

19

concentrations of sulphate and methane while the second type was absent of sulphate but rich in methane. Further details on the advantages and disadvantages of working with an open and closed system were previously discussed by Pedersen (2008).

1.5.1 SURE 1

SURE 1 investigated the influence of elevated H2 and methane concentrations on the microbial sulphate reducing activity of microorganisms in groundwater and biofilms sampled from an ONK-PVA6 aquifer situated at −318.7 masl. This groundwater was known to have significant concentrations of sulphate and methane and small amounts of H2. The presence of relatively large numbers of SRB in Olkiluoto groundwater from a range of depths similar to this ONKALO drillhole had been confirmed previously with an MPN analysis (Figure 1-2, Figure 1-3) and recently with 16S rDNA sequencing (Figure 1-4). Based on results from the Äspö HRL (Pedersen 2012a), it was expected that the rate of sulphate-reducing activity would increase rapidly with the addition of H2 (Eq. 3). The presence of AOM activity has not been confirmed in the groundwater of deep hard rock aquifers. An addition of methane was therefore carried out to investigate if such activity could be detected (Eq 5).The results were compared with the effect of air that was expected to inhibit the activity of all anaerobic microorganisms such as SRB.

Pressure-resistant, gas-tight circulating systems with FCs were installed in ONKALO to enable the investigation of attached and planktonic anaerobic microbial populations in sulphate-rich groundwater. The experiment was performed under in situ groundwater pressure (2.4 MPa), microbial diversity, dissolved gas, and chemistry conditions (Pedersen 2013a; Pedersen et al. 2013). Groundwater was circulated through crushed rock from the drill core of ONK-PVA06 in FCs for 110 days. Three parallel flow cell cabinet systems (FCCSs) were then configured in the laboratory to allow the observation of the influence of 11 mM methane, 11 mM methane plus 10 mM H2, or 2.1 mM O2 plus 7.9 mM N2 (i.e., air) on microbial metabolic activity. The concentrations of these gases and of organic acids and carbon, sulphur chemistry, pH and Eh, were analysed. The numbers of CHAB, SRB, NRB, IRB, MRB, AA, methanogens and virus-like particles (VLP) as well as TNC and the amounts of planktonic and attached biomass measured as ATP were determined. The microbial diversities of the attached microbial diversities were examined using a microarray hybridisation method (Phylochip) of bacterial 16S rDNA.

1.5.2 SURE 2

The MPN numbers of SRB diminish when the depth approaches −400 masl in Olkiluoto (Figure 1-2, Figure 1-3). Likewise, 16S rDNA sequencing did not detect SRB below this depth (Pedersen et al. 2015). It was expected that the construction of the ONKALO repository may induce mixing of deep sulphate-poor, methane-rich groundwater with shallower, sulphate rich groundwater. The SURE 2 experiment was set to investigate the influence of adding sulphate to sulphate-poor, methane-rich groundwater on microbial activity. Will microbial sulphate-reducing activity start upon addition of sulphate and if so, at what rate?

Deep groundwater from a methane-rich, sulphate-poor aquifer from a depth of −388 masl (ONK-KR15) was circulated through crushed rock from the drill core of ONK-

20

KR15 in FCs for 70 days at in situ pressure. Sulphate was then added to the circulating groundwater, and metabolic activity, numbers of cultivable microorganisms, and 16S rDNA diversity in biofilms and groundwater were studied for 103 d. The effect of adding a 10% portion of groundwater from ONK-PVA6, containing a large number of SRB, to methane-rich, sulphate-poor groundwater devoid of SRB was also investigated. The microbial diversities of cultures were analysed using cultivation and cloning analysed with 16S rDNA Sanger sequencing, while the attached and planktonic microbial diversities were examined using 454 pyrotag and Illumina HiSeq sequencing of the bacterial and archaeal v4v6 and v6 regions of 16S rDNA, respectively. The concentrations of H2, CH4, sulphate, sulphide, ferrous iron, organic acids, and carbon as well as pH and Eh were analysed. The numbers of CHAB, SRB, NRB, IRB, AA, methanogens and VLP as well as TNC and the amounts of planktonic and attached biomass measured as ATP were also determined.

1.5.3 SURE 3

SURE 1 and 2 used crushed rock from the respective drill core as support for the attachment and growth of microorganisms. This support was replaced with glass beads and garnet grains in SURE 3 to investigate the influence of solid phases on microbial diversity and activity. It was expected that the crushed rock could influence the circulating groundwater by release of iron and possibly other elements released when crushing opened a large surface area of fresh rock to the groundwater. In addition, fresh rock surfaces are unusual in deep aquifers because they are often leached by long exposure to groundwater and covered with secondary minerals. The glass beads used as support for biofilm formation were inert and the crystalline and polished character of the garnets was expected to be much less reactive than crushed rock. The addition of H2 in SURE 1 induced the expected increase in sulphate-reducing activity, but the effect of methane addition was less clear. Therefore, because the effect of 4 - 11 mM methane on sulphate reducing activity was small in SURE 1 and 2, this third experiment investigated the effect of considerably higher concentrations of methane on microbial sulphate-reducing activity.

In the third SURE, the effects on microbial growth and activity of sequential additions of methane and sulphide to groundwater with and without sulphate were investigated. Deep groundwater from the sulphate-rich, methane-poor aquifer of ONK-PVA6 and from the methane-rich, sulphate-poor aquifer of ONK-KR15 was circulated through grains of garnets and glass beads in FCs for 70 days at in situ pressure. Three parallel FCCSs were then configured to allow the observation for 209 days of the influence on microbial metabolic activity of sulphate additions to one of the ONK-KR15 FCCSs and the effect of saturation with CH4 on microbial sulphate reduction in the ONK-PVA6 and the ONK-KR15 FCCSs. Attached and planktonic microbial diversities were examined using 454 pyrotag and Illumina HiSeq bacterial v4v6 and v6 regions of 16S rDNA, respectively. The concentrations of H2, CH4, sulphate, sulphide, ferrous iron, organic acids, and carbon as well as pH and Eh were analysed. The numbers of CHAB, SRB, NRB, AA and VLP as well as TNC and the amounts of planktonic and attached biomass measured as ATP were determined.

21

1.6 This report

This report compiles all the results of groundwater characterisation and the three SUREs. A selection of results from SURE 1 and SURE 2 have been published (Pedersen 2013a; Pedersen et al. 2013; Pedersen et al. 2014). SURE 3 is reported here for the first time. The report is divided into three main parts. The first part present methods and data for ONK-PVA6 and ONK-KR15 groundwater samples that were repeatedly obtained during a period of 3.5 and 2.5 years, respectively. All groundwater characterisation results are assembled, presented and discussed in this report. The second part deals with SURE. Each SURE comprised three sub-experiments. All 9 experiments have been merged and are arrayed as a series of different treatments using combinations of the two investigated groundwater types. The third part discusses observations and results of how SURE has contributed to our present understanding of microbial sulphate reduction in deep Olkiluoto groundwater.

23

2 DRILLING, INSTRUMENTATION AND CHARACTERISATION OF ONK-PVA6 AND ONK-KR15

2.1 Drilling and rock analysis

Two 76-mm-diameter drillholes were core drilled in the ONKALO tunnel for use in SUREs. The ONK-PVA6 drillhole was drilled at a depth of 318.7 m (Toropainen 2009). The drilling was conducted at a 14.8° inclination on 3–4 November 2009 to a total length of 35.15 m. A part of the obtained drill core between 32–33 m drillhole length was crushed by ALS Scandinavia, Luleå, Sweden, concomitant with an analysis of elemental composition (analysis name is G-0: metals in geological samples, (www.alsglobal.se) (Table 2-1). The ONK-KR15 drillhole was drilled at a depth of 387.9 m (Toropainen 2011). The drilling was conducted at an 8.6° inclination on 23–28 February 2011 to a total length of 79.96 m. A part of the obtained drill core between 75–76 m length was crushed by ALS Scandinavia, Luleå, Sweden, concomitant with an analysis of elemental composition (analysis G-0, Table 2-1).

http://www.alsglobal.se/

24

Table 2-1. Elemental composition of a part of the ONK-PVA6 and ONK-KR15 drill cores sampled at 32−33 m and 75−76 m drillhole length, respectively.

Element ONK-PVA6

ONK-KR15 Element

ONK-PVA6

ONK-KR15 Element

ONK-PVA6

ONK-KR15

mg kg−1 mg kg−1 mg kg−1 mg kg−1 % %

Ba 453 962 Nb 0.861 7.96 SiO2 74.7 76.7

Rb 139 122 Er 0.814 1.74 Al2O3 14 11.3

Sr 103 192 Gd 0.633 4.06 K2O 4.39 1.34

Cr 24.3 173 Eu 0.583 1.03 Na2O 3.12 4.27

Zr 23.7 183 Th 0.546 10.7 Fe2O3 1.61 3.98

Ce 16.6 66.5 Hf 0.507 5.6 CaO 0.835 1.34

Ni 15.6 72 Sm 0.458 4.61 MgO 0.487 0.0585

Ga 14.4 6.52 Lu 0.405 0.11 P2O5 0.2 2.15

Y 8.42 15 Ho 0.272 0.64 MnO 0.0255 0.145

V 7.77 58.4 Ta 0.209 0.76 TiO2 0.0175 0.379

La 5.38 32.7 Tm 0.145 0.231

W 4.55 <50 Tb 0.14 0.565

Be 4.38 1.57 Co <6 8.31

U 2.6 3.01 Mo <2 <5

Nd 2.06 26.2 Sc <1 7.58

Dy 1.05 3.12 Pr <1 7.36

Yb 1.03 1.67 Sum 99.4 101.7

2.2 Drillhole instrumentation

A metal-free packer system isolated an aquifer in the ONK-PVA6 drillhole located 32.7–32.9 m from the tunnel rock face at a depth of 327 m; the transmissivity of the aquifer was 1.07 × 10−9 m2 s−1. The packer system is illustrated in Figure 2-1 and is described in detail elsewhere (Pedersen 2005). The system was modified by using a 6-mm stainless steel tube to shield the PEEK tubing in the part of the drillhole exposed to air. A similar metal-free packer system isolated an aquifer in the ONK-KR15 drillhole located 75.0–75.2 m from the tunnel rock face at a depth of 399 m; the transmissivity of the aquifer was 7.6 × 10−9 m2 s−1. The packer systems were installed approximately one year after the drilling of each drillhole.

25

Figure 2-1. The packer system. The yellow sections are expandable polyurethane packers, while the green rings are Teflon-coated stainless steel casings. The grey components are made of PEEK, as are the 1/8-inch (outer diameter) sampling tubes. Groundwater sampling and circulation are mediated via two small holes opposite each other in the grey portion in the middle of the packer assembly (the dark spot in the top drawing). The grey sampling section was 100 mm long.

2.3 Characterization scheme

A total of three discrete sulphate reduction experiments have been performed, denoted SURE 1, SURE 2 and SURE 3 as shown in Table 2-2 The experiments lasted from September 2010 to January 2014. The chemical composition of the respective groundwater was analysed for the first time after the installation of the packers and thereafter at regular intervals. The gas composition of groundwater in each drillhole was analysed three times over a time period of approximately 2 years.

Microbiological enumerations and cultivations of groundwater samples were performed in connection with each SURE. DNA analyses were performed on the groundwater and on the biofilms that grew on introduced surface materials.

26

Table 2-2. A scheme showing the positions in time of the three SUREs and sampling activities for groundwater chemistry and gas composition, microbiology and DNA analyses. Each activity is explained in detail in the text.

Year 2010 2011 2012 2013 2014

Month 6 7 8 9 10

11

12 1 2 3 4 5 6 7 8 9 1

0 11

12 1 2 3 4 5 6 7 8 9 1

0 11

12 1 2 3 4 5 6 7 8 9 1

0 11

12 1 2 3 4 5 6

SURE 1 PVA6

SURE 2 KR15

SURE 3 KR15/PVA6

ChemistryPVA6

Chemistry KR15

Gas PVA6

Gas KR15

Microbiology PVA6

Microbiology KR15

DNA water PVA6

DNA water KR15

DNA biofilms PVA6

DNA biofilms KR15

27

2.4 Groundwater chemistry

Groundwater chemistry was repeatedly analysed in 2010 through 2014 for ONK-PVA6 and ONK-KR15 and the samples were transferred to the laboratory of Teollisuuden Voima Oy (TVO) directly after sampling for analysis. The chemical analyses were performed by TVO according to their protocols, or were subcontracted to external laboratories as described in detail in Table A-1 and Table A-2. The detailed results of these analyses are presented in Table A-3 and Table A-4. For an analysis of 34S, samples were sent to Iso-analytical Ltd, Cheshire, England (http://www.iso-analytical.co.uk ) and for a 13C analysis to the University of Helsinki.

2.5 Sampling and analysis of dissolved gas in groundwater

Groundwater was collected from ONK-PVA6 and ONK-KR15 with a PAVE pressure vessel directly from each drillhole that had been kept closed for several weeks. Pressure drop was kept below 100 kPa during filling to avoid the degassing of dissolved gasses in the aquifers.

Water samples were transferred from the pressure vessels within a couple of minutes to a vacuum container and any gas in the water was boiled off under vacuum (i.e., at water vapour pressure). The extraction time was approximately 20–30 min at room temperature (RT; 20°C). The extracted gas volume was compressed and transferred to a 10 mL syringe (SGE Analytical Science, Melbourne, Victoria, Australia) and subsequently to a 6.6 mL glass vial provided with a butyl rubber stopper and sealed with aluminium crimp seals. A silica gel dehydrator was added to the sample containers to adsorb any traces of water remaining in the gas. The vials were evacuated and flushed twice with nitrogen (N2), in two cycles, and left under high vacuum (1 Pa), prior to sampling. The volumes of the sample water and the extracted gas were measured and the volume of gas at 20 °C and standard pressure (1013 mbar) was calculated with the ideal gas law. An analysis was then performed using gas chromatography.

Several different gas chromatographs and detectors were used for analysis. During SURE 1, hydrogen (H2) (<20 ppm) was analysed with a KAPPA-5/E-002 analytical gas chromatograph (AMETEK/Trace Analytical, formerly Trace Analytical, Menlo Park, CA, USA) equipped with a 156 × 1/16-inch stainless steel HAYESEP column in line with a 31 × 1/8-inch stainless steel molecular sieve 5A column which was subsequently attached to a reductive gas detector (RGD) with N2 used as the carrier gas. During SURE 2 and 3, a Bruker 450 gas chromatograph equipped with a CP7355 PoraBOND Q (50 m x 0.53 mm, ID) and a CP7536 MOLSIEVE 5A PLOT (25 m x 0.32 mm, ID) and a Pulsed Discharge Helium Ionization Detector (PDHID) was employed for the trace concentrations of H2, O2, Ar, CO2 and hydrocarbon gases (C1-C3), (Bruker Daltonics Scandinavia AB, Vallgatan 5, SE-17067 Solna, Sweden).

He, H2, N2, O2 and CH4 >20 ppm were analysed with a Varian Star 3400CX gas chromatograph (Varian AB) using a Thermal Conductivity Detector (TCD). The temperatures of the oven, the detector, and the filament were 65, 120, and 250°C, respectively. The gases were separated using a Porapak-Q column (2 m × 1/8 inch diameter) followed by a molecular sieve 5A column (6 m × 1/8 inch) on the TCD with Ar as the carrier gas. CH4, CO2 and CO <20 ppm were analysed with a Varian Star 3400CX gas chromatograph using a Flame Ionization Detector (FID) with an oven temperature of 65°C and a detector temperature of

http://www.iso-analytical.co.uk/

28

200°C. The gases were separated using a Porapak-Q column (2 m × 1/8 inch diameter) and analysed with the FID using N2 as the carrier gas. N2 and CH4 >20 ppm were alternatively analysed with Varian CP-3800 gas chromatograph (Agilent Technologies Inc., CA, USA) with a 30 m high resolution capillary column (Bruker, SELECT PERMANENT GASES/CO2 HR, CP7430) using He as the carrier gas. The gases were detected using a thermal conductivity detector (TCD) at 120°C detector temperature, with a filament temperature of 220°C and a column temperature of 45°C. CH4 and hydrocarbon gases (C1-C3) <20 ppm were alternatively analysed with Varian CP-3800 gas chromatograph using carboxen column (2 m × 1/8 inch diameter) and analysed with the FID using N2 as the carrier gas.

All chromatographs were calibrated using certified gas mixes that mimic the gas composition of the analysed samples.

2.6 Sampling and analysis of microorganisms in groundwater

Groundwater was collected from ONK-PVA6 and ONK-KR15 directly from each drillhole in 120 mL sterile, oxygen-free serum bottles. The results of the analysis of the relation between the flushing volume and the numbers of microorganisms at Äspö HRL have shown that microbial numbers decrease by one order of magnitude or more when prolonged flushing is applied before sampling (Pedersen 2013b). The drillholes were, therefore, kept closed without a flow for at least a week before sampling started. The void volume of the packer system (Figure 2-1) is less than 100 mL and the sampling of aquifer groundwater could commence safely after flushing approximately 1000 mL of groundwater.

2.6.1 ATP analysis

The ATP Biomass Kit HS no. 266-311; BioThema, Handen, Stockholm, was used to determine total ATP in the cells living in groundwater. The ATP biomass method used here has been described, tested in detail, and evaluated for use with Fennoscandian Shield groundwater (Eydal and Pedersen 2007). The method was also used for biomass attached to the crushed rock, glass beads and garnet grains, but with the following modification: Approximately 1.5 g of material was sampled from the FCs and placed in 1 mL of ATP extraction solution and analysed. A detailed description of this method, with advantages and disadvantages, is given in Pedersen et al. (2012).

2.6.2 Total number of cells

TNC mL−1 was determined using the acridine orange direct count (AODC) method as devised by Hobbie et al. (1977) and modified by Pedersen and Ekendahl (1990). A detailed description of this method, with advantages and disadvantages, is given in Pedersen et al. (2012).

2.6.3 Analysis of cultivable heterotrophic aerobic bacteria

The medium was prepared for CHAB as described by Hallbeck and Pedersen (2008). The cultivation time was one week. A detailed description of this method, with advantages and disadvantages, is given in Pedersen et al. (2012).

29

2.6.4 Analysis of most probable numbers of culturable anaerobic microorganisms

The media were prepared for the most probable number analysis of NRB, IRB, MRB, SRB, AA, and methanogens as described by Hallbeck and Pedersen (2008). The cultivation time was about eight weeks to ensure that slow-growing microorganisms would be included in the results. A detailed description of this method, with advantages and disadvantages, is given in Pedersen et al. (2012).

2.7 Nucleic acids analysis

2.7.1 DNA extraction from groundwater

Groundwater from ONK-PVA6 and ONK KR15 was pressure filtrated using a high pressure, stainless steel 47 mm filter holder (X4504700, Millipore AB, Solna, Sweden) with water filters from MO BIO Power Water kit filter units. The filter holder was equipped with a pressure relief valve (Swagelok SS-RL3S6MM; SWAFAB, Sollentuna, Sweden) and a manometer that enabled the adjustment of a pressure drop over the filter between 200 and 400 kPa relative to the ambient aquifer pressure. The drillholes were kept closed for at least a week before sampling. Filtration was performed for the DNA analysis on three consecutive dates according to Table 2-2.

• 2012-04-17: Approximate filtered volumes of groundwater were 57 L for ONK-KR15 and 16.8 L for ONK-PVA6. Flow rates of water during filtration were 140 mL min−1 for ONK-KR15 and 25 mL min−1 for low permeability drillhole ONK-PVA6.

• 2012-09-04: Approximate filtered volumes of groundwater were 153 L for ONK-KR15 and 30.6 L for ONK-PVA6. Flow rates of water during filtration were 150 mL min−1 for ONK-KR15 and 30 mL min−1 for low permeability drillhole ONK-PVA6.

• 2013-04-17: Approximate filtered volumes of groundwater were 107 L for ONK-KR15 and 7.4 L for ONK-PVA6. Flow rates of water during filtration were 90 mL min−1 for ONK-KR15 and 11 mL min−1 for low permeability drillhole ONK-PVA6. TNC were determined in the samples collected at the start and end of the filtration period. This was done to investigate the effect on TNC of releasing a large volume of groundwater from the respective aquifer.

The total genomic DNA was extracted from groundwater using the manufacturer’s protocol for the MO BIO Power Water DNA isolation kit (cat. no. 12888) of MO BIO Laboratories, Carlsbad, CA, USA. The total extracted nucleotide concentrations were measured using a ND-1000 UV-vis spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) and the double stranded (ds) DNA concentrations were measured fluorometrically using the Stratagene MX3005p fluorometer with MXPro software (Agilent Technologies Inc., Santa Clara, CA, USA) and the Quant-it™ Picogreen reagent kit of Molecular Probes (cat. no. P7589; Invitrogen, San Diego, CA, USA) according to the manufacturer’s specifications. The extracted DNA was stored at -20°C and subsequently used for sequencing.

2.7.2 Amplicon 16S rDNA sequencing

The sequence analysis of the extracted DNA was performed with the method indicated for each drillhole in Table 2-3 and primers and primers according to Table 2-4. Detailed method descriptions are given in subsequent sections.

30

Table 2-3. Sequencing procedures used to analyse DNA diversity in groundwater samples from ONK-PVA6 and ONK-KR15

ONK-PVA6 ONK-KR15

Date 2012-04-17 2012-09-04 2013-04-17 2012-04-17 2012-09-04 2013-04-17

Bacteria v4v6 454 FLX Titanium × × × × × ×

Archaea v6 Illumina HiSeq × × - × × -

Table 2-4. Primers and probes used in this study.

Name Sequence (5'-3') Reference/Comment

cl

one

27F AGAGTTTGATCCTGGCTCAG (Lane 1991) 1492R GGTTACCTTGTTACGACTT (Lane 1991) M13rev(-29) CAGGAAACAGCTATGAC (Eurofins genomics) M13uni(-21) TGT AAAACGACGGCCAGT (Eurofins genomics)

pyro

sequ

enci

ng B

v4-6

518F CCAGCAGCYGCGGTAAN (Marteinsson et al. 2013) 565F-a TGGGCGTAAAG informatics landmark only 1064R CGACRRCCATGCANCACCT (Huber et al. 2007)

Av6

958F AATTGGANTCAACGCCGG (Huber et al. 2007) 1048R CGRCRGCCATGYACCWC (Huber et al. 2007)

Bv6

967F CTAACCGANGAACCTYACC (Huber et al. 2007) CNACGCGAAGAACCTTANC CAACGCGMARAACCTTACC ATACGCGARGAACCTTACC

1046R CGACRRCCATGCANCACCT (Huber et al. 2007)

2.7.2.1 454 FLX Titanium 16S rDNA v4v6 pyrosequencing

A Bacterial 16S rDNA v4v6 amplicon library for sequencing was generated using a forward (518F) primer and a degenerated reverse primer (1064R) (Table 2-4). The conditions for the PCR reaction were; 1× Platinum HiFi Taq polymerase buffer, 1.6 units Platinum HiFi polymerase, 3.7 mM MgSO4, 200 µM dNTPs (PurePeak polymerization mix, ThermoFisher), and 400 nM primers. Sample DNA in an amount of 5 to 25 ng was added to a master mix to a final volume of 100 μL and this was divided into three replicate 33 μL reactions. No-template negative control for each sample series was included. Cycling conditions included initial denaturation at 94°C for 3 minutes; 30 cycles at 94°C for 30 seconds, 57-60°C for 45 seconds, and 72°C for 1 minute; and a final extension at 72°C for 2 minutes using a Bio-Rad mycycler. The quality and the concentration of the amplicon library were evaluated with the Agilent Tapestation 2000 instrument from Agilent according to the manufacturer’s protocol. The reactions were cleaned and products under 300 base pairs were removed using Ampere beads at 0.75 × volume (Beckman Coulter, Brea CA). The final products were resuspended in 100 μL of 10mM Tris-EDTA + 0.05% Tween-20, quantified with PicoGreen Quant-IT assay (Life Technologies), and assayed once more with the Tapestation 2000 instrument. The amplicons

31

were further titrated in equimolar concentration before emulsion-PCR, based on their dsDNA concentrations. A GS-FLX Sequencer was used to generate pyrotag sequence reads with the Roche Titanium reagents. After sequencing, the data were run through a quality control process. Each read was trimmed for primer bases at the beginning and the end of each read, and sequences likely to be of low-quality based on an assessment of pyrosequencing error rates were removed (Huse et al. 2007). Bioinformatic Trimming anchor site (565F-a) TGGGCGTAAAG was used to trim the sequences. 454 pyrotag sequence processing was carried out to assign a taxonomic classification using tag mapping methodology Global Alignment for Sequence Taxonomy (GAST) (Huse et al. 2008) where the reference database of 16S rRNA genes, RefSSU, was based on the SILVA database (Pruesse et al. 2007). If two-thirds or more of the full-length sequences shared the same assigned genus, the tag was assigned to that genus. Tags that did not match any reference tag by BLAST were not given a taxonomic assignment. After the classification of data, the representativeness of the sequences was tested by a rarefaction analysis and Chao (Unbiased richness estimate) as well as Abundance based Coverage Estimator (ACE) indexes were used to estimate richness. Samples were not normalized or subsampled when the mean species diversity in samples, alpha-diversity, was analysed (McMurdie and Holmes 2014). In order to statistically estimate abundance and evenness for each sample, Shannon and Simpson indices were calculated. Distance calculations for sequence similarities were carried out using the Morsita-Horn or Bray-Curtis algorithms.

2.7.2.2 Illumina HiSeq 16S Archaea rDNA v6 sequencing

The amplicon library preparation of the 16S Archaea rRNA gene hypervariable v6 region was performed by amplification with custom fusion primers consisting of the Illumina bridge adaptor, 12 different inline barcodes on the forward primer or 8 different indices on the reverse primer, and the 16S specific target sequence; the forward primer was (958F) and the reverse primer was (1048R), Table 2-4.

The PCR was carried out in triplicate reactions in a 33 μL reaction volume of 2.5 U Platinum Taq Hi-Fidelity Polymerase (Life technologies, Carlsbad CA), 1× Hi-Fidelity Buffer, 200 μM dNTP PurePeak DNA Polymerase mix (Pierce Nucleic acid Technologies, Milwaukee, WI), 1.5 mM MgSO4, 0.3 μM of each primer and ~10 ng of template DNA. Each primer pair had a no-template control. Cycling conditions were; an initial 94°C denaturation for 3 minutes, 30 cycles of: 94°C for 30 sec., 60°C for 60 sec. and 72°C for 90 sec. followed by a final 10 minute extension at 72°C.

The triplicate PCR reactions were pooled before purification on a QIAquick PCR clean-up 96 well plate (Qiagen, Valencia Ca). The purified DNA was quantified using the Picogreen dsDNA reagens (Life Technologies, Carlsbad, CA) and pooled in equimolar amounts. Further, amplicons were size selected for products in the range of 200-240 base pairs on 1% agarose using Pippin prep (SageScience Beverly MA). qPCR was used to measure concentrations prior to 100 nucleotide paired-end run sequencing on Illumina HiSeq for 100 cycles. PhiX DNA served as the control DNA for the run.

CASAVA 1.8.2 was used to identify reads by their index and a custom Phyton script was used to resolve barcodes in the dataset. Paired-end recovers DNA sequence from both ends of the DNA template giving an overlap between the reads. Quality filtering was carried out to remove sequencing errors by requiring a complete overlap of forward and reverse paired-end

http://www.arb-silva.de/

32

reads. Further, the first 6 nucleotides of the distal primer were required to be of a perfect match. A search for the initial 10 nucleotides that match a consensus sequence for the proximal primers (967F-AQ, 967F-UC3, 967F-PP and 967F-UC12) was performed. Sequences that did not have a perfect match in either the forward or the reverse read were discarded as a pair (Eren et al. 2013). The v6 complete overlap analysis pipeline may be accessed at GitHub; https://github.com/meren/illumina-utils. Alpha diversity indexes, the observed richness and sequence similarities were calculated using the Visualization and Analysis of Microbial Population Structure (VAMPS) software.

2.7.3 Statistical analyses and data visualization

The amplicon 16S rDNA sequencing data were analysed and evaluated using the VAMPS web site (www.vamps.ml.edu) and the Quantitative Insights into Microbial Ecology software (QIIME) version 1.8.0. The graphics design of data and the statistical analyses were performed in Statistica 10 (Statsoft Inc., Tulsa, OK, USA).

https://github.com/meren/illumina-utils

33

3 SULPHATE REDUCTION EXPERIMENTS – MATERIALS, CONFIGURATIONS AND METHODS

The SUREs were divided into two phases; the first phase was performed in the field and comprised the collection of biofilms and planktonic microorganisms as well as groundwater from ONKALO for use in laboratory experiments. The second phase comprised laboratory experiments with treatments and observations of the growth of microorganisms as well as production and consumption of metabolic components over time.

3.1 Field phase with flow cell field circulation systems

Three identical Flow Cell Field Circulation Systems (FCFCSs) comprising four or five FCs each, a micropump (Micropump GAH series V21 J with a PEEK impeller; Labinett, Göteborg, Sweden), two pressure meters (S-11, 40 Bar 4-20 G1 ⁄ 2; WIKA – AB Svenska Industri Instrument, Göteborg, Sweden), a flow meter (Promag 50; Endress+Hauser Flowtech AG, Sollentuna, Sweden), and a 4-L expansion vessel were prepared for the experiments (Figure 3-1).

Groundwater from ONK-PVA6 and/or ONK-KR15 was pumped from the respective aquifer isolated with the packer systems (Figure 2-1) to the FCFCSs and then back to the aquifer via two parallel, 1/8-inch polyetheretherketone (PEEK) thermoplastic tubes of high-pressure liquid chromatography quality (IDEX Health and Science, Oak Harbor, WA, USA). Each of the three specific SURE configurations are explained next.

3.1.1 Flow cell field circulation system configuration for SURE 1

Three FCFCSs were installed in a container placed in a niche at a depth of −319 masl in the ONKALO tunnel and connected to the packer system in ONK-PVA6. The FCs had a stainless steel shell (length 300 mm, diameter 65 mm) and were lined with polyvinyldifluoride (PVDF) plastic (for details, see Pedersen 2005). Each FC had a 120 mm long PVDF insert with a 22 × 32 mm opening that supported 110 g of the crushed rock grains (2–4 mm diameter, Figure 3-2) described in Section 2.1, offering a theoretical rock surface area of 895 cm2 per FC for microbial adhesion and biofilm development, assuming spherical rock grains with an average diameter of 3 mm. The rock grains were heat sterilized at 160 °C for 5 h. Three flow stabilizers at each end of the insert ensured even distribution and a slow laminar flow of water through each FC (Pedersen 1982). The FCFCSs were installed on 24 November 2010 and circulated with groundwater under the in situ pressure of 2.4 MPa for 110 d at a flow rate from and to the aquifer of 17–18 mL min−1. The total volumes of groundwater circulated were 2597, 2840, and 2758 dm3 in the three field systems, respectively, as registered by the flow meters.

34

Figure 3-1. Top image: Two flow cell field circulation systems installed for SURE 3 in ONK-KR15 in a container, −388 masl underground in the ONKALO research tunnel. Bottom image: Five flow cells with a micro-pump, flow meter, pressure meters, and expansion vessel installed for SURE 3 in ONK-PVA6 in a container, 319 m underground in the ONKALO research tunnel.

35

Figure 3-2. Left image shows a flow cell insert with crushed rock from ONK-PVA6 used during SURE 1. Right image shows a flow cell insert with crushed rock from ONK-KR15 used during SURE 2.

Figure 3-3. Expansion vessels with a volume of 4 L were used for transportation of groundwater from drillholes ONK-PVA6 and ONK-KR15 under in situ pressure. The vessels had an aluminium shell (length 435 mm, diameter 160 mm) and were lined with polyvinyldifluoride plastic. Details can be found elsewhere (Pedersen 2005).

36


Three identical FCFCSs were installed in a container placed in the ONKALO tunnel at a depth of −388 masl and connected to the packer system in ONK-KR15. Each FC was configured as described above for SURE 1, except for the crushed rock that was obtained from ONK-KR15 (Figure 3-2) as described in Section 2.1. The FCFCSs were installed on 7 February 2012. Groundwater was circulated through them at an in situ pressure of 3.2 MPa for 70 d at a flow rate from and to the aquifer of 22–25 mL min−1. The total volumes of groundwater circulated were 2562, 2381, and 2287 L in the three field systems, respectively, as registered by the flow meters.


Two identical FCFCSs with five FCs were installed in a container placed in the ONKALO tunnel at a depth of −388 masl and connected to the packer system in ONK-KR15, and one FCFCS with five FCs was installed in a niche at a depth of −319 masl and connected to ONK-PVA6 (Figure 3-1). Four of the FCs were loaded with two layers of different solid material. There were ~100 g of garnet grains (0.7 mm in diameter) followed by ~100 g of glass beads (1 mm in diameter, see Figure 3-5) offering a theoretical surface area of 1376 cm2 per FC for microbial adhesion and biofilm development, assuming spherical grains and beads with an average diameter of 0.85 mm. The garnet grains were delivered by MoBio Laboratories (USA) and were of molecular grade meaning they were sterile, DNA-free and RNase/DNase-free. The 0.7 mm garnet grains contain 30% FeO and 2% Fe2O3 according to the manufacturer. The glass beads were of the brand Assistant and purchased from WVR International with product number 201-0276. They were sterilized by heating to 450 °C for 5 hours in a muffle furnace. The last FC contained 40 glass slides (60 × 24 × 1 mm) for fluorescent in situ hybridization (FISH) followed by microscopy. These glass slides were sterilized by heating to 450 °C for 5 hours in a muffle furnace. The flow cells were installed on 17 April 2013 and circulated with groundwater for 70 d under the in situ pressures of 2.4 and 3.2 MPa and at flow rates to and from the aquifers of 25 mL min−1 and of 23 mL min−1 for ONK-PVA6 and ONK-KR15, respectively. The total volumes of groundwater circulated were 2478, 2458 and 2393 dm3 in the three field systems, respectively, as registered by the flow meters.

3.1.4 Water collection and transportation

Pressure resistant expansion vessels were used to collect and transport groundwater from ONK-PVA6 and ONK-KR15 under in situ pressure (Figure 3-3). They were filled using the pressure of the respective drillhole with a pressure drop not exceeding 400 MPa.

3.2 Flow cell circulation systems for laboratory experiments

Flow cell circulation systems (FCCSs) were originally developed and tested for research activities in the Äspö HRL tunnel where they were connected to the investigated aquifer (Pedersen 2005; Pedersen 2012a, b). These FCCSs were modified for laboratory work as shown in Figure 3-4. Detailed descriptions can be found elsewhere (Pedersen 2005).

Each FCCS had four pressure-resistant microsensor Eh electrode couples that consisted of one platinum micro-electrode with a tip diameter of 400–600 µm (RD500; Unisense A/S, Aarhus, Denmark) and one Ag/AgCl reference electrode with a tip diameter of 90–110 µm in gel-

37

stabilized electrolyte (REF100; Unisense). The electrodes were installed in line in each FCCS. The electrodes represented an adaptation of the standard glass Unisense microsensors mounted in the stainless steel flow cells. The electrodes were connected to two eight-channel mV amplifiers that transformed the recorded voltages into digital signals, subsequently collected and stored in Microsoft Office Excel files every 600 sec. using SensorTrace Basic software (version 1.9; Unisense A/S). Each amplifier collected data from two electrode couples per FCCS, thereby creating a back-up function in case one of the amplifiers should fail. The temperature was monitored in each FCCS during the experiment with digital thermometers installed at two different points.

Figure 3-4. Schematic representation of a flow cell cabinet system shown in Figure 3-5, the Eh electrode loop is not shown. Groundwater is circulated via the external expansion vessel during filling of the circulation. Next, the external vessel is used to set the pressure in the circulation with nitrogen gas pushing on groundwater in the external expansion vessel that communicates with the internal expansion vessel and transmits the pressure to the circulation system. When groundwater is extracted during sampling, water is moved from the external vessel to the internal vessel thereby retaining the pressure. The total circulating volume decreases in relation to the sample volume.

micro-pump

FlowCell 2

FlowCell 1

Expansionvessel

Sampleconnection

De-gassing

Pressure meter

Flow meter

FlowCell 3

FlowCell 4

Temperaturecontrolled area

16- 18 °C

= valve

Pumpcontrol

inout

By-pass

= PistonNitrogengas tank

Pressuretransmittingexpansionvessel

38

Figure 3-5. Lower left image; three flow cell cabinets systems installed in the laboratory, showing the flow cells from the circulation set-ups depicted in Figure 3-1. Micro-pumps, pressure meters, flow meters, and expansion vessels similar to those shown in Figure 3-1 were permanently installed in these cabinets. The Eh electrodes were installed via a loop of the circulation system on top of the cabinets as shown in the image. Upper image; The flow stabilizer insert line-up. Lower right image: Image of glass beads and garnet grains which were placed inside the flow cells.

3.3 Laboratory phase experiments with flow cell circulation systems

There were three SUREs with treatments as described in Table 3-1. Each SURE configuration is described in detail next. The interior tubes and valves of the FCCS were sterilized with 20 mg L−1chlorine dioxide, rinsed with sterile water, and filled with N2 before the installation of the FCs.

39

Table 3-1. Summary of treatments in the three SURE showing dates for the respective field and laboratory phases, type of biofilm support and the treatments applied to each FCCS. The names to the right of the table are used throughout this report to denote the respective treatment.

SURE no.

Dates Days Flow cell biofilm support

FCCS*No.

Groundwater source

Treatment Name

1 2010-11-24 ONKALO 2011-03-15 Laboratory 2011-06-28

110 / 105

Crushed rock (2- 4 mm) from ONK-PVA6 drillcore

1 ONK-PVA6 2.2 mM O2 and 7.9 mM N2 (air) 1-6-air 2 ONK-PVA6 11 mM CH4 1-6-CH4

3 ONK-PVA6 10 mM H2 and 11 mM CH4 1-6-H2-CH4

2

2012-02-07 ONKALO 2012-04-26 Laboratory 2012-08-07

70

/

103

Crushed rock (2- 4 mm) from ONK-KR15 drillcore

1 ONK-KR15 None 2-15

2 ONK-KR15 5 mM Na2SO4 2-15-SO4

3 ONK-KR15 5 mM Na2SO4 and 500 mL ONK-PVA6 groundwater 2-15-SO4-6

3

2013-04-17 ONKALO 2013-06-26 Laboratory 2014-01-23

70

/

209

50% Garnet grains (0.7 mm)

50% glass beads (1 mm)

Glass slides

1 ONK-KR15 Day 106, 62.5 µM Na2S; Day 134, 2.5 MPa CH4 3-15

2 ONK-KR15 5 mM Na2SO4; Day 106, 62.5 µM Na2S; Day 134, 25 mM CH4

3-15-SO4

3 ONK-PVA6 Day 106, 62.5 µM Na2S; Day 134, 25 mM CH4 3-6

*Flow cell circulation system

40

3.4 Configuration and sampling of growth experiments, SURE 1

The 12 FCs exposed to ONK-PVA6 groundwater for 110 d (3.1.1) were transported below RT under pressure from the ONKALO tunnel to the laboratory in Mölnlycke and installed four by four in three FCCSs. Each cabinet was temperature controlled (16–18 °C) and a pressure of 2.4 MPa was maintained. A total of seven expansion vessels (Figure 3-3) were filled with groundwater from ONK-PVA6, shipped pressurized with the FCs and used to fill the FCCSs with a total of 5000 mL of groundwater at the start of the experiments. Next, gases were added as follows. Three Teflon-lined, 500 mL stainless steel cylinders (304L-HDF4-500-T; Swagelok, Göteborg, Sweden) were filled at RT with:

1. Air to a pressure of 220 kPa 2. Methane to a pressure of 220 kPa 3. 50% H2 and 50% methane to a total pressure of 440 kPa

The cylinders were then filled with groundwater under pressure (2.4 MPa), resulting in a total circulating volume of 5500 mL per FCCS. The gas additions thus corresponded to final theoretical concentrations of 7.9 mM N2 and 2.1 mM O2, 11 mM methane, and 11 mM methane and 10 mM H2, respectively. These treatments are hereafter denoted 1-6-air, 1-6-CH4, and 1-6-H2-CH4, respectively (SURE experiment number, drillhole number, treatment). The start date was 15 March 2011 (day 0) and the end date was 28 June 2011, giving an experimental duration of 105 d. The circulation flow rate was kept at 20 mL min−1 corresponding to a flow of approximately 1 mm s−1 over the rock grains.

3.4.1 Sampling procedures

Complete sampling was performed six times, i.e., on days 3, 21, 42, 63, 84, and 105, for analysis according to Table 3-2 and associated descriptions. A pilot sampling was performed on day 0 before the gas additions to test the sampling procedures. On each sampling occasion, 20 mL of circulating water was drained and disposed of; 5 mL of water was then sampled and analysed for pH, and 10 mL of water was collected in a sterile 15 mL polypropylene (PP) tube (Sarstedt, Landskrona, Sweden) for an immediate analysis of ATP. After that, 4 × 10 mL of water was collected, using syringes, in butyl rubber-stoppered anaerobic glass tubes (no. 2048-00150; Bellco Glass, Vineland, NJ, USA) and 5 mL was collected for an analysis of CHAB. Two 10 mL volumes of water were collected in PP tubes, preserved with 0.02 µm of filtered, neutralized formaldehyde to a final concentration of 2.5%, and analysed for TNC and VLP, respectively. Next, 8 mL of water was sampled for a sulphate analysis, 9 mL was sampled for a sulphide analysis, and two × 5 mL volumes were sampled using a 0.2 µm syringe filter (Minisart, Sartorius syringe filter, hydrophilic; Fisher Scientific, Göteborg, Sweden) and stored at −20°C until analysed for acetate and lactate, respectively. Then, 25 mL of water was sampled using a 0.2 µm syringe filter (Minisart) for an immediate analysis of ferrous iron; then 100 mL of water was sampled and filtered for DNA extraction and qPCR analysis. This analysis was unfortunately unsuccessful, probably due to the small biomass in 100 mL of groundwater combined with the low extraction efficiency of the employed extraction kit (DNeasy Blood and Tissue Kit; Qiagen, Hilden, Germany) as described elsewhere (Lloyd et al. 2010). Finally, 10 mL of groundwater was used for the analysis of Eh and 190 mL was collected for the analysis of gas on sampling occasions 1–3, while approximately 70 mL was sampled on the three remaining sampling occasions. In total, 452 mL of water was sampled on each of the first three sampling occasions and 332 mL was

41

collected on the last three sampling occasions. After sampling the water, approximately 10 + 10 batches of rock grains were collected from two FCs in each FCCS for subsequent analysis of the amount of attached ATP and DNA. Samples were collected for analysis of the δ34SV-

CDT values (‰) of sulphate on days 0, 3 and 105; 100 mL of water was sampled using a 0.2µm syringe filter (Minisart), 2 drops of hydrochloride acid was added before the samples were deep frozen until analysis. For analysis of the δ13CV-PDB values (‰) of dissolved inorganic carbon (DIC), 20 mL water was collected through a 0.2 µm syringe filter (Minisart) injected in evacuated butyl rubber-stoppered anaerobic glass tubes using an injection needle.


The 12 FCs exposed to ONK-KR15 groundwater for 70 d (3.1.2) were transported below RT under pressure from the ONKALO tunnel to the laboratory in Mölnlycke and installed four by four in three FCCSs. Each cabinet was temperature controlled (16–18 °C) and a pressure of 2.4 MPa was maintained. A total of six expansion vessels (4 L each) were filled with groundwater from ONK-KR15, shipped pressurized with the FCs and used to fill the FCCSs with a total of 5000 mL of groundwater at the start of the experiments. Sulphate and ONK-PVA6 groundwater were then added as follows. Three Teflon-lined, 500 mL stainless steel cylinders (304L-HDF4-500-T) were filled at RT with:

1. 500 mL of ONK-KR15 groundwater 2. 5 mmol Na2SO4 dissolved in 500 mL of ONK-KR15 groundwater 3. 5 mmol Na2SO4 dissolved in 500 mL of ONK-PVA6 groundwater

Each cylinder was connected in line with the circulating groundwater in one FCCS, resulting in a total circulating volume of 5500 mL per FCCS. These treatments are hereafter denoted 2-15, 2-15-SO4, and 2-15-SO4-6. The start date for these groundwater circulations was 26 April 2012 and the end date was 7 August 2012, with the duration of the experiment being 103 d. The flow rate was kept at 22–25 mL min−1, corresponding to a flow of approximately 1.2 mm s−1 over the rock grains.


Complete sampling was performed six times, i.e., on days 0, 7, 19, 40, 61, 82, and 103, for analysis as described below. On each sampling occasion, 20 mL of circulating water was drained and discharged; two 25 mL volumes of water were collected in sterile 50 mL polypropylene (PP) tubes and deep frozen until the sulphate analysis, and 10 mL of water was collected in a sterile 15 mL PP tube for an immediate ATP analysis. Six 10 mL volumes of water were collected, using syringes, in butyl rubber-stoppered anaerobic glass tubes for an MPN analysis and 10 mL was collected for a CHAB analysis. Two 10 mL volumes of water were collected in PP tubes, preserved with 0.02 µm of filtered neutralized formaldehyde to a final concentration of 2.5%, and analysed for TNC and VLP, respectively. After that, 9 mL of water was sampled for a sulphide analysis, and two 5 mL volumes were sampled using a 0.2µm syringe filter (Minisart) and stored at −20°C until acetate and lactate analyses could be performed. Next, 25 mL of water was sampled using a 0.2 µm syringe filter (Minisart) for an immediate ferrous iron analysis. Two 10 mL volumes of water were sampled using a 0.2 µm syringe filter and deep frozen until the DOC analysis. For analysis of the δ34SV-CDT values (‰) of sulphate, 100 mL of water was sampled using a 0.2 µm syringe filter (Minisart), 2 drops of hydrochloride acid was added before the samples were deep frozen until analysis. Finally, 10

42

mL of groundwater was collected for pH analysis and 100 mL for gas analysis. In total, 434 mL of water was sampled on each sampling occasion. After sampling the water on days 0 and 103, one batch of rock grains was collected from each of the two FCs in each FCCS for a subsequent analysis of the amount of attached ATP and 16S rDNA diversity.


The 15 FCs exposed to groundwater for 70 d (3.1.3) were transported under pressure from the ONKALO tunnel to the laboratory in Mölnlycke, Sweden and installed five by five in three FCCSs. Each cabinet was temperature controlled (16–18 °C) and a pressure of 3.2 MPa was maintained. A total of seven expansion vessels (4 L each) were filled with groundwater; 4 from ONK-KR15 and 3 from ONK-PVA6, shipped pressurized with the FCs and used to fill the FCCSs with a total of 5000 mL of groundwater at the start of the experiments. After that, sulphate was added as follows. Three Teflon-lined, 500 mL stainless steel cylinders (304L-HDF4-500-T) were filled at RT with:

1. 500 mL ONK-KR15 groundwater 2. 5 mmol Na2SO4 dissolved in 500 mL ONK-KR15 groundwater 3. 500 mL ONK-PVA6 groundwater

These treatments are hereafter denoted 3-15, 3-15-SO4 and 3-6, respectively. The start date was 26 June 2013 (day 0) and the end date was 23 January 2014, giving an experimental duration of 209 d. The flow rate was kept at approximately 25 mL min−1 corresponding to a flow of approximately 1.2 mm s−1 over the garnet grains and glass beads.

After 106 days, sulphide (as Na2S) was added to each system to a final sulphide concentration in the FCCSs of 2 mg L−1. This was done to lower the Eh to a level that is typical of sulphate-reducing environments. Methane was added after 134 days as follows. Three Teflon-lined, 500 mL stainless steel cylinders (304L-HDF4-500-T) were filled at RT with a free gas phase of methane to a pressure of 0.5 MPa and installed in each FCCS to achieve a saturated concentration of approximately 25 mM of methane in the circulating groundwater at a pressure of 3.2 MPa.


Complete sampling was performed five times for analysis as described below, i.e., on days 2, 64, 97, 169 and 206-207-209; sampling for 3-15 took place on day 206, for 3-15-SO on day4 207 and for 3-6 on day 209. On each sampling occasion, 20 mL of circulating water was drained and disposed of; 2 × 10 mL of water was collected in sterile 15 mL polypropylene (PP) tubes and deep frozen until a sulphate analysis; 10 mL of water was collected in a sterile 15 mL PP tube for an immediate analysis of ATP. Then water was collected for an MPN analysis, 3 × 10 mL using syringes, in butyl rubber-stoppered anaerobic glass tubes and 25 mL using syringes, in a butyl rubber-stoppered anaerobic glass bottle. For the analysis of CHAB, 10 mL was collected in a sterile 15 mL polypropylene (PP) tube. Two 30 mL volumes of water were collected in 50 mL PP tubes, preserved with 0.02 µm of filtered, neutralized formaldehyde to a final concentration of 2%, and analysed for TNC and VLP, respectively. Thereafter, 9 mL was sampled for sulphide analysis, and 5 mL were sampled using a 0.2 µm syringe filter (Minisart) and stored at −20°C until the analysis of acetate. Then 50 mL water was sampled using a 0.2 µm syringe filter (Minisart) and deep frozen until the analysis of

43

DOC. For analysis of the δ34SV-CDT values (‰) of sulphate, 100 mL of water was sampled using a 0.2 µm syringe filter (Minisart), 2 drops of hydrochloride acid was added before the samples were deep frozen until analysis. After that, 25 mL water was sampled using a 0.2 µm syringe filter (Minisart) for an immediate analysis of ferrous iron. For analysis of the δ13CV-

PDB values (‰) of dissolved inorganic carbon (DIC), 20 mL water was collected through a 0.2 µm syringe filter (Minisart) injected in evacuated butyl rubber-stoppered anaerobic glass tubes using an injection needle. Finally, 10 mL of groundwater was used for the analysis of Eh and pH and 50 mL was collected for the analysis of gas. In total, 419–469 mL of water was sampled on each of the sampling occasions. After sampling the water on day 2 and day 206-207-209, batches of garnet grains were collected from FCs in each FCCS for subsequent analysis of the amount of attached ATP and DNA. After the day 2 sampling occasion, the removed garnet batch was replaced with a new garnet batch to ensure further experiments in the FCCSs.

Table 3-2. Summary of performed analyses.

Analysis name water biofilm Description SURE 1

SURE 2

SURE 3

Adenosine triphosphate ATP × × 2.6.1 × × × Total number of cells TNC × 2.6.2 × × × Virus like particles VLP × 3.7.1 × × × Cultivable, aerobic, heterotrophic bacteria

CHAB × 2.6.3 × × ×

Nitrate-reducing bacteria NRB × 2.6.4 × × × Iron-reducing bacteria IRB × 2.6.4 × × Manganese-reducing bacteria MRB × 2.6.4 ×

Sulphate-reducing bacteria SRB × 2.6.4 × × × Autotrophic acetogens AA × 2.6.4 × × × Methanogens MM × 2.6.4 × Phylochip 16S rDNA diversity

16S rDNA × 3.7.7 ×

Sequencing 16S rDNA diversity

16S rDNA × × 2.7.2 × ×

Sulphate SO4 × 3.7.4 × × × Sulphide S2− × 3.7.3 × × × Acetate Acetate × 3.7.3 × × × Lactate Lactate × 3.7.3 × Dissolved organic carbon DOC × 3.7.3 × × Fe2+ Fe2+ × 3.7.3 × × ×

δ34S of sulphate δ34SV-

CDT × 3.7.4 × × ×

δ13C of inorganic carbon δ13CV-

PDB × 3.7.5 ×

pH pH × 3.7.2 × × ×

44

Redox potential Eh × 3.1.2 3.7.2 × × ×

Oxygen gas O2 × 2.5 × Hydrogen gas H2 × 2.5 × × × Methane CH4 × 2.5 × × × Helium He × 2.5 × × × Carbon dioxide CO2 × 2.5 × × ×

3.7 Methods for measurements and analyses

In addition to the methods described in Section 2.6, the following analyses were also performed.

3.7.1 Total number of virus-like particles

The total number of virus-like particles was determined using a direct count method with SYBR Gold (Invitrogen, Eugene, OR, USA) (Chen et al. 2001; Noble and Fuhrman 1998).

3.7.2 Ph and Eh analysis using a portable meter

Eh was analysed using a HACH HQ40d portable multi-parameter meter (HACH Lange AB, Sköndal, Sweden) equipped with an MTC101-05 probe (HACH Lange AB) installed in a flow cell connected to each analysed FCC. This installation prevented contact with air, which might have influenced Eh due to the degassing of H2, carbon dioxide, and sulphide from the samples. The pH of 5 mL subsamples was determined immediately following extraction from the FCCSs, using Schott CG84310 pH meter fitted with a BlueLine 13 pH electrode (VWR, Stockholm, Sweden) calibrated according to the manufacturer’s instructions.

3.7.3 Acetate, lactate, organic carbon, ferrous iron, and sulphide analysis

Acetate and lactate concentrations were determined with the enzymatic UV method (kit no. 10148261035 for acetate and kit no. 10139084035, for lactate; Boehringer Mannheim/R-Biopharm AG, Darmstadt, Germany) using a Genesys 10UV spectrophotometer (Thermo Fisher Scientific) for detection. Samples of 50 mL for the analysis of dissolved organic carbon (DOC) were deep frozen at −20 °C until the analysis at ALS Scandinavia AB according to the CSN EN 1484 method. The uncertainty was ±20% of the analysed values. Sulphide was analysed using the colorimetric methylene blue method with an uncertainty of ±17% (Swedish Standard Method SIS 028115). Ferrous iron concentrations were determined using the 1-10 phenanthroline method (method no. 8146, programme 255, range 0.4–54 mM with 95% confidence limits of distribution of ±11%; HACH Lange AB).

3.7.4 Analysis of sulphate and δ34SV-CDT values of sulphate

Samples were collected for the sulphate analysis in sterile 15 mL PP tubes and frozen at −20°C until the analysis using the SulfaVer 4 method (method #8051, programme 680, range 0.03–0.73 mM with 95% confidence limits of distribution of ±10%; HACH Lange). Samples for the analysis of the values of sulphate were collected in 125 mL PP bottles (Nalgene–Thermo Fisher Scientific, Göteborg Sweden) and sent for analysis with an elemental analyser-isotopic ratio mass spectrometer by Iso-Analytical Limited, UK (www.iso-analytical.com).

http://www.iso-analytical.com/

45

3.7.5 Analysis of δ13CV-PDB in dissolved inorganic carbon

20 mL of water was collected through a 0.2 µm syringe filter (Minisart) injected in a evacuated butyl rubber-stoppered anaerobic glass tube using an injection needle and then sent for analysis to the University of Helsinki, Department of Geosciences and Geography as described elsewhere (Atekwana and Krishnamurthy 1998). In short, the DIC was extracted by injecting 85% phosphoric acid and allowing the sample to react at 50 ºC for 30 minutes. Then the DIC, now in the gas phase, was purified in a vacuum line which removed water with an ethanol and dry ice trap, and collected the DIC into a liquid nitrogen trap. The sample collection time was 10 minutes.

3.7.6 DNA extraction from biofilms and MPN cultures

3.7.6.1 SURE 1

The SURE 1 FCCSs were kept in circulation after day 103 when the last sampling of circulating water was performed, until day 120 when there was time available in the laboratory to sample and analyse DNA from biofilms. DNA was extracted using the MO BIO Power Biofilm DNA isolation kit (catalogue no. 24000-50; Immuno Diagnostic Oy, Hämeenlinna, Finland) from the biomass attached to the rock grains collected from the 1-6-CH4, and 1-6-H2-CH4 FCs on day 118. Four extractions, each from 10 grains, were performed according to the manufacturer’s protocol and the extracted DNA was concentrated on a Microcon centrifugal filter (Ultracel, YM-100, 42424; Millipore Sweden AB, Solna, Sweden). The 1-6CH4 FCs were re-sampled on day 120 and a total of 40 extractions of 10 grains each were concentrated to one sample. DNA concentrations were determined using the Quant-iT PicoGreen dsDNA reagent (catalogue no. P7589; Invitrogen AB, Lidingö, Sweden) and a total of 84, 450, and 87 ng DNA were obtained from the 1-6-CH4×4, CH4×40, and 1-6-H2-CH4 ×4 extractions, respectively. The DNA was sent deep frozen (on CO2 pellets) for a microarray PhyloChip analysis to Second Genome Inc (San Bruno, CA, USA).

3.7.6.2 SURE 2

From selected MPN cultures, 9 mL of culture was filtered onto 0.22 µm water filters (cat. no. 14880-100-WF; MO BIO Laboratories) using vacuum suction. Using sterile, DNA-free tweezers, 40 rock grains (2 g of rock) were collected from each of the three FCCS and pooled on day 0 (total of 120 grains of rock, 6 g), and 80 rock grains (4 g of rock) were collected from each FCCS on day 103, and placed directly into empty biofilm DNA extraction vessels provided by the manufacturer. The total genomic DNA from the biomass attached to the rock grains and MPN cultures was extracted using the MO BIO Power Biofilm DNA isolation kit (cat. no. 24000-50) and the MO BIO PowerWater DNA isolation kit (cat. no. 12888), respectively, both from MO BIO Laboratories, Carlsbad, CA, USA. The extraction volume of the MO BIO extraction kits used in this work was 100 µL. DNA concentrations were determined using the Quant-iT PicoGreen dsDNA reagent (catalogue no. P7589; Invitrogen AB, Lidingö, Sweden)

3.7.6.3 SURE 3

Using sterile, DNA-free tweezers, approximately 6 g of garnet grains, corresponding to a surface area of ~34 cm2 assuming the average diameter of grains to be 0.7 mm, was collected from each FCCS on days 0 and 206-207-207 using sterile equipment and placed directly into

http://www.immunodiagnostic.fi/

46

DNA extraction vessels provided by the manufacturer. The total genomic DNA was extracted using the manufacturer’s protocol for the MO BIO Power Water DNA isolation kit (cat. no. 12888) from MO BIO Laboratories, Carlsbad, CA, USA. DNA concentrations were determined using the Quant-iT PicoGreen dsDNA reagent (catalogue no. P7589; Invitrogen AB, Lidingö, Sweden)

3.7.7 16S rDNA sequence analysis on biofilms and MPN cultures

The DNA diversity of biofilms and MPN cultures was gauged using different sequencing protocols and microarrays as outlined in Table 3-3. Methods for 454 FLX Titanium and Illumina HiSeq are described in Section 2.7.2.

3.7.7.1 Analysis using the G3 PhyloChip assay

This analysis was performed by Second Genome Inc. (San Bruno, CA, USA) as briefly outlined here; the methodology is completely described by Hazen et al. (2010). The bacterial 16S rRNA genes were amplified using degenerate forward primer 27F.1 5’-AGRGTTTGATCMTGGCTCAG-3’and non-degenerate reverse primer 1492R.jgi 5’-GGTTACCTTGTTACGACTT-3’. For each sample, amplified products were concentrated using a solid-phase reversible immobilization method to purify the PCR products, and quantified by electrophoresis using an Agilent 2100 Bioanalyser (Agilent, Santa Clara, CA, USA). PhyloChip Control Mix was added to each amplified product. Thirty-five cycles of bacterial 16S rRNA gene PCR amplification were performed. The labelled bacterial products were fragmented, biotin labelled, and hybridized to the PhyloChip Array, version G3. The PhyloChip arrays were washed, stained, and scanned using the GeneArray scanner (Affymetrix, Santa Clara, CA, USA); each scan was captured using standard Affymetrix software (GeneChip Microarray Analysis Suite). The hybridization values, i.e., the fluorescence intensity, were calculated for each taxon as a trimmed average, with maximum and minimum values removed before averaging.

Table 3-3. Sequence analysis procedures used to analyse DNA diversity in biofilm samples from SURE 1 – 3, and from MPN cultures from SURE 2. s = start of experiment; e = end of experiment.

Date SURE 1s SURE 1e SURE 2s SURE 2e SURE 3s SURE3e Phylochip × Bacteria v4v6 454 FLX Titanium × × × Bacteria v6 Illumina ×

Archaea v6 Illumina × × ×

16S Bacteria rDNA Cloning ×

3.7.7.2 Cloning and 16S rDNA sequencing

The species diversity of MPN culture microorganisms from SURE 2 was gauged by their bacterial 16S rDNA sequence. PCR amplification used universal 16S rDNA forward and

47

reverse primers 27f and 1492r, respectively (Table 2-4). The thermal cycling conditions were 98 °C for 30 s, 30 cycles at 98 °C for 30 s, 60 °C for 30 s and 72 °C for 30 s with a final extension of 72 °C for 5 min. The amplification products were visualized using gel electrophoresis on 1% agarose gel, stained with ethidium bromide and illuminated by UV exposure. The amplification products were then purified using a QIAquick Gel Extraction Kit (cat. no. 28704; QIAGEN, Solna, Sweden) following the manufacturer’s protocol. In order to produce 3´A overhangs of the blunt-ended iProof polymerase product, 1 µL of Taq polymerase (cat. no. 18038-042; Invitrogen) and additional dATP were added to the reactions, which were then incubated for 30 min at 72 °C.

The purified samples were cloned into the linearized PCR 2.1-TOPO vector and transformed into chemically competent TOP10´F Escherichia coli cells using the TOPO TA cloning kit (cat. no. K4550-01; Invitrogen) following the manufacturer’s protocol. White clones containing the insert were randomly selected and each colony was inoculated onto LB agar plates containing kanamycin (40 mg mL–1) and incubated overnight at 37 °C. The recombinant plasmids were extracted and subsequently sequenced using the Value Read Plate service (Eurofins MWG Operon, Ebersberg, Germany) with the M13rev(-29) sequencing primer and the M13uni(-21) sequencing primer provided by Eurofins MWG for the Value Read Plate service for the 16S rDNA clones. Raw data sequences were screened for chimeric sequences using the Bellerophon program (Huber et al. 2004). Sequence data were analysed and aligned using the Geneious 6.0.3 software package (Biomatters, Auckland, New Zealand). The 16S rDNA reference gene E. coli Brosius with accession number J01695 was used as a sequence mask for the alignment of the 16S rDNA clones (Huber et al. 2004). In addition, the clones were compared to sequences available in the BLAST nucleotide database. Sequence homology was analysed using either the nucleotide–nucleotide algorithm or the 16S rDNA microbial algorithm. Sequences that were <99.9% similar to database records were submitted to the GenBank database under accession numbers KC676781 to KC676786.

3.7.7.3 Illumina HiSeq 16S Bacteria rDNA v6 sequencing

For the amplicon library preparation of the Bacteria 16S rRNA gene hypervariable v6 region, a slightly different approach compared to v6 for Archaea (2.7.2) was used. The forward primer was (967F) and the reverse primer was (1064R) (Table 2 4). The PCR was carried out in triplicate reactions in a 33 μL reaction volume of 1.0 U Platinum Taq Hi-Fidelity Polymerase (Life technologies, Carlsbad CA), 1X Hi-Fidelity Buffer, 200 μM dNTP PurePeak DNA Polymerase mix (Pierce Nucleic acid Technologies, Milwaukee, WI), 1.5 mM of MgSO4, 0.2 μM of each primer and ~10 ng of template DNA. Each primer pair had a no-template control. The cycling conditions were; an initial 94 °C 3-minute denaturation, 30 cycles of; 94 °C for 30 sec., 60 °C for 60 sec. and 72 °C for 90 sec. followed by a final 10 min. extension at 72 °C. All of the sequence quality filtering and data processing was performed as for the 16S Archaea rDNA v6 amplicons described in 2.7.2

49

4 GEOCHEMICAL AND MICROBIAL CHARACTERISTICS OF ONK-PVA6 AND ONK-KR15 OVER TIME

4.1 Geochemistry and dissolved gases

The complete data set on the groundwater geochemistry of ONK-PVA6 and ONK-KR15 over time is given in Table A-3 and Table A-4. The amount of total dissolved solids (TDS) was significantly higher in ONK-KR15 than in ONK-PVA6 (Figure 4-1). While the amount of TDS decreased only slightly over a period of 3 years in ONK-KR15, in ONK-PVA6 it dropped from 8500 mg L−1 to 6000 mg L−1 over a period of 4 years. Chloride concentrations had a similar evolution over time. In contrast, the concentration of sulphate and alkalinity was much higher in ONK-PVA6 than in ONK-KR15 and both parameters increased over time in ONK-PVA6. There was also an increase in sulphide concentration over time in ONK-PVA6. Sulphide and sulphate were both below detection limits in ONK-KR15. There were no clear trends for DOC over time in either of the two drillhole groundwaters and all but three observations of DOC in ONK-KR15 were below detection limits. Ferrous iron was below detection limits in all but one sample from ONK-KR15 and ferrous iron concentration was generally lower than 0.1 mg in ONK-PVA6 groundwater (Appendix 1).

The gas analysis results correlated with the geochemistry trends with some exceptions (Table 4-1). The concentration of methane decreased over time in ONK-PVA6, while in ONK-KR15 it was constant at approximately 6 mM (Figure 4-2). H2 was at or below the detection limit in ONK-PVA6 and increased in the last two samples from ONK-KR15 (Figure 4-2). This coincided with an increase in volume of dissolved gases, methane and helium, suggesting that groundwater from deeper layers may have moved upwards to this aquifer during summer 2012, possibly as a consequence of the drainage of the aquifer via the packer system that would have generated a pressure drop. If the hydraulic conductivity is larger in the fractures going deeper than in the fractures going upwards, deep groundwater will move upwards, so-called up-coning. This phenomenon has been observed for many years in the Äspö HRL (e.g. Pedersen 2013b). Consequently, there was a peak in chlorine and TDS which supports the assumption of a temporary up-coning event to ONK-KR15. The total amount of dissolved gas was the highest in ONK-KR15 at between 230 and 280 mL L−1 groundwater. The amount of dissolved gas decreased over time in ONK-PVA6 and most of this decrease was due to a decrease in the concentration of methane from 4.5 mM to 1.7 mM (Table 4-1).

The trends in Figure 4-1 indicate a change in the groundwater composition of ONK-PVA6 and a stagnant situation in ONK-R15. There appears to have been a drawdown of shallower brackish groundwater with a high concentration of sulphate, a low concentration of methane and slightly higher alkalinity to the aquifer of ONK-PVA6 compared to ONK-KR15. This groundwater mixed with the more saline groundwater with a composition related to ONK-KR15. The two studied drillholes consequently represent two very different conditions for microbial studies. ONK-KR15 represented groundwater reproducible over time and the SURE 2 and 3 experiments were consequently performed with a similar type of groundwater with respect to geochemistry. ONK-PVA6 was used in SURE 1, 2 and 3 in different combinations and each SURE experiment was performed with a different groundwater geochemistry. These differences and similarities between groundwater types may have had an influence on the microbiology of the corresponding groundwater which will be reported next.

50

Figure 4-1. Evolution of dissolved solids and alkalinity over time in ONK-PVA6 () and ONK-KR15 (). The fitting line was omitted for DOC in ONK-KR15 due to several data points being below detection limits. Connection dates SURE 1: 2010-11-24 − 2011-03-15; SURE 2: 2012-02-07 − 2014-04-26; SURE 3: 2013-04-17 − 2013-06-26.

2010-08-102011-02-26

2011-09-142012-04-01

2012-10-182013-05-06

2013-11-222014-06-10

Sample date

0.12

0.16

0.20

0.24

0.28

0.32

0.36

0.40

Tota

l alk

alin

ity (m

mol

− 1)

2010-08-102011-02-26

2011-09-142012-04-01

2012-10-182013-05-06

2013-11-222014-06-10

Sample date

3000

3500

4000

4500

5000

5500

6000

6500

7000

Chl

orid

e (m

g L− 1

)

2010-08-102011-02-26

2011-09-142012-04-01

2012-10-182013-05-06

2013-11-222014-06-10

Sample date

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5D

isso

lved

org

anic

car

bon

(mg

L− 1)

2010-08-102011-02-26

2011-09-142012-04-01

2012-10-182013-05-06

2013-11-222014-06-10

Sample date

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Sul

phid

e (m

g L− 1

)

2010-08-102011-02-26

2011-09-142012-04-01

2012-10-182013-05-06

2013-11-222014-06-10

Sample date

0

20

40

60

80

100

120

140

160

180

Sul

phat

e (m

g L− 1

)

2010-08-102011-02-26

2011-09-142012-04-01

2012-10-182013-05-06

2013-11-222014-06-10

Sample date

5000

6000

7000

8000

9000

10000

11000

Tota

l Dis

solv

ed S

olid

s (m

g L− 1

)

51

Table 4-1. Concentration of dissolved gases over time in ONK-PVA6 and ONK-KR15. The total gas shows the amount of dissolved gas per L of groundwater.

Gas unit ONK-PVA6 ONK-KR15

2010-09-23

2011-03-14

2012-09-18

2012-01-11

2012-04-17

2012-06-26

2014-05-15

Total mL L−1 196 133 97.7 243 235 259 281 H2 µM 0.078 <0.01 0.21 1.89 3.66 39.7 14 He µM 267 89.8 79.6 208 216 243 246 Ar µM 58.3 29.5 19.8 32.2 32.5 43 34.3 N2 µM 3270 3200 2740 3720 3640 4510 5220 CO µM 0.047 0.17 0.03 0.06 0.21 0.03 0.24 CO2 µM 2.73 3.67 1 0.44 11 1.2 1.1 CH4 µM 4540 2780 1660 6100 6190 6700 5980 C2H6 µM 16.5 - 8.42 21.7 20 18.3 16.2

Figure 4-2. Concentration of CH4 and H2 over time in ONK-PVA6 () and ONK-KR15 ().

4.2 Microbiology

All microbiology parameters were analysed three times for ONK-PVA6 groundwater and twice for ONK-KR15 groundwater. These analyses were generally coordinated in time with the start of the SURE experiments. Data are shown in Table 4-2.

4.2.1 Total number of cells

The TNC of ONK-PVA6 increased approximately 5 times over the experimental time period of almost three years. The average TNC in Olkiluoto groundwater is 1.07 × 105 cells mL−1 calculated from 78 observations (Pedersen et al. 2015). This range of TNC was more than 2 orders of magnitude and all three ONK-PVA6 results were within this range. The increase in TNC over time may be due to the observed evolution towards more dilute groundwater (Figure 4-1) which may have stimulated microbial growth and activity.

2010-01-22 2010-08-10

2011-02-26 2011-09-14

2012-04-01 2012-10-18

2013-05-06 2013-11-22

2014-06-10 2014-12-27

Sample date

1000

2000

3000

4000

5000

6000

7000

CH

4 (µ

M)

2010-01-22 2010-08-10

2011-02-26 2011-09-14

2012-04-01 2012-10-18

2013-05-06 2013-11-22

2014-06-10 2014-12-27

Sample date

0

5

10

15

20

25

30

35

40

H2

(µM

)

52

The TNC of ONK-KR15 increased 10 times over a period of 1.5 years. This increase may have been due to the significant increase in the concentration of H2 (Figure 4-2) which has previously been found to boost microbial activity significantly (Pedersen 2012a).

The increases in TNC over time in both groundwater systems consequently show that all three SUREs were started with different TNC which may infer that growth conditions also were different at the start of the experiments due to variations in groundwater geochemistry.

4.2.2 ATP

The ATP concentration relates to the biomass and the activity of microbial populations. The quotient of ATP over TNC indicates viability and activity (Eydal and Pedersen 2007). This indicator suggests that the microbial population was significantly less active in the ONK-PVA6 groundwater in 2012 compared to the sampling occasions in 2010 and 2013 Table 4-2. Similarly, the microbial population was more active in ONK-KR15 on the 2013 sampling occasion compared to the 2012 sampling occasion.

4.2.3 CHAB

The numbers of CHAB correlated with the ATP and to some extent also with the TNC values being low in 2012 in ONK-PVA6 and ONK-KR15. The highest values were found in the 2013 samples which were also the samples with the highest ATP values.

4.2.4 Nitrate-reducing bacteria

NRB have previously been found to correlate very well with CHAB numbers in deep groundwater (Pedersen et al. 2015) and such a correlation was found here as well. The NRB values were the lowest in the 2012 samples. The NRB value was higher than the TNC value in the 2010 ONK-PVA6 sample, but TNC was still within the 95% confidence interval. It seems likely that TNC may have somewhat underestimated the true TNC number in that sample for reasons unknown.

4.2.5 Iron-reducing bacteria

The analysis indicated a relatively high number of IRB in the ONK-PVA6 groundwater, in the upper region of what has been analysed previously in deep Olkiluoto groundwater (Pedersen et al. 2015). The MPNs for IRB were below detection limits in ONK-KR15 in the analysis.

4.2.6 Manganese-reducing bacteria

The analysis also indicated a relatively high number of MRB in the ONK-PVA6 groundwater, again in the upper region of what has been analysed previously in deep Olkiluoto groundwater (Pedersen et al. 2015). The MPNs for MRB were low in ONK-KR15 in the analysis.

53

Table 4-2. The total number of cells (TNC), ATP, cultivable heterotrophic aerobic cells (CHAB) and the most probable numbers (MPN) of cultivable nitrate, iron, manganese and sulphate-reducing bacteria (NRB, IRB, MRB and SRB, respectively) and acetogenic and methanogenic microorganisms in groundwater from drillholes ONK-PVA6 and ONK-KR15. SD = standard deviation for TNC, ATP and CHAB with three observations for each average number, CI = 95% confidence interval for MPN analyses. n.a. = not analysed.

ONK-PVA6 ONK-KR15

Analysis Date 2010-09-23 2012-04-17 2013-06-28 2012-01-11 2013-06-28 Unit Number SD/CI number SD/CI number SD/CI number SD/CI number SD/CI

TNC cells mL−1 37 000 6 100 49 000 10 000 190 000 40 000 18 000 5 000 180 000 22 000

ATP amol mL–1 27 300 3 170 10 600 2 230 108 000 10 100 4 500 810 111 000 11 900

ATP/TNC amol cell–1 0.74 - 0.27 - 0.57 - 0.25 - 0.62 -

CHAB cells mL−1 16 300 3 210 70 17 36 300 12 300- 2 500 436 91 700 5 300

NRB cells mL−1 90 000 30 000–290 000 35 16–82 90 000 30 000–

290 000 800 300–2500 30 000 10 000–

130 000

IRB cells mL−1 500 200–2000 900 300–

2900 n.a. - <0.2 - n.a. -

MRB cells mL−1 220 30–250 110 40–300 n.a. - 8.0 3.0–25 n.a. -

SRB cells mL−1 80 30–250 7 3–21 170 70–480 <0.2 - 0.2 0.1–1.1

Acetogens cells mL−1 110 40–300 <0.2 - 0.8 0.3–2.4 <0.2 - <0.2 -

Methanogens cells mL−1 <0.2 - <0.2 - n.a. - <0.2 - n.a. -

54

4.2.7 Sulphate-reducing bacteria

There were fairly small MPNs of SRB in the ONK-PVA6 2010 and 2013 samples compared to what has been analysed in deep Olkiluoto groundwater, while the 2012 observation was close to detection limits. SRB were at or below the limit of detection in ONK-KR15.

4.2.8 Acetogens

The MPNs for acetogens were only observed in significant numbers in ONK-PVA6 on the 2010 sampling occasion; on other occasions the values were close to or below the limit of detection.

4.2.9 Methanogens

The MPNs for methanogens were below the limit of detection in all samples.

4.2.10 Overall evaluation of microbiological observations

A clear difference in cultivable diversity was found between ONK-PVA6 and ONK-KR15. While ONK-PVA6 generally had high MPNs of IRB, MRB, SRB and acetogens, these groups were not cultivable from ONK-KR15. The only cultivable groups found in groundwater from both drillholes were NRB and CHAB. The TNC and ATP results attest to the presence of microorganisms in ONK-KR15 groundwater in numbers equal to what was observed in ONK-PVA6. The observed difference in cultivability is probably related to both the geochemical composition of the groundwater and the presence of mixing groundwater in ONK-PVA6 and absence of mixing in ONK-KR15. It should be noted that although only a few groups of microorganisms were cultivated from ONK-KR15, there may have been other types of microorganisms present not covered by the MPN-protocols applied as indicated next.

4.3 Groundwater 16S rDNA sequence diversity

Because the amount of DNA can be very low in deep groundwater, and because all DNA extraction methods have losses, we did filter the groundwater to increase the amount of cells for extraction. In addition to obtaining more DNA, the filtering of a large volume of groundwater, > 1 L, will reduce possible variations in cell numbers and diversity over volume compared to smaller volumes, < 1 L. The less DNA recovered from a sample, the higher the risk that the reagent and laboratory contamination impact the sequence results (Salter et al. 2014). Therefore, procedures should be adopted that ensure as large DNA samples as possible. In the case of groundwater sampling, the filtering of large groundwater volumes will reduce the risk for bias of the results due to contamination by trace DNA in reagents and laboratory materials. In this work, we filtered large volumes of groundwater before applying our DNA extraction protocols.

4.3.1 DNA recovery in groundwater extractions

The total amounts of extracted DNA obtained on 2012-04-17 ranged from 10 to 243 × 10–9 g (Table 4-4). The average amount of DNA in a typical groundwater bacterium, for example D. aespoeensis (Motamedi and Pedersen 1998), is 649 Daltons/base pair × 3,629,109 bases (Locus CP002431) = 2.36 × 109 Daltons cell–1 = 2.36 × 109 Daltons cell–1 × 1.6605402 × 10−24 g Dalton−1 = 3.9 × 10–15 g DNA cell–1. Approximately 57 L of groundwater was filtered on 2012-04-17 from ONK-KR15 and the DNA recovery was 63 × 10–9 g of DNA (Table 4-4),

55

which corresponds to 1.62 × 107 average-sized cells based on the DNA estimate for D. aespoeensis. This number of cells was found to be sufficient in the work by Salter et al. (2014) to avoid laboratory contamination with a MoBIO kit. There were 1.8 × 104 cells mL–1 in the ONK-KR15 groundwater at the start of filtration. The calculated average DNA recovery then becomes 1.6%. Calculated in the same way, the DNA recovery was 9.2% for ONK-PVA6 groundwater.

It has previously been observed that the number of cells in deep groundwater tends to decrease during flow in aquifers (Pedersen 2013b). Therefore, TNC was determined both at the start and end of filtration of ONK-PVA6 and ONK-KR15 groundwater on 2013-04-17. DNA recovery was much higher if the TNC at the end of the filtration was used for calculations instead of the TNC at the start (Table 4-3). If this occurred also at the 2012-04-17 filtration, the DNA recovery was significantly higher than calculated above, because a decrease in the number of TNC during filtration will increase the observed amount of DNA over the total amount of cells captured on the filter.

Table 4-3. DNA recovery calculated with the TNC obtained at the start and end of filtration. ONK-PVA10 was filtered for another project, and is included here only to demonstrate the effect of groundwater flow in an aquifer on the TNC.

Drillhole TNC at start (cells mL-1)

TNC at end (cells mL-1)

Start TNC DNA recovery

(%) End TNC

DNA recovery (%)

ONK-PVA6 1.49 ×105 ± 9.5 ×103 1.40 ×104 ± 2.5 ×103 3.06 32.41 ONK-PVA10 7.20 ×105 ± 1.4 ×103 1.30 ×103 ± 1.4 ×102 3.37 19.05 ONK-KR15 5.20 ×105 ± 4.7 ×102 2.00 ×103 ± 5.9 ×102 0.46 1.19

4.3.2 Groundwater 16S Bacteria rDNA v4v6 sequence diversity

Rarefaction curves showed that almost all of the 16S rDNA diversity was captured for each sample (Figure 4-3). Except for 1.3% and 1.9% Desulfosporosinus reads on 2012-09-04 and 2013-04-17, sequences related to SRB were absent in the sequence library from ONK-KR15 groundwater (Figure 4-5, Table 4-6). Sulphur-reducing Desulfuromonas was found in the ONK-KR15 2012-04-17 library but this microorganism cannot reduce sulphate. The general absence of SRB in the ONK-KR15 sample sequence libraries agrees well with the lack of cultivable SRB in ONK-KR15 (Table 4-2). The dominant sequences were closely related to hydrogen-oxidizing genus Hydrogenophaga followed by Hoeflea, Lutibacter, Pseudomonas and Thiobacillus related sequences (Figure 4-5).

Unlike ONK-KR15 groundwater, the sequence library from ONK-PVA6 groundwater was dominated by sequences related to several genera of SRB with Desulfobacula and Desulfobulbaceae dominating (Figure 4-5). Three of the most abundant genera from ONK-KR15, Hydrogenophaga, Hoeflea, and Pseudomonas were found to some extent also in the ONK-PVA6 sequence library. The incidence of similar sequences in the ONK-PVA6 and ONK-KR15 sequence libraries is reasonable, because hydrogeochemical results show that intermediate, sulphate-rich groundwater containing SRB penetrates downwards and mixes with the deep, sulphate-poor groundwater in the aquifer region to which ONK-PVA6 is connected (Aalto et al. 2011). There was generally a rather high proportion of sequences that affiliated with not annotated sequences.

56

Figure 4-3. Rarefaction curves for ONK-PVA6 and ONK-KR15 16S Bacteria rDNA v4v6 dataset. Each curve represents a single sample and sampling occasion.

Figure 4-4. Rarefaction curves for ONK-PVA6 and ONK-KR15 16S Archaea rDNA v6 dataset. Each curve represents a single sample and sampling occasion.

0 5000 10000 15000 20000 25000 30000 35000 40000

Sample size

0

20

40

60

80

100

120

140Ta

xa c

ount

PVA6 2012-04-17 PVA6 2012-09-04 PVA6 2013-04-17 KR15 2012-04-17 KR15 2012-09-04 KR15 2013-04-17

0E-01 1E+06 2E+06 3E+06 4E+06 5E+06 6E+06 7E+06

Sample size

0

20

40

60

80

100

120

Taxa

cou

nt

ONK-KR15 2012-04-17 ONK-KR15 2012-09-04 ONK-PVA6 2012-09-04 ONK-PVA6 2012-04-17

57

4.3.3 Groundwater 16S Archaea rDNA v6 sequence diversity

Rarefaction curves for archaeal v6 16S rDNA sequences all reached a plateau at the end of the respective curve (Figure 4-4). This attests to a sufficient sampling depth for the dataset. All samples had almost the same number of OTUs, i.e., richness, at >0% OTUs level assignment (Table 4-5). However, the sample from ONK-PVA6 2012-04-17 was more diverse which is indicated by higher CHAO, Shannon and inverse Simpson indexes. The major archaeal OTUs in the ONK-PVA6 2012-04-17 groundwater were Crenarchaeota (29.45%), Halobacteriales (12.44%) and the Thermoplasmata group (31.01%) of which the Thermoplasmata, South African Goldmine (SAG) Group represented 11.83% (Figure 4-6, Table 4-7). The ONK-PVA6 2012-09-04 groundwater library exhibited similar OTUs as the ONK-PVA6 sample 2012-04-17 but had less sequences in the Crenarcheaota group together with a higher number of sequences clustering with the Thermoplasmata; South African Goldmine Group (34.48%).

There was a clear difference between the groundwater sequence libraries from the two drillholes. ONK-PVA6 harboured more sequences related to methanogenic sequences than ONK-KR15. The major archaeal OTUs in ONK-KR15 2012-04-17 and 2012-09-04 groundwater were Thermoplasmata; South African Goldmine Group (67.97% and 56.54%) and Thermoplasmata (19.18 and 23.78%) (Figure 4-6). There was a small but significant amount of sequences, approximately 0.25%, related to consortia with a capacity to run AOM processes.

4.3.4 16S Bacteria and Archaea rDNA sequence diversity in groundwater over time and by drillhole

ONK-PVA6 and ONK-KR15 were each sampled three times over a period of 1 year from 2012-04-17 to 2013-04-17 to explore the stability of diversity over time. While the groundwater geochemistry of ONK-PVA6 changed significantly over this time period, the geochemistry of ONK-KR15 remained the same (Figure 4-1). A comparison between the 6 samples in a combined cluster-bar plot for bacterial v4v6 rDNA shows that ONK-PVA6 and ONK-KR15 groundwater sequence libraries did separate in two coherent clusters (Figure 4-5). The 2012-04-17 ONK-PVA6 library was more separated from the other two ONK-PVA6 libraries than were the ONK-KR15 libraries for the corresponding dates. Consequently, the bacterial diversity did change over time in both drillhole groundwater systems, but most of this change was due to changing percentages of representation of present OTUs and not to changes in the OTUs representation as demonstrated by Figure 4-5. The archaeal libraries showed a large difference between the two ONK-PVA6 libraries but the ONK-KR15 libraries differed much less than the ONK-PVA6 libraries (Figure 4-6).

The numbers of unique bacterial OTUs at the genus level or higher were in the 114 to 187 range (Table 4-4). The trend was increasing numbers in the respective drillhole library over time. ONK-PVA6 increased from 114 to 187 observed OTUs at genus level. The highest calculated richness was found in the ONK-PVA6 2013-04-17 sample with a calculated richness between 203 up to 227 using the abundance-based coverage estimator (ACE) and the unbiased richness estimate denoted CHAO. However, the Shannon and inverse Simpson index shows that ONK-PVA6 2012-09-04 is more evenly distributed and that this sequence library has the largest diversity. For the ONK-KR15 samples, diversity only showed a minor change over the sampling occasions.

58

The major OTUs (>1%) were in the range of 13–19 and if minor OTUs (>0.1%) are included, there were 36 to 66 OTUs on genus level or higher in the data set. Consequently, approximately 2/3 of the total OTUs were observed at a frequency <0.1% and may be assigned to the rare biosphere (Bowen et al. 2012). It is likely that microorganisms related to the major OTUs in each sequence dataset had the largest influence on the SURE experiments. Their representation in the experimental biofilms is presented in Section 5.7.

4.3.4.1 Deposition of nucleotide sequences

Nucleotide sequences for groundwater have been deposited in the NCBI short read archive (SRA) database as follows: 2012-04-17 and 2012-09-04 Bacteria v4v6 groundwater samples have Bioproject alias number PRJNA1956613, SRA study accession numbers SRP021177 and SRP041926. Biosample accession numbers are: SRS605770, SRS605771, SRS414375 and SRS414378.

2013-04-17 Bacteria v4v6 groundwater samples have Bioproject alias number PRJNA246527, SRA study accession number SRP041926 and Biosample accession numbers are: SRS795318. Experiment RUN accession numbers are: SRR1720371, SRR1773429

Archaea v6 groundwater sequences have Bioproject alias number PRJNA246527, SRA study accession number SRP041926 and Biosample accession numbers: SRS605770, SRS605771, SRS414375 and SRS414378.

59

Table 4-4. Amounts of extracted double-stranded bacterial DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated diversity at genus level or higher rank (>0% abundance) in groundwater 16S Bacteria rDNA v4v6 sequence libraries. The >0.1% and >1% abundance OTU number was generated at gens level or the highest annotated rank.

1 Abundance-based coverage estimator; 2 Unbiased richness estimate

Sample

Amount of extracted

DNA (g × 10−9)

Sampling depth, i.e., number of sequences

Number of OTUs at

>0% abundance

Number of OTUs at ≥0.1%

abundance

Number of OTUs at

≥1% abundance

ACE1 CHAO2

Shannon-Weaver diversity

Index

Inverse Simpson diversity

Index

ONK-PVA6 2012-04-17 243 12795 114 36 13 130.3 155.3 1.85 3.37

ONK-PVA6 2012-09-04 230 12818 139 66 15 152.7 190.2 2.32 3.74

ONK-PVA6 2013-04-17 132 45999 187 66 16 203.0 227.3 2.31 3.65

ONK-KR15 2012-04-17 63 18134 121 35 13 136.3 175 2.03 3.54

ONK- KR15 2012-09-04 130 17405 138 44 17 157.7 194.0 2.19 3.69

ONK- KR15 2013-04-17 10 34685 150 52 19 166.5 193.2 2.3 3.75

60

Table 4-5. Amounts of extracted double-stranded archaeal DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated diversity at genus level or higher rank (>0% abundance) in groundwater 16S Archaea rDNA v6 sequence libraries. The >0.1% and >1% abundance OTU number was generated at genus level or the highest annotated rank.


Sample

Amount of

extracted DNA

(g × 10−9)

Sampling depth,

i.e., number

of sequences

Number of OTUs at

>0% abundance


abundance

Number of OTUs at

≥1% abundance

ACE1 CHAO2


Index


Index

ONK-PVA6 2012-04-17 243 403651 101 30 8 106.1 157.3 2.03 3.64

ONK-PVA6 2012-09-04 230 217372 89 31 10 92.7 97.3 1.89 3.45

ONK-KR15 2012-04-17 63 6651830 101 12 5 102.4 103.3 1.27 2.68

ONK- KR15 2012-09-04 130 1159183 100 16 7 103.3 112.1 1.45 2.93

61

Figure 4-5. Frequency of taxonomic assignment for 16S Bacteria rDNA v4v6 pyrotag sequence libraries for samples of the ONK-PVA6 and ONK-KR15 groundwater. Sequences with ≥1% abundance frequency are shown. Bar designations: Sampling dates from left 2012-04-17, 2012-09-04, 2013-04-17, 2012-04-17, 2012-09-04 and 2013-04-17. The clustering graph on top of the bar graph shows a Morisita–Horn distance measure illustrated using the unweighted pair group method with arithmetic mean (UPGMA) for the tree construction with taxonomic depth at genus level.

Acholeplasma Acinetobacter Aquabacterium Bacteriodetes Brevundimonas Chitinophagaceae Chlorobiales Coriobacteriaceae Cyanobacteria Deferribacterales Dehalobacter Dehalogenimonas Desulfobacterium Desulfobacula Desulforudis Desulfosporosinus Desulfurivibrio Desulfuromonadales Desulfuromonas Dethiosulfatibacter Fusibacter Hoeflea Hydrogenophaga Lachnospiraceae Lutibacter Methylomonas Methylophilus Nitrospiraceae Pseudidiomarina Pseudomonas Rhodoferax Roseovarius Seohaeicola Sphingobacteriales Sphingopyxix Thermoplasmata Thiobacillus Unknown, NA <1 %P

VA

6 20

12-0

4-17

PV

A6

2012

-09-

04

PV

A6

2013

-04-

17

KR

15 2

012-

04-1

7

KR

15 2

012-

09-0

4

KR

15 2

013-

04-1

7

0

20

40

60

80

100

Fre

quen

cy (

%)

62

Figure 4-6. Frequency of taxonomic assignment for Archaeal 16S rDNA v6 pyrotag sequence libraries for samples of the ONK-PVA6 and ONK-KR15 groundwater. Sequences with ≥1% abundance frequency are shown. Bar designations: Sampling dates from left 2012-04-17, 2012-09-04, 2012-04-17and 2012-09-04. The clustering graph on top of the bar graph shows a Morisita–Horn distance measure illustrated using the unweighted pair group method with arithmetic mean (UPGMA) for the tree construction with taxonomic depth at genus or the highest annotated rank level.

< 1 % Archaea;phylum_NA Methanosarcinales;GOM_Arc_I Thermoplasmatales Methanolobus Methermicoccus Methanobacteriaceae Thermoplasmata; SAG Halobacteriales Crenarchaeota

PVA6

201

2-04

-17

PV

A6

2012

-09-

04

KR

15 2

012-

04-1

7

KR

15 2

012-

09-0

4

0

20

40

60

80

100

Freq

uenc

y (%

)

63

Table 4-6. Percent occurrence of OTUs in 16S Bacteria rDNA v4v6 sequence libraries from groundwater at ≥1% abundance as illustrated in Figure 4-5. The major electron acceptor is shown for sulphide producing OTUs.

ONK-PVA6 ONK-KR15

OTU e-acc 2012-04-17

2012-09-04

2013-04-17

2012-04-17

2012-09-04

2013-04-17

Acholeplasma 1.4 1.3 1.8 Acinetobacter 1.11 1.7 Aquabacterium 1.1 Bacteriodetes 16.7 6.7 2.4 Brevundimonas 8.9 3.5 Chitinophagaceae 1.9 Chlorobiales 5.4 27.9 Coriobacteriaceae 3.2 4.3 Cyanobacteria 9.7 3.6 Deferribacterales 1.4 1.67 2.8 Dehalobacter 1.7 Dehalogenimonas 1.1 Desulfobacterium SO4

2− 1.4 1.5 1.2 Desulfobacula SO4

2− 33.3 7.6 6.2 Desulforudis SO4

2− 1.2 Desulfosporosinus SO4

2− 1.3 1.9 Desulfurivibrio S, S2O3

2− 23.2 4 3.6 Desulfuromonas S 2.9 Desulfuromonadales S, S2O3

2− 3.1 Dethiosulfatibacter 1.1 Fusibacter 7.9 5.18 4.2 Geobacter Hoeflea 3.7 5.4 5.7 4.9 10.2 1.2 Hydrogenophaga 5.1 6.4 4.3 30.4 9 5.2 Lachnospiraceae 1.8 2.4 12.1 Lutibacter 4.4 3.8 5.5 8.3 12.2 Methylophilus 19.9 Methylomonas 1.8 1.6 Nitrospiraceae 1.9 1.2 4.3 Pseudidiomarina 3.6 6.5 1.9 Pseudomonas 1.2 4 4.1 8.9 7 13.3 Rheinheimera Rhodoferax 2.2 1.5 Roseovarius 1.4 Seohaeicola 1.5 Sphingobacteriales 2.6 Sphingopyxix 1.1 Thermoplasmata 3.5 Thiobacillus 12.1 8.8 4.3 6 Unknown, NA 3.9 1 1.9 9.5 6.1 7.8 <1% 7.8 17.9 21.3 9.69 10.25 12.6

64

Table 4-7. Percent occurrence of OTUs in 16S Archaea rDNA v6 sequence libraries from groundwater at ≥1% abundance as illustrated in Figure 4-6.

ONK-PVA6 ONK-KR15 OTU 2012-04-17 2012-09-04 2012-04-17 2012-09-04 Crenarchaeota 29.5 11.8 4.65 Halobacteriales 12.4 7.61 1.16 Thermoplasmata;SAG 11.8 34.5 68.0 56.5 Methanobacteriaceae 5.61 2.88 Methermicoccus 4.44 8.31 8.56 Methanolobus 8.32 11.2 Thermoplasmatales 19.2 14.1 19.2 23.8 Methanosarcinales;GOM_Arc_I 6.5 6.28 1.32 1.02 Archaea;phylum_NA 1.77 < 1% 6.66 5.5 3.21 4.29

65

5 RESULTS FROM SULPHATE REDUCTION EXPERIMENTS 1, 2 AND 3

5.1 TNC

In SURE 1, the TNC mL–1 did not differ significantly over time between 1-6-CH4 and the 1-6-H2-CH4 FCCSs, but the numbers were significantly lower in the 1-6-air FCCS (Figure 5-1). The addition of air obviously had a negative effect on the growth and survival of the microorganisms in the ONK-PVA6 groundwater. The TNC increased during the first 60 days in the 1-6-CH4 and 1-6-H2-CH4 FCCSs after which the numbers levelled out. In SURE 2, the total number of cells mL−1 increased by approximately one order of magnitude but they did not differ significantly between the FCCSs for most of the experimental time period (Figure 5-1). In SURE 3, the TNC increased somewhat (3-6) or did not change much during the first 60 -100 days after which all three FCCSs showed decreasing values. This decrease coincided with the sulphide and methane amendments (Figure 5-1).

Except for the air amended FCCS, the TNC was comparable between all FCCS and averaged around 105 cells mL−1 ± <1 order of magnitude. This is within the typical TNC range observed in Olkiluoto groundwater (Pedersen et al. 2015), attesting that the SURE experiments reproduced natural numbers of TNC in deep groundwater well.

Figure 5-1. Total number of cells (TNC) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue). Whiskers indicate ±1 standard deviation.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

5.4

5.6

5.8

6.0

10Lo

g(TN

C) c

ells

mL-1

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

66

5.2 Virus-like particles

In SURE 1, the number of VLP increased rapidly by orders of magnitude during the first 20 days in both the 1-6-CH4 and the 1-6-H2-CH4 FCCSs, while the VLP counts decreased to below detection limits (<100 VLP mL–1) in the 1-6-air FCCS after 20 days (Figure 5-2). In SURE 2, the numbers of VLP were similar in all FCCSs. In SURE 3, the VLP in 3-15 and 3-15-SO4 reproduced the VLP in the SURE 2 2-15 and 2-15-SO4 after 60 days and the 3-6 FCCS had the largest number of VLP of all nine sub-experiments.

The presence of a viral mechanism of population control in deep groundwater, similar in function to a marine viral shunt (Suttle 2007), was indicated in previous FCCS experiments (Pedersen 2012a, b). It was hypothesized that bacteriophages exerted a significant mitigating effect on the numbers of microbial cells and on the observed production rate of sulphide, but VLP data were not obtained. In SURE, the VLP cell−1 data indicated that bacteriophages increased from 0.2 to 5 and 9 bacteriophages cell–1 after day 20 in the 1-6-CH4 and the 1-6-H2-CH4 FCCSs, respectively (Figure 5-3), and the data also showed that the TNC did not exceed 2.6 × 105 cells mL–1 in any of the FCCSs (Figure 5-1). In contrast, VLP were below detection limits in the 1-6-air FCCS, which indicates that the metabolic activity of microorganisms in that system was probably inactivated by the O2 addition to a level at which lytic phage activity was arrested. Taken together, it seems reasonable to conclude that bacteriophages controlled the number of cells in SURE 1, keeping it at a cell density within the range observed in deep Olkiluoto groundwater, i.e., between 5 × 103 and 1 × 106 cells mL−1 (Pedersen et al. 2012).

The number of VLP cell−1 was high during the first half of SURE 2 and decreased to between 2 and 4 VLP cell−1 after 60 days (Figure 5-3). This decrease coincided with the increase in TNC in all FCCS. Further, the numbers of VLP cell−1 were the highest in the 2-15 and the 2-15-SO4 FCCSs while this ratio was lower in the 2-15-SO4-6 FCCS. The VLP cell−1 decreased significantly at about 60 days and became close to 1 for the remaining experimental time period (Figure 5-3). It seems as if the phages exerted a significant mitigating effect on the numbers of microbial cells also in SURE 2 during the first 60 days after which the phage mitigation effect decreased.

There was a clear difference in the numbers of VLP between sub-experiments using ONK-KR15 (3-15, 3-15-SO4) and ONK-PVA6 (3-6) groundwater in SURE 3. The 3-6 experiment exhibited the largest TNC and VLP numbers of all 9 sub-experiments. The 3-15 and 3-15-SO4 experiments reproduced the VLP and VLP cell−1 numbers in the previous sub-experiments using ONK-KR15 groundwater. The high TNC, VLP and the increasing VLP cell−1 numbers in 3-6 suggest that microbial activity was high in this sub-experiment and that the viral shunt was effective in mitigating the TNC within the range of TNC in Olkiluoto groundwater.

The presence of viruses that attack microorganisms in groundwater must originate from lytic infections of host microorganisms. The investigation of Äspö HRL groundwater for virus abundance returned large numbers of a diverse virus population and the average ratio of VLP cell−1 was 12 (Kyle et al. 2008). Active and lytic viruses constitute important predators that will control microorganism numbers and activity. Furthermore, their presence suggests that their prey, the microorganisms, are active and growing, and absence as observed in 1-6-air, suggests that microorganism are inactive.

67

Figure 5-2. Number of virus-like particles (VLP) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue). Whiskers indicate ±1 standard deviation.

Figure 5-3 Number of virus-like particles (VLP) per cell in circulating groundwater calculated as the quotient of VLP mL-1 over TNC mL-1. SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

1

2

3

4

5

6

7

10Lo

g(V

LP) v

irus

mL-1

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0

5

10

15

20

25

30

VLP

cel

ls-1

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

68

5.3 ATP

5.3.1 Circulating groundwater

In SURE 1, the amounts of ATP mL–1 were the highest over time in the 1-6-CH4 FCCS followed by the 1-6-H2-CH4 FCCS, and significantly lower in the 1-6-air FCCS (Figure 5-4). In agreement with this, the amounts of ATP g–1 rock grains differed only slightly between the FCCSs (Figure 5-5). There were no obvious trends in the amounts of ATP g–1 rock grains and ATP mL–1 over time.

In SURE 2, the amount of ATP mL−1 differed somewhat between the FCCSs with the largest amounts detected in the 2-15-SO4 FCCS and the trends were increasing over time (Figure 5-4) The amount of ATP g–1 rock grains was almost 10 times lower in all FCCSs compared to what was analysed in SURE 1 and SURE 3 and there were no significant differences over time except for 2-15-SO4 that increased 3.5 times from day 0 to day 103 (Table 5-1).

In SURE 3, ATP mL–1 was the highest of all three SUREs until day 97 after which sulphide and methane were added on days 106 and 134 respectively. ATP mL–1 then dropped down to values about 10 times lower than before the additions. The ATP g–1 garnet grains did not change over time from day 0 to 206/207/209 as shown in Table 5-1 indicating a more robust microbial population in the biofilms compared to the circulating water since no decrease in ATP could be observed.

The TNC and ATP data correlated well over the measured range of two orders of magnitude with the exception of the last two SURE 3 sampling days when the ATP/TNC quotients decreased significantly (Figure 5-6). Previously, the correlation between TNC and ATP data was calculated for shallow and deep Olkiluoto and ONKALO groundwater and the following regression data were found (Pedersen et al. 2015):

10Log(ATP) = 0.80 × 10Log(TNC) + 0.53; r = 0.75, p = 0.00001, n = 123.

The regression line for SURE 1, 2 and 3 TNC and ATP data in Figure 5-6 was found to be almost identical:

10Log(ATP) = 0.84 × 10Log(TNC) + 0.31; r = 0.68, p = 0.00001, n = 54

Obviously, the conditions in the SURE FCCSs reproduced the relation between natural groundwater ATP and TNC data in an excellent way (except for the last 100 days in SURE 3).

69

Figure 5-4. ATP in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue). Whiskers indicate ±1 standard deviation.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

5.2

5.4

5.610

Log(

ATP

) am

ol m

L-1 1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

70

Figure 5-5. ATP in biofilms from SURE 1. Whiskers indicate ±1 standard deviation. Table 5-1. ATP in biofilms from SURE 2 and SURE 3. Standard deviations ranged from 10 to 100 %.

Time (days)

1ATP amol g−1

2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

0 1.35×105 0.81×105 0.71×105 1.45×106 1.45×106 1.26×106

103 1.74×105 0.66×105 2.63×105

206 0.91×106

207 0.39×106

209 1.38×106

0 20 40 60 80 100 120

Time (days)

4.0

4.5

5.0

5.5

6.0

6.5

7.010

Log(

ATP

) am

ol g

-1

1-6-air 1-6-CH4 1-6-H2-CH4

71

Figure 5-6. The relationship between total numbers of cells (TNC) in SURE 1, 2 and 3 (Figure 5-1) and ATP concentrations (Figure 5-4). The least squares regression line for TNC versus ATP is shown (10Log(ATP) = 0.84 × 10Log(TNC) + 0.31; r = 0.68, p = 0.00001, n = 54). Symbols 1-6-air; 1-6-CH4; 1-6-H2-CH4; 2-15; 2-15-SO4; 2-15-SO4-6; 3-15; 3-5-SO4: 3-6.

5.4 Cultivable heterotrophic aerobic bacteria

In SURE 1 the numbers of CHAB were the highest in 1-6-CH4 FCCS throughout the experiment, while in 1-6-H2-CH4 and 1-6-air FCCSs the CHAB were comparable (Figure 5-7). Air should not act as an inhibitor for CHAB that can grow in the presence of O2. At the end of SURE 1, the numbers of CHAB levelled out at approximately 4 × 103 cells mL–1 in the 1-6-H2-CH4 and 1-6-air FCCSs and 2.5 × 104 cells mL–1 in the 1-6-CH4 FCCS.

In SURE 2, the numbers of CHAB were on an approximately similar level throughout the experiment in all FCCSs with an average of approximately 5 × 104 cells mL−1 (Figure 5-7). The 2-15-SO4 FCCS had the highest numbers of CHAB at the end of the experiment.

In SURE 3, the numbers of CHAB were approximately similar in all three FCCSs throughout the experiment (Figure 5-7). The trend was generally decreasing and the CHAB numbers were two orders of magnitude lower at the end of the experiment compared to the start values.

3.6 4.0 4.4 4.8 5.2 5.610Log(TNC) cells mL-1

3.6

4.0

4.4

4.8

5.2

5.6

10Lo

g(AT

P) a

mol

mL-1

72

Figure 5-7. Cultivable heterotrophic anaerobic bacteria (CHAB) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue). Whiskers indicate ±1 standard deviation.

5.5 Most probable number of bacteria

Anaerobic media for determining the most probable number (MPN) of various anaerobic and aerobic microorganisms in groundwater have been developed and tested according to the procedures described by Hallbeck and Pedersen (2008). The specific media details were formulated based on previously measured chemical data from Olkiluoto. This allowed the formulation of artificial media that most closely mimicked in situ groundwater chemistry for optimal microbial cultivation (Haveman and Pedersen 2002). The methods and results for Olkiluoto and ONKALO have been described and discussed by Pedersen et al. (2015; 2012). In SURE, a selection of these MPN methods was used as found appropriate for each SURE.

The MPN procedures resulted in protocols for tubes that score positive or negative for growth. The results of the analyses can rate positive or negative compared with control levels. Three dilutions of five parallel tubes are used to calculate the MPN of each group, according to the calculations found in Greenberg et al. (1992). The lower and upper 95% confidence intervals for the MPN method applied to five parallel tubes equal to 1/3 and 3 times the obtained values, respectively. Consequently, there have to be relatively large differences in the MPN numbers before differences can be regarded as significant.

5.5.1 Nitrate-reducing bacteria

In SURE 1, the MPNs of NRB were the highest in the 1-6-CH4 FCCS with some overlap with the MPNs of NRB in the 1-6-H2-CH4 FCCS. The MPNs of NRB decreased to <500 cells mL–

1 in the 1-6-air FCCS (Figure 5-8). In SURE 2, the MPN of NRB was the highest in the 2-15-

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

2.0

2.5

3.0

3.5

4.0

4.5

5.010

Log(

CH

AB

) CFU

mL-1

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

73

SO4-6 FCCS after 40 days (Figure 5-8). At the start and the end of the experiment, all three FCCSs had a similar MPN of NRB. In SURE 3 again, the MPNs of NRB were similar and showed a decreasing trend just as for the CHAB data (Figure 5-8). The over-all distribution patterns for MPNs of NRB in Sure 1, 2 and 3 were scattered and except for SURE 3, trends over time are difficult to find. Consequently, this cultivation approach was successful, revealing that microbial populations in SURE 1, 2 and 3 were generally dominated by cultivable microorganisms capable of nitrate reduction.

CHAB and NRB data have previously been demonstrated to correlate well over a range of four orders of magnitude in Olkiluoto and ONKALO (Pedersen et al. 2012). The following regression data was found:

10Log(NRB) = 0.71 × 10Log(CHAB) + 0.32; r = 0.61, p = 0.00001, n = 82.

The regression line was found to be almost identical for SURE 1, 2 and 3 NRB and CHAB data in Figure 5-9:

10Log(NRB) = 0.667 × 10Log(CHAB) + 1.04; r = 0.53, p = 0.00003, n = 54

Obviously, the conditions in the SURE FCCSs not only reproduced the relation between natural groundwater ATP and TNC data in an excellent way, they also reproduced the relation between natural groundwater NRB and CHAB data very well. Because many NRB are facultative anaerobes, they should be expected to grow on the CHAB medium as well, which is confirmed by the good correlation between CHAB and NRB.

Figure 5-8. The most probable number (MPN) of nitrate-reducing bacteria (NRB) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

10Lo

g(N

RB

) cel

ls m

L-1

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

74

Figure 5-9. The relationship between cultivable heterotrophic bacteria (CHAB) in SURE 1, 2 and 3 (Figure 5-7) and the most probable numbers of nitrate-reducing bacteria (NRB) (Figure 5-8). The least squares regression line for CHAB versus NRB is shown (10Log(NRB) = 0.667 × 10Log(CHAB) + 1.04; r = 0.53, p = 0.00003, n = 54). Symbols 1-6-air; 1-6-CH4; 1-6-H2-CH4; 2-15; 2-15-SO4; 2-15-SO4-6; 3-15; 3-5-SO4: 3-6.

5.5.2 Iron-reducing bacteria

In SURE 1, the MPN of IRB was higher on day 105 than on day 0 in the 1-6-CH4 FCCS (Table 5-2) and this value of 103 cells mL−1 reproduced the MPN of IRB in ONK-PVA6 at the time of water collection (Table 4-2). In contrast, the MPNs of IRB and MRB decreased to values close to detection limits in the 1-6-air FCCS and to approximately 101 cells mL−1 in the 1-6-H2-CH4 FCCS. In SURE 2, all MPNs of IRB dropped to close to detection limits (0.2 cells mL−1 ) at the start of the experiment after which 2-15-SO4-6 increased to approximately 100 cells mL−1 (Figure 5-10). This is consistent with the data from ONK-KR15 where the MPNs of IRB were below detection limits at the time of groundwater collection (Table 4-2). The MPN of IRB was not analysed in SURE 3.

Table 5-2 The 10Log of MPN of iron-reducing bacteria (IRB) in circulating groundwater from SURE 1.

Time (days)

1-6-air (10log(IRB) cells mL−1)

1-6-CH4 (10log(IRB) cells mL−1)

1-6-H2-CH4 (10log(IRB) cells mL−1)

0 2.15 2.15 1.90

105 0.36 3.20 1.11

2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.610Log (CHAB) cells mL-1

2.0

2.4

2.8

3.2

3.6

4.0

4.4

4.8

5.2

5.610

Log(

NR

B) c

ells

mL-1

75

Figure 5-10. The most probable number (MPN) of iron-reducing bacteria (IRB) in circulating groundwater from SURE 2.

5.5.3 Manganese-reducing bacteria

In SURE 1, the MPN of MRB 1-6-CH4 FCCS, was approximately the same on days 0 and 105 (Table 5-2) and this value of 500 cells mL−1 reproduced the MPN of MRB in ONK-PVA6 at the time of water collection (Table 4-2). In contrast, the MPNs of IRB and MRB decreased to values close to detection limits in the 1-6-air and 1-6-H2-CH4. The MPN of MRB was not analysed in SURE 2 and 3. Table 5-3. The most probable number (MPN) of manganese-reducing bacteria (MRB) in circulating groundwater from SURE 1.

Time (days)

1-6-air (10log(MRB) cells mL−1)

1-6-CH4 (10log(MRB) cells mL−1)

1-6-H2-CH4 (10log(MRB) cells mL−1)

0 2.70 2.48 2.95 105 0.04 2.70 0.23

0 20 40 60 80 100 120

Time (days)

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.510

Log(

IRB)

cel

ls m

L-1

2-15 2-15-SO4

2-15-SO4-6

76

5.5.4 Sulphate-reducing bacteria

The MPN of SRB increased to between 102 and 104 cells mL−1 in most anaerobic FCCSs that contained dissolved sulphate in the circulating groundwater, i.e., to between 103 and 104 cells mL−1 in the 1-6-CH4, 1-6-H2-CH4, 2-15-SO4, 2-15-SO4-6 FCCSs and to >102 cells mL−1 in the 3-6 FCCS (Figure 5-11). There was a very small increase in the MPN of SRB in the 3-15-SO4 FCCS for the first 100 days after which the MPN decreased again. The remaining three FCCSs had MPNs of SRB that were at or below detection limits or 0.2 cells mL−1. The absence or presence of sulphate thus appeared to be an important factor for the growth of SRB, which was expected.

Figure 5-11. The most probable number (MPN) of sulphate-reducing bacteria (SRB) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue). 5.5.5 Autotrophic acetogens

In SURE 1, the MPN of AA increased concomitantly in the 1-6-CH4 and the 1-6-H2-CH4 FCCSs to at most 1 × 104 AA mL–1 after 60 days (Figure 5-12). The AA decreased to below detection limits in the 1-6-air FCC. The presence of AA in the FCC was consistent with the observation of AA in the ONK-PVA6 groundwater used to fill the FCCSs (Table 4-2). In SURE 2, the MPN of AA was at the limit of detection in all samples but for one of the 2-15-SO4-6 samples that was approximately 10 AA mL−1. In SURE 3, all MPNs of AA were below 1 AA mL–1 or below the detection limit of 0.2 cells mL–1. The MPNs of AA in the groundwater used to fill SURE 2 and 3 FCCSs were at or below the limit of detection.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

-1

0

1

2

3

4

5

10Lo

g(S

RB

) cel

ls m

L-1

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

77

Figure 5-12. The most probable number (MPN) of autotrophic acetogens (AA) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

5.5.6 Methanogens

The MPNs of methanogens were below detection limits on all sampling occasions in all FCCSs in SURE 1. Likewise, the MPNs of methanogens were below detection limits on all sample occasions in all FCCSs in SURE 2. Methanogens were not cultivated for in SURE 3.

5.6 Gases and chemistry

5.6.1 H2

In SURE 1, the H2 concentration decreased exponentially in the H2 amended 1-6-H2-CH4 FCCS from just below 10 mM at the start to 0.1 mM after 105 d as a result of microbial consumption and diffusion out of the system (Figure 5-13). Similar decreasing trends of H2 in the FCCSs have been observed and discussed previously (Pedersen 2012a).

In SURE 2 and 3 the amounts of gases showed values approximately similar to the low levels observed in ONK-PVA6 groundwater and much lower than those observed in ONK-KR15 groundwater (Table 4-1). Because H2 is rapidly consumed by microorganisms to concentrations below 1 µM (Pedersen 2012a) and slowly diffuses out from the FCCSs, these low levels were expected.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

-2

-1

0

1

2

3

4

510

Log(

AA

) cel

ls m

L-1 1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

78

Figure 5-13. Concentration of dissolved H2 in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

5.6.2 Methane

In SURE 1, both the 1-6-CH4 and the 1-6-H2-CH4 FCCSs were amended with 8 mM methane, which supplemented the in situ concentration of 2.7 mM as analysed in the -1-6-air system, resulting in a total of just below 11 mM of methane (Figure 5-14). The methane concentration decreased in the 1-6-CH4 and the 1-6-H2-CH4 FCCSs from just below 11 mM at the start to 5.5 mM after 105 d, due possibly partly to microbial consumption and partly to the diffusion out of these FCCSs. Diffusion of methane out of the system, or AOM, did not occur in the inactivated, non-amended 1-6-air FCCS, suggesting that the out-diffusion of methane observed in the amended FCCs was dependent on concentration. In SURE 2, there was no amendment of methane and the concentrations of methane were similar and decreased slowly in the FCCSs. In SURE 3, all FCCSs were saturated with methane after 134 days to a concentration of approximately 25 mM. All start values on day 0 in (Figure 5-14) agree well with the concentrations of methane in the ONK-PVA6 and ONK-KR15 groundwater used to fill the FCCSs. In general, there was a slow, constant, pressure related release of methane via diffusion though the various parts of the FCCSs.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

H2

(µM

)

0

2000

4000

6000

8000

10000H

2 (µ

M) (

1-6-

H2-

CH

4) 1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

79

Figure 5-14. Concentration of dissolved methane in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

5.6.3 pH

In SURE 1, pH increased from a starting value of approximately 7.4 to approximately 8.5 in the 1-6-H2-CH4 FCCS after 40 days and decreased in the other two FCCSs (Figure 5-15). This increase in pH was due to intensive sulphate reduction because protons are consumed by this process of sulphate reduction with H2 (i.e., 4 H2 + SO4

2– + H+ → HS– + 4 H2O). In SURE 2, pH decreased at roughly the same rate in all of the FCCs from a start value of approximately 8.2 ending at about 7.5 after 103 days (Figure 5-15). In SURE 3, the pH increased from 7.6 to 8.5 in FCCS 3-6 and both 3-15 as well as 3-15-SO4 FCCs showed increases from 7.8 to 8.3 (Figure 5-15) after 106 days probably as a result of the di-sodium-sulphide amendment that also consumed protons (Na2S + H2O → HS– + OH– + 2 Na+) at the present pH range where most of the sulphide is present as HS–.

5.6.4 Carbon dioxide

The proportions of dissolved CO2, carbonate and bi-carbonate in water are strongly dependent on pH. In SURE 1, the increase in pH in the 1-6-H2-CH4 FCCS reduced the amount of dissolved CO2 gas significantly due to the formation of bi-carbonate and carbonate ions from dissolved CO2 gas. In contrast, the lowering of pH by 0.3 – 0.7 units in 1-6-air and 1-6-CH4 may explain some or all of the increase of carbon dioxide gas. Alkalinity was higher, i.e., >240 µmol L–1 in ONK-PVA6 compared to ONK-KR15 and a small change in pH can, therefore, have resulted in the observed dissolved CO2 in ONK-PVA6.

In SURE 2 and SURE 3, the concentrations of CO2 showed approximately similar trends in all three FCCSs. The pH varied less in SURE 2 than in SURE 1 and the concentration of DIC

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0

5000

10000

15000

20000

25000C

H4

(µM

)

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

80

and bicarbonate was lower in ONK-KR15 groundwater compared to ONK-PVA6 groundwater (Figure 4-1) which may explain the low variation in CO2 over the experimental time period relative to pH. Just as observed for the 1-6-H2-CH4 FCCS, dissolved CO2 decreased in all SURE 3 FCCSs after the addition of sodium sulphide that increased pH.

Figure 5-15. pH in SURE 1 (black) and SURE 2 (red). In SURE 3 (blue) measured using an external multimeter.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

6.5

7.0

7.5

8.0

8.5

9.0

pH

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

81

Figure 5-16. Concentration of dissolved carbon dioxide in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

5.6.5 δ13CV-PDB in dissolved inorganic carbon

The logic behind this analysis is discussed in detail by Wersin et al (2014). The analysis of δ13C V-PDB in DIC produced data that are difficult to interpret. A general observation is that the δ13C values decreased in two of the SURE 1 FCCSs with ONK-PVA6 groundwater and increased initially in two of the FCCSs in SURE 3 using ONK-KR15 groundwater. The rapid drop in SURE 1 from day 0 to day 3 must be due to technical or analytical factors, microbial activity would be too slow in these nutrient poor groundwaters to explain this change. Because the data on δ13C in DIC in SURE 1 changed so rapidly over 3 days, the data were not included in the previous SURE 1 report. In this report, they are presented mainly for the sake of the completeness of data reporting. However, changes over time were relatively small and not consistent with other data, e.g. CO2 that increased in 1-6-air and 1-6-CH4 while δ13C decreased in 1-6-air and increased in 1-6-CH4. The expected result would be similar trends for both these FCCSs that had identical start values of alkalinity and similar pH fluctuations and very low microbial activity. It cannot be excluded that the variability in data is mainly due to analytical variability, including sampling, transportation and storage procedures. Some of these samples were stored for a long time at the analysis laboratory (months) due to technical problems with instruments and lack of time to run the analyses quickly. Although the samples were sterile filtered, long storage times may invoke microbial activity if single cells managed to pass the filters. We, therefore, refrain from drawing any conclusions based on the obtained δ13C data.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0

5

10

15

20

25

30

35

40C

O2

(µM

) 1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

82

Figure 5-17. Concentration of dissolved inorganic 13C in circulating groundwater from SURE 1 (black) and SURE 3 (blue).

5.6.6 Dissolved organic carbon

In SURE 1, the concentrations of DOC increased to 1.3 and 1.7 mM in the 1-6-CH4 and the 1-6-H2-CH4 FCCs, respectively, and DOC increased to at most 0.8 mM in the 1-6-air FCCS (Figure 5-18). DOC concentrations did not change over the experimental time period in SURE 2. Weak increasing trends of DOC were observed in SURE 3 with more DOC in 3-6 compared to the other two FCCSs. Because viruses were active (Figure 5-2), only a small net increase in TNC was observed (Figure 5-1). Possibly, growing cells were lysed by viruses and the released cytoplasmic material contributed to the increase in DOC concentration over time.

5.6.7 Acetate

In SURE 1, there was strong acetate production and growth of AA (Figure 5-12) in the 1-6-CH4 and 1-6-H2-CH4 FCCSs but in the 1-6-air FCCS acetate concentration was only doubled (Figure 5-19), which further attests to the inhibiting effect of aeration. These increases correlate very well with the increase in DOC which corroborates that acetate production was microbiological in these systems and in the preceding experiments (Pedersen 2012a, b) and not an artefact of the degradation of the plastic construction materials of the FCCS. The produced acetate constituted approximately 1/3 of the DOC in the 1-6-CH4 and 1-6-H2-CH4 FCCSs.

In SURE 2 and 3, the concentrations of acetate and DOC did not change much compared to the respective start values at sampling time 0 (Figure 5-19). Acetate data were rather scattered over time and the experiment in SURE 2 and 3 without any clear correlation with AA or DOC.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

-30

-25

-20

-15

-10δ13

CV

-PD

B (‰

) 1-6-air 1-6-CH4 1-6-H2-CH4 3-15 3-15-SO4 3-6

83

Figure 5-18. Concentration of DOC in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

Figure 5-19. Acetate concentration in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8D

OC

(mM

) 1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0

20

40

60

80

100

Ace

tat (

µM)

0

50

100

150

200

250

300

350

Ace

tat (

µM) (

1-6-

CH

4; 1

-6-H

2-C

H4)

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

84

5.6.8 Lactate

In SURE 1, lactate was present at low concentrations, i.e., 10–16 µM, until day 40 when the concentration of this compound declined to below detection limits in all three FCCSs (Figure 5-20). Lactate was not found in SURE 2 with one exception. Lactate was not analysed for in SURE 3.

Figure 5-20. Lactate concentration in circulating groundwater from SURE 1 (black) and SURE 2 (red).

5.6.9 Redox potential

At the start of each experiment, water was transported from ONKALO in expansion vessels and transferred to the FCCSs in the laboratory. It is difficult to avoid traces of O2 entering the circulations. Eh-electrodes are very sensitive to O2. Therefore, relatively high start values of Eh were registered that rapidly decreased when the tiny traces of O2 were consumed by minerals and microbes. In addition, the internal Eh-electrodes needed some time to stabilize when the environment changed from a N2 atmosphere to a water environment which is observed as high Eh values at the beginning of the experiments. It has been previously observed that stabilization of Eh measurements with in situ electrodes in deep groundwater may take up to 10 days (Grenthe et al. 1992). Sampling the rock grains for ATP and DNA analysis inevitably introduced small amounts of O2 when the FCs were opened, as indicated by the peaks in Eh after each sampling occasion in SURE 1 (Figure 5-22). This effect diminished within a few days, as expected based on previous Äspö HRL results, indicating the effect of adding 0.1–0.2 mM of O2 (Pedersen 2012a). The internal Eh electrodes reported these effects in continuous mode and were superior in sensitivity to the external Eh electrode (Figure 5-21).

In SURE 1, the Eh of the 1-6-H2-CH4 FCCS rapidly decreased to approximately –400 mV, as registered by the external and internal electrodes (Figure 5-21, Figure 5-22). The Eh of the 1-

0 20 40 60 80 100 120

Time (days)

0

5

10

15

20

25

Lact

ate

(µM

)

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6

85

6-CH4 FCCS decreased as well, reaching a steady level of –300 mV after 35 d, while the Eh of the 1-6-air FCCS slowly decreased to 0 mV by the end of the experiment. A technical problem occurred with the electrodes in the 1-6-air, which lost contact with the FCCS between days 90 and 110.

In SURE 2, the Eh of the 2-15 and the 2-15-SO4 FCCSs decreased and reached a steady level at about −260 mV after 70 days as registered by the internal microelectrodes (Figure 5-23). The Eh of the 2-15-SO4-6 slowly decreased to about −140 mV at the end of the experiment. The external Eh electrode roughly reproduced the Eh signal trends of the internal electrodes but at higher potentials (Figure 5-21).

In SURE 3, Eh decreased in all FCCS during the experiment. After the addition of sulphide on day 106, all FCCSs immediately decreased internal electrode readings to approximately −300 mV (Figure 5-24). The external electrode reacted more sluggishly than did the internal electrodes; the difference was approximately 100 mV. Technical problems occurred with three of the internal electrodes in the 3-15-SO4 FCCS and with one in the 3-15 FCCS. These electrode readings were discarded from the data files used to produce the Figure.

Figure 5-21. Eh measured using an external multimeter (mV) in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

-400

-300

-200

-100

0

100

200

300

400

Eh

(mV

)

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

86

Figure 5-22. Eh measured using internal electrode couples, the average of four electrode couple signals in the SURE 1 FCCSs; 1-6-air (blue symbol), 1-6-CH4 (green symbol), and 1-6-H2-CH4 (red symbol).

Figure 5-23. Eh with internal electrode couples, the average of 4 electrode couple signals SURE 2 FCCS; 2-15 (blue symbol), 2-15-SO4 FCCS (red symbol) and 2-15-SO4-6 (green symbol).

0 10 20 30 40 50 60 70 80 90 100 110 120

Time (days)

-500

-400

-300

-200

-100

0

100

200

300

400

500E h

(mV)

0 10 20 30 40 50 60 70 80 90 100

Time (days)

-500

-400

-300

-200

-100

0

100

200

300

400

500

Eh

(mV

)

87

Figure 5-24. Eh with internal electrode couples, the average of 1-4 electrode couple signals SURE 3 FCCS; 3-15 (blue symbol, 3 electrodes), 3-15-SO4 FCCS (red symbol, 1 electrode) and 3-6 (green symbol 4 electrodes).

5.6.10 Sulphate

In SURE 1, sulphate concentration decreased by approximately 700 µM by day 105 relative to the starting concentration in the 1-6-H2-CH4 FCCS (Figure 5-25). Sulphate decreased by 200 µM in the 1-6-CH4 FCCS and did not change significantly in the 1-6-air system. Sulphate reduction was consequently indicated in the 1-6-H2-CH4 FCCS from day 21 to day 63 as judged from the decrease in sulphate (Figure 5-25), while in the 1-6-CH4 FCC sulphate reduction appeared later, between days 63 and 105.

In SURE 2, the additions of sulphate resulted in 1 mM of sulphate in the 2-15 FCCS and a somewhat higher concentration in 2-15-SO4-6; this difference was due to a higher than 1 mM sulphate concentration in the added ONK-PVA6 groundwater (1.27 mM). The concentration of sulphate did not change over the experimental time period and was below detection limits in the 2-15 FCCS (Figure 5-25).

In SURE 3, there was a small decrease in sulphate in 3-6 between days 60 and 106. When methane was added on day 134, sulphate concentrations decreased by approximately 0.4 mM in both sulphate containing FCCSs; sulphate was below detection limits in the 3-15 FCCS.

The case of sulphate in the different experiments is addressed further in Section Discussion (6.5).

0 20 40 60 80 100 120 140 160 180 200

Time (days)

-500

-400

-300

-200

-100

0

100

200

300

400

500

E h (m

V)

88

5.6.11 δ34SV-CDT in SO4

The logic behind this analysis is discussed in detail by Wersin et al (2014). The evolution of all the analysed δ34SV-CDT values of sulphate is shown in Figure 5-26.

In SURE 1, the MPN of SRB increased in the 1-6-CH4 and 1-6-H2-CH4 FCCSs, with the highest values observed in the 1-6-CH4 FCCS, while in the 1-6-air FCCS the MPN of SRB decreased to below detection limits (0.2 cells mL–1) (Figure 5-11). The MPN of SRB in circulating water was five to ten times higher in the 1-6-CH4 FCCS than in the 1-6-H2-CH4 FCC, but sulphate reduction, observed as a decrease in SO4

2– (Figure 5-25), was only 200 µM compared to the 700 µM decrease in the 1-6-H2-CH4 FCCS. While 700 µM sulphate was consumed, 250 µM sulphide was observed (Figure 5-28); the remaining sulphide was probably precipitated as iron sulphide, which was observed as a black precipitate in the flow cells when sampling the rock grains. There were statistically significant 1.6 ‰ and 2.8 ‰ increases in δ34SV-CDT over time for sulphate in the 1-6-CH4-H2 and 1-6-CH4 FCCSs, respectively, compared to the inactive 1-6-air FCCS where a decrease was observed, which indicates that microbiological sulphate reduction did occur (Table 5-4).

Table 5-4. Student’s t-test for independent samples by variables. The variables were over FCCSs that showed increases in δ34SV-CDT (‰) over time. The mean values of δ34SV-CDT (‰) at the start and end of the experiments are shown. FCCS Mean

start δ34SV-CDT (‰)

Std. Dev. start

Mean end δ34SV-CDT (‰)

Std. Dev. end

t-value df p F-ratio variances

P variances

1-6-CH4 30.044 0.2058 31.69 0.1944 -9.693 4 0.000634 1.121 0.9429 1-6-H2-CH4 29.85 0.2021 32.64 0.1944 -17.220 4 0.000067 1.081 0.9609 3-15-SO4 0.4781 0.1208 0.9770 0.1110 -5.267 4 0.006223 1.185 0.9155 3-6 30.85 0.1864 32.04 0.1387 -8.855 4 0.000898 1.807 0.7126

89

Figure 5-25. Concentration of sulphate in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

Figure 5-26. δ 34S-values for sulphate in FCCSs with natural or added concentrations of sulphate.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

SO

42- (m

M)

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

90

Isotopic fractionation factors (ε, ‰) were determined according to Detmer et al. (2001) for FCCSs that showed statistically significant increases in δ34SV-CDT and decreases in the concentration of sulphate over time (cf Table 5-4). The start and end values were used for the calculation of ε on the basis of the isotopic composition of sulphate and the fraction of remaining sulphate (SO4

2−) according to the following equation:

δ34ST1SO4 = −ε ln (SO42−) + δ34ST0SO4 Eq. 14

The values of ε are shown in Table 5-5. The fractionation factor was approximately 4 times higher in the 1-6-CH4 FCCS compared to 1-6-H2-CH4. The significance of these values is addressed in detail in Section Discussion (6.5).

Table 5-5. Sulphate fractionation factors for FCCS experiments that showed statistically significant increases in δ34SV-CDT and decreases in the concentration of sulphate over time.

FCCS ε (‰) Std. Dev. (n=3) 1-6-CH4 12.24 2.75 1-6-H2-CH4 2.85 0.18 3-15-SO4 1.30 0.38 3-6 2.32 0.30

In SURE 2, most of the sulphate was added in the laboratory and the δ34SV-CDT value was approximately 1 ‰ in the sulphur of that chemical. The addition of 500 mL of ONK-PVA6 groundwater that had a δ34SV-CDT value of approximately 31 ‰ increased the value of mixed groundwater (2-15-SO4-6) by 3 ‰ to approximately 4 ‰, i.e., a 10-times dilution of ONK-PVA6 groundwater. There was no change in the δ34S ratio for any of the FCCSs during the experimental time period (Figure 5-26) which agrees well with the absence of a decreasing concentration of sulphate (Figure 5-25).

In SURE 3, there were increases in the δ34S ratios of 0.47‰ and 1.16‰ in 3-15-SO4 and 3-6, respectively (Table 5-4). Most of this increase occurred after the addition of methane on day 134 and agreed with a decrease in sulphate (Figure 5-25). The values of ε are shown in Table 5-5. The fractionation factors were low. The significance of these values is addressed in detail in Section Discussion (6.5).

5.6.12 Ferrous iron

In SURE 1, ferrous iron increased to approximately 50 µM in the 1-6-CH4 FCCS and was below detection limits in the other FCCSs, except on day 20 in the 1-6-H2-CH4 FCCS when the concentration was 20 µM (Figure 5-27). In SURE 2, ferrous iron increased in a similar way to approximately 20 µM in all FCCSs. In SURE 3, ferrous iron increased to just below 40 µM at day 106. The addition of 62.5 µM (2 mg L−1) of sulphide on day 106 would have precipitated most or all of this ferrous iron. The increase in ferrous iron indicates that IRB may have been active reducing the ferric iron possibly present in the crushed rock, ferric iron that may have been present in the stainless steel tubes and other FCCSs parts, and expressed the metabolic activities that correlated with the MPN results. The 0.7 mm garnet grains used in SURE 3 contain 30% FeO and 2% Fe2O3 according to the manufacturer. It cannot be confirmed from the data if these garnets have released ferrous iron to the circulating groundwater, or if IRB were active.

91

Figure 5-27. Ferrous iron concentration in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue). 5.6.13 Sulphide

In SURE 1, sulphide concentration increased to approximately 250 µM in the 1-6-H2-CH4 FCCS and was below detection limits (0.01 µM) in the other two FCCSs except for day 84 in the 1-6-CH4 FCCS (Figure 5-28). In SURE 2, the concentration of sulphide was below detection limits in all FCCSs at all sample times. In SURE 3, sulphide increased after day 106, but this increase must mainly be due to the added sulphide of which most was precipitated with ferrous iron (Figure 5-27). Generally, it is not recommended to use sulphide concentrations as quantitative indications of sulphate reduction in systems where metals are present. This is because sulphide easily reacts with metals and forms precipitates that escape the analysis of dissolved sulphide. The decrease in sulphate concentrations concomitant with an increase in δ34S is a better combination of indicators that can be supported by sulphide data.

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0

10

20

30

40

50

60

Fe2+

(µM

) 1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

92

Figure 5-28. Concentration of sulphide in circulating groundwater from SURE 1 (black), SURE 2 (red) and SURE 3 (blue).

5.7 16S rDNA sequence diversity

During the execution time of the SURE experiments, in 2010 – 2014, there has been a rapid development of methods for the analysis of 16S rDNA sequence diversity on the market. Before the introduction of high throughput sequencing, cloning and sequencing was the commonly used method, used in this work to characterize MPN cultures (see Section 5.7.2). The use of microarrays was growing rapidly in scientific literature some years ago (Hazen et al. 2010). Then the high throughput 454 sequencing platform became the standard (e.g. Marteinsson et al. 2013), but it was later replaced with the use of the Illumina sequencing platform. This was because the 454 sequencing platform was bought by Hoffman LaRoche who soon afterward announced the discontinuation of the 454 sequencing platform in 2013 in favour of their own Illumina platform. Today there are several other platforms as well for high throughput sequencing (e.g. MySeq and Torrent). In SURE 1, 2 and 3, the 16S rDNA diversity of biofilms and groundwater was gauged using different sequencing protocols and microarrays as outlined in Table 3-3, basically following the development of the methods on the market. The methods for 454 FLX Titanium and Illumina HiSeq are described in Section 2.7.2 and the PhyloChip method is described in Section 3.7.7.1. More detailed descriptions of sequencing and other tools for evaluating microbial diversity are presented and evaluated by Pedersen et al. 2012.

Because the amount of water in the FCCSs was limited, it was not possible to sample this water for the 16S rDNA sequence diversity analysis. The sample volumes would have been too small to give enough extracted DNA for quality-assured results as discussed in Section 4.3. The less DNA recovered from a sample, the higher the risk that the reagent and laboratory contamination impact the sequence results (Salter et al. 2014). Therefore, MPN was used to analyse microbial cultivable diversity in the circulating water. The main advantage of MPN cultivation is the small sample volume needed (1 mL) and a very good detection limit (0.2 cells mL−1). Further, MPN is a quantitative method for cultivable microorganisms, while sequencing at best indicates abundance that should be interpreted with

0 20 40 60 80 100 120 140 160 180 200 220

Time (days)

0

1

2

3

S2-

(µM

)

0

50

100

150

200

250

300

S2-

(µM

) (1-

6-H 2

-CH 4

)

1-6-air 1-6-CH4 1-6-H2-CH4 2-15 2-15-SO4 2-15-SO4-6 3-15 3-15-SO4 3-6

93

caution (See Pedersen et al. 2012 for explanations) Hence, it is important to understand that DNA methods are generally superior for a diversity analysis, but MPN cultivation is superior for a diversity and quantitative analysis of small samples with low cell numbers such as those detected in deep groundwater.

Data on 16S rDNA sequence diversity are presented in this Section with comments on the most important observations and conclusions, by SURE experiment. The analysis of DNA diversity with 16S data should be regarded as profiling of microbial populations. General conclusions on metabolisms can be inferred, but details would require a metagenome analysis (Wu et al. 2015). Brief comments on metabolic pathways are given in this Section. A more thorough synthesis evaluation and result analysis of DNA and MPN diversities over all three experiments is given in Section 6.3.

5.7.1 SURE 1: Phylochip 16S rDNA diversity of biofilms

The PhyloChip assay is a microarray-based method that identifies and measures the relative abundance of more than 50,000 individual microbial OTUs. This approach relies on the analysis of the 16S ribosomal RNA gene sequence. This gene is present in every bacterial genome but varies in a way that provides a fingerprint for specific microbial types. Unlike sequencing methods, PhyloChip relies on the analysis of all nine variable regions of the 16S gene, providing more in-depth taxonomic classification than other common approaches. With 1.2 million probes per chip, the microarray-based hybridization approach ensures that measurements on important low abundance bacteria are not overwhelmed by commonplace, dominant microbial community members. Here, the method was used to analyse the effect on the community profile of the addition of CH4 and H2 to the ONK-PVA6 groundwater in comparison with ONK-PVA6 groundwater in which only CH4 was added.

The H2 addition narrowed the subfamily richness in the 1-6-H2-CH4 FCCS, revealed by the PhyloChip analysis, versus the 1-6-CH4 FCCS. Adding excess H2 to a microbial ecosystem adapted to high concentrations of methane and only traces of H2 narrowed the subfamily richness and markedly changed the species abundance (Figure 5-29) compared with the in situ diversity reflected in the CH4 FCCS. Bacterial subfamily richness was 199, 301, and 373 in the 1-6-H2-CH4 × 4, 1-6-CH4 × 4, and 1-6-CH4 × 40 extraction samples, respectively, while archaeal subfamily richness was 3, 4, and 7, respectively (see Section 3.7.7 for an explanation of the ×4 and ×40 extractions). Methanomicrobiales and Methanosarcinales were present in all extractions in low but significant and comparable amounts. A larger percentage of Pseudomonadaceae appeared to be present in the 1-6-CH4 × 40 than the 1-6-CH4 × 4 sample, but a larger percentage of Pseudomonadaceae was present in the 1-6-CH4 × 4 than the 1-6-H2-CH4 × 4 sample. A larger percentage of Xanthomonadaceae was present in the 1-6-H2-CH4 × 4 sample than in the other two samples; the 1-6-H2-CH4 × 4 sample had no Micrococcaceae, while this family comprised 4% of the CH4 samples. The number of operational taxonomic units (OTUs) in the Deltaproteobacteria class accounted for 3.9, 4.0, and 2.7% of the OTUs in the 1-6-H2-CH4 × 4, 1-6-CH4 × 4, and 1-6-CH4 × 40 samples, respectively. The Deltaproteobacteria were represented by Desulfuromonadaceae, Desulfobacteraceae, Desulfobulbaceae and Desulfovibrionaceae. A circular tree showed that Firmicutes and Bacteroidetes OTUs varied in their abundance scores between the two treatments (Figure 5-29). In particular, some of the largest differences were found for a Clostridium sequence (OTU 32005) that was much more abundant in the 1-6-H2-CH4 than the CH4 samples and for Bacteroidales sequences (OTUs 46869 and 47213) that were much more abundant in the 1-6-CH4 than the 1-6-H2-CH4 samples. Large differences were evident in Proteobacteria, as

94

suggested by the different percentages of Pseudomonadaceae and Xanthomonadaceae in the samples. The tree consequently showed that most of the OTU abundance scores diverged between the 1-6-CH4 and the 1-6-H2-CH4 FCCSs, i.e., the different gas treatments resulted in different OTU abundances and diversities between the samples.

Figure 5-29. A circular phylogenetic tree comparing the microarray hybridization results for DNA extracted from biomass attached to rock grains from flow cells treated with methane (CH4), and hydrogen and methane (1-6-H2-CH4). DNA was obtained from pooled 1-6-CH4 × 4, 1-6-CH4 × 40, and 1-6-H2-CH4 × 4 extractions, respectively. In total, 4694 OTUs were present in at least one of the samples. The three samples had an abundance score for each OTU. OTUs for which the difference between the maximum and minimum abundance scores exceeded 300 were selected, bringing the total to 1193 OTUs, in which 182 families were represented. A representative 16S rRNA gene from each of the 182 families was aligned and used to infer a phylogenetic tree. The tree, taxonomy labels, and abundance data were rendered using Interactive Tree of Life Software (iTOL; http://itol.embl.de). The rings around the tree, from innermost to outermost, are 1-6-CH4 × 40, 1-6-CH4 × 4, and 1-6-H2-CH4 × 4. The tree shows each sample’s abundance score for an OTU divided by the average abundance score for that OTU for all three samples. Blue indicates that the OTU was less abundant in that sample, and red indicates that the OTU was more abundant. The colour saturation indicates the degree of difference in the mean abundance score of the three samples, dark blue indicating a ratio of 0.90, white = 1.0, and dark red = 1.17.

95

5.7.2 SURE 2: Cloning and 16S rDNA Sanger sequencing of MPN cultures

DNA extracted from the highest dilutions of positive NRB and SRB cultures inoculated on days 82 and 103 was cloned and sequenced (Table 5-6). The sequence data revealed a total of nine clone OTUs from the studied MPN cultures. The sequences found represented Deltaproteobacteria, Alphaproteobacteria, and Gammaproteobacteria phyla. The dominant species affiliations were to Desulfovibrio aespoeensis and Pseudomonas stutzeri with 41 and 14 clone observations, respectively; these clones were 99.9–100% similar to the database records in NCBI GenBank.

Table 5-6. OTUs detected in clones from MPN cultures of SRB and NRB sampled during SURE 2 on days 82 and 103 from 2-15-SO4 and 2-15-SO4-6 flow cell circulation systems.

Sample ID Most similar annotated record in database

Number of clones

2-15-SO4 2-15-SO4-6

SRB cultures Desulfovibrio aespoeensis 23 18 NRB cultures

Pseudomonas stutzeri 11 3 Flavobacteriaceae bacterium 6 Rhizobium selenitireducens 5 Desulfovibrio aespoeensis 3 Hoeflea alexandrii 2 Marispirillum indium 1 Agrobacterium tumefaciens 1 Rhizobium sp. 1

5.7.3 SURE 2: 16S rDNA bacterial diversity analysis of biofilms

Rarefaction curves showed that almost all of the 16S rDNA diversity was captured for each sample (Figure 5-30). A clear phylogenetic similarity was observed among biofilm samples, and the diversity profile of each FCCS treatment indicated little change over 103 days relative to the biofilm diversity on day 0 (Figure 5-32). Many of the OTUs found in 2012-04-17 ONK-KR15 groundwater were also found in the FC biofilms (Table 5-7). However, some genera seemed to prefer the planktonic state as they were not found, or found at very low frequencies, in the biofilms. Sequences related to the Fusibacter, Thermoplasmata, and Nitrospira OTUs were not found in the biofilm sequence libraries. Others, such as the Brevundimonas OTU, were ten times more abundant in the biofilms than in the groundwater libraries. Otherwise, most OTUs found in the groundwater were also represented in a generally comparable order of frequency-abundance in all biofilm samples. The Hydrogenophaga, Lutibacter, and Pseudomonas OTUs together constituted most of the sequence reads in all biofilm samples at >50% sequence abundance and constituted 45% of the sequence reads in 2012-04-17 ONK-KR15 groundwater. The biofilm diversity appeared to constitute a good predictor of groundwater diversity and vice versa.

The MPNs of SRB in the sulphate-amended FCCSs, 2-15-SO4 and 2-15-SO4-6 were 5000 cells mL−1 on day 103 in both FCCSs, indicated by cloning to be D. aespoeensis (Table 5-6), while the 2-15 FCCS without sulphate was below the detection limit (<0.2 cells mL−1) (Figure

96

5-11, Table B-7). The sequence data agreed with this, with only 11 reads related to Desulfovibrio in the 2-15 biofilm library on day 103 while the 2-15-SO4 biofilm library had a total of 218 reads related to Desulfovibrio, of which 168 (0.81%) were related to D. aespoeensis. The 2-15-SO4-6 biofilm library similarly contained 211 reads related to Desulfovibrio with 43 related to D. aespoeensis and 22 related to the Desulfobacula OTU. Details on the frequency-abundance of SRB are presented in Table 5-11.

The numbers of unique OTUs observed were in the 108 to 135 range (Table 5-9). There were similar numbers in groundwater and biofilm libraries. The largest calculated richness was found in 2012-04-17 ONK-KR15 groundwater sample with a calculated richness between 157 and 213 using the ACE and CHAO algorithms. The diversity was slightly higher in the 2-15-SO4 biofilm on day 103 determined by inspecting the Shannon and Simpson indices. Major OTUs (>1%) were in the range of 13-17 and if minor OTUs (>0.1%) are included, there were 37 to 47 OTUs in the data set. Consequently, approximately 2/3 or somewhat less of the total OTUs were observed at a frequency <0.1% and may be assigned to the rare biosphere (Bowen et al. 2012). The microorganisms related to the major OTUs in the ONK-KR15 groundwater sequence dataset were well reproduced in the biofilms and consequently had the largest influence on the SURE 2 experiment. The ONK-PVA6 diversity did not reproduce well in the biofilms, possibly because the sites for attachment were already occupied by microorganisms from 2012-04-17 ONK-KR15 during the field experiment period.

5.7.4 SURE 2: 16S rDNA archaeal diversity analysis of biofilms

Rarefaction curves showed that all of the 16S rDNA diversity was captured for each sample (Figure 5-31). A phylogenetic similarity was observed among biofilm samples, and the diversity profile of the untreated 2-15 FCCS indicated little change over 103 days relative to the biofilm diversity on day 0 (Figure 5-33). The SO4-treated FCCSs (2-15-SO4 and 2-15-SO4-6) developed methanogenic groups (Methanolobus, Methanosaeta) that were not found above 1% in the untreated biofilm after 103 days. The largest difference was found for Methanolobus in the 2-15-SO4 biofilm (Table 5-8). Crenarchaeota related sequences increased significantly in all biofilms after 103 days. The numbers of unique OTUs observed were in the 81 to 97 range (Table 5-10). The largest calculated richness was found in the day 0 and 2-15-SO4-6 samples with a richness of between 100 and 118 calculated using the ACE and CHAO algorithms. CHAO was higher in 2-15-SO4 due to the low sampling depth giving the possibility that not all OTUs have been captured for that sample. Major OTUs (>1%) were in the range of 6-9 and if minor OTUs (>0.1%) are included, there were 24 to 32 OTUs in the data set. Consequently, approximately 2/3 of the total OTUs were observed at a frequency <0.1% and may be assigned to the rare biosphere (Bowen et al. 2012). In comparison, the archaeal diversity was approximately 2/3 lower than what was found for the Bacterial diversity.


16S Bacteria clone sequences in SURE2 that were <99.9% similar to database records were submitted to the GenBank database under accession numbers KC676781 to KC676786.

The next generation nucleotide sequences for Bv4v6 SURE2 have been deposited in the NCBI short read archive (SRA) database under Bioproject alias number PRJNA1956613 and SRA study accession number SRP021177.

97

Biosample accession numbers for groundwater samples are: SRS414375 and SRS414378 and for biofilm samples: SRS414379, SRS414380, SRS414381 and SRS414382.

Bv6 sequences in SURE2 have been deposited in the NCBI short read archive (SRA) database under Bioproject alias number PRJNA246527 and SRA study accession number SRP041926.

Biosample accession numbers for biofilm samples are: SRS659266, SRS659267 and SRS659268.

Figure 5-30. Rarefaction curves for SURE2 16S Bacteria rDNA v4v6 dataset biofilms at day 0 and 103. Each curve represents a single sample and sampling occasion.

02000

40006000

800010000

1200014000

1600018000

20000

Sample size

0

20

40

60

80

100

Taxa

cou

nt

2-15 & 2-15-SO4 & 2-15-SO4-6, Biofilm day 0 2-15, Biofilm day 103 2-15-SO4, Biofilm day 103 2-15-SO4-6, Biofilm day 103

98

Figure 5-31. Rarefaction curves for SURE2 16S Archaea rDNA v6 dataset biofilms at day 2 and 103. Each curve represents a single sample and sampling occasion.

0E-01 1E+06 2E+06 3E+06 4E+06 5E+06 6E+06 7E+06

Sample size

0

20

40

60

80

100

Taxa

cou

nt

2-15 & 2-15-SO4 & 2-15-SO4-6, Biofilm day 0 2-15, Biofilm day 103 2-15-SO4, Biofilm day 103 2-6, Biofilm day 103

99

Figure 5-32. Frequency of taxonomic assignment for v4v6 pyrotag sequence libraries for samples of ONKALO groundwater and FCCSs biofilms at day 0 and 103. Sequences with ≥1% frequency-abundance are shown. ONK-KR15 groundwater was sampled on 17 April 2012; ONK-PVA6 groundwater was sampled on 17 April 2012. NA: not annotated / unknown. The tree above the bar graph depicts a Morisita–Horn distance measure, constructed using the unweighted pair group method with arithmetic mean (UPGMA) with taxonomic depth at the species level. The scale bar represents 5% nucleotide substitutions.

Acholeplasma Actinobacter Algoriphagus Brevundimonas Deferribacteriales Desulfobacterium Desulfobacula Dethiosulfatibacter Desulfurivibrio Desulfuromonas Fusibacter Geobacter Hoeflea Hydrogenophaga Lachnospiraceae Lutibacter Methylophilus Nitrospiraceae Novispirillum Ovenweeksia Phenylobacterium Porphyrobacter Pseudidiomarina Pseudomonas Reheinheimera Rosevarius Seohaeicola Sphingobacteriales Thermoplasmata Thiobacillus Unknown, NA <1 %

ON

K-PV

A6

2-15

-SO

4-6

2-15

-SO

4

2-15

biof

ilm d

ay 0

ON

K-KR

15

0

20

40

60

80

100

Freq

uenc

y (%

)

0.05

100

Table 5-7. Occurrence of OTUs in Bacteria v4v6 pyrotag sequence libraries from groundwater and biofilms from SURE 2 at ≥1% abundance as illustrated in Figure 5-32. NA: not annotated / unknown.

Groundwater Biofilm day 0 Biofilm day 103 OTU KR15

2012-04-17 PVA6

2012-04-17 KR15 2-15 2-15-SO4 2-15-SO4-6

Acholeplasma 1.4 Actinobacter 1.1 Algoriphagus 1.4 1.9 1.9 1.4 Brevundimonas 5.4 5.4 6.6 5.3 Deferribacteriales 1.4 Desulfobacterium 1.4 Desulfobacula 33.3 Desulfurivibrio 23.2 1.1 Desulfuromonas 2.9 2.3 2.4 1.2 Dethiosulfatibacter 1.1 Fusibacter 7.9 Geobacter 1.8 Hoeflea 4.9 3.7 9.8 3.9 3 1.8 Hydrogenophaga 30.4 5.1 12 14 8.5 5.9 Lachnospiraceae 1.8 Lutibacter 5.5 6 15.6 20.7 28.7 Methylophilus 2.4 Nitrospiraceae 1.9 Novispirillum 1.8 Ovenweeksia 2.4 Phenylobacterium 3 1 Porphyrobacter 1.1 Pseudidiomarina 3.6 2.7 1.4 1.6 2.1 Pseudomonas 8.9 1.2 33.6 28.6 24 20.5 Reheinheimera 6.5 1.7 2.1 1.8 Rosevarius 1.4 3.6 6 5.4 6.5 Seohaeicola 1.5 2.8 2.1 1.8 Sphingobacteriales 2.6 Thermoplasmata 3.5 Thiobacillus 8.8 12.1 4.3 4.1 5.4 5.4 Unknown, NA 9.5 3.9 2.5 <1% 9.7 7.8 10.7 10.5 11.5 9.6

101

Figure 5-33. Frequency of taxonomic assignment for Archaea v6 sequencing libraries for SURE 2 samples of FCCSs biofilms at day 0 and day 103. Sequences with ≥1% frequency-abundance are shown. Bar designation for Biofilm day 0 = 2-15, 2-15-SO4, 2-15-SO4-6. NA: not annotated / unknown. Table 5-8 Occurrence of OTUs in sequence libraries from SURE 2 Archaea v6 biofilms at ≥1% abundance as illustrated in Figure 5-33. NA: not annotated / unknown.

Biofilm day 0 Biofilm day 103 OTU No treatment 2-15 2-15-SO4 2-15-SO4 -6 Crenarchaeota 6.22 28.24 13.4 38.53 Methanosarcinales GOM_Arc_1 11.6 1.92 5.76 11.77 Methermicoccus 6.78 5.21 1.82 2.07 Halobacteriales 4 2.57 4.02 Methanolobus 24.91 Methanosaeta 1.14 Thermoplasmata; SAG 49.92 34.27 16.98 12.80 Thermoplasmata 19.29 19.95 12.81 11.72 Archaea; NA 7.25 1.10 <1% 6.19 6.41 14.5 16.85

<1 % Archaea; NA Methanosaeta Methanolobus Halobacteriales Crenarchiota Methermicoccus Methanosarcinales GOM_Arc_1 Thermoplasmata Thermoplasmata; SAG

Biof

ilm d

ay 0

2-15

2-15

-SO4

-6

2-15

-SO4

0

20

40

60

80

100

Freq

uenc

y (%

)

102

Table 5-9. Amounts of extracted double-stranded DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated v4v6 bacterial diversity at genus OTU level (>0% sequence abundance) in groundwater and biofilm sequence libraries in SURE 2.

Sample Amount of

extracted DNA

(g × 10−9)


Number of OTUs at

>0% abundance


abundance

Number of OTUs at

≥1% abundance

ACE1 CHAO2 Shannon-Weaver diversity

index

Simpson diversity

index

ONK-KR15 2012-04-17 63 18134 135 38 14 157 213 2.7 0.87

Biofilm day 0 50 19769 126 37 12 139 158 2.55 0.85

Biofilm 2-15, day 103 26 15330 108 39 13 117 147 2.6 0.86

Biofilm 2-15-SO4, day 103 46 20632 119 45 14 138 264 2.74 0.88

Biofilm 2-15-SO4-6, day 103 80 19074 113 47 17 119 131 2.68 0.86

ONK-PVA6 2012-04-17 243 12795 116 37 13 133 157 2.33 0.81 1 Abundance-based coverage estimator; 2 Unbiased richness estimate

103

Table 5-10. Amounts of extracted double-stranded DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated v6 archaeal diversity at genus OTU level (>0% sequence abundance) in biofilm sequence libraries in SURE 2.

Sample Amount of

extracted DNA

(g × 10−9)


Number of OTUs at

>0% abundance


abundance

Number of OTUs at

≥1% abundance


index

Simpson diversity

index

Biofilm day 0. 50 585723 97 24 6 100.1 107.1 1.59 3.1

Biofilm 2-15, day 103 26 135524 89 32 7 91.1 93.9 1.75 3.33

Biofilm 2-15-SO4, day 103 46 44864 81 35 9 86.2 179 2.01 3.57

Biofilm 2-15-SO4-6, day 103 80 135367 97 32 9 103.6 118.3 1.94 3.5


104

Table 5-11. Occurrence of sulphide-producing OTUs (Deltaproteobacteria and Firmicutes) in sequence libraries from biofilms and groundwater at ≥0.1% abundance and observed major OTUs in the libraries and their reported sample sites in the GenBank nucleic acid database Sample Observed SRB OTUs in

descending order of abundance (%)

Total SRB OTUs

abundance (%)

Observed major OTUs in descending order of abundance (%)

Reported sample sites for sequences in the GenBank database with BLAST similarities >99%

ONK-KR15 2012-04-17 Desulfuromonas (2.9) - Hydrogenophaga Pseudomonas Thiobacillus Fusibacter Lutibacter

Subsurface microbial community, marine bacteria, biofilm in bed reactor, Wuliangsuhai Lake, Biofilms at Hot Creek, Baltic Sea, subsurface microbial community, deep argillite geological formation, Antarctic lake, deep coal seam groundwater, activated sludge hydrocarbon wastewater

Biofilm day 0. - - Pseudomonas Lutibacter Hydrogenophaga Roseovarius Brevundimonas Thiobacillus Hoeflea

Packed-bed sulphur reactor, Antarctic lake, coastal marine sediment, salt marsh sediments, Tibetan lake, Himalayan soil, Bering Sea, mud volcano, polar seas, subsurface microbial community, anaerobic digester, salt marsh rhizosphere soil, Lake Kauhako, marine microalgae, gas field formation saline fluids, natural gas brines and seawaters, Columbia River, Northam Platinum Mine fissure water, Lake Shira, Qianshan Iron Mine, mine drainage, groundwater from 297 m in Olkiluoto, Finland

Biofilm 2-15, day 103 Desulfuromonas (2.3) -

Biofilm 2-15-SO4, day 103 Desulfovibrio aespoeensis (0.81) Desulfurivibrio (0.24) Desulfuromonas (2.4)

1.05

Biofilm 2-15-SO4-6, day 103

Desulfurivibrio (1.13) Desulfovibrio aespoeensis (0.23) Desulfobacula sp. (0.12)

1.48

ONK-PVA6 2012-04-17

Desulfobacula sp. (33.3) Desulfurivibrio (23.2) Desulfobacterium sp. (1.4) Desulfosporosinus (0.54) Desulfotignum sp. (0.24) Desulfuromonas (1.2)

58.7 Desulfobacula Desulfurivibrio Thiobacillus Hydrogenophaga Hoeflea

Biofilm in packed-bed reactor, low-sulphate Lake Pavin, marine hydrocarbon cold seeps, Antarctic lakes, shellfish aquaculture, activated sludge, river-recharged Fredericton aquifer, biofilm-activated sludge system, drainage water from a magnesite mine, anaerobic digester-like effluent, rhizosphere soil of salt marshes, Lake Kauhako, dinoflagellate Alexandrium minutum, marine microalgae

105

5.7.5 SURE 3: 16S rDNA diversity analysis of biofilms

Rarefaction curves were calculated for all samples and showed a good coverage for all samples (Figure 5-34 - Figure 5-36). Biofilms day 2 were analysed with the Illumina v6 method which delivers many more sequences than does the 454 method. The Illumina method then reported many more OTUs in the rare biosphere, i.e., an abundance at ≤0.1% which in turn will bias the richness indices compared to the sequence libraries obtained with the 454 method. Therefore, we compare numbers at >1% abundance. The ONK-KR15 biofilms day 2 had 17/17 numbers of OTUs ≤1% and the ONK-PVA6 biofilm had 23/7 for Bacteria/Archaea as shown in Table 5-14 and Table 5-15, respectively. These numbers agree well with the numbers obtained in SURE 2 (Table 5-9 and Table 5-10).

The UPGMA dendogram in Figure 5-37 shows the Bray-Curtis distance measure relationship among replicate biofilm samples from 3-15, 3-15-SO4 and 3-6. The reproducibility of the method is clearly demonstrated by the closer relationships between replicate samples compared to the relationships between the treatments. Further, biofilm samples 3-6 originating from ONK-PVA6 groundwater are clearly separated from the two biofilm samples originating from OK-KR15 groundwater.

The Bar graphs in Figure 5-38 and Figure 5-39 visualise the bacterial and the archaeal variation among the FCCSs treatments. The bacterial diversity in the biofilm formed in ONK-KR15 groundwater, denoted Biofilm day 2, was dominated by Lutibacter (13.5%) and Desulfuromonas (9.7%) and the ONK-PVA6, Biofilm day 2, was dominated by Lutibacter (9.8%) (Table 5-12). Bacterial diversity in the 3-15 biofilm was dominated by the genus Desulfuromonas (32.4%), Lutibacter (17.7%) and Pseudomonas (8.0%). The major groups in the 3-15-SO4 biofilm were Desulfuromonas (38.9%), Lutibacter (13.3%) and Pseudomonas (7.08%). The 3-6 biofilm samples at days 2 and 209 had the Methylococcaceae group at 7.3 and 11.9%, respectively, which did not exist above >1% cut off in the other samples. Further, Desulfuromonas was found at 28.0% and Lutibacter at 14.1% in the 3-6 biofilm. The FCCS containing ONK-PVA6 groundwater had different OTUs compared to the FCCSs containing ONK-KR15 groundwater; the most important OTUs were Desulfurivibrio, Desulfobacula, Methylophilaceae, Hyphomonas, Chlorobiales and Bacteriodetes. The archaeal diversity of 3-15 and 3-15-SO4 biofilms was dominated by Methanosarchinales (24.4%), Thermoplasmata (35.6%) of witch the South African Goldmine (SAG) group represented (11.9%). Crenarcheota were found at 17.9%. The archaeal diversity of the 3-6 biofilm was dominated by Crenarcheota (41.5%) and Thermoplasmata (33.4%) of which the South African Goldmine group represented (13.0%). Halobacteria were found at 11.9%.

For Biofilm day 206, 207 and 209 the observed OTU numbers at > 0% were 87 to 140. The highest number of OTUs was found in the 3-6 biofilm (Table 5-14). At > 1% OTUs abundance, richness was again higher for the 3-6 samples at 17 OTUs compared to 3-15 and 3-15-SO4 which each had 14 OTUs. Shannon and inverse Simpson indices show that 3-15 and 3-6 biofilms had an even distribution of OTUs and were more diverse than other biofilm samples. The 2-4 FCs of the 3-6 FCCS were not sampled twice and refilled with new garnet grains on day 2. The community in this FC may therefore have stayed closer to the original diversity state.

A >1% cut off was chosen for all comparisons in bar graphs and abundance tables. This was because the different sample dataset had a large number of rare OTUs at <1% that would be very time consuming and costly to evaluate. For example, the v6 bacteria dataset for

106

groundwater at genus level returned 788 different OTUs of which 23 represent genus above the 1% abundance cut-off. However, the rare OTUs pool does constitute a source for microbial communities to respond to environmental changes and should therefore not be neglected when exploring microbial diversity in natural ecosystems. Therefore, the SURE 3 dataset was screened for sequences related to bacteria involved in AOM by assigning taxonomy to each sequence including the singleton sequences (= sequences that only appeared once in the libraries). The cell numbers in the biofilms were calculated from ATP measurements of garnet grains, assuming an average of 0.4 amol ATP per cell (Eydal and Pedersen 2007). Using this cell density for the biofilms, “singletons” would in a case of 400 000 tags (sequence reads) per sample at a cell number of 4.8 × 105 cells cm-2 correspond to approximately 1.2 cells cm-2, “duplicons” would correspond to ~2.4 cells cm-2 and “triplicons” to ~3.6 cells cm-2 and so forth. The rare OTUs have been shown to be a dependable source for the evaluation of diversity and should be taken into consideration when comparing among datasets. The most abundant OTUs do not always alone have the most valuable information (Bowen et al. 2012).

In general three archaeal groups; ANME-1, ANME-2, and ANME-3, have been identified as involved in AOM. ANME-2 and ANME-3 Archaea belong to the order Methanosarcinales, and ANME-1 archaea are more distantly related to the orders Methanosarcinales and Methanomicrobiales. ANME-1 often occurs as single cells. ANME-2 is associated with Gram negative SRB of the Desulfosarcina-Desulfococcus branch forming structured consortia. The third group, ANME-3, seems to be closer related to cultivated genera Methanococcoides spp. than the other ANME groups. ANME-3 archaea occur as single cells and form either shell-type aggregates associated with the SRB of Desulfobulbus relatives or mixed-type aggregates associated with a so far unidentified bacterial partner.

The rare OTUs in SURE 3 harboured sequences associated with ANME. Archaea v6 sequences obtained from both ONK-PVA6 and ONK-KR15 groundwater samples showed that the dataset harboured ANME-1 (~0.2%) as well as ANME-2 (~0.007%); the ANME-3 pool was very small (~0.003%). Further, a genus belonging to the SRB associated ANME partner Desulfobulbaceae was abundant, a total of 3.5% in ONK-PVA6 groundwater, Table 5-12. The Deltaproteobacteria of the Desulfosarcina group were found at about 0.5% in both ONK-PVA6 and ONK-KR15.


The next generation nucleotide sequences for SURE3 have been deposited in the NCBI short read archive (SRA) database under bio-project alias number PRJNA246527, SRA study accession number SRP041926.

For Bv4v6 and Bv6 sequences the bio-sample accession number is SRS795318 and for Av6 biofilms day 2 the bio-sample accession numbers are: SRS659269 and SRS659270.

Experiment RUN accession numbers for the groundwater samples are: SRR1720371, SRR1773429, for Bv6 day 2 biofilms the numbers are SRR1773550, SRR1773554 and for Bv4v6 biofilms at day 206, 207, 209 the numbers are SRR1773430, SRR1773431, SRR1773432, SRR1773434, SRR1773436 and SRR1773437.

107

Figure 5-34. Rarefaction curves for SURE 3 16S Bacteria rDNA v6 dataset biofilms at day 2. Each curve represents a single sample and sampling occasion.

Figure 5-35. Rarefaction curves for SURE 3 16S Bacteria rDNA v4v6 dataset biofilms at day 206, 207 and 209. Each curve represents a single sample and sampling occasion.

0.00E-01 1.00E+05 2.00E+05 3.00E+05 4.00E+05

Sample Size

0

100

200

300

400

500

600

700

800Ta

xa c

ount 3-6

3-15 & 3-15-SO4

0E-015E+04

1E+052E+05

2E+053E+05

3E+054E+05

4E+055E+05

Sample Size

0

20

40

60

80

100

120

140

160

Taxa

cou

nt

3-6 cell 1-3 3-6 cell 2-4 3-15-SO4 cell 1-3 3-15-SO4 cell 2-4 3-15 cell 1-3 3-15 cell 2-4

108

Figure 5-36. Rarefaction curves for SURE 3 16S Archaea rDNA v6 dataset biofilms at day 2. Each curve represents a single sample and sampling occasion.

Figure 5-37. UPGMA dendrogram showing the dissimilarity relationship among biofilm samples from day 206, 207, 209 in SURE 3. Each FCCS was sampled in replicate cells of two; FC 1-3 and 2-3. The cluster analysis was carried out using the Bray-Curtis distance measure. The tree was constructed based on normalized counts at the genus taxonomic level at >1%.

0E-01 1E+06 2E+06 3E+06 4E+06 5E+06 6E+06 7E+06

Sample size

0

20

40

60

80

100Ta

xa c

ount

3-6 3-15 & 3-15-SO4

109

Figure 5-38. Frequency of taxonomic assignment for 16S Bacteria rDNA v6 and v4v6 sequence libraries for samples from SURE 3 groundwater (gw) and biofilms (bf) at genus or the highest annotated rank level. ONK-KR15 and ON-PVA6 biofilms are from v6 sequence libraries from biofilms day 2, the others are from v4v6 sequence libraries from groundwater 203-04-17 and biofilms day 206, 207, 209. Sequences with ≥1% abundance frequency are shown.

Acholeplasma Acinetobacter Alteromonas Arcobacter Bacteroidetes Brevundimonas Chitinophagaceae Chlorobiales Comamondaceae Coriobacteriaceae Cornyebacterium Cyanobacteria Deferribacteriales Dehalobacter Dehalogenimonas Desulfobacterium Desulfobacula Desulfobulbaceae Desulforudis Desulfosporosinus Desulfurivibrio Desulfuromonas Desulfuromondacea Enterobacteriales Fusibacter Hoflea Hydrogenophaga Hyphomonas Lachnospiraceae Leucobacter Lutibacter Mesorhizobium Methylococcaceae Methylomonas Methylophilaceae Methylophilus Nitrospiraceeae Owenweeksia Propionibacterium Pseudidiomarina Pseudomonas Rhodoferax Roseovarius SAR 11 Seohaeicola Staphylococcus Thiobacillus < 1%

KR

15 g

w

PV

A6

gw

KR

15 b

f

PV

A6

bf

3-15

bf

3-15

-SO

4 bf

3-6

bf

0

20

40

60

80

100Fr

eque

ncy

(%)

110

Table 5-12. Percent occurrence of OTUs in 16S Bacteria rDNA v6 and v4v6 sequence libraries from SURE 3 sequence libraries at ≥1% abundance as illustrated in Figure 5-38.

v4/v6 Groundwater

v6 Biofilms day 2

v4v6 Biofilms days 206, 207, 209

OTU KR15 2013-04-17

PVA6 2013-04-17

KR15 PVA6 3-15 3-15-SO4 3-6

Acholeplasma 1.8 1.4 Acinetobacter 1.7 7.2 3.6 Alteromonas 4.6 5 Arcobacter 1.1 Bacteroidetes 2.4 6.7 2.1 1.6 Brevundimonas 3.5 1.5 1.8 1.4 3.4 Chitinophagaceae 1.9 Chlorobiales 27.9 2.9 2 Comamondaceae 7.3 3.6 Coriobacteriaceae 4.3 1.9 2.4 1.3 Cornyebacterium 1.3 1.4 Cyanobacteria 3.6 Deferribacteriales 2.8 Dehalobacter 1.7 Dehalogenimonas 1.1 Desulfobacterium 1.4 Desulfobacula 6.2 1.5 2.8 Desulforudis 1.2 Desulfosporosinus 1.9 Desulfurivibrio 3.6 1.7 3.4 3.3 Desulfuromonas 9.7 1.4 32.4 38.9 28 Desulfuromondacea 1.6 Enterobacteriales 1.1 1.3 Fusibacter 4.2 Hoflea 1.2 5.7 1.5 1.3 1.1 Hydrogenophaga 5.2 4.3 6.1 5.4 5.1 Hyphomonas 4.4 Lachnospiraceae 12.1 Leucobacter 1.2 Lutibacter 12.2 3.8 13.5 9.8 17.7 13.3 14.1 Mesorhizobium 2 1.9 Methylococcaceae 7.3 11.9 Methylomonas 1.6 Methylophilaceae 1.9 Methylophilus 2.2 1 3.2 4 Nitrospiraceeae 4.3 Owenweeksia 2.9 Propionibacterium 5.3 5.8 Pseudidiomarina 1.9 Pseudomonas 13.3 4.1 5.8 3.2 8 7.1 1.1 Rhodoferax 1.5 4.7 6.5 4.3 Roseovarius 3.4 2.9 SAR 11 1.36 1.5 Seohaeicola 1.5 4.3 4 Staphylococcus 1.1 1.3 Thiobacillus 6 1.3 1 1.2 1 Unknown, NA 7.8 1.9 1 0.5 0.3 0.3 0.3 <1% 12.6 21.1 32.4 36.1 11.9 9.9 12.4

111

Figure 5-39. Frequency of taxonomic assignment for 16S Archaea rDNA v6 sequence libraries for samples from SURE 3 biofilms at day 2 at genus or the highest annotated rank level. Sequences with ≥1% abundance frequency are shown. Table 5-13. Percent occurrence of OTUs in 16S Archaea rDNA v6 sequence libraries from biofilms at day 2 at ≥1% abundance as illustrated in Figure 5-39.

OUT ONK-KR15 ONK-PVA6 Crenarchaeota 17.87 41.5 Halobacteria 5.13 11.9 Methanoregula 5.46 Methanosaeta 2.64 Methermicoccus 1.26 Methanosarcinales; GOM_Arc_1 24.38 Methanolobus 1.01 1.94 Thermoplasmata;SAG 11.87 12.98 Thermoplasmatales 23.70 20.41 < 1% 6.68 11.27

> 1% Thermoplasmathales Thermoplasmata; SAG Methanolobus Methanosarcinales; GOM_Arc_1 Methermicoccus Methanosaeta Methanoregula Halobacteria Crenarchaeota

ON

K-K

R15

ON

K-PV

A6

0

20

40

60

80

100Fr

eque

ncy

(%)

112

Table 5-14. Amounts of extracted double-stranded DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated diversity at genus level (>0% abundance) in SURE 3 16S Bacteria rDNA v4v6 and v6 sequence libraries on garnet biofilms from flow cells (FC). The >0.1% and >1% abundance OTUs number was generated at genus level or the highest annotated rank.


Sample day Sequenced region

Amount of extracted

DNA (g × 10−9)


Number of OTUs at

>0% abundance


abundance

Number of OTUs at

≥1% abundance


Index


Index

ONK-KR15 2 v6 400 540572 798 92 17 836 949 2.69 3.82

ONK-PVA6 2 v6 1936 398116 788 105 23 821 875 2.85 3.88

3-15 FC 1-3 206 v4/v6 6000 29081 103 36 14 110 119 1.92 3.43

3-15 FC 2-4 206 v4/v6 7000 30449 97 36 14 101 117 1.99 3.48

3-15-SO4 FC 1-3 207 v4/v6 6000 21790 92 36 14 97 111 1.88 3.34

3-15-SO4 FC 2-4 207 v4/v6 6680 18373 87 36 14 89 93 1.92 3.4

3-6 FC 1-3 209 v4/v6 1400 21997 115 42 17 120 125 1.99 3.47

3-6 FC 2-4 209 v4/v6 1200 26054 140 42 17 149 159 2.13 3.6

113

Table 5-15. Amounts of extracted double-stranded DNA analysed fluorometrically using the Stratagene MX3005p fluorometer with MXPro software and the Quant-it Picogreen reagent kit from Molecular Probes; observed and estimated diversity at genus level (>0% abundance) in SURE#3 16S Archaea rDNA v6 sequence libraries from biofilms at day 2. The >0.1% and >1% abundance OTU numbers were generated at genus level or the highest annotated rank.


Sample

Amount of

extracted DNA

(g × 10−9)

Sampling depth,

i.e., number

of sequences

Number of OTUs at

>0% abundance


abundance

Number of OTUs at

≥1% abundance

ACE1 CHAO2


Index


Index

ONK-KR15 400 70780 85 36 17 91.9 107.5 2.07 3.63

ONK-PVA6 1936 121351 93 33 7 97.6 109.1 1.91 3.44

115

6 UNDERSTANDING MICROBIAL REDUCTION OF SULPHATE TO SULPHIDE IN DEEP OLKILUOTO GROUNDWATER

A total of 9 experiments were performed that included two different groundwater types and varying treatments with additions of sulphate, H2 and methane. The experiments consequently evaluated the effect of sulphate, H2 and methane on microbial activity and diversity. An extensive analysis programme was applied to follow how these variables influenced mainly sulphate-reducing activity by SRB. However, many other parameters were analysed as well to ensure that our understanding of sulphate reduction in deep Olkiluoto groundwater is significantly deepened and increased.

6.1 Experimental approach – choice of methodology

Samples for investigations of microbial numbers and diversity in Olkiluoto have been obtained frequently using depressurized samples withdrawn to the surface with pumps through very long tubing or using pressurised sample vessels with limited sample volumes. These sampling procedures may change pressure-related parameters, such as gas solubility, which in turn may influence pH and Eh relative to the in situ conditions in the aquifers. Moreover, it is difficult to create realistic conditions for the analysis of in situ microbial activity using pumped groundwater or small volumes of groundwater collected with pressurised sample vessels. The obvious way to better understand in situ microbial activity is to install experiments underground under in situ pressure conditions as was done in this work. The experimental equipment used in SURE could be directly connected to aquifers in ONKALO via short tubes using the natural aquifer pressure.

The pressure-resistant FCCSs were constructed to enable the investigation of attached and unattached microbial populations under in situ pressure, diversity, dissolved gas, and chemistry conditions in the ONKALO tunnel. They were set up in an underground laboratory container, combining controlled laboratory conditions with the in situ conditions prevailing in the intersected aquifers of ONK-PVA6 and ONK-KR15. The FCCSs were previously used in the Äspö HRL to investigate sorption of radionuclides onto biofilms on glass and rock surfaces (Anderson et al. 2007; Anderson et al. 2006), after which the nitrate reduction activity, using lactate as a carbon source, was studied (Nielsen et al. 2006). Later, the influence of organic carbon and reduced gases was investigated (Pedersen 2012a, b; Pedersen 2013b). The experience gained from these studies was used in developing the SURE research.

6.1.1 The choice of material for flow cell circulation systems

The material in the tubing, valves and FCs can be either stainless steel or plastic. While many plastic materials are inert and do not react with water, stainless steel may corrode anaerobically under the production of H2 and ferrous iron. Exposure to air between experiments may oxidize ferrous iron to ferric iron. On the other hand, gases diffuse readily through many plastic materials. The use of a plastic material, PEEK, compared with the use of stainless steel has been evaluated previously (Pedersen 2012b). An FCCS outfitted with PEEK tubing, pump, and valves was compared with an FCCS outfitted with stainless steel tubing, pump, and valves. This comparison clearly demonstrated that the steel-outfitted system was superior at keeping gases in the circulating water; in contrast, gases rapidly diffused out of the PEEK-outfitted FCCS. Although the stainless steel used was corrosion resistant, some anaerobic corrosion may have taken place. However, if that was so, the H2 production rate was less than what could be detected.

116

Because the SURE experiments included the addition of gases, stainless steel was used. However, the interior of the FCs and the expansion vessels were lined with PVDF; the vessels used to add gases were lined with Teflon. Consequently, the FCCSs only exposed stainless steel to the circulating groundwater in tubing and valves. It cannot be excluded that some anaerobic corrosion was ongoing, indicated in Figure 5-27 by a slow, similar increase in ferrous iron concentrations in all experiments except in the 1-6-H2-CH4 FCCS with a high concentration of sulphide that will have precipitated all produced ferrous iron as FeS (Figure 5-28) and in the aerobic 1-6-air FCCS (excluding an anaerobic corrosion process).

6.1.2 Ratios between attached and planktonic biomass

Solids in the form of crushed rock, glass beads and garnet grains were used as support for the attachment and growth of microbial biofilms. This was done to mimic the situation in hard rock aquifers where surfaces dominate over volume. Still, there was a relatively small surface to volume ratio in the SURE experiments. The total volumes of groundwater at the start of the experiments were 4600 mL for FCCSs with 4 FCs and 4750 mL for FCCSs with 5 FCs (used in SURE 3). The corresponding surface areas in each FCCS, including the solids and the surface in the expansion vessel and tubing, were approximately 5500 cm2 in SURE 1 and 2 and 7000 cm2 in SURE 3. The corresponding surface (cm2) to volume (mL) ratios were then 1.16 and 1.47, respectively. These ratios decreased over time as each sampling occasion reduced the volume with 300 - 400 mL to a remaining volume at the end of the experiments of about 50 % of the start volume. A surface/volume (cm2/mL) ratio of 2 would correspond to a fracture with an aperture of 10 mm. In reality, the aperture of most fractures in hard rock is 10 to 1000 times smaller (1 – 0.001 mm). Consequently, the SURE experiment mimicked a larger water phase relative to the solid phase surface than normally found in hard rock aquifers.

The total amount of attached and planktonic biomass at the start and end of the experiments can be calculated from the values of ATP in Table 5-1 and Appendix Tables B-3, C-3 and D-3, and the volumes and surface areas given above (all data are compiled in Table D-1). The results show ratios of ATP cm−2 / ATP mL−1 in the range from approximately 1 to up to 80 (Figure 6-1). The start values for the ratios were between 4 and 10 in SURE 1 and these ratios decreased towards a value of 1 in the gas treated FCCSs. The ratio will change if one or both of the values change. In SURE 1, most of the change was due to a decrease in the amount of attached ATP (Table 5-1). In SURE 2, ratios for 2-15 and 2-15-SO4 did not change over the 103 days the experiment lasted, whereas 2-15-SO4-6 increased in the attached ATP giving the highest of the 6 ratios for SURE 2. In SURE 3, the attached biomass did not change much (Table 5-1) and the amount of ATP mL−1 groundwater did not change much from day 0 to day 97. However, after the methane and sulphide additions, there was a large decrease in planktonic ATP which explains the large increase in the ratios (Figure 6-1).

In summary, the results in Figure 6-1 show that there was from less than 1 to up to 10 times more ATP on the solids compared to the groundwater. There was a trend of more attached ATP in FCCS with ONK-PVA6 groundwater. The most important conclusion that can be drawn from Figure 6-1 is that the SURE experiments investigated active attached as well as planktonic microorganisms. This has important implications for the interpretation of the analysed numbers and diversity of MPN and 16S rDNA, respectively, as discussed next.

117

Figure 6-1. The ratios between the amounts of ATP cm−2 of solid surfaces over the amounts of ATP mL−1 of circulating water in the FCCSs.

0 2 4 6 8 10

1-6-air, day 3

1-6-air, day 105

1-6-CH4, day 3

1-6-CH4, day 105

1-6-H2-CH4, day 3

1-6-H2-CH4, day 105

0 2 4 6 8 10

2-15, day 3

2-15, day 103

2-15-SO4, day 3

2-15-SO4, day 103

2-15-SO4-6, day 3

2-15-SO4-6, day 103

0 2 4 6 8 10

3-15, day 0

3-15, day 97

3-15-SO4, day 0

3-15-SO4, day 97

3-6, day 0

3-6, day 97

0 20 40 60 80 100

Ratio (ATP cm2 / ATP mL)

3-15, day 2063-15-SO4, day 207

3-6, day 209

118

6.1.3 Cultivation of planktonic microorganisms and 16S rDNA sequence analysis of attached microorganisms in circulation systems

The 16S rDNA methodology used in this report was designed for the mapping of microbial diversity as a function of time and treatments. There is a defined lower limit for how much DNA is needed for a non-biased result (Salter et al. 2014). The limit corresponds to approximately 5 × 106 cells with approximately 30 × 10−9 g of DNA. With less DNA in a sample, there is an increased risk for reagent contamination biases. This limit was reached with most biofilm samples and filtered groundwater samples. There are losses at all the steps of filtering and DNA extraction so a TNC of 105 cells mL−1 in the circulating groundwater means that at least 1000 mL would need to be filtered to reach the quality limit. There was only 4600 mL water in the FCCS and, therefore, the 16S rDNA diversity mapping of the circulating groundwater was not possible. Finally, it should be noted that the sequencing method is not quantitative although some understanding of abundance can be inferred from the frequencies of taxonomic assignments.

Cultivation with MPN is very sensitive in the sense that only 1 mL is needed for the analysis. We used this method to analyse the cultivable diversity and enumeration of cultivable metabolic groups of prokaryotic microorganisms in the circulating water of the FCCSs. The method is well suited for water samples, but difficult to apply to solid samples with attached biomass.

The groundwater used to fill the FCCSs was analysed with both the cultivation and the 16S rDNA method. There was a good agreement between the obtained results. SRB and representatives of NRB, IRB and MRB were found with both methods (Table 4-2, Table 4-6 and Table 4-7). There were some differences between the MPN results of planktonic microorganisms in the circulating groundwater and the 16S rDNA results of attached microorganisms. This was clearly demonstrated in SURE 2, where the MPN of planktonic SRB increased by more than three orders of magnitude in the sulphate amended FCCSs (Figure 5-11) while a representation of SRB in the 16S rDNA results above 1 % frequency was absent (Table 5-7). Instead, sulphur-reducing microorganisms (Desulfuromonas) were observed in the sequencing results of attached microorganisms in SURE 2. Similarly, 16S rDNA sequences related to methanogenic archaea were observed among the attached microorganisms, but the MPN analysis of methanogens was below detection limits in all samples (5.5.6). This result highlights one weakness of the MPN analysis: far from all prokaryotic microorganisms can be cultivated with a few medium types. The media for methanogens were of a general type that may not work with the methanogens detected with the 16S rDNA analysis.

Consequently, both MPN analysis and 16S rDNA sequencing have advantages and disadvantages. The combined use of these methods in SURE were selected to have these methods covering up as much as possible for their respective disadvantages.

6.2 Effect of total 3-year duration of three consequtive experiments using ONKALO groundwater

The water chemistry changed in both ONK-PVA6 and ONK-KR15 during the time period the SURE experiments were preformed (Figure 4-1, Table A-3 and Table A-4). The changes were most pronounced in ONK-PVA6. Similarly, we observed differences of 1 – 2 orders of magnitude in the TNC, ATP and MPN results over time in the respective drillhole

119

groundwater (Table 4-2). This means that although groundwater from the same drillhole was used repeatedly in SURE 1, 2 and 3, there were large differences in their geochemical and microbiological composition between the experiments. Therefore, caution is needed when the results from SURE 1, 2 and 3 are interpreted and compared. The following general differences are noted:

• The ONK-PVA6 groundwater for SURE 3 had much higher values for TNC, ATP and CHAB compared to SURE 1 and 2. SURE 2 had much lower numbers of ATP, CHAB, NRB and SRB than SURE 1. SURE 1 had a 16S rDNA diversity that differed more from SURE 2 and 3 than SURE 2 and 3 differed between each other (Figure 4-5). In particular, SURE 1 had a much higher number of reads related to sulphate- and sulphur-reducing bacteria than had SURE 2 and 3.

• The ONK-KR15 groundwater used for SURE 3 had much higher values for TNC, ATP, CHAB and NRB than the groundwater used for SURE 2. There were modest differences in the 16S rDNA diversity in the groundwater (Figure 4-5).

6.3 16S rDNA diversity in sequence libraries – interpretations and implications for results and conclusions

This Section deals with the diversity and frequency of occurrence of sequence reads in the sequence libraries produced with amplicon 16S rDNA sequencing methodology (Methods see 2.7.2) The phylochip data cannot be interpreted as occurring OTUs at various percentages of all reads due to the very different methodology used to produce those data (Methods, see 3.7.7.1).

The 16S rDNA diversity data show genera and families most similar, but not necessarily identical to the obtained sequences. Each OTU then represents a more or less coherent group of sequences in the sample sequence libraries. Conclusions about metabolism, growth rates and other characteristics of the reported OTU must be on a general level. Exact understanding of all possible metabolic pathways of microorganisms in a sampled environment requires a metagenomics approach (e.g. Wu et al. 2015). Such analyses have been costly and very time consuming in the past but are now becoming less expensive. However, the bioinformatics is still very time consuming. The sequence data and related OTU of planktonic and attached prokaryotic microorganisms have been summarised and arranged according to overall abundance in Table 6-1 and Table 6-2. The occurrence of the 60 OTUs at ≥ 1 % is given for all 15 samples and for water and biofilms.

6.3.1 Bacteria

A large proportion, 15%, of all observed bacterial sequence reads were found at occurrences ≤ 1% of all reads (Table 6-1). There were more than 1000 OTUs on genus level in this portion of occurring OTUs while the number of major OTUs (>1%) was 60. Consequently more than 94% of the total OTUs were observed at a sequence read occurrence of ≤ 1%. It is likely that microorganisms related to the major OTUs in each sequence dataset had the greatest influence on the SURE experiments. However, many of the gaps in Table 6-1 can be filled in with numbers ≤ 1%, indicating microorganisms that had small or no influence on the SURE experiments at the time of sampling and sequencing. If there were changes in the growth conditions of any of the SURE experiment that favoured a ≤ 1% OTU, it is likely that the occurrence of this OTU did increase with a concomitant increase in metabolic activity and influence on the SURE results. In other words, OTUs that appear in a SURE experiment at the

120

end of the experiment, but were absent in tables and figures at the start of the experiments, must have been present throughout the experiment “hidden” in the ≤ 1% group because microbial species are not generated spontaneously.

The core bacterial community of the SURE experiments was composed of a few OTUs found in 12 or more of the total of 15 samples examined with amplicon 16S rDNA sequencing (Methods see 2.7.2). The Pseudomonas OTU was found in all 15 samples and constituted 11.4 % of all observed OTUs (Table 6-1). The Pseudomonas genus comprises a very diverse group of generally heterotrophic aerobic, and facultative anaerobic bacteria. They degrade a multitude of organic compounds and many can respire with nitrogen in nitrate and these will, therefore, appear in the MPN results of NRB. NRB were found in all groundwater samples and in all SURE samples (Figure 5-8) which agrees well with the finding of Pseudomonas OTUs in all 15 sequence libraries. Community analysis by other research groups confirms Pseudomonas OTUs to constitute a major core group in Olkiluoto groundwater (Bomberg et al. 2015) and in groundwater from the deep Outokompu drillhole (Purkamo et al. 2015a).

Lutibacter was found in all but one groundwater sample at > 1 % occurrence, comprising 11.6% of all observed OTUs. This OTU was more common in the biofilm communities than in the groundwater communities (Table 4-6, Table 5-7 and Table 5-12). Lutibacter is commonly found in marine mud and the found species are heterotrophic, living with organic compounds as carbon and energy sources. They are Gram-negative, can be facultatively anaerobic, non-spore-forming, rod shaped and non-motile, belonging to Flavobacteriaceae in the order Bacteriodetes. These characteristics makes Lutibacter likely to be included in the CHAB and MPN of NRB results. The Hoeflea OTU was present in 13 of the sample libraries, comprising 3.7% of all observed OTUs and this genus has many characteristics in common with Lutibacter, being Gram-negative, rod shaped, heterotrophic, non-spore-forming and non-motile. It may also have nitrogen fixing properties because this genus belongs to the order Rhizobiales. The fact that the heterotrophic genera Pseudomonas, Lutibacter and Hoeflea belong to the core group suggests that heterotrophy is an important metabolic pathway in deep groundwater as recently suggested by Purkamo et al. (2015b). The sequence libraries comprise samples of planktonic prokaryotic microorganisms in groundwater at the start of the experiments and attached microorganisms in biofilms. The diversity of the cultivated bacteria was analysed by cloning and Sanger sequencing and the results agree with the sequencing results (Table 5-6). Pseudomonas, Flavobacteriaceae and Hoeflea were found suggesting that these genera were present in the circulating water. There were 0.1 to 1.8 mM of DOC and 10 to 300 µM of acetate in the circulating groundwater of the experiments (Figure 5-18 and Figure 5-19) confirming that heterotrophy was possible. Because phages were present and active (Figure 5-2), it is possible that some of this DOC (including acetate) was cycled from cells being lysed by the phages into growing cells not attacked by the phages.

The genus Hydrogenophaga comprised 7.8% of all observed OTUs and was found in all groundwater sequence libraries and in 7 of the biofilm sequence libraries at > 1% occurrence and at ≤ 1% in the 2 remaining sequence libraries. The cells of this genus are Gram-negative, straight or slightly curved rods or spirilla. Most genera are motile and endospores are not formed. Their metabolism is chemoorganotrophic or facultatively chemolithotrophic with H2 or CO oxidation as sources of energy. Some species can respire with nitrates.

The occurrence of Brevundimonas Pseudidiomarina, Rosevarius, Seohaeicola and Methylophilus related OTUs was strongly biased towards the attached communities compared

121

to their occurrence in the groundwater from the drillhole aquifers. These species consequently appear to prefer an attached life in deep groundwater.

6.3.1.1 Sulphur-related OTUs

There were two groups of sulphur-related OTUs in the SURE sequence libraries. One group comprised SRB and the highest occurrence was found for Desulfobacula and Desulfobacterium (Table 6-1). The cloning results of the MPN cultivations for SRB showed a high occurrence of the genus Desulfovibrio (Table 5-6). Desulfobacterium was only found in groundwater. The groundwater from ONK-KR15 had very few OTU belonging to SRB, e.g. Desulfovibrio was totally absent in that groundwater. The second group comprised sequences related to sulphur-reducing and sulphur-oxidizing bacteria. Desulfuromonas and Desulfurivibrio are sulphur-reducing bacteria and they comprised 8.0 % and 2.7 % of all observed OTUs, respectively. Desulfuromonas was, with one exception, only found in the biofilm sequence libraries. Thiobacillus related OTUs comprised 3.7 % of all OTUs. These genera oxidise sulphur to sulphate with oxygen or nitrate. The large representation of sulphur-related OTUs is very intriguing, because it suggests that a cryptic sulphur cycle was present in the SURE experiments as is discussed in detail in Section 6.8.

6.3.2 Archaea

A large proportion, 8.1 %, of all the observed archaeal sequence reads were found at occurrences ≤ 1% of all reads (Table 6-2). There were 123 OTUs on genus level in this portion of occurring OTUs while the number of major OTUs (>1%) was 12. Consequently, 90% of the total OTUs were observed at an occurrence of ≤ 1%. Just as for the bacteria, it is likely that microorganisms related to the major OTUs in each sequence dataset had the greatest influence on the SURE experiments. However, all gaps in Table 6-2 can be filled in with numbers ≤ 1%, indicating microorganisms that had small or no influence on the SURE experiments at the time of sampling and sequencing.

Almost 50 % of all sequence reads were related to the thermoacidophilic class of Thermoplasmata and they occurred in all 10 analysed samples. Many of the members of this group grow best at temperatures above 50 – 60 °C and anaerobic growth is enhanced by elemental sulphur, which is reduced to H2S via respiration. This group then seems to be misplaced in deep Olkiluoto groundwater. However, other, independent investigations have observed a similar dominating number of sequences related to Thermoplasmata in Olkiluoto groundwater (Bomberg et al. 2015). They may originate from deeper and hotter layers in Olkiluoto, or do they represent not yet described and cultured mesophilic members of this order. Members of the phylum Crenarchaeota represented 19.2% of all sequence reads and many are thermophilic and have sulphur-related metabolisms.

The methane related OTUs comprised 17% of all archaeal reads representing 7 different phylogenetic groups: GOM_Arc_1, Methanosacrinales, Methaobacteriaceae, Methermicoccus, Methanolobus, Methanosaeta and Methanoregula (Table 6-2). Underlined groups were also detected by Bomberg et al. (2015) in similar proportions. ANME-1 and ANME-1a, b and ANME 2a, b were present among the ≤ 1% reads, with ANME-1a at a several orders of magnitude larger number of reads than the other ANME OTUs. The largest group, the Gulf Of Mexico (GOM) Archaea_1 is also known as ANME-2d, i.e., they can oxidise methane under anaerobic conditions.

122

Table 6-1. Observed percentage of all sequence reads for each OTU in the 16S Bacteria rDNA sequence libraries sorted in order of appearance. The total number of sequence libraries was 15 from 6 groundwater and 9 biofilm samples. The numbers of libraries with OTUs occurring at > 1 % of all sequence reads are shown for each of the listed OTUs. Data have been collected from Table 4-6, Table 5-7 and Table 5-12.

OTU Sum of sequence

reads (%)

Part of sum of sequence

reads (%)

Total number of libraries

Libraries from water (Total = 6)

Libraries from biofilms

(Total = 9)

1 <1% 224.55 14.97 15 6 9 2 Pseudomonas 170.4 11.36 15 6 9 3 Lutibacter 173.6 11.57 14 5 9 4 Hoeflea 53.5 3.57 13 6 7 5 Hydrogenophaga 117.4 7.83 13 6 7 6 Unknown, NA 35.1 2.34 12 6 6 7 Thiobacillus 54.9 3.66 12 4 8 8 Brevundimonas 43.2 2.88 10 2 8 9 Desulfuromonas 119.2 7.95 9 1 8 10 Desulfurivibrio 40.3 2.69 7 3 4 11 Pseudidiomarina 19.8 1.32 7 3 4 12 Roseovarius 29.2 1.95 7 1 6 13 Seohaeicola 18 1.20 7 1 6 14 Methylophilus 32.7 2.18 6 1 5 15 Bacteriodetes 29.5 1.97 5 3 2 16 Coriobacteriaceae 13.1 0.87 5 2 3 17 Desulfobacula 51.4 3.43 5 3 2 18 Fusibacter 17.28 1.15 5 3 2 19 Rhodoferax 19.2 1.28 5 2 3 20 Acholeplasma 5.9 0.39 4 3 1 21 Acinetobacter 13.6 0.91 4 2 2 22 Algoriphagus 6.6 0.44 4 4 23 Chlorobiales 38.2 2.55 4 2 2 24 Reheinheimera 12.1 0.81 4 4 25 Desulfobacterium 4.1 0.27 3 3 26 Nitrospiraceae 7.4 0.49 3 3 27 Alteromonas 9.6 0.64 2 2 28 Comamondaceae 10.9 0.73 2 2 29 Cornyebacterium 2.7 0.18 2 2 30 Cyanobacteria 13.3 0.89 2 2 31 Deferribacterales 5.87 0.39 2 2 32 Desulfosporosinus 3.2 0.21 2 2 33 Enterobacteriales 2.4 0.16 2 2 34 Lachnospiraceae 16.3 1.09 2 2 35 Mesorhizobium 3.9 0.26 2 2 36 Methylococcaceae 19.2 1.28 2 2 37 Methylomonas 3.4 0.23 2 2 38 Ovenweeksia 5.3 0.35 2 2 39 Phenylobacterium 4 0.27 2 2 40 Propionibacterium 11.1 0.74 2 2 41 SAR 11 2.9 0.19 2 2 42 Staphylococcus 2.4 0.16 2 2 43 Desulfuromonadales 3.1 0.21 1 1 44 Aquabacterium 1.1 0.07 1 1 45 Arcobacter 1.1 0.07 1 1 46 Chitinophagaceae 1.9 0.13 1 1 47 Dehalobacter 1.7 0.11 1 1

123

Table 6-1. Continued

OTU Sum of sequence

reads (%)

Part of sum of sequence

reads (%)



Libraries from biofilms

(Total = 9)

48 Dehalogenimonas 1.1 0.07 1 1 49 Desulforudis 1.2 0.08 1 1 50 Desulfuromondacea 1.6 0.11 1 1 51 Dethiosulfatibacter 1.1 0.07 1 1 52 Geobacter 1.8 0.12 1 1 53 Hyphomonas 4.4 0.29 1 1 54 Leucobacter 1.2 0.08 1 1 55 Methylophilaceae 1.9 0.13 1 1 56 Novispirillum 1.8 0.12 1 1 57 Porphyrobacter 1.1 0.07 1 1 58 Sphingobacteriales 2.6 0.17 1 1 59 Sphingopyxix 1.1 0.07 1 1 60 Thermoplasmata 3.5 0.23 1 1 Sum of all data 100 248 97 151

Table 6-2. Observed percentage of all sequence reads for each OTU in 16S Archaea rDNA sequence libraries sorted in order of appearance. The total number of sequence libraries was 10 from 4 groundwater and 6 biofilm samples. The numbers of libraries with occurrences ≥ 1 % of the sequence reads are shown for each of the listed OTUs. Data have been collected from Table 4-7, Table 5-8 and Table 5-13. OTU Sum of

sequence reads (%)

Part of sum of sequence reads (%)



Libraries from

biofilms (Total = 9)

1 < 1% 81.6 8.16 10 4 6 2 Thermoplasmata; SAG 309.6 31.0 10 4 6 3 Thermoplasmatales 184.1 18.4 10 4 6 4 Halobacteriales 48.8 4.9 9 3 5 5 Methanosarcinales

GOM_Arc_1 70.6 7.06 9 4 4 6 Crenarchaeota 192 19.2 9 3 6 7 Methermicoccus 38.5 3.85 8 3 4 8 Archaea_NA 10.1 1.01 3 1 2 9 Methanolobus 47.4 4.74 3 2 3 10 Methanobacteriaceae 8.5 0.85 2 2 0 11 Methanosaeta 3.8 0.38 2 0 2 12 Methanoregula 5.5 0.55 1 0 1 Sum of all data 100 76 30 45

6.4 Groundwater types and effect of sulphate on microbial diversity

Prokaryotic microorganisms generally have wide ranges of many growth parameters with optimum, minimum and maximum values. A good example for temperature, salinity and pH is given with Methanobacterium subterraneum in Figure 6-2. This species has a temperature range for growth from <5 °C to up to <50 °C, a pH range of 2 units and it tolerates at least 1 M dissolved NaCl. Similarly, prokaryotes tolerate large ranges of most other dissolved

124

species. They do need carbon, phosphate and nitrogen sources and trace elements. Because of very efficient uptake and transport systems into the cells, concentrations can be low. Growth rates will slow down at low concentrations, but the microorganisms will survive and metabolise. Because of this efficient ability to grow and survive at different concentrations of nutrients and in varying environmental conditions (temperature, salinity, pH) subsurface microorganisms are, consequently, not severely influenced by moderate changes in groundwater composition, unless there is a complete absence of an essential nutrient.

Figure 6-2. Metanobacterium sub-terraneum is a genus of Archaea that has been isolated from the Äspö hard rock aquifers and characterised (Kotelnikova et al. 1998). It has temperature (A), pH (B) and salt (C) requirements that include typical values of these parameters in the Olkiluoto groundwater.

The construction of the ONKALO tunnel causes a drawdown of sulphate-rich groundwater that mixes with the deep methane-rich groundwater. It has been found that the mixing zone is slowly moving deeper in the fractures intersected by the ONKALO tunnel. The ONK-PVA6 drillhole was drilled on 3–4 November 2009 and the groundwater at that time was sulphate poor. The sulphate concentration has, due to the drawdown, been slowly rising since then to 1.6 mM (Figure 4-1). The FCCS experiments reported here were designed to mimic this human-induced transition in groundwater geochemistry that is slowly working its way deeper in ONKALO, triggering SRB growth in the mixing zone as demonstrated by Bomberg et al. (2015).

The only major difference in geochemistry between ONK-KR15 and ONK-PVA6 groundwater was the absence of sulphate, some nitrogen compounds and ferrous iron from ONK-KR15 groundwater (Table 6-3). However, only sulphate exhibits a very large difference. Ferrous iron and the nitrogen compounds were close to detection limits in ONK-PVA6 groundwater and below detection limits in ONK-KR15 groundwater. This difference in

0 10 20 30 40 50 60

Incubation temperature (°C)

0

0.05

0.10

0.15

0.20

Gro

wth

rate

(h-1

)

A

6 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10

pH

0

0.10

0.20

0.30

0.40

0.50

Gro

wth

rate

(h-1

)

_ B

0 0.25 0.50 0.75 1.00 1.25 1.5

NaCl (M)

0

0.05

0.10

0.15

0.20

0.25

Gro

wth

rate

(h-1

)

C

125

sulphate concentration profoundly influenced the observed numbers of cultivable SRB and the 16S rDNA diversity of SRB, which were high in ONK-PVA6 groundwater and virtually absent in ONK-KR15 (Figure 4-5, Table 4-6). This difference in diversity was reproduced in both FCCSs without sulphate, i.e., in 2-15 (Table 5-7) and 3-15 (Table 5-12) were sequences related to SRB were absent or only found in the rare biosphere at ≤0.1% abundance. In contrast, all FCCSs that circulated ONK-PVA6 groundwater showed a large or very large presence and diversity of SRB related sequences, i.e., 1-6-CH4, 1-6-H2-CH4 and 3-6. Further, it was found that the addition of sulphate to ONK-KR15 groundwater triggered SRB to grow and increase in cultivable numbers in the circulating groundwater (Figure 5-11). There was a less significant increase in the proportion of SRB related sequences in the attached populations of sulphate treated ONK-KR15 FCCSs, i.e., 2-15-SO4, 2-15-SO4-6 and 3-15 SO4 (Table 5-7, Table 5-12). The attachment and growth of SRB in biofilms obviously takes longer time than growth in the water phase after sulphate addition because they must compete for attachment sites with already attached microorganisms. However, sulphate alone will not activate microbial populations as demonstrated by Rajala et al. (2015), but a source of energy must be available as discussed next.

Table 6-3. Geochemical parameters of experimental groundwater and a comparative ratio for each parameter. The Table shows data for April 2012, testing on other dates delivered similar results.

Analysis Unit ONK-PVA6

ONK-KR15

PVA6/ KR15

Sample date 2012-04-02 2012-04-12 1.00 Total dissolved solids °C 7212 10560 0.68 pH g L−1 7.8 7.9 0.99 Conductivity mS cm−1 1279 1830 0.70 Total alkalinity, HCl uptake mM 0.3 0.17 1.76 Ammonium, NH4

+ mg L−1 0.02 0.02 1.00 Bicarbonate, HCO3 mg L−1 18 10 1.80 Dissolved Inorg. Carbon mg L−1 3.3 <3 >1.10 Non Purgeable Org. Carbon mg L−1 2.5 3.2 0.78 Bromide, Br mg L−1 26 47 0.55 Calcium, Ca mg L−1 880 1400 0.63 Chloride, Cl mg L−1 4350 6580 0.66 Fluoride, F mg L−1 1.5 1.5 1.00 Iron, Fe2+ mg L−1 0.16 <0.02 >8.00 Iron, Fe total mg L−1 0.14 <0.025 >5.60 Magnesium, Mg mg L−1 18 37 0.49 Nitrogen, N total mg L−1 0.098 <0.05 >1.96 Phosphate, PO4 mg L−1 0.2 <0.1 >2.00 Potassium, K mg L−1 7.1 12 0.59 Silica, SiO2 mg L−1 8.8 6.3 1.40 Sodium, Na mg L−1 1760 2450 0.72 Strontium, Sr mg L−1 6.9 11 0.63 Sulphate, SO4

2- mg L−1 135 <0.8 >169.00 Sulphide, S2- mg L−1 0.04 0.02 2.00

126

6.5 Observed microbial sulphate reduction and reported isotopic sulphur fractionation factor ε (‰)

Sulphate-reducing bacteria fractionate 34S/32S in sulphate in the sulphate-reduction process. Therefore, variations in sulphur isotope ratios, δ34SV-CDT, pyrite and sulphate are often used to reveal past and present activity of SRB (Drake et al. 2013). Fractionation factor ε (‰) varies markedly from species to species and also as a function of the rate at which sulphur in sulphate is reduced (Detmers et al. 2001). Complete lactate oxidizing species often have a larger fractionation factor than do incomplete lactate oxidizing species. Desulfovibrio, an incomplete oxidizer, typically shows a fractionation factor among the smallest factors in the range of 2 – 5 ‰. The paper of Detemer et al. (2001) concludes that “different metabolic pathways and variable regulation of sulphate transport across the cell membrane all potentially affect isotope fractionation. Previous models that explained fractionation only in terms of sulphate reduction rates appear to be oversimplified. The species-specific physiology of each sulphate reducer thus needs to be taken into account to understand the regulation of sulphur isotope fractionation during dissimilatory sulphate reduction”. This conclusion presents a challenge for the interpretation of the fractionation factors calculated in SURE because these factors are not known for the ANME detected in the sequence libraries of Olkiluoto.

Each decrease in sulphate concentration of SURE correlated with an increase in the fractionation factor of sulphur in sulphate compared to the values before the additions (Table 5-5) which attests that it was a microbiological process that consumed sulphate. The 1-6-H2-CH4 experiment had a large sulphate reducing activity by SRB as demonstrated by the high MPN of SRB (Figure 5-11), mainly Desulfovibrio assuming an MPN diversity similar to what was found in SURE 2 (Table 5-6), and a consumption of 0.7 mM of sulphate (Figure 5-25). The corresponding fractionation factor was 2.85 which is within the range for the Desulfovibrio species (Detmers et al. 2001). The highest fractionation factor, 12.24 ‰ was obtained with methane in 1-6-CH4. If there is an AOM process in Olkiluoto, it is plausible that such a process would be the most active in the sulphate-methane mixing zone where ONK-PVA6 is situated as indicated by Bomberg et al. (2015). The fractionation factors were much lower in SURE 3 where 3-6 had a higher fractionation factor than 3-15-SO4. In summary, the fractionation factors indicated sulphate reduction, in all SURE with sulphate and methane. Fractionation with H2 was expected, but whether the addition of methane would result in sulphate-reduction and concomitant fractionation was not known at the start of SURE. However, as the SURE experiments were performed from 2010 to 2014, new results from field work (Bomberg et al. 2015; Rajala et al. 2015) have made an AOM process in Olkiluoto more likely to occur. The metabolic pathways of that process remain to be explored. A hypothetical model, based on our present knowledge base, is presented last in this report (6.8).

6.6 Effects of H2 on microbial activity

H2 is found in Olkiluoto groundwater at concentrations of 0–1 µM in groundwater above −300 masl. Below this depth the concentration of H2 increases and can be 100 µM at −1000 masl (Pedersen et al. 2015). H2 can also originate from anaerobic corrosion of steel in a repository according to Eqs 10–12 (Figure 1-1). Because SRB readily reduce sulphate to sulphide with H2 according to Eq 3, the effect of H2 on the microbial activity of microbial communities in Olkiluoto groundwater was deemed important to investigate.

127

Adding H2 in the 1-6-H2-CH4 FCCS strongly affected Eh (Figure 5-22). This effect was the greatest at the start of the experiment when the H2 concentration was the highest. Eh was approximately −400 mV throughout the experiment. pH was also strongly influenced soon after the H2 addition probably due to proton consumption by the sulphate-reduction process with H2 (4 H2 + SO4

2– + H+ → HS– + 4 H2O). Molecular H2 is a key intermediate in the metabolic processes of many microorganisms, including SRB. The genus Desulfovibrio is known to use H2 for sulphate reduction as the sole electron donor (Barton and Fauque 2009), but few members of the genus can oxidize acetate (Detmers et al. 2001). Acetate did indeed increase in 1-6-H2-CH4 (Figure 5-19) suggesting that the present SRB did not metabolize this compound during the experimental time period. This genus, represented by Desulfovibrio aespoeensis (Motamedi and Pedersen 1998) was cultivated from the 2-15-SO4 and the 2-15-SO4-6 FCCSs (Table 5-6) and was also found in ONK-PVA6 groundwater (Table 5-7) and in the sulphate amended ONK-KR15 biofilms (Table 5-11). The concentration of H2 in the 2-15-SO4-6 FCCS was <1 µM (Figure 5-13) and sulphate-reduction based on H2 is slow at values below 1 µM. The half-saturation constants (Km) and maximum uptake rates (Vmax) (Figure 6-3) for sulphate and H2 have been determined for two thermophilic SRB by Sonne-Hansen et al. (1999). Km was approximately 3 µM for sulphate and approximately 2 µM for H2, in line with the constants determined for mesophilic SRB (Ks = 1 µM) (Kristjansson et al. 1982; Robinson and Tiedje 1984). The experiments with the FCCSs at the Äspö HRL corroborate these literature data for deep groundwater; the SRB Km value for H2 was determined at ≈1 µM of H2 (Figure 6-4) which is the same as the limiting value found in the 2-15-SO4-6 FCCS. These results suggest that sulphate-reducing activity will be vivid at H2 concentrations above 1 µM, for instance as shown for the MINICAN experiments where SRB corroded steel and iron at a very high rate (Smart et al. 2014). In SURE 2 and 3, H2 was, therefore, not expected to support the observed growth and sulphate reduction.

Figure 6-3. Vmax represents the maximum rate achieved by the system, at maximum (saturating) substrate concentrations. Michaelis constant Km is the substrate concentration at which the reaction rate is half of Vmax.

Figure 6-4. Sulphide production rate in groundwater from KJ0052F01 at Äspö HRL in the three flow cells between days 0–21 and 21–43 versus the average H2 concentration in these time intervals for the respective flow cell. (Figure from Pedersen 2012a).

128

6.7 Effect of CH4 on microbial activity

Methane was added to the FCCSs in SURE 1 and SURE 3 but not in SURE 2. This gave a range of methane concentrations in the FCCSs over time (Figure 5-14). There was a decrease in sulphate concentrations in several of the FCCSs in which methane and sulphate were added (Figure 5-25). This sulphate concentration decrease was very likely due to microbial reduction to sulphide, but the corresponding increase in concentration of dissolved sulphide (Figure 5-28) was mitigated by precipitation as iron sulphide. Therefore, sulphide concentrations did not reflect the total sulphate reduction in the FCCS. Instead, the decrease in sulphate concentration can be used for calculations of sulphate reduction. The sulphate consumption per day was calculated for the time intervals where there was a clear decrease in the concentration of sulphate for SURE 1 and 3 FCCSs but not for SURE 2 that showed only a small change (Table 6-4). Plotting the sulphate consumption rate versus the concentrations of methane (Figure 6-5) shows a Michaelis-Menten type of relation (Figure 6-3) very similar to the relation found for H2. Unfortunately, there were no methane concentrations close to Km which renders the curve in the graph a shape that does not fit the expected asymptotic relationship. However, the graph suggests that Vmax for sulphate reduction with methane was approximately 14 µM sulphate day−1 and that Km was in the range of 10 to 14 mM methane. An even higher Km for methane of 37 mM has been reported for an AOM process in sediments (Zhang et al. 2010). Further, the graph suggests that sulphate reduction with methane as an electron donor is very slow at methane concentrations below 4 mM. The concentration of methane was between 1.7 and 4.5 mM in ONK-PVA6 and approximately 6 mM in ONK-KR15. It was only ONK-PVA6 that contained sulphate; if sulphate reduction with methane occurred there, the rate must have been very slow. At the beginning of a mixing process in Olkiluoto groundwater between deep methane-rich and sulphate-poor groundwater, and sulphate-rich groundwater, relatively fast reduction of sulphate with methane can occur that will approach nil when methane is consumed below 4 mM. The presence of sequences related to AOM in the sequence libraries (Table 6-2) attests that AOM is possible when deep and shallow groundwater mixes as long as methane concentration is higher than 4 mM.

Evidence for the presence of active ANME in the sulphate-methane mixing zone of Olkiluoto has recently been published (Bomberg et al. 2015). In addition, it has recently been demonstrated that the addition of sulphate and methane to microbial communities from deep groundwater induced significant metabolic response of sulphate- and nitrate-reducing bacteria (Rajala et al. 2015). This is in agreement with the results from SURE 1 and 2 where the MPN numbers of SRB and NRB increased upon methane addition (Figure 5-8, Figure 5-11). A novel linage of ANME was recently shown to perform AOM with nitrate as an electron acceptor (Haroon et al. 2013) and in another work, elemental sulphur was demonstrated as a key intermediate in AOM (Milucka et al. 2012). Consequently, at least three different metabolic pathways are now known for the process of AOM and all three are conceptually possible in Olkiluoto groundwater as discussed next.

129

Table 6-4. Change in sulphate concentrations over time for selected time intervals where concentration of sulphate decreased, and analysed concentration of methane at the start of these intervals.

FCCS Day Interval days

SO4 first day

µM

SO4 last day

µM

∆SO4µM

SO4 µM/day

CH4 mM

1-6-CH4 63-105 42 1220 1040 180 6.58 6.25 2-15-SO4 0-103 103 940 920 20 0.2 4.0

2-15-SO4-6 0-103 103 1040 980 60 0.6 3.3 3-15-SO4 128-160 32 1120 680 440 13.8 25.7

3-6 128-160 32 1530 1040 490 15.3 24.0

Figure 6-5. Sulphate consumption rate in FCCSs in which methane and sulphate were added; data taken from Table 6-4.

6.8 A model of possible metabolic and geochemical pathways of sulphur in Olkiluoto

The SURE results have corroborated field observations from Olkiluoto where microbial abundance and diversity correlated positively with increasing concentrations of sulphate and methane (Figure 1-3). Similar results were recently published for microcosms in bottles of groundwater from Outokumpu where the combined addition of sulphate and methane activated microorganisms (Rajala et al. 2015). Further, Bomberg et al. (2015) found the zone with mixing sulphate- and methane-rich groundwater to be inhabited by active microbial communities. It seems safe to conclude that the combination of methane and sulphate in deep hard rock aquifer groundwater stimulates microbial sulphide producing activity. However, there are several possible alternative metabolic and geochemical pathways that may operate the sulphur transformations observed in SURE and in the groundwater aquifers of Olkiluoto. Figure 6-6 summarize these pathways and they are discussed in more detail next.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

CH4 (mM)

0

2

4

6

8

10

12

14

16

Sulp

hate

con

sum

ptio

n (µ

M d

ay-1

)

130

6.8.1 Sulphate-reducing bacteria produce sulphide from sulphate with H2 and/or organic matter

There is a large diversity of SRB that reduce sulphur in sulphate to sulphide with organic matter and/or H2, (Eqs 15 − 17). Many can oxidize organic compounds to carbon dioxide and these are denoted complete oxidizers (Detmers et al. 2001). The incomplete oxidizers cannot degrade acetate which therefore is excreted during the degradation of organic matter. The increase in acetate in SURE (Figure 5-19) can then be produced from organic matter by incomplete oxidizing SRB, or by autotrophic acetogens from H2 and CO2, or by heterotrophic acetogens that, similar to the incomplete oxidizing SRB, excrete acetate. A continuous reduction of sulphate to sulphide by SRB depends on a continuous supply of reducing power, i.e., H2 or organic matter (Number 1 in Figure 6-6). While H2 to some extent may continuously be supplied to sulphate-rich groundwater from deeper layers, organic matter must be produced at depth or transported from the photosynthetic processes on the ground surface and surface water. Because infiltration from the surface is very slow, as probably is also the transport of H2 from deep layers, sulphide production by SRB with H2 and organic matter as reducing agents will also be very slow in deep groundwater, unless there are larger amounts of H2 in the rock matrix that slowly diffuse into groundwater. An alternative possibility that should be considered is the anaerobic oxidation of methane with sulphate.

2(CH2O) + SO42−

+ 2H+→ 2HCO3− + HS− + 3H+ Eq. 15

2 lactate + SO42− + 2H+ → 2 acetate + 2CO2 + HS− + H+ + 2H2O Eq. 16

4H2 + SO42− + 2H+ → H2S + 4H2O Eq. 17

6.8.2 Anaerobic methanotrophs (ANME) type 1 and sulphate-reducing bacteria (SRB) oxidize methane with sulphate to carbon dioxide and sulphide

Sequences related to ANME type 1 were found in small percentages of the total number of reads in SURE and in some deep Olkiluoto groundwater samples (Bomberg et al. 2015). This type of ANME oxidize methane with SRB partners to carbon dioxide and sulphide (Eq 19, Number 2 in Figure 6-6). (Knittel and Boetius 2009). The typical SRB partners are found among the Desulfosarcina/Desulfococcus genera and members of these genera were observed at low relative abundance in the deep Olkiluoto groundwater samples. The fact that members of the ANME type 1 with its SRB partners were not found, or found in low relative abundance in SURE and deep Olkiluoto groundwater, indicates that they do not contribute to an AOM process in Olkiluoto.

6.8.3 Anaerobic methanotrophs (ANME) type 2 oxidize methane with sulphate to carbon dioxide and sulphur and disulphide

A new type of sulphate dependent AOM process was published in 2012 by Milucka et al. (2012). This type might not be an obligate syntrophic process between ANME and SRB, but may be carried out by ANME alone (Number 3 in Figure 6-6). These ANME deposit elemental sulphur from sulphate and expel disulphide (HS2

−) according to the following equations that sum up to Eq 5:

131

4HS2− +4H2O SO4

− + 7HS– + 5H+ Eq 18

7CH4 +8SO42− + 5H+ 4HS2

− + 7HCO3– + 11H2O Eq 19

CH4 + SO42– HCO3

– + HS– + H2O Eq 20

This type of ANME are found among the type 2 members to which the GOM_Arc_1 group belongs. Both the field investigations in Olkiluoto (Bomberg et al. 2015) and the SURE experiments had a high relative number of sequence reads for this ANME indicating that an AOM process according to Eqs 19 and 20 is possible in Olkiluoto.

These three SURE experiments were planned to investigate methane oxidation derived sulphate reduction, but unambiguous arguments for this hypothesis were not achieved and there are still some unresolved issues regarding AOM in Olkiluoto. Metagenomic studies of the DNA obtained in this study could possibly show if metabolic pathways for AOM were present in the experiment or not.

6.8.4 Sulphur-reducing bacteria produce sulphide from sulphur

The sulphur produced by ANME type 2 will be deposited on surfaces where sulphur-reducing bacteria can produce sulphide from the sulphur (Number 4 in Figure 6-6). In SURE we found a large relative abundance of Desulfuromonas almost exclusively in the biofilms (Table 6-1). This genus has a sulphur reducing metabolism (Pfennig and Biebl 1976) and it was found in biofilms were ANME type 2 would possibly deposit sulphur.

6.8.5 Sulphate-reducing bacteria disproportionate disulphide to sulphate and sulphide in a sulphide-sulphate loop

The disulphide produced by ANME 2 is expelled to the water phase were members of Desulfobulbaceae can disproportionate it to sulphate and sulphide (Number 5 in Figure 6-6). Desulfurivibrio was found both in groundwater from ONK-PVA6 and in the two SURE 2 ONK-PVA6 biofilms and one SURE 2 ONK-KR15 biofilm (Table 4-6, Table 5-7). This genus is known to disproportionate sulphur (Sorokin et al. 2008). The predominance in ONK-PVA6 groundwater is due to the presence of sulphate and methane which may support an AOM process by ANME type 2 while the absence of sulphate in ONK-KR15 will hinder Desulfurivibrio form proliferation.

6.8.6 A cryptic sulphur cycle produces ferrous iron and sulphur from ferric iron and sulphide

Dissimilatory sulphate reduction can be coupled to ferric iron reduction without the involvement of iron-reducing bacteria in high-sulphate systems (Kwon et al. 2014). Sivan et al. (2014) have shown that iron oxides stimulate sulphate-driven AOM indicating an important role for ferric iron in the AOM process. Sulphur (re)cycling was recently suggested to be a dominant process in iron cycling even in low-sulphate systems and in a manner difficult to predict using the classical thermodynamic ladder (Hansel et al. 2015). The authors convincingly demonstrated that despite low sulphate concentrations and regardless of an iron oxide substrate (ferrihydrite, Al-ferrihydrite, goethite, hematite), sulphidization was a dominant pathway in iron reduction. The process is mediated by the (re)cycling of sulphur upon the reaction of sulphide and iron oxides to support continued sulphur-based respiration −

132

a cryptic sulphur cycle involving the generation and consumption of sulphur intermediates (Number 6 in Figure 6-6). A cryptic sulphur cycle may consequently have contributed to the precipitation of iron sulphide in SURE. Such a cycle would be fed with sulphide from the AOM-related processes described above. Half of the produced sulphide ions would efficiently be precipitated as iron sulphide while the other half would be oxidized to sulphur which, in the presence of organic matter or H2, would be reduced to sulphide by sulphur-reducing bacteria and so on in a cycle as illustrated in Figure 6-6. Eventually most of the produced sulphide would end up as iron sulphide, as long as ferric iron is available to react with the biogenic sulphide.

In SURE at most 0.7−0.4 mM of sulphate was consumed (SURE 1 and SURE 3, respectively) and probably reduced to sulphide although similar concentrations of dissolved sulphide were not observed. If some of the sulphate was reduced to sulphur by ANME 2, that sulphur would of course need to be subtracted from the highest possible dissolved sulphide concentration. If ferric and ferrous iron contacted the sulphide produced, it would rapidly have precipitated as iron sulphide, directly via ferrous iron and indirectly via the cryptic sulphur cycle with ferric iron (Figure 6-6). However, such precipitation must be balanced by access to iron in the solids. There were at most 5 L of water in each FCCS. The total amount of consumed sulphate was then at most 3.5 mmol FCCS−1 in the 1-6-H2-CH4 FCCS. If all sulphate was reduced to sulphide the SRB would at most have produced 3.5 mol of sulphide FCCS−1 or, 3.5 mmol sulphur would have been produced via the ANME 2 AOM process (number 3 in Figure 6-6). If all sulphate was reduced to sulphide, precipitation with iron would require 3.5 mmol ferrous/ferric iron which in weight would correspond to 196 mg Fe. Each FCCS had 440 g of crushed rock in SURE 1 and 2, and in SURE 3 each FCCS had 400 g of garnets. The required amount of Fe, 0.20 g, corresponds to 0.05−0.06 weight % of the total weight of rock and garnets, respectively, in each FCCS. This is only a fraction of the available 1.5−4% iron in the crushed rock (Table 2-1) and the garnets (Sobolev et al. 1999). It, therefore, seems likely that there was more than enough available iron in the FCCSs to allow precipitation of all or most of the produced sulphide. Any elemental sulphur produced from sulphate would also be present as a solid.

133

Figure 6-6. A model of possible metabolic and geochemical pathways of sulphur in deep Olkiluoto aquifers. See text for details. Red text indicates solids; black text indicates dissolved solids. Blue arrows indicate reduction; red arrows indicate oxidation; purple arrows indicate disproportionation; black arrows indicate transport. 1. Sulphate-reducing bacteria produce sulphide from sulphate with H2 and/or organic matter. 2. Anaerobic methanotrophs (ANME) type 1 and sulphate-reducing bacteria (SRB) oxidize methane with sulphate to carbon dioxide and sulphide. 3. Anaerobic methanotrophs (ANME) type 2 oxidize methane with sulphate to carbon dioxide and sulphur and disulphide. 4. Sulphur-reducing bacteria produce sulphide from sulphur. 5. Sulphate-reducing bacteria disproprotionate disulphide to sulphate and sulphide in a sulphide-sulphate loop. 6. A cryptic sulphur cycle (yellow arrows) produces ferrous iron and sulphur from ferric iron and sulphide.

Groundwater CH3COO −

G r o u n d w a t e r

S0

SO4

HS2−

HS− HS −

ANME 1

Fe(III)

Fe2+ FeS

HS −

H2Organic matter

H2O CO2

CH4

CO2

3

43

2

5

5

4

2

1

1

6

6

SO4

HS−CO2 HS−

1

Sulphate-reducing bacteria

Desulfurivibrio

DesulfuromonasANME 2

4

5 5SRB

5

6

Fe2+

134

6.9 Proposed reduction-oxidation pathways for sulphur in Olkiluoto groundwater

The SURE results, supported by results from field investigations in Olkiluoto and Outokumpu, imply an AOM process that reduces sulphate to disulphide and sulphur coupled to a process that disproportionates sulphur to sulphate and sulphide, and a cryptic sulphur cycle that reduces ferric iron to ferrous iron which eventually precipitates sulphide as iron sulphide.

There is one additional sulphur-related process indicated by the high relative numbers of sequence reads related to sulphide- and sulphur-oxidizing Thiobacillus. However, at present, we lack proof for the presence in deep Olkiluoto aquifers of relevant oxidizing agents of reduced sulphur, such as O2 or nitrate, for these sulphur-oxidizing bacteria. If oxidation of reduced sulphur compounds does occur in parallel with the sulphur-reduction processes discussed above, a complete sulphur cycle could be speculated to exist in deep aquifers that would keep the concentrations of each of the sulphur cycle constituents in steady state. Such a cycle could then be driven by H2, CH4 and organic matter.

135

7 REFERENCES

Aalto, P., Aaltonen, I., Ahokas, H., Andersson, J., Hakala, M., Hellä, P., Hudson, J., Johansson, E., Kemppainen, K., Koskinen, L., Laaksoharju, M., Lahti, M., Lindgren, S., Mustonen, M., Pedersen, K., Pitkänen, P., Poteri, A., Snellman, M., Ylä-Mella, M., 2009. Programme for repository host rock characterisation in the ONKALO (ReRoC). Posiva Working Report 2009-31. Posiva Oy, Eurajoki, Finland, 1-68.

Aalto, P., Helin, J., Lindgren, S., Pitkänen, P., Ylä-Mella, M., Ahokas, H., Heikkinen, E., Klockars, J., Lahdenperä, A.-M., Korkealaakso, J., Lamminmäki, T., Pedersen, K., Karvonen, T., 2011. Baseline report for infiltration experiment. Posiva Working Report 2011-25. Posiva Oy, Olkiluoto, Finland, 1-152.

Anderson, C., Jakobsson, A.M., Pedersen, K., 2007. Influence of in situ biofilm coverage on the radionuclide adsorption capacity of subsurface granite. Environ. Sci. Technol. 41, 830-836.

Anderson, C., Pedersen, K., Jakobsson, A.M., 2006. Autoradiographic comparisons of radionuclide adsorption between subsurface anaerobic biofilms and granitic host rocks. Geomicrobiol. J. 23, 15-29.

Atekwana, E.A., Krishnamurthy, R.V., 1998. Seasonal variations of dissolved inorganic carbon and 13C of surface waters: application of a modified gas evolution technique. Journal of Hydrology 205, 265-278.

Barton, L.L., Fauque, G.D., 2009. Chapter 2 biochemistry, physiology and biotechnology of sulfate‐reducing bacteria. Adv. Appl. Microbiol. 68, 41-98.

Bomberg, M., Nyyssonen, M., Pitkanen, P., Lehtinen, A., Itavaara, M., 2015. Active Microbial Communities Inhabit Sulphate-Methane Interphase in Deep Bedrock Fracture Fluids in Olkiluoto, Finland. Biomed Research International Article id. 979530, 1-17.

Bowen, J.L., Morrison, H.G., Hobbie, J.E., Sogin, M.L., 2012. Salt marsh sediment diversity: a test of the variability of the rare biosphere among environmental replicates. ISME J. 6, 2014-2023.

Brysch, K., Schneider, I.C., Fuchs, G., Widdel, F., 1987. Lithoautotrophic growth of sulfate-reducing bacteria, and description of Desulfobacterium autotrophicum gen. nov., sp. nov. Arch. Microbiol. 148, 264-274.

Chen, F., Lu, J.R., Binder, B., J. Liu, Y.C., Hodson, R.E., 2001. Application of digital image analysis and flow cytometry to enumerate marine viruses stained with SYBR gold. Appl. Environ. Microbiol. 67, 539-545.

Cord-Ruwisch, R., Widdel, F., 1986. Corroding iron as a hydrogen source for sulphate reduction in growing cultures of sulphate-reducing bacteria. Appl. Microbiol. Biotechnol. 25, 169-174.

Cross, M.M., Manning, D.A.C., Bottrell, S.H., Worden, R.H., 2004. Thermochemical sulphate reduction (TSR): experimental determination of reaction kinetics and implications of the observed reaction rates for petroleum reservoirs. Org. Geochem. 35, 393-404.

Detmers, J., Bruchert, V., Habicht, K.S., Kuever, J., 2001. Diversity of sulfur isotope fractionations by sulfate-reducing prokaryotes. Appl. Environ. Microbiol. 67, 888-894.

Drake, H., Åström, M.E., Tullborg, E., Whitehouse, M., Fallick, A.E., 2013. Variability of sulphur isotope ratios in pyrite and dissolved sulphate in granitoid fractures down to 1km depth – Evidence for widespread activity of sulphur reducing bacteria. Geochim. Cosmochim. Acta 102, 143-161.

136

Ekendahl, S., Pedersen, K., 1994. Carbon transformations by attached bacterial populations in granitic groundwater from deep crystalline bed-rock of the Stripa research mine. Microbiology 140, 1565-1573.

Enning, D., Garrelfs, J., 2014. Corrosion of iron by sulfate-reducing bacteria: new views of an old problem. Appl. Environ. Microbiol. 80, 1226-1236.

Eren, A.M., Vineis, J.H., Morrison, H.G., Sogin, M.L., 2013. A filtering method to generate high quality short reads using illumina paired-end technology. PLoS One 8, e66643.

Eydal, H.S., Pedersen, K., 2007. Use of an ATP assay to determine viable microbial biomass in Fennoscandian Shield groundwater from depths of 3-1000 m. J. Microbiol. Methods 70, 363-373.

Greenberg, A.E., Clesceri, L.S., Eaton, A.D., 1992. Estimation of Bacterial Density, 18 ed. American Public Health Association, Washington.

Grenthe, I., W. Stumm, W., M., L., Nilsson, A.-C., Wikberg, P., 1992. Redox potentials and redox reactions in deep groundwater systems. Chem. Geol. 98, 131-150.

Hallbeck, L., Pedersen, K., 2012. Culture-dependent comparison of microbial diversity in deep granitic groundwater from two sites considered for a Swedish final repository of spent nuclear fuel. FEMS Microbiol. Ecol. 81, 66-77.

Hansel, C.M., Lentini, C.J., Tang, Y., Johnston, D.T., Wankel, S.D., Jardine, P.M., 2015. Dominance of sulfur-fueled iron oxide reduction in low-sulfate freshwater sediments. ISME J 9, 2400-2412.

Haroon, M.F., Hu, S., Shi, Y., Imelfort, M., Keller, J., Hugenholtz, P., Yuan, Z., Tyson, G.W., 2013. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567-570.

Haveman, S.A., Pedersen, K., 2002. Distribution of culturable microorganisms in Fennoscandian Shield groundwater. FEMS Microbiol. Ecol. 39, 129-137.

Hazen, T.C., Dubinsky, E.A., DeSantis, T.Z., Andersen, G.L., Piceno, Y.M., Singh, N., Jansson, J.K., Probst, A., Borglin, S.E., Fortney, J.L., Stringfellow, W.T., Bill, M., Conrad, M.E., Tom, L.M., Chavarria, K., Alusi, T.R., Lamendella, R., Joyner, D.C., Spier, C., Baelum, J., Auer, M., Zemla, M.L., Chakraborty, R., Sonnenthal, E.L., D'Haeseleer, P., Holman, H.Y., Osman, S., Lu, Z., Van Nostrand, J.D., Deng, Y., Zhou, J., Mason, O.U., 2010. Deep-sea oil plume enriches indigenous oil-degrading bacteria. Science 330, 204-208.

Hobbie, J.E., Daley, R.J., Jasper, S., 1977. Use of nucleopore filters for counting bacteria by fluorescence microscopy. Appl. Environ. Microbiol. 33, 1225-1228.

Huber, J.A., Mark Welch, D.B., Morrison, H.G., Huse, S.M., Neal, P.R., Butterfield, D.A., Sogin, M.L., 2007. Microbial population structures in the deep marine biosphere. Science 318, 97-100.

Huber, T., Faulkner, G., Hugenholtz, P., 2004. Bellerophon: a program to detect chimeric sequences in multiple sequence alignments. Bioinformatics 20, 2317-2319.

Huse, S.M., Dethlefsen, L., Huber, J.A., Mark Welch, D., Relman, D.A., Sogin, M.L., 2008. Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. PLoS Genet. 4, e1000255.

Huse, S.M., Huber, J.A., Morrison, H.G., Sogin, M.L., Welch, D.M., 2007. Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol 8, R143.

Jägevall, S., Rabe, L., Pedersen, K., 2011. Abundance and diversity of biofilms in natural and artificial aquifers of the Äspö Hard Rock Laboratory, Sweden. Microb. Ecol. 61, 410-422.

137

King, F., Lilja, C., Pedersen, K., Pitkänen, P., Vähänen, M., 2011. An update of the state-of-the-art report on the corrosion of copper under expected conditions in a deep geologic repository. Posiva report 2011-01. Posiva Oy, Eurajoki, Finland, 1-252.

Knittel, K., Boetius, A., 2009. Anaerobic oxidation of methane: progress with an unknown process. Annu. Rev. Microbiol. 63, 311-334.

Kotelnikova, S., Macario, A.J., Pedersen, K., 1998. Methanobacterium subterraneum sp. nov., a new alkaliphilic, eurythermic and halotolerant methanogen isolated from deep granitic groundwater. Int. J. Syst. Bacteriol. 48 Pt 2, 357-367.

Kristjansson, J.K., Schönheit, P., Thauer, R., 1982. Different Ks values for hydrogen of methanogenic bacteria and sulfate reducing bacteria: An explanation for the apparent inhibition of methanogenesis by sulfate. Arch. Microbiol. 131, 278-282.

Kwon, M.J., Boyanov, M.I., Antonopoulos, D.A., Brulc, J.M., Johnston, E.R., Skinner, K.A., Kemner, K.M., O’Loughlin, E.J., 2014. Effects of dissimilatory sulfate reduction on FeIII (hydr)oxide reduction and microbial community development. Geochim. Cosmochim. Acta 129, 177-190.

Kyle, J.E., Eydal, H.S., Ferris, F.G., Pedersen, K., 2008. Viruses in granitic groundwater from 69 to 450 m depth of the Äspö hard rock laboratory, Sweden. ISME J. 2, 571-574.

Lane, D.J., 1991. 16S/23S rDNA sequencing, in: Stackebrandt, E., Goodfellow, M. (Eds.), Nucleic acid techniques in bacterial systematics. John Wiley & Sons Ltd., Chichester, England.

Losekann, T., Knittel, K., Nadalig, T., Fuchs, B., Niemann, H., Boetius, A., Amann, R., 2007. Diversity and abundance of aerobic and anaerobic methane oxidizers at the Haakon Mosby Mud Volcano, Barents Sea. Appl. Environ. Microbiol. 73, 3348-3362.

Marteinsson, V.T., Runarsson, A., Stefansson, A., Thorsteinsson, T., Johannesson, T., Magnusson, S.H., Reynisson, E., Einarsson, B., Wade, N., Morrison, H.G., Gaidos, E., 2013. Microbial communities in the subglacial waters of the Vatnajokull ice cap, Iceland. ISME J. 7, 427-437.

Masurat, P., Eriksson, S., Pedersen, K., 2010. Evidence of indigenous sulphate-reducing bacteria in commercial Wyoming bentonite MX-80. Applied Clay Science 47, 51-57.

McMurdie, P.J., Holmes, S., 2014. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comp. Biol. 10, e1003531.

Milucka, J., Ferdelman, T.G., Polerecky, L., Franzke, D., Wegener, G., Schmid, M., Lieberwirth, I., Wagner, M., Widdel, F., Kuypers, M.M., 2012. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541-546.

Motamedi, M., Pedersen, K., 1998. Desulfovibrio aespoeensis sp nov., a mesophilic sulfate-reducing bacterium from deep groundwater at Äspö hard rock laboratory, Sweden. Int. J. Syst. Bacteriol. 48, 311-315.

Nielsen, M.E., Fisk, M.R., Istok, J.D., Pedersen, K., 2006. Microbial nitrate respiration of lactate at in situ conditions in ground water from a granitic aquifer situated 450 m underground. Geobiology 4, 43-52.

Noble, R.T., Fuhrman, J.A., 1998. Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat. Microb. Ecol. 14, 113-118.

Nyyssönen, M., Bomberg, M., Kapanen, A., Nousiainen, A., Pitkänen, P., Itävaara, M., 2012. Methanogenic and Sulphate-Reducing Microbial Communities in Deep Groundwater of Crystalline Rock Fractures in Olkiluoto, Finland. Geomicrobiol. J. 29, 863-878.

Pedersen, K., 1982. Method for studying microbial biofilms in flowing-water systems. Appl. Environ. Microbiol. 43, 6-13.

Pedersen, K., 2005. Äspö Hard Rock Laboratory. The MICROBE framework. Site descriptions, instrumentation, and characterisation. SKB International Progress Report

138

IPR-05-05. Swedish Nuclear Fuel and Waste Management Co, Stockholm, Sweden, 1-85.

Pedersen, K., 2008. Microbiology of olkiluoto groundwater 2004 – 2006. Posiva Report 2008-02. Posiva Oy, Olkiluoto, Finland, 156.

Pedersen, K., 2012a. Influence of H2 and O2 on sulphate-reducing activity of a subterranean community and the coupled response in redox potential. FEMS Microbiol. Ecol. 82, 653-665.

Pedersen, K., 2012b. Subterranean microbial populations metabolize hydrogen and acetate under in situ conditions in granitic groundwater at 450 m depth in the Aspo Hard Rock Laboratory, Sweden. FEMS Microbiol. Ecol. 81, 217-229.

Pedersen, K., 2013a. Metabolic activity of subterranean microbial communities in deep granitic groundwater supplemented with methane and H2. ISME J 7, 839-849.

Pedersen, K., 2013b. The MICROBE project. Achievements of a 10-year research program. SKB report R-13-49. Swedish Nuclear Fuel and Waste Management Co, Stockholm Sweden, 1-40.

Pedersen, K., Arlinger, J., Bengtsson, A., Edlund, J., Eriksson, L., Hallbeck, L., Johansson, J., Pääjärvi, A., Rabe, L., 2013. Sulphate reduction experiment SURE-1. Posiva Working Report 2013-57. Posiva Oy, Olkiluoto, Finland, 1-48.

Pedersen, K., Arlinger, J., Eriksson, S., Hallbeck, A., Hallbeck, L., Johansson, J., 2008. Numbers, biomass and cultivable diversity of microbial populations relate to depth and borehole-specific conditions in groundwater from depths of 4-450 m in Olkiluoto, Finland. ISME J. 2, 760-775.

Pedersen, K., Bengtsson, A., Edlund, J., Eriksson, L., 2014. Sulphate-controlled diversity of subterranean microbial communities over depth in deep groundwater with opposing gradients of sulphate and methane. Geomicrobiol. J. 31, 617-631.

Pedersen, K., Bengtsson, A., Edlund, J., Eriksson, L., Johansson, L., Rabe, L., 2015. Microbiology of Olkiluoto and ONKALO groundwater. Results and interpretations 2010 - 2013. Posiva Working Report 2015-42. Posiva Oy, Eurajoki, FInland, 1-66.

Pedersen, K., Bomberg, M., Itävaara, M., 2012. Summary report: Microbiology of Olkiluoto and ONKALO groundwater. Posiva Report 2012-42. Posiva Oy, Olkiluoto, Finland, 1-102.

Pedersen, K., Ekendahl, S., 1990. Distribution and activity of bacteria in deep granitic groundwaters of southeastern sweden. Microb. Ecol. 20, 37-52.

Pedersen, K., Ekendahl, S., 1992a. Assimilation of CO2 and introduced organic compounds by bacterial communities in groundwater from southeastern Sweden deep crystalline bedrock. Microb. Ecol. 23, 1-14.

Pedersen, K., Ekendahl, S., 1992b. Incorporation of CO2 and introduced organic compounds by bacterial populations in groundwater from the deep crystalline bedrock of the Stripa mine. J. Gen. Microbiol. 138, 369-376.

Pfennig, N., Biebl, H., 1976. Desulfuromonas acetoxidans gen. nov. and sp. nov., a new anaerobic, sulfur-reducing, acetate-oxidizing bacterium. Arch. Microbiol. 110, 3-12.

Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W., Peplies, J., Glockner, F.O., 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188-7196.

Purkamo, L., Bomberg, M., Kietäväinen, R., Salavirta, H., Nyyssönen, M., Nuppunen-Puputti, M., Ahonen, L., Kukkonen, I., Itävaara, M., 2015a. The keystone species of Precambrian deep bedrock biosphere belong to Burkholderiales and Clostridiales. Biogeosci. Disc. 12, 18103-18150.

139

Purkamo, L., Bomberg, M., Nyyssonen, M., Kukkonen, I., Ahonen, L., Itavaara, M., 2015b. Heterotrophic communities supplied by ancient organic carbon predominate in deep Fennoscandian bedrock fluids. Microb. Ecol. 69, 319-332.

Rajala, P., Bomberg, M., Kietäväinen, R., Kukkonen, I., Ahonen, L., Nyyssönen, M., Itävaara, M., 2015. Rapid Reactivation of Deep Subsurface Microbes in the Presence of C-1 Compounds. Microorganisms 3, 17-33.

Reardon, E.J., 1995. Anaerobic corrosion of granular iron: measurement and interpretation of hydrogen evolution rates. Environ. Sci. Technol. 29, 2936-2945.

Robinson, J.A., Tiedje, J.M., 1984. Competition between sulfate-reducing and methanogenic bacteria for H2 under resting and growing conditions. Arch. Microbiol. 137, 26-32.

Salter, S.J., Cox, M.J., Turek, E.M., Calus, S.T., Cookson, W.O., Moffatt, M.F., Turner, P., Parkhill, J., Loman, N.J., Walker, A.W., 2014. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87.

Sivan, O., Antler, G., Turchyn, A.V., Marlow, J.J., Orphan, V.J., 2014. Iron oxides stimulate sulfate-driven anaerobic methane oxidation in seeps. Proc Natl Acad Sci U S A 111, E4139-4147.

SKB, 2010. Design and production of the KBS‑3 repository. SKB Technical Repoert TR-10-12. wedish Nuclear Fuel and Waste Management Co, Stockholm, Sweden.

Smart, N.R., Rance, A.P., Reddy, B., Hallbeck, L., Pedersen, K., Johansson, A.J., 2014. In situ evaluation of model copper-cast iron canisters for spent nuclear fuel: a case of microbiologically influenced corrosion (MIC). Corrosion Engineering, Science and Technology 49, 548-553.

Sobolev, V.N., McCammon, C.A., Taylor, L.A., Snyder, G.A., Sobolev, N., V., 1999. Precise Mössbauer milliprobe determination of ferric iron in rock-forming minerals and limitations of electron microprobe analysis. Am. Mineral. 84, 78-85.

Sonne-Hansen, J., Westermann, P., Ahring, B.K., 1999. Kinetics of sulfate and hydrogen uptake by the thermophilic sulfate-reducing bacteria thermodesulfobacterium sp. Strain JSP and thermodesulfovibrio sp. Strain R1Ha3. Appl. Environ. Microbiol. 65, 1304-1307.

Sorokin, D.Y., Tourova, T.P., Mussmann, M., Muyzer, G., 2008. Dethiobacter alkaliphilus gen. nov. sp. nov., and Desulfurivibrio alkaliphilus gen. nov. sp. nov.: two novel representatives of reductive sulfur cycle from soda lakes. Extremophiles 12, 431-439.

Suttle, C.A., 2007. Marine viruses--major players in the global ecosystem. Nature reviews. Microbiology 5, 801-812.

Svensson, D., Dueck, A., Nilsson, U., Olsson, S., Sandén, T., Lydmark, S., Jägerwall, S., Pedersen, K., Hansen, S., 2011. Alternative buffer material. Status of the ongoing laboratory investigation of reference materials and test package 1. SKB Technical Report TR-11-06. Swedish Nuclear Fuel & Waste Management Co, Stockholm, Sweden, 1-146.

Thomsen, T.R., Finster, K., Ramsing, N.B., 2001. Biogeochemical and molecular signatures of anaerobic methane oxidation in a marine sediment. Appl. Environ. Microbiol. 67, 1646-1656.

Toropainen, V., 2009. Core drilling of drillholes ONK-PVA06 and ONK-PVA07 in ONKALO at Olkiluoto 2009. Posiva Working Report 2009-121. Olkiluoto, Finland, 1-61.

Toropainen, V., 2011. Core drilling of drillholes ONK-KR13, ONK-KR14 and ONK-KR15 in ONKALO at Olkiluoto 2010–2011. Posiva Working Report 2011-39. Posiva Oy, Olkiluoto, Finland, 1-127.

140

Trinchero, P., Delos, A., Molinero, J., Dentz, M., Pitkänen, P., 2014. Understanding and modelling dissolved gas transport in the bedrock of three Fennoscandian sites. Journal of Hydrology 512, 506-517.

Wersin, P., Alt-Epping, P., Pitkänen, P., Román-Ross, G., Trinchero, P., Mol inero, J., Smith, P., Snellman, M., Filby, A., Kiczka, M., 2014. Sulphide fluxes and concentrations in the spent nuclear fuel repository at Olkiluoto. Posiva Report 2014-01. Posiva Oy, Olkiluoto, Finland, 1-245.

Widdel, F., Bak, F., 1992. Gram-negative, mesophilic sulphate-reducing bacteria. Springer-Verlag, New York.

Wu, X., Holmfeldt, K., Hubalek, V., Lundin, D., Astrom, M., Bertilsson, S., Dopson, M., 2015. Microbial metagenomes from three aquifers in the Fennoscandian shield terrestrial deep biosphere reveal metabolic partitioning among populations. ISME J.

Zehnder, A.J., Brock, T.D., 1980. Anaerobic methane oxidation: occurrence and ecology. Appl. Environ. Microbiol. 39, 194-204.

Zhang, Y., Henriet, J.P., Bursens, J., Boon, N., 2010. Stimulation of in vitro anaerobic oxidation of methane rate in a continuous high-pressure bioreactor. Bioresour. Technol. 101, 3132-3138.

141

A. APPENDIX 1

Table A-1. Pre-treatment of deep groundwater samples.

Parameter Container (L) N2-

shielding / Filtering

Preserving chemicals and details Laboratory

Conductivity, density, pH, NH4

1 × 0.5 HDPE -/- - TVO

Alkalinity, acidity

1 × 0.5 Duran bottle x/x Sampling with titration sampler TVO

S2– 3 × 0.1 measuring bottle

x/x 0.5 mL Zn(Ac)2 and 0.5 mL 0.1 M NaOH. Sampling with sampler

TVO

Cl, Br, SO4, Stot

1 × 0.25 HDPE -/x TVO

F 1 × 0.25 HDPE -/x TVO

Fe2+, Fetot

6 × 0.05 measuring bottle

x/x

Adding a ferrozine reagent to Fe2+ samples in nitrogen atmosphere. Sampling with sampler

TVO

Sodium fluorescein

1 × 0.05 measuring bottle

-/x TVO

DIC/DOC 1 × 0.05 brown glass bottle -/x Sampling with sampler TVO

Na, Ca, K, Mg, Fe, Mn, SiO2

1 × 0.25 PE, acid washed -/x 2.5 mL conc. HNO3 / 250 mL TVO

PO4 1 × 0.25 HDPE -/x 2.5 mL 4 M H2SO4/ 250 mL TVO

Sr, Btot 1 × 0.1 HDPE, acid washed -/x 1 mL suprapur HNO3 / 100 mL VTT

Ntot, NO2, NO3 1 × 0.25 HDPE -/x Rauman

ymp. lab.

142

Table A-2. Methods and detection limits for groundwater chemistry.

PARAMETER APPARATUS AND METHOD

DETECTION LIMIT

UNCERTAINTY OF THE MEASUREMENT

pH pH meter ISO-10532

0.05 pH units

Conductivity Conductivity analyser SFS-EN-27888

5 µS/cm 5%

Sodium fluorescein

Fluorometry 0.7 µg/L 6% at level 15 µg/L 5% at level 200 µg/L 1% at level 275 µg/L

Alkalinity *Titration SFS 3005 to the appropriate extent

0.05 mmol/L 12% at level 0.16 mmol/L

# Titration SFS 3005 and SFS-EN ISO 9963-1 to the appropriate extent

0.03 mmol/L

22% at level 0.03-1 mmol/L 2% at level 1-12 mmol/L

Acidity & Titration SFS 3005 to the appropriate extent

0.05 mmol/L 10%

¤ Titration SFS 3005 and SFS-EN ISO 9963-1 to the appropriate extent

0.05 mmol/L 20% at level 0.05-0.1 mmol/L 16% at level 0.1-0.5 mmol/L 11% at level >0.5 mmol/L

DIC SFS-EN 1484 to the appropriate extent

0.3 mg/L 27% at level 1 mg/L 2.9% at level 20 mg/L 2.4% at level 70 mg/L

NPOC 0.4 mg/L 6.3% at level 20 mg/L 7.0% at level 70 mg/L

Al Ca Fetot

K Mg Mn Na Si Sr

ICP-OES 2 µg/L 0.2 mg/L 0.005 mg/L 0.1 mg/L 0.005 mg/L 0.003 mg/L 0.2 mg/L 0.01 mg/L 0.002 mg/L

18% at level 2-10 µg/L 10% at level 10-100 µg/L 16% 32% at level 0.005-0.05 mg/L 5% at level 0.05-10 mg/L 12% 12% 11% at level 2-20 mg/L 10% at level 0.05-2 mg/L 10% at level 2-20 mg/L 12% at level 20-500 mg/L 15% at level 0.01-0.1 mg/L 6% at level 0.1-20 mg/L 30% at level 0.002-0.05 mg/L 6% at level 0.05-20 mg/L

Fe2+ Spectrophotometry ASTM E1615-08 to the appropriate extent

0.02 mg/L 20% at level 0.04 mg/L 4.3% at level 0.3 mg/L 2.3% at level 0.5 mg/L

143


DETECTION LIMIT


Co, Pb, Btot, Ba, Cd, Cu, As, Ni, Zn, U

ICP-MS (high resolution) 0.5 µg/L 2 µg/L 5 µg/L 0.2 µg/L

10% Near detection limit 30%

Hg CVAAS 0.02 µg/L 20% (0.05 µg/L) Cl *Titration

SFS 3006 to the appropriate extent

5 mg/L

3.3% at level 100 mg/L 1.7% at level 1000 mg/L

Cl #Titration SFS 3006 to the appropriate extent

50 mg/L 6.5% at level 50-30000 mg/L

IC, conductivity detector

0.1 mg/L 11% at level 1 mg/L 11% at level 5 mg/L 4% at level 10 mg/L

Br IC, conductivity detector 0.1 mg/L 11% at level 1 mg/L 11% at level 5 mg/L 4% at level 10 mg/L

F *ISE (Mettler D58)

0.09 mg/L 27% at level 0.1 mg/L 2.7% at level 1.2 mg/L 1.9% at level 3 mg/L

#ISE /Metrohm 905, Titrando 0.05 mg/L 3.3% at level 0.1- 5 mg/L IC, conductivity detector 0.1 mg/L 11% at level 1 mg/L

11% at level 5 mg/L 4% at level 10 mg/L

PO4 IC, conductivity detector 0.1 mg/L 11% at level 1 mg/L 11% at level 5 mg/L 4% at level 10 mg/L

S2- Spectrophotometer SFS 3038 to the appropriate extent

0.02 mg/L 40% at level 0.04 mg/L 11% at level 0.15 mg/L 7.8% at level 0.5 mg/L

SO4 IC, conductivity detector 0.1 mg/L 11% at level 1 mg/L 11% at level 5 mg/L 4% at level 10 mg/L

Stot H2O2 oxidation + IC 0.2 mg/L 20% at level 1 mg/L 6.8% at level 3 mg/L

NH4

Spectrophotometer SFS 3032 to the appropriate extent

0.02 mg/L 16% at level 0.1 mg/L 4.7% at level 0.5 mg/L

Total nitrogen, Ntot

FIA method, SFS-EN ISO 11905-1

0.05 mg/L 10%

Nitrate nitrogen. NO3-N

*FIA method, SFS-EN ISO 13395

0.02 mg/L 10%

#IC, conductivity detector 0.4 mg/L 11% at level 1 mg/L 11% at level 5 mg/L 4% at level 10 mg/L

Nitrite nitrogen, *FIA method, SFS-EN ISO 0.010 mg/L 10%

144


DETECTION LIMIT


NO2-N 13395 #IC, conductivity detector 0.2 mg/L 11% at level 1 mg/L

11% at level 5 mg/L 4% at level 10 mg/L

18O MS < 0.1‰ 18O (SO4) MS 0.5‰ 3H Fluid scintillation

spectrometry (LSC) after electrolytic enrichment. measured in Tritium units (TU)

0.2 TU ~ 0.3-1.0 TU

2H MS 1‰ 13C (DIC) MS Precision is ∼ 0.1‰ 14C (DIC) AMS Precision is ∼ 0.5% 87Sr/86Sr MS 0.003‰ 34S (SO4) MS 0.1 mBq/L 0.2‰ * Method or apparatus is used before 1st of March 2013. # Method or apparatus is used since 1st of March 2013. & Method or apparatus is used before 25th of November 2013 ¤ Method or apparatus is used since 25th of November 2013

145

Table A-3. Analysis results of ONK-PVA6. Results with uncertainties are shown in italics. Analysis Unit ONK-PVA6 Sample date 2010-11-08 2011-07-06 2012-01-10 2012-04-02 2012-07-03 2012-11-06 TDS mg L−1 8580 7560 7250 7212 7270 6888 Charge balance % −0.32 −4.27 −1.94 −1.58 +2.45 −0.74 pH 7.6 8.0 8.1 7.8 7.6 7.8 Conductivity mS m−1 1520 1370 1290 1279 1280 12.36 Sodium fluorescein G L−1 <1 <1 <1 <1 <1 <1 Total alkalinity, HCl uptake mmol L−1 0.23 0.24 0.28 0.3 0.30 0.33 Carbonate alkalinity, HCl uptake mmol L−1 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 Total acidity, NaOH uptake mmol L−1 0.07 <0.05 0.05 0.07 <0.05 <0.05 Ammonium, NH4

+ mg L−1 0.03 <0.02 <0.02 0.02 <0.02 <0.02 Bicarbonate, HCO3 mg L−1 14 15 17 18 18 20 Dissolved Inorg. Carbon mg L−1 3.2 3.4 3.3 3.3 3.2 3.7 Non Purgeable Org. Carbon mg L−1 2.5 3.5 2.3 2.5 2.8 2.5 Bromide, Br mg L−1 34 28 26 26 25 24 Calcium, Ca mg L−1 1100 860 870 880 890 790 Chloride, Cl mg L−1 5190 4670 4390 4350 4250 4110 Fluoride, F mg L−1 1.4 1.5 1.4 1.5 1.5 1.4 Iron, Fe2+ mg L−1 0.02 0.04 0.05 0.16 0.09 0.09 Iron, Fetotal mg L−1 19 0. 033 0.044 0.14 0.11 0.09 Magnesium, Mg mg L−1 18 18 19 18 20 18 Nitrate, NO3 mg L−1 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 Nitrite, NO2 mg L−1 <0.01 <0.010 <0.010 <0.010 <0.010 <0.01 Nitrogen, Ntot mg L−1 0.071 0.15 0.060 0.098 0.11 0.063 Phosphate, PO4 mg L−1 - - <0.1 0.2 <0.1 <0.1 Potassium, K mg L−1 7.2 6.8 7.7 7.1 8.1 7.9 Silica, SiO2 mg L−1 7.3 8.3 8.6 8.8 8.9 9.0 Sodium, Na mg L−1 2100 1820 1770 1760 1910 1760 Strontium, Sr mg L−1 11 8.5 7.2 6.9 7.7 6.8 Sulphate, SO4

2- mg L−1 96 120 128 135 130 141 Sulphide, S2- mg L−1 <0.02 <0.02 <0.02 0.04 0.05 0.08 Sulphur, Stotal mg L−1 35 41 41 43 43 47

146

Analysis results of ONK-PVA6 (continued.). Results with uncertainties are shown in italics. Analysis Unit ONK-PVA6 Sample date 2013-01-15 2013-04-17 2013-06-26 2013-10-29 2014-01-21 TDS mg L−1 6637 6090 6321 6292 6092 Charge balance % 0 1.1 -1.63 -2.8 -3.65 pH 7.7 7.1 7.6 7.8 7.8 Conductivity mS m−1 11.88 11.28 11.18 11.33 10.99 Sodium fluorescein G L−1 <1 <1 <1 <1 <1 Total alkalinity, HCl uptake mmol L−1 0.36 0.38 0.35 0.36 0.4 Carbonate alkalinity, HCl uptake mmol L−1 <0.05 <0.05 <0.05 <0.05 <0.05 Total acidity, NaOH uptake mmol L−1 0.06 0.06 <0.05 <0.05 <0.05 Ammonium, NH4

+ mg L−1 <0.02 <0.02 <0.02 <0.02 <0.02 Bicarbonate, HCO3 mg L−1 22 23 21 22 24 Dissolved Inorg. Carbon mg L−1 4.2 4.7 4.3 4.9 4.3 Non Purgeable Org. Carbon mg L−1 3 2.8 4.3 2.7 3 Bromide, Br mg L−1 23 20 23 22 20 Calcium, Ca mg L−1 750 700 700 670 660 Chloride, Cl mg L−1 3920 3540 3770 3790 3680 Fluoride, F mg L−1 1.5 1.3 1.3 1.5 1.4 Iron, Fe2+ mg L−1 0.04 0.06 0.15 0.04 <0.02 Iron, Fetotal mg L−1 0.039 0.052 0.15 0.041 <0.02 Magnesium, Mg mg L−1 17 18 18 16 16 Nitrate, NO3 mg L−1 <0.40 <0.40 <0.40 <0.40 <0.40 Nitrite, NO2 mg L−1 <0.20 <0.20 <0.20 <0.20 <0.20 Nitrogen, Ntot mg L−1 0.076 0.06 0.064 0.067 0.049 Phosphate, PO4 mg L−1 <0.10 <0.10 <0.10 <0.10 <0.10 Potassium, K mg L−1 7.3 6.7 6.8 6.7 6.7 Silica, SiO2 mg L−1 9.2 9.3 9.2 9.1 9.2 Sodium, Na mg L−1 1730 1600 1610 1600 1510 Strontium, Sr mg L−1 6.8 6.2 6.2 6.1 5.6 Sulphate, SO4

2- mg L−1 150 166 157 148 159 Sulphide, S2- mg L−1 0.08 0.1 0.07 0.08 0.09 Sulphur, Stotal mg L−1 49 56 51 48 51

147

Table A-4. Analysis results of ONK-KR15/75.0–75.2 m. Results with uncertainties are shown in italics. Analysis Unit ONK-KR15 75 – 75.2 m Sample date 2012-01-10 2012-02-06 2012-03-13 2012-04-12 2012-06-12 2012-09-11 TDS mg L−1 10180 10320 10640 10560 10460 10160 Charge balance % −1.73 −2.37 −1.84 −1.75 +0.72 −0.49 pH 7.8 8.1 8.1 7.9 8.0 7.8 Conductivity mS m−1 1790 1840 1860 1830 1820 1820 Sodium fluorescein G L−1 <1 <1 <1 <1 <1 <1 Total alkalinity, HCl uptake mmol L−1 0.17 0.17 0.17 0.17 0.17 0.18 Carbonate alkalinity, HCl uptake mmol L−1 <0.05 <0.05 <0.05 <0.05 <0.05 <0.05 Total acidity, NaOH uptake mmol L−1 <0.05 <0.05 0.05 <0.05 <0.05 <0.05 Ammonium, NH4

+ mg L−1 0.03 <0.02 0.02 0.02 0.03 0.03 Bicarbonate, HCO3 mg L−1 10 10 10 10 10 11 Dissolved Inorg. Carbon mg L−1 2.1 <3 <2.4 <3 <3 <3 Non Purgeable Org. Carbon mg L−1 <2 <3 4.0 3.2 <3.5 <3 Bromide, Br mg L−1 46 49 48 47 47 48 Calcium, Ca mg L−1 1320 1270 1450 1400 1310 1240 Chloride, Cl mg L−1 6340 6450 6640 6580 6390 6260 Fluoride, F mg L−1 1.5 1.4 1.4 1.5 1.5 1.5 Iron, Fe2+ mg L−1 <0.02 <0.02 <0.02 <0.02 <0.02 <0.02 Iron, Fetotal mg L−1 <0.005 0.010 0.010 <0.025 <0.05 <0.025 Magnesium, Mg mg L−1 34 36 34 37 36 34 Nitrate, NO3 mg L−1 <0.02 0.02 <0.02 - <0.02 <0.02 Nitrite, NO2 mg L−1 <0.010 <0.010 <0.010 - 0.01 <0.010 Nitrogen, Ntot mg L−1 <0.05 0.11 <0.05 <0.05 <0.05 <0.05 Phosphate, PO4 mg L−1 <0.1 <0.1 0.2 <0.1 <0.2 <0.1 Potassium, K mg L−1 10 12 10 12 11 11 Silica, SiO2 mg L−1 6.6 6.6 6.3 6.3 6.4 6.4 Sodium, Na mg L−1 2400 2470 2430 2450 2640 2540 Strontium, Sr mg L−1 11 11 11 11 11 11 Sulphate, SO4

2- mg L−1 <0.8 <0.8 <0.8 <0.8 <0.8 <0.5 Sulphide, S2- mg L−1 <0.02 <0.02 <0.02 0.02 <0.02 <0.02 Sulphur, Stotal mg L−1 <0.3 <0.3 0.32 <0.3 <0.3 <1.5

148

Analysis results of ONK-KR15 (continued). Results with uncertainties are shown in italics. Analysis Unit ONK-KR15 Sample date 2013-01-08 2013-04-17 2013-06-26 2013-11-06 2014-01-30 TDS mg L−1 10160 10060 10060 10010 9884 Charge balance % -1.8 -1.32 -1.1 -0.95 -3.21 pH 7.9 7.8 8.1 8.1 8 Conductivity mS m−1 18.12 17.76 17.8 17.43 17.39 Sodium fluorescein µg L−1 <1 <1 <1 <1 <1 Total alkalinity, HCl uptake mmol L−1 0.18 0.14 0.14 0.14 0.14 Carbonate alkalinity, HCl uptake mmol L−1 <0.05 <0.05 <0.05 <0.05 <0.05 Total acidity, NaOH uptake mmol L−1 <0.05 <0.05 <0.05 <0.05 <0.05 Ammonium, NH4

+ mg L−1 0.02 0.02 0.02 0.02 0.04 Bicarbonate, HCO3 mg L−1 11 8.5 8.5 8.5 8.5 Dissolved Inorg. Carbon mg L−1 <2.40 <3 <3 2 <3 Non Purgeable Org. Carbon mg L−1 <2.40 <3 5.3 <3 <3 Bromide, Br mg L−1 48 47 48 46 45 Calcium, Ca mg L−1 1320 1260 1260 1230 1210 Chloride, Cl mg L−1 6330 6240 6230 6190 6220 Fluoride, F mg L−1 1.5 1.8 1.7 1.6 1.8 Iron, Fe2+ mg L−1 <0.02 <0.02 <0.02 <0.02 <0.02 Iron, Fetotal mg L−1 <0.0250 <0.02 <0.0250 <0.02 <0.02 Magnesium, Mg mg L−1 33 33 33 34 32 Nitrate, NO3 mg L−1 <0.40 <0.40 <0.80 <0.40 <0.80 Nitrite, NO2 mg L−1 <0.20 <0.20 <0.40 <0.20 <0.40 Nitrogen, Ntot mg L−1 <0.05 <0.05 <0.04 <0.04 <0.04 Phosphate, PO4 mg L−1 <0.10 <0.10 <0.20 <0.10 <0.20 Potassium, K mg L−1 9.9 11 11 9.9 9.3 Silica, SiO2 mg L−1 6.1 6.3 6.1 6.4 6.3 Sodium, Na mg L−1 2390 2440 2450 2470 2340 Strontium, Sr mg L−1 11 10 11 10 10 Sulphate, SO4

2- mg L−1 <0.50 <0.40 <0.40 <0.20 0.3 Sulphide, S2- mg L−1 <0.02 <0.02 <0.02 <0.02 <0.02 Sulphur, Stotal mg L−1 <1.50 <0.60 <0.60 <0.60 <0.30

149

B. APPENDIX SURE 1

Table B-1. pH, Eh, and chemistry data.

Drillhole Sampled (Y-M-D)

Time (day) pH Redox

mV Fe2+

µM Sulphide

µM Sulphate

mM Acetate

µM Lactate

µM 1-6-air 2011-03-14 0 7.45 80.7 1.08 0.00 1.27 9.7 10.7 1-6-CH4 2011-03-14 0 7.48 79.3 0.72 0.00 1.23 19.3 16.0 1-6-H2-CH4 2011-03-14 0 7.64 76.3 0.90 0.00 1.19 19.4 21.4 1-6-air 2011-03-18 3 7.37 140 0.18 0.00 1.25 38.4 10.7 1-6-CH4 2011-03-18 3 7.23 146 0.54 0.00 1.19 28.8 10.7 1-6-H2-CH4 2011-03-18 3 7.45 –155 0.36 0.00 1.17 19.2 10.7 1-6-air 2011-04-05 21 7.10 200 0.54 0.00 1.25 48.2 10.7 1-6-CH4 2011-04-05 21 7.24 –89.9 20.4 0.00 1.25 57.8 16.0 1-6-H2-CH4 2011-04-05 21 7.68 –309 20.6 3.81 1.19 38.7 16.0 1-6-air 2011-04-26 42 6.88 181 1.08 0.00 1.23 67.3 16.0 1-6-CH4 2011-04-26 42 7.36 –134 38.5 0.00 - 151 0.0 1-6-H2-CH4 2011-04-26 42 8.45 –342 0.36 80.6 0.90 114 10.7 1-6-air 2011-05-17 63 6.98 200 1.08 0.00 - 48.4 0.0 1-6-CH4 2011-05-17 63 7.51 –133 19.7 0.00 1.22 188 0.0 1-6-H2-CH4 2011-05-17 63 8.73 –342 0.72 208 0.47 188 0.0 1-6-air 2011-06-07 84 6.86 171 0.36 0.00 1.21 95.7 0.0 1-6-CH4 2011-06-07 84 7.21 –168 53.8 1.44 1.10 312 0.0 1-6-H2-CH4 2011-06-07 84 8.47 –338 0.90 259 0.45 233 0.0 1-6-air 2011-06-28 105 7.20 125 0.36 0.00 1.21 67.5 0.0 1-6-CH4 2011-06-28 105 7.26 –161 48.2 0.00 1.04 320 0.0 1-6-H2-CH4 2011-06-28 105 8.41 –333 0.72 261 0.44 286 0.0

150

Table B-2. Gas data.


Time (day)

H2 µM

CH4 mM

CO2 µM

He µM

O2 µM

1-6-air 2011-03-14 0 <0.01 2720 5.33 88.8 <10 1-6-CH4 2011-03-14 0 <0.01 2772 3.67 89.8 <10 1-6-H2-CH4 2011-03-14 0 <0.01 2680 2.41 88.8 <10 1-6-air 2011-03-18 3 <0.01 2170 5.28 45.5 2221.94 1-6-CH4 2011-03-18 3 <0.01 9900 8.35 72.5 <10 1-6-H2-CH4 2011-03-18 3 9830 10790 7.65 81.4 <10 1-6-air 2011-04-05 21 <0.01 2370 12.2 40.8 1881.32 1-6-CH4 2011-04-05 21 <0.01 9280 14.1 36.1 <10 1-6-H2-CH4 2011-04-05 21 6240 9170 8.8 29.8 <10 1-6-air 2011-04-26 42 <0.01 2424 17.3 36.2 1355.33 1-6-CH4 2011-04-26 42 <0.01 8190 - 22.5 <10 1-6-H2-CH4 2011-04-26 42 3410 8310 1.33 17.3 <10 1-6-air 2011-05-17 63 <0.01 2160 27 27.3 752.39 1-6-CH4 2011-05-17 63 <0.01 6560 17.3 10.4 <10 1-6-H2-CH4 2011-05-17 63 1680 6640 1.08 14.6 <10 1-6-air 2011-06-07 84 <0.01 2430 29.0 26.2 410.64 1-6-CH4 2011-06-07 84 <0.01 6890 28.0 9.1 <10 1-6-H2-CH4 2011-06-07 84 1320 7240 0.0 6.9 <10 1-6-air 2011-06-28 105 <0.01 2200 35.7 21.6 216.17 1-6-CH4 2011-06-28 105 <0.01 5950 32.3 11.9 <10 1-6-H2-CH4 2011-06-28 105 538 5530 0.00 8.0 <10

151

Table B-3. Microbiology data

FCC Sampled (Y-M-D)

Time (day)

TNC (cells mL−1)

SD n VLP (virus mL−1)

SD n ATP (amol mL−1)

SD n ATP (amol g−1) SD n

CHAB (cells mL−1)

SD n

1-6-air 2011-03-14 0 180000 22000 3 37800 - 1 1370000 80200 3 1.25E+06 1.26E+05 6 69000 18500 3 1-6-CH4 2011-03-14 0 170000 49000 3 40000 - 1 2980000 93800 3 1.16E+06 5.90E+04 6 108000 17700 3 1-6-H2-CH4 2011-03-14 0 160000 14000 3 100 - 1 731000 20400 3 1.24E+06 2.48E+04 6 60700 15500 3 1-6-air 2011-03-18 3 93000 19000 3 814 - 1 32900 1710 3 9.30E+05 1.58E+05 6 7830 651 3 1-6-CH4 2011-03-18 3 88000 6300 3 100 - 1 30000 2580 3 1.55E+06 6.33E+04 6 4070 351 3 1-6-H2-CH4 2011-03-18 3 68000 13000 3 100 - 1 24600 1560 3 1.72E+06 1.48E+05 6 2600 1280 3 1-6-air 2011-04-05 21 9800 890 3 100 - 1 7410 415 3 1.15E+05 7.27E+04 6 1100 120 3 1-6-CH4 2011-04-05 21 97000 41000 3 6840 - 1 108000 4010 3 8.65E+05 2.47E+05 6 16000 2500 3 1-6-H2-CH4 2011-04-05 21 110000 7600 3 18700 - 1 44300 3820 3 9.84E+05 1.85E+05 6 14000 800 3 1-6-air 2011-04-26 42 110000 24000 3 480000 - 1 14300 407 3 4.26E+05 2.37E+05 6 7500 1100 3 1-6-CH4 2011-04-26 42 200000 36000 3 956000 - 1 83400 6890 3 8.34E+05 2.63E+05 6 28000 1500 3 1-6-H2-CH4 2011-04-26 42 170000 11000 3 364000 - 1 35400 3860 3 3.71E+05 8.65E+03 6 6900 250 3 1-6-air 2011-05-17 63 62000 4800 3 304000 - 1 5510 252 3 1.38E+06 5.79E+04 6 3600 200 3 1-6-CH4 2011-05-17 63 150000 52000 3 221000 - 1 42600 1850 3 1.21E+06 9.20E+04 6 16000 800 3 1-6-H2-CH4 2011-05-17 63 200000 39000 3 79400 - 1 28600 1060 3 1.57E+06 1.19E+05 6 3400 200 3 1-6-air 2011-06-07 84 31000 9800 3 11700 - 1 7660 799 3 2.28E+05 4.28E+04 6 5800 529 3 1-6-CH4 2011-06-07 84 260000 98000 3 980000 1 65300 2490 3 7.90E+05 1.42E+05 6 63300 14600 3 1-6-H2-CH4 2011-06-07 84 160000 23000 3 64300 1 36800 4060 3 1.21E+05 2.07E+04 6 3400 265 3 1-6-air 2011-06-28 105 51000 6300 3 155000 1 10100 1160 3 2.61E+05 6.76E+04 6 5030 808 3 1-6-CH4 2011-06-28 105 160000 40000 3 178000 1 47000 3170 3 2.35E+05 9.86E+03 6 16000 1990 3 1-6-H2-CH4 2011-06-28 105 150000 20000 3 94900 1 20600 1080 3 7.01E+04 6.94E+03 6 3970 1100 3

152

Table B-4. Most probable number data; L and U limits are 95% confidence values.


Time (day)

NRB (cells mL−1)

L limit

U limit

SRB (cells mL−1)

L limit U limit AA

(cells mL−1)

L limit

U limit

1-6-air 2011-03-14 0 <160000 - - 220 100 580 220 100 580

1-6-CH4 2011-03-14 0 <160000 - - 170 70 480 170 70 400

1-6-H2-CH4 2011-03-14 0 <160000 - - 3000 1000 11000 28 12 69

1-6-air 2011-03-18 3 17000 7000 48000 5 2 17 1 0 3

1-6-CH4 2011-03-18 3 1700 700 4800 8 3 25 0.4 0.1 1.5

1-6-H2-CH4 2011-03-18 3 9000 4000 25000 30 10 120 0.2 0.1 1

1-6-air 2011-04-05 21 1700 800 4100 0 0 1 0.4 0.1 1.5

1-6-CH4 2011-04-05 21 30000 10000 130000 7000 3000 21000 7 3 21

1-6-H2-CH4 2011-04-05 21 30000 10000 130000 3300 1500 7700 14 6 36

1-6-air 2011-04-26 42 5000 2000 17000 0.8 0.3 2.4 0.2 0.1 1.1

1-6-CH4 2011-04-26 42 50000 20000 170000 13000 5000 39000 50 20 170

1-6-H2-CH4 2011-04-26 42 30000 10000 120000 3000 1000 12000 3 1 12

1-6-air 2011-05-17 63 170 80 410 0.2 - - 0.2 - -

1-6-CH4 2011-05-17 63 80000 30000 250000 24000 10000 94000 14000 6000 36000

1-6-H2-CH4 2011-05-17 63 17000 7000 48000 5000 2000 17000 7000 3000 21000

1-6-air 2011-06-07 84 800 300 2400 0.2 - - 0.2 - -

1-6-CH4 2011-06-07 84 80000 30000 250000 22000 10000 58000 130 50 390

1-6-H2-CH4 2011-06-07 84 11000 4000 30000 1700 700 4800 7 3 21

1-6-air 2011-06-28 105 350 160 820 0.2 0.1 1.1 0.2 - -

1-6-CH4 2011-06-28 105 13000 5000 39000 24000 10000 94000 3 1 12

1-6-H2-CH4 2011-06-28 105 70000 30000 210000 3000 1000 12000 17 7 48

153

Appendix SURE 2

Table B-5. pH, Eh, and chemistry data.


Time (day) pH Redox

mV Fe2+

µM Sulphide

µM Sulphate

mM Acetate

µM DOC mM

2-15 2012-04-26 0 8.23 102.90 5.19 >0.01 0.01 18.67 0.297 2-15-SO4 2012-04-26 0 8.24 15.50 4.30 >0.01 0.94 9.33 0.253 2-15-SO4-6 2012-04-26 0 8.21 8.40 4.12 >0.01 1.04 18.67 0.223 2-15 2012-05-03 7 8.11 36.20 2.33 >0.01 0.00 18.83 0.236 2-15-SO4 2012-05-03 7 8.06 74.30 - >0.01 0.94 9.50 0.218 2-15-SO4-6 2012-05-03 7 7.94 150.40 1.97 >0.01 0.98 18.83 0.237 2-15 2012-05-15 19 7.96 32.60 6.27 >0.01 0.00 18.67 0.239 2-15-SO4 2012-05-15 19 7.90 27.10 4.83 >0.01 0.96 28.00 0.254 2-15-SO4-6 2012-05-15 19 7.90 0.70 3.04 >0.01 0.98 28.17 0.273 2-15 2012-06-05 40 7.83 5.20 7.34 >0.01 0.00 18.67 0.270 2-15-SO4 2012-06-05 40 7.84 -34.70 7.34 >0.01 0.88 28.00 0.274 2-15-SO4-6 2012-06-05 40 7.96 -39.20 5.91 >0.01 0.97 46.83 0.264 2-15 2012-06-26 61 7.72 -32.40 11.10 >0.01 0.00 28.00 0.274 2-15-SO4 2012-06-26 61 7.71 -33.30 10.21 >0.01 0.90 28.00 0.233 2-15-SO4-6 2012-06-26 61 7.80 -71.10 8.24 >0.01 1.02 28.00 0.263 2-15 2012-07-17 82 7.62 -78.70 15.22 >0.01 0.00 28.00 0.253 2-15-SO4 2012-07-17 82 7.59 -92.30 14.86 >0.01 0.92 28.00 0.258 2-15-SO4-6 2012-07-17 82 7.72 -114.80 13.25 >0.01 0.98 37.17 0.228 2-15 2012-08-07 103 7.42 -46.60 20.23 >0.01 0.00 37.33 0.238 2-15-SO4 2012-08-07 103 7.35 -63.30 21.49 >0.01 0.92 37.33 0.245 2-15-SO4-6 2012-08-07 103 7.48 -83.10 17.91 >0.01 0.98 28.17 0.229

154

Table B-6. Gas data.


Time (day)

H2 µM

CH4 mM

CO2 µM

He µM

2-15 2012-04-26 0 0.4 4367 39 75 2-15-SO4 2012-04-26 0 0.3 4735 5 71 2-15-SO4-6 2012-04-26 0 0.2 3845 10 47 2-15 2012-05-03 7 0.5 4286 6 72 2-15-SO4 2012-05-03 7 0.5 4245 10 75 2-15-SO4-6 2012-05-03 7 0.4 3547 24 57 2-15 2012-05-15 19 0.2 4033 6 7 2-15-SO4 2012-05-15 19 0.2 4286 10 46 2-15-SO4-6 2012-05-15 19 0.6 3616 14 40 2-15 2012-06-05 40 0.7 3829 12 18 2-15-SO4 2012-06-05 40 0.7 4061 8 58 2-15-SO4-6 2012-06-05 40 0.4 3661 12 25 2-15 2012-06-26 61 0.5 3567 13 50 2-15-SO4 2012-06-26 61 0.3 4163 9 8 2-15-SO4-6 2012-06-26 61 0.3 3388 9 13 2-15 2012-07-17 82 0.2 3082 13 4 2-15-SO4 2012-07-17 82 0.2 3735 10 6 2-15-SO4-6 2012-07-17 82 0.2 3800 9 9 2-15 2012-08-07 103 0.2 4286 12 7 2-15-SO4 2012-08-07 103 0.2 3363 12 15 2-15-SO4-6 2012-08-07 103 0.2 2796 8 9

155

Table B-7. Microbiology data

FCC Sampled (Y-M-D)

Time (day)

TNC (cells mL−1)



SD n CHAB (cells mL−1)

SD n

2-15 2012-04-26 0 29000 1 560000 1 12300 1380 3 13500 3710 3 2-15-SO4 2012-04-26 0 29000 1 700000 1 13400 1040 3 14200 2110 3 2-15-SO4-6 2012-04-26 0 29000 1 510000 1 33700 6700 3 17000 6060 3 2-15 2012-05-03 7 24000 3200 3 490000 88000 3 10000 680 3 14700 1280 3 2-15-SO4 2012-05-03 7 26000 3500 3 170000 8400 3 16000 2060 3 12500 3310 3 2-15-SO4-6 2012-05-03 7 19000 1900 3 940000 16000 3 13700 1500 3 11400 1850 3 2-15 2012-05-15 19 34000 2700 3 980000 21000 3 20800 1100 3 23000 4880 3 2-15-SO4 2012-05-15 19 42000 6600 3 270000 34000 3 31300 2070 3 36900 3950 3 2-15-SO4-6 2012-05-15 19 41000 4800 3 1000000 25000 3 20300 650 3 33500 4100 3 2-15 2012-06-05 40 55000 7400 3 1200000 180000 3 27100 1640 3 21300 3100 3 2-15-SO4 2012-06-05 40 71000 14000 3 870000 45000 3 35700 4490 3 44500 11200 3 2-15-SO4-6 2012-06-05 40 59000 3400 3 270000 13000 3 25200 3470 3 37100 5730 3 2-15 2012-06-26 61 120000 7200 3 400000 17000 3 44100 2650 3 19100 1240 3 2-15-SO4 2012-06-26 61 130000 2100 3 88000 12000 3 63700 5120 3 64800 23600 3 2-15-SO4-6 2012-06-26 61 110000 16000 3 470000 23000 3 42700 1520 3 34100 6580 3 2-15 2012-07-17 82 130000 31000 3 510000 23000 3 29800 8310 3 17500 3880 3 2-15-SO4 2012-07-17 82 180000 79000 3 310000 11000 3 54200 9550 3 69300 15300 3 2-15-SO4-6 2012-07-17 82 170000 38000 3 330000 9100 3 22600 2360 3 21700 3280 3 2-15 2012-08-07 103 100000 13000 3 530000 4000 3 56900 6450 3 19000 2660 3 2-15-SO4 2012-08-07 103 170000 37000 3 96000 3900 3 81700 3090 3 73300 25800 3 2-15-SO4-6 2012-08-07 103 81000 8500 3 59000 100000 3 12300 1380 3 9950 1090 3

156


FCC Sampled (Y-M-D)

Time (day)

NRB (cells mL−1)

L limit

U limit

IRB (cells mL−1) L limit U limit SRB

(cells mL−1) L limit

U limit

2-15 2012-04-26 0 2200 1000 5800 110 40 300 0.2 0.1 1.1

2-15-SO4 2012-04-26 0 1100 400 3000 140 60 360 1.3 0.5 3.8

2-15-SO4-6 2012-04-26 0 5000 2000 15000 350 160 820 1.7 0.7 4.6

2-15 2012-05-03 7 500 200 1700 0.4 0.1 1.5 <0.2 - -

2-15-SO4 2012-05-03 7 170 70 480 0.9 0.3 2.5 <0.2 - -

2-15-SO4-6 2012-05-03 7 600 300 1800 14 6 36 <0.2 - -

2-15 2012-05-15 19 1700 800 4100 1.7 0.7 4 <0.2 - -

2-15-SO4 2012-05-15 19 1400 600 3600 0.4 0.1 1.3 <0.2 - -

2-15-SO4-6 2012-05-15 19 5000 2000 17000 3.4 1.6 8 <0.2 - -

2-15 2012-06-05 40 2300 900 8600 0.4 0.1 1.7 <0.2 - -

2-15-SO4 2012-06-05 40 13000 5000 39000 0.6 0.2 1.8 3 1 12

2-15-SO4-6 2012-06-05 40 160000 60000 530000 160 60 530 1.3 0.5 3.8

2-15 2012-06-26 61 8000 3000 25000 17 7 46 0.4 0.1 1.7

2-15-SO4 2012-06-26 61 8000 3000 25000 <0.2 - - 5000 2000 17000

2-15-SO4-6 2012-06-26 61 90000 30000 290000 140 60 360 30 10 120

2-15 2012-07-17 82 3000 1000 12000 7 2 21 0.4 0.1 1.7

2-15-SO4 2012-07-17 82 28000 12000 69000 <0.2 - - 8000 3000 25000

2-15-SO4-6 2012-07-17 82 22000 10000 58000 900 300 2900 1700 700 4800

2-15 2012-08-07 103 2300 900 86000 0.8 0.3 2.4 <0.2 - -

2-15-SO4 2012-08-07 103 3000 1000 11000 11 4 30 5000 2000 17000

2-15-SO4-6 2012-08-07 103 5000 2000 15000 8 3 25 5000 2000 17000

157


FCC Sampled (Y-M-D)

Time (day)

AA (cells mL−1)

L limit

U limit

MM (cells mL−1) L limit U limit

2-15 2012-04-26 0 0.4 0.1 1.7 <0.2 - - 2-15-SO4 2012-04-26 0 0.4 0.1 1.7 <0.2 - - 2-15-SO4-6 2012-04-26 0 0.8 0.3 2.4 <0.2 - - 2-15 2012-05-03 7 <0.2 - - <0.2 - - 2-15-SO4 2012-05-03 7 <0.2 - - <0.2 - - 2-15-SO4-6 2012-05-03 7 <0.2 - - <0.2 - - 2-15 2012-05-15 19 <0.2 - - <0.2 - - 2-15-SO4 2012-05-15 19 0.2 0.1 1.1 n.a. n.a. n.a. 2-15-SO4-6 2012-05-15 19 0.2 0.1 1.1 n.a. n.a. n.a. 2-15 2012-06-05 40 0.2 0.1 1.1 <0.2 - - 2-15-SO4 2012-06-05 40 0.4 0.1 1.7 <0.2 - - 2-15-SO4-6 2012-06-05 40 0.6 0.2 1.8 <0.2 - - 2-15 2012-06-26 61 <0.2 - - <0.2 - - 2-15-SO4 2012-06-26 61 0.4 0.1 1.7 <0.2 - - 2-15-SO4-6 2012-06-26 61 0.7 0.2 2.1 <0.2 - - 2-15 2012-07-17 82 <0.2 - - <0.2 - - 2-15-SO4 2012-07-17 82 1.7 0.7 4.6 <0.2 - - 2-15-SO4-6 2012-07-17 82 13 5 39 <0.2 - - 2-15 2012-08-07 103 0.4 0.1 1.7 <0.2 - - 2-15-SO4 2012-08-07 103 <0.2 - - <0.2 - - 2-15-SO4-6 2012-08-07 103 0.4 0.1 1.5 <0.2 - -

159

C. APPENDIX SURE 3

Table C-1. pH, Eh, and chemistry data.


Time (day) pH Redox

mV Fe2+

µM Sulphide

µM Sulphate

mM Acetate

µM DOC mM

3-6 2013-06-28 2 7.62 278.00 2.87 -0.56 1.60 19.17 0.212 3-15 and 3-15-SO4 2013-06-28 2 7.76 269.90 1.61 -0.47 0.00 28.67 0.050 3-6 2013-08-29 64 7.45 -36.90 1.75 -0.44 1.64 28.67 0.249 3-15-SO4 2013-08-29 64 7.81 63.90 34.02 -0.50 1.15 19.17 0.083 3-15 2013-08-29 64 7.63 55.30 26.50 -0.50 0.00 38.17 0.071 3-6 2013-10-01 97 7.54 -21.00 36.89 -0.22 1.54 9.83 0.271 3-15-SO4 2013-10-01 97 7.60 -45.30 39.04 -0.37 1.15 19.50 0.107 3-15 2013-10-01 97 7.62 -35.80 36.89 -0.44 0.00 9.83 0.093 3-6 2013-12-12 169 8.53 -73.70 4.30 2.46 1.09 0.00 0.363 3-15-SO4 2013-12-12 169 8.36 -171.90 8.42 1.03 0.73 38.17 0.256 3-15 2013-12-12 169 8.19 -173.70 9.31 0.90 0.00 57.17 0.221 3-6 2014-01-20 206 8.54 -94.70 0.25 1.75 0.99 28.67 0.388 3-15-SO4 2014-01-21 207 8.27 -193.50 1.04 0.59 0.78 9.67 0.265 3-15 2014-01-23 209 8.26 -175.90 1.00 0.47 0.02 0.00 0.247

160

Table C-2. Gas data.


Time (day)

H2 µM

CH4 µM

CO2 µM

He µM

3-6 2013-06-28 2 0.01 1306 4.86 86 3-15 and 3-15-SO4 2013-06-28 2 0.02 1784 1.89 132 3-6 2013-08-29 64 0.06 551 9.88 0.12 3-15-SO4 2013-08-29 64 0.13 3441 16.00 51 3-15 2013-08-29 64 0.20 3506 13.30 0.12 3-6 2013-10-01 97 0.05 882 14.00 191 3-15-SO4 2013-10-01 97 0.02 3057 6.33 9 3-15 2013-10-01 97 0.02 2527 3.62 8.57 3-6 2013-12-12 169 0.30 24041 5.55 0.31 3-15-SO4 2013-12-12 169 0.44 25714 3.14 0.34 3-15 2013-12-12 169 0.36 23673 3.42 0.31 3-6 2014-01-20 206 0.04 19673 6.37 0.28 3-15-SO4 2014-01-21 207 0.34 22204 5.06 0.29 3-15 2014-01-23 209 0.03 22408 4.98 0.30

161

Table C-3. Microbiology data

FCC Sampled (Y-M-D)

Time (day)

TNC (cells mL−1)



SD n CHAB (cells mL−1)

SD n

3-6 2013-06-28 2 190000 40000 3 320000 5600 3 108000 10130 2 36300 12300 6

3-15 and 3-15-SO4 2013-06-28 2 180000 180000 3 59000 21000 2 111000 11930 2 91700 23000 6

3-6 2013-08-29 64 520000 93000 3 2900000 32000 3 220000 16350 3 26800 5300 6 3-15-SO4 2013-08-29 64 240000 14000 3 230000 33000 3 121000 12710 3 20200 6550 6

3-15 2013-08-29 64 180000 180000 3 230000 13000 3 110000 2090 3 32000 4590 6

3-6 2013-10-01 97 440000 93000 3 1600000 60000 3 192000 17210 3 21400 4910 6 3-15-SO4 2013-10-01 97 380000 58000 3 580000 44000 3 229000 15900 3 27200 8470 6 3-15 2013-10-01 97 210000 28000 3 240000 22000 3 136000 11670 3 13200 5120 6

3-6 2013-12-12 169 120000 8900 3 1100000 370000 3 10200 1080 2 443 72.3 3

3-15-SO4 2013-12-12 169 110000 17000 3 8600 1100 3 8500 490 2 587 25.2 3 3-15 2013-12-12 169 100000 6300 3 31000 5100 3 12700 930 2 1550 85.6 3

3-6 2014-01-20 206 11000 42000 3 1500000 110000 3 10400 2110 2 367 47.3 3

3-15-SO4 2014-01-21 207 50000 17000 3 32000 3800 3 3600 440 3 757 179 3 3-15 2014-01-23 209 73000 20000 3 28000 3400 3 4300 630 3 947 46.2 3

162

Table C-4. Most probable number data; L and U limits are 95% confidence values.

FCC Sampled (Y-M-D)

Time (day)

NRB (cells mL−1)

L limit

U limit

SRB (cells mL−1) L limit U limit AA

(cells mL−1) L limit U limit

3-6 2013-06-28 2 90000 30000 290000 170 70 480 0.8 0.3 2.4 3-15 and 3-15-SO4

2013-06-28 2 30000 10000 130000 0.2 0.1 1.1 <0.2 0 0

3-6 2013-08-29 64 3000 1000 12000 900 400 2500 0.9 0.3 2.4 3-15-SO4 2013-08-29 64 14000 6000 36000 0.7 0.2 21 <0.2 0 0 3-15 2013-08-29 64 17000 7000 48000 0.4 0.1 1.7 0.2 0.1 1.1 3-6 2013-10-01 97 5000 2000 17000 300 100 1200 0.2 0.1 1.0. 3-15-SO4 2013-10-01 97 8000 3000 25000 1.3 0.5 3.8 <0.2 0 0 3-15 2013-10-01 97 17000 7000 48000 <0.2 - - 1.4 0.6 3.5 3-6 2013-12-12 169 300 100 1200 300 100 1200 <0.2 0 0 3-15-SO4 2013-12-12 169 1700 700 4800 0.4 0.1 1.7 <0.2 0 0 3-15 2013-12-12 169 1700 700 4800 0.2 0.1 1.1 0.2 0.1 1.1 3-6 2014-01-20 206 500 200 1700 500 200 1700 0.2 0.1 1.1 3-15-SO4 2014-01-21 207 500 200 1700 <0.2 - - 0.2 0.1 1.1 3-15 2014-01-23 209 2200 1000 5800 <0.2 - - <0.2 0 0

163

D. APPENDIX SURE 1-3

Table D-1. Data for calculation of ratios of attached ATP over ATP in circulating water. SURE Experiment

Analysis day

Volume in FCCS

(mL)

ATP amol mL-1

ATP pmol water

FCCS-1

ATP amol g-1

ATP pmol

g-1

Bead weight

g

Bead surface

cm2

ATP pmol cm-2

total surface

area cm-2

ATP pmol

surface FCCS-1

Ratio: ATP water / ATP surface

Ratio: ATP surface /

ATP water

1-6-air day 3 4600 32900 151 930000 0.930 440 3600 0.114 5500 625 0.24 4.13 1-6-air day 105 2600 10100 26 261000 0.261 440 3600 0.032 5500 175 0.15 6.68 1-6-CH4 day 3 4600 30000 138 1550000 1.550 440 3600 0.189 5500 1042 0.13 7.55 1-6-CH4 day 105 2600 47000 122 235000 0.235 440 3600 0.029 5500 158 0.77 1.29 1-6-H2CH4 day 3 4750 24600 117 1720000 1.720 440 3600 0.210 5500 1156 0.10 9.89 1-6-H2CH4 day 105 2750 20600 57 70100 0.070 440 3600 0.009 5500 47 1.20 0.83 2-15 day 3 4600 10000 46 134896 0.135 440 3600 0.016 5500 91 0.51 1.97 2-15 day 103 2600 56900 148 173780 0.174 440 3600 0.021 5500 117 1.27 0.79 2-15-SO4 day 3 4600 16000 74 81283 0.081 440 3600 0.010 5500 55 1.35 0.74 2-15-SO4 day 103 2600 81700 212 66069 0.066 440 3600 0.008 5500 44 4.78 0.21 2-15-SO4-6 day 3 4750 13700 65 70795 0.071 440 3600 0.009 5500 48 1.37 0.73 2-15-SO4-6 day 103 2750 12300 34 263027 0.263 440 3600 0.032 5500 177 0.19 5.23 3-15 day 0 4600 108000 497 1445440 1.445 800 5500 0.210 7000 1472 0.34 2.96 3-15 day 97 2600 192000 499 1380384 1.380 800 5500 0.201 7000 1405 0.36 2.82 3-15 day 206 2600 10400 27 1380384 1.380 800 5500 0.201 7000 1405 0.02 51.98 3-15-SO4 day 0 4600 111000 511 1445440 1.445 800 5500 0.210 7000 1472 0.35 2.88 3-15-SO4 day 97 2600 229000 595 389045 0.389 800 5500 0.057 7000 396 1.50 0.67 3-15-SO4 day 207 2600 3600 9 389045 0.389 800 5500 0.057 7000 396 0.02 42.32 3-6 day 0 4750 220000 1045 1258925 1.259 800 5500 0.183 7000 1282 0.82 1.23 3-6 day 97 2750 136000 374 912011 0.912 800 5500 0.133 7000 929 0.40 2.48 3-6 day 209 2750 4300 12 912011 0.912 800 5500 0.133 7000 929 0.01 78.53

understanding microbial production of sulphide in deep … › files › 4411 › wr_2016-48.pdf ·...

Documents