Analysis of Water Quality Using Physico-Chemical Parameters in Different Cities of Pakistan
Bibliographic record
Abstract
Present study was carried out to assess different parameters (physiochemical) of water for public health safety. 25 samples from majority of areas of Khyber-Pakhtunkhwa (KP) along with Rawalpindi and Islamabad were chosen. Total twelve parameter which include color, odour, pH, TSS (total suspended solids), TDS (total dissolved solids), Na (Sodium), K (Potassium), hardness, alkalinity, EC (Electric Conductivity), Cl (Chloride) and SO4 (Sulphate) were assessed for each sample and were compared with WHO and other limits. Results showed that very few samples 3 out of 14 in Mardan, 5/12 in Bannu, 2/11 in Buner, 3/4 in Batkhela, 1/6 in Malakand, 2/4 in Shangla, 3/18 in Mansehra, 2/7 in Karak, 1/17 in Kohat, 2/5 in Upper Dir, 3/12 in Islamabad, 3/11 in Abbottabad and 2 out of 17 samples were found to have objectionable color and odour. None of the samples exceeded the permissible limit of WHO for pH, TDS, alkalinity, Cl and SO4. While, other parameters like TSS was higher in 9 samples, Na in 2, K in 6, hardness in 1 and EC in 7 as compare to permissible limit. It was recommended that proper water treatment especially effective chlorination with residual chlorine be rendered at all sources in order to achieve safe water quality up to the consumer’s end. However, it is also important to investigate other potential water contaminations such as chemicals and microbial and radiological materials for a longer period of time, including human body fluids, in order to assess the overall water quality of Pakistan.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".