Electrodialysis reversal (EDR) technology: a viable solution for addressing water quality challenges in the dry zone, Sri Lanka
Bibliographic record
Abstract
ABSTRACT Chronic Kidney Disease of Unknown Etiology (CKDu) affects rural Sri Lankan agricultural populations, with poor-quality ground and surface water suspected as the root cause. Hence, we conducted this study to explore the effectiveness of Electrodialysis Reversal (EDR) technology in treating water quality issues related to CKDu in dry zone of Sri Lanka. The EDR plant in Kahatagasdigiliya, Anuradhapura district, managed by the National Water Supply and Drainage Board (NWS&DB) was selected. We measured both physical (colour, turbidity, pH) and chemical (electrical conductivity, total dissolved solids, chloride, alkalinity, hardness, nitrate, nitrite, sulfate, fluoride, total phosphate, iron, manganese) parameters of the EDR process. The parameters of the permeate stage of the EDR plant were validated by comparison with data from SLS 614:2013, and removal efficiencies were assessed. The results revealed that all parameters consistently fell within the permissible limits in the permeate stage of the EDR plant. Turbidity (62.65%), sulfate and manganese (50%), colour (47.37%), fluoride (44.19%), and hardness (35.71%) showed high removal efficiencies in the EDR process. The study demonstrated the effectiveness of EDR technology in addressing water quality challenges, validating its potential for groundwater treatment and this contributes to the improvement of groundwater quality in CKDu-prevalent areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".