Potential Role of Dissolved Oxygen and Manganese Concentration on the Development of Biofilms Causing Reduction in Hydraulic Capacity of a Gravity-Fed Irrigation System
Bibliographic record
Abstract
Water from the Blyderiver dam in the Mpumalanga province, South Africa is used for gravity-fed irrigation. Biofilm development in the pipelines causes an increase in pipeline surface roughness, reduced hydraulic capacity, and water delivery below design capacity. The role of manganese (Mn) concentration on biofilm development is of interest, since the water is currently extracted at depth near the bottom of the reservoir (45–50 m when full) where high Mn levels were measured during four sampling events over 2 years. In the water body, dissolved oxygen (DO) and Mn concentrations showed a strong, inverse correlation, with rapid decrease in DO at increased depth, mirrored by an increase in total and soluble Mn. The depth of this inflection point correlated with the reservoir's water level. DO concentrations typically remained constant between 8 and 9 mg l−1 in the upper regions of the water column, followed by a rapid decline to lower than 2 mg l−1 at deeper zones. Similarly, Mn concentrations remained constant with increasing depth, ranging between 10 and 100 μg l−1, followed by a rapid increase once the depth is reached where DO levels started to decline, to 8,631 μg l−1 near the bottom. In the main, 1.5 m diameter pipeline, Mn concentrations decreased with distance; from 8,631 μg l−1 at the extraction point to 134 μg l−1 at 23 km downstream in the bulk aqueous phase, while in the biofilm biomass, Mn concentrations decreased from 30105.4 mg kg−1 at 4.5 km to 23501.9 mg kg−1 at 12.5 km, and 13727.7 mg kg−1 at 28.4 km downstream. This decrease in Mn concentration with distance suggests that biofilm accumulation has not yet reached a steady state. The aesthetic and operational problems caused by manganese in drinking water systems is well-documented, with biological removal gaining increasing interest as alternative to physical and chemical removal strategies. This study showed that the microbial processes that are being harnessed in drinking water treatment, i.e., Mn accumulation through biofilm formation supported by Mn cycling in sand filters, may have undesirable consequences in bulk water distribution systems by causing notable reduction in flow.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".