Improving conventional household greensand treatment for efficient Mn(II) removal from drinking water
Bibliographic record
Abstract
Geogenic contaminants pose a global threat to ensuring access to safe drinking water. Manganese (Mn) is a naturally-occurring redox-active element which in its reduced form, Mn(II), is a widespread groundwater contaminant. Prolonged consumption of water containing high levels of Mn has been linked to adverse effects on memory, attention, motor skills, and nervous system function, particularly in vulnerable groups including pregnant individuals and young children. In addition, Mn can lead to aesthetic issues such as altered taste, clogging, and damage to plumbing systems. While Mn has historically been regulated as an aesthetic concern, a mounting body of evidence linking health issues to Mn exposure through drinking water has heightened the challenges of using groundwater to meet drinking water needs. Recently, Health Canada established a health guideline which set a maximum acceptable concentration for total Mn in drinking water of 120 μg/L. Greensand (GS) filters are commonly used in conventional drinking water treatment systems for Mn(II) removal due to their cost-effectiveness and high exchange capacity. However, under certain conditions conventional GS systems may have a low Mn(II) removal efficiency (Galangashi et al. 2021) and encounter challenges related to Mn leaching (Outram et al. 2018), resulting in failure to meet health-related standards. Furthermore, GS filters typically undergo regeneration using potassium permanganate (KMnO 4 ), a mild oxidant that results in the release of additional Mn waste byproducts during the regeneration of the filter. Recent research suggests that Mn-containing materials can effectively activate peroxymonosulfate (PMS) and produce reactive oxygen species (ROS) that facilitate contaminant degradation. This study aims to investigate whether PMS may be used to improve greensand treatment systems and enhance Mn(II) removal from drinking water. Batch experiments were performed to test the Mn(II) removal efficiency across a range of PMS concentrations (0-500 μM) and GS mass (0.1-3 g). Aqueous Mn concentrations were measured over time using inductively coupled plasma optical emission spectrometry (ICP-OES). Solid-phase reaction products were characterized by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The mechanisms of Mn oxidation were identified by quenching experiments and electron paramagnetic resonance (EPR) spectroscopy. The activation of PMS by greensand significantly increased the removal efficiency of Mn(II) compared to the conventional method (e.g., 96.83(±3.77)% at PMS = 500 µM vs. 5.77(±11.1)% at PMS = 0 µM). Our results attribute the mechanism underlying increased Mn removal in our improved treatment method to advanced oxidation processes that involve free radicals (e.g., hydroxyl radical (·OH) and sulfate radical (SO 4 · - )) and non-free radical pathways, with Mn oxides as the main oxidation products. This study provides a new treatment method for more efficient Mn(II) removal while simultaneously reducing Mn wastes produced during the regeneration cycle which allows for a more sustainable and holistic management of invaluable groundwater resources.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".