Tracing and quantifying microbes in riverbank filtration sites combining online flow cytometry and integrated surface water – groundwater modelling
Bibliographic record
Abstract
Understanding microbial transport behaviour in river-aquifer systems is crucial for drinking water management. Particularly after heavy rain and peak flow events, the quality of groundwater pumped near streams might be impacted by high microbial loads. Dissolved noble gases have been shown to be conservative tracers of river-aquifer interactions and provide information on pathways and travel times of alluvial groundwater. However, due to size exclusion, microbes appear to travel faster than solutes and dissolved gas tracers might therefore not provide insights representative for microbial transport. Recently, online flow cytometry (FCM) has been shown to be a promising tool to track on site, continuously, and in near-real time the movement of microbes in riverbank filtration settings (Besmer et al., 2016). Beyond direct cell counting, unique microbial community patterns such as high (HNA) and low (LNA) nucleic acid content microbes, often referred to as larger and smaller prokaryotes, can be distinguished by FCM.Aiming to identify preferential transport pathways of microbes and develop a quantitative tool for riverbank filtration site modeling, we combine online FCM and noble gas analyses with integrated surface-subsurface hydrological modelling (ISSHM). We use a dual-permeability approach with a two-site kinetic deposition mode which enables the co-simulation of fast preferential microbial transport and slower bulk transport, along with attachment and detachment of the microbes in high and low permeability regions of the pore space (after Bradford et al., 2009). The formulation was implemented in the ISSHM HydroGeoSphere (HGS; Aquanty, Inc.) and enables multispecies transport, e.g., to represent HNA and LNA groups.An 8-month measurement campaign at a riverbank filtration site in Switzerland showed that cell concentrations and microbial community patterns are sensitive to surface water infiltration and travel distance in the alluvial aquifer. Distinctly different changes in microbial patterns could be observed for peak flow events, river restoration activities, and spring snowmelt periods. The observed reactive microbial transport behaviour was reproduced and quantified by systematic numerical experiments on the wellfield scale using the transport of conservative dissolved noble gases as a benchmark.In summary, the interdisciplinary approach combining online flow cytometry, dissolved (noble) gas analysis and explicit microbial transport simulations with an ISSHM is a promising tool to understand and quantify the reactive transport of microbes from rivers into and through alluvial aquifers. REFERENCESBesmer, M. D., Epting, J., Page, R. M., Sigrist, J. A., Huggenberger, P., & Hammes, F. (2016): Online flow cytometry reveals microbial dynamics influenced by concurrent natural and operational events in groundwater used for drinking water treatment. Sci. Rep., 6, Article 38462. https://doi.org/10.1038/srep38462Bradford, S. A., Torkzaban, S., Leij, F., Šimůnek, J., & van Genuchten, M. T. (2009). Modeling the coupled effects of pore space geometry and velocity on colloid transport and retention. Water Resources Research, 45(2). https://doi.org/10.1029/2008WR00709
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".