Improving Water Quality Using Broad-Crested Weirs Under Laboratory Flume Conditions
Bibliographic record
Abstract
Maintaining sustainable water quality remains a major challenge for protecting ecosystems and ensuring safe water for people.Among the many hydraulic structures used in rivers, weirs play an important role in controlling flow and water levels.Their ability to create turbulence makes them particularly useful for improving water quality.The objective of the current study is to quantify the changes in dissolved oxygen (DO), turbidity (TUR), total dissolved solids (TDS), electrical conductivity (EC), and pH downstream of a broad-crested weir under controlled laboratory conditions.Also, establish an empirical relationship between aeration efficiency and flow rate.Experiments were conducted in a 12 m long, 0.30 m wide, and 0.46 m deep glass-sided flume equipped with a closed-loop pumping system and precise flow control.The dimensions of a broad-crested weir model were 0.29 m width, 0.25 m height, and 0.40 m length.The models were installed at a distance of 3.27 m upstream of the flume.The location of water quality sensors was at a distance of 1 m upstream and downstream of the weir.The range of discharges was from 0.003 to 0.009 m³ /s.The results indicated that DO is increased by 8-12% in the downstream, with the maximum increment of 0.9 mg/L at discharge (Q) = 0.009 m³ /s.The maximum aeration efficiency was 13.69%.TDS slightly increased in the downstream for the given discharge.The percentage of reduction was approximately 14.5%.Turbidity showed a clear decreasing trend with increasing discharge, and the percentage of reduction was approximately 17.5%.pH remained stable (7.25-7.30),while EC generally decreased with discharge but was slightly higher downstream.Aeration efficiency was 13.69% in the present study compared to 13% by previous study.The main significance of this study is to show the efficiency of the broadcrested weirs as hydraulic structures capable of improving dissolved oxygen levels and reducing pollutant concentrations under controlled laboratory conditions, while also highlighting the applicability and limitations of such findings for real river systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".