Examining microbial carbon source cycling in arsenic contaminated Bangladesh aquifers through lipid and isotopic analyses
Bibliographic record
Abstract
Understanding how organic matter is microbially cycled through Bangladesh aquifers is a key component in understanding the spatial and temporal patterns of arsenic release into groundwater occurring on wide regional scales. There is a current gap in the literature for how overall microbial carbon cycles are functioning in Bangladesh aquifers, how these microbial metabolisms factor into arsenic release, including methodology as to approach these questions in situ. This research aimed to provide insight into carbon sources and cycling of the microbial communities in Bangladesh aquifers through a complimentary applied suite of lipidomic, isotopic and inorganic analytical approaches on in situ sediments and groundwater from Bangladesh aquifers. Through radiocarbon analyses of phospholipid fatty acids (PLFA's), bacterial populations in a shallow Holocene-aged and high arsenic aquifer were found to be predominantly utilizing younger organic matter as their carbon source rather than older sedimentary carbon. At the sites studied, the sources of younger organic matter that coincide with zones where increased reductive dissolution of iron and arsenic release is occurring were consistent with human and livestock waste identified through sedimentary sterol distributions (phytosterols and coprstonaol) and Cl/Br mass ratios in groundwater. Since poor sanitation is widespread across Bangladesh, sewage-derived waste should be considered a prevalent potential microbial carbon source is these systems. An examination of sediment- versus groundwater-associated microbial communities in Bangladesh aquifers (through PLFA analysis) suggested that the former is 5-6 orders of magnitude more abundant than the latter. Archaeal communities, examined through both groundwater methane and sedimentary archaeal lipid (archaeol and glycerol dialkyl glycerol tetraether (GDGT)) analysis, are suggested to be highly active (depths 5-240 m) but to varying degrees in Bangladesh aquifers. Methanogenesis, dominantly being carried out through CO2 reduction, appears to be spatially associated at some sites with zones of iron/arsenic reductive dissolution in the Bangladesh aquifers. The analytical approaches and conceptual frameworks applied throughout this dissertation have been demonstrated to be effective strategies to understand how microbial carbon cycling is occurring at a community level and intimately involved in arsenic release.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".