Microplastics attenuation from surface water to drinking water: Impact of treatment and managed aquifer recharge – and identification uncertainties
Bibliographic record
Abstract
River water can be used to recharge aquifers exploited for drinking water production. Several recent studies reported microplastics (MPs) in river water, and therefore, the potential contamination of groundwater by MPs is a growing concern among stakeholders and citizens. In this research, we investigate the fate of MPs (> 20 μm) along six different stages of a major Managed Aquifer Recharge (MAR)-water supply system in Switzerland. About 20 l of water were filtered using steel meshes at each location in triplicates. In the laboratory, MPs deposited on the anodisc filters were identified using Focal Plane Array (FPA) micro-Fourier-Transform-InfraRed (μFTIR) spectroscopy. The obtained hyperspectral data were processed using the imaging software Microplastics Finder for MPs identification and classification. Our results revealed a 20-fold decrease in MPs concentration from the Rhine River bed water (112 ± 27.4 MPs/l) to after the coagulation, flocculation and sedimentation (5.5 ± 2.2 MPs/l), a further 3-fold decrease to after the sand-filtration system (1.8 ± 0.9 MPs/l), corresponding to an overall removal efficiency of 98.4 %. The MPs concentrations remained low following MAR (2.7 ± 0.7 MPs/l) through a Quaternary gravel aquifer. Activated carbon filters did not substantially further reduce MPs concentrations. The percentage of fragments (≈95 %) prevailed over fibers (≈5 %) at all locations, with fibers being longer and more abundant in the river water. Overall, this study demonstrates the effectiveness of the treatment systems to remove MPs larger than 20 μm. Finally, we calculated an uncertainty in MPs concentrations of one order of magnitude depending on the user-defined parameters inside the MPs identification and classification model. The Quality Assurance/Quality Control approach followed during laboratory analysis highlighted an accumulation of surrogate particles at the edges of the disc, which would have an impact for MPs number upscaling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".