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Microbial diversity and capacity for arsenic biogeochemical cycling in aquifers associated with thermal mobilization

2025· article· en· W4409418461 on OpenAlexafffundabout
Molly Chen, Daniel S. Grégoire, Pascale St-Germain, Carolina Berdugo‐Clavijo, Laura A. Hug

Bibliographic record

VenueThe Science of The Total Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsImperial Oil (Canada)Carleton UniversityUniversity of Waterloo
FundersImperial Oil ResourcesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsImperial Oil Limited
KeywordsBiogeochemical cycleMobilizationArsenicCyclingAquiferEnvironmental scienceDiversity (politics)Environmental chemistryEarth scienceGeologyChemistryGroundwaterGeographyPolitical scienceGeotechnical engineeringForestryArchaeology

Abstract

fetched live from OpenAlex

Thermal recovery technologies for in-situ bitumen extraction can result in the heating of surrounding aquifers, potentially mobilizing arsenic naturally present in the sediments to the groundwater. The relative toxicity of dissolved arsenic is related to its speciation, with As(V) being less toxic than As(III). Microorganisms have various mechanisms of arsenic detoxification and metabolism, which include genes for efflux, methylation, and reduction/oxidation of As(V)/As(III). We characterized the microbial communities along two aquifer transects associated with thermally mobilized arsenic near Northeastern Alberta oil sands deposits. 16S rRNA amplicons and metagenomic sequencing data of biomass from filtered groundwater indicated major changes in the dominant taxa between wells, especially those currently experiencing elevated arsenic concentrations. Annotation of arsenic-related genes indicated that efflux pumps (arsB, acr3), intracellular reduction (arsC) and methylation (arsM) genes were widespread among community members but comparatively few organisms encoded genes for arsenic respiratory reductases (arrA) and oxidases (arxA, aioA). While this indicates that microbes have the capacity to exacerbate arsenic toxicity by increasing the relative concentration of As(III), some populations of iron oxidizing and sulfate reducing bacteria (including novel Gallionella and Thermodesulfovibrionia populations) show potential for indirect bioremediation through formation of insoluble iron/sulfide minerals which adsorb or coprecipitate arsenic. An unusually high proportional abundance of a single Paceibacteria population that lacked arsenic resistance genes was identified in one high‑arsenic well, and we discuss hypotheses for its ability to persist. Overall, this study describes how aquifer microbial communities respond to thermal and arsenic plumes, and predicts potential contributions of microbes to arsenic biogeochemical cycling under this disturbance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.189
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes3
Has abstractyes

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