Assessment of Drinking Water of Al-Abbasia River using Water Quality Index
Bibliographic record
Abstract
Water Quality Index (WQI) is one of the most effective indicators assessing the water quality of the surface water. The Water Quality Index was used to determine the quality of raw water used for drinking purposes at the AL-Abbasia River. The water samples were taken from six stations along AL-Abbasia River for during a twenty - month period started from January to October in (2019) to (2020). fifteen parameters of water quality were evaluated including Temperature (C°), Hydrogen power (pH), Total Hardness (T.H), Turbidity (Turb.), Total Dissolved Solid (T.D.S), Chloride (Cl-), Calcium (Ca+2), Electrical Conductivity (E.C.), Magnesium (Mg+2), Sodium (Na+), Potassium (K+), Sulfate (SO4 -2), Total Suspended Solids (T.S.S), Aluminum (Al). Data analysis revealed that some of these parameters exceeded the World Health Organization (WHO) standards such as Total Hardness (T.H), Total Dissolved Solid (T.D.S), Magnesium (Mg+2) and Sulfate (SO4 -2). Other parameters were within the WHO standards. In this thesis three different methods were used to calculate WQI, the WAWQI, CWQI, and Nemerow’s Pollution Index. For WAWQI, the computed overall WQI value of all samples and stations along AL-Abbasia River was (64.5) which implies that the water was generally "poor". The computed monthly overall WQI along AL-Abbasia River for all samples and stations was (64.114). The monthly WQI variation ranged to a higher value of (78.42) in February 2019, due to high pollutants and heavy rainfall in this month and a lower value of (53.92) in July 2019 along AL-Abbasia River, and classified as (very poor) in terms of drinking water quality. The annual river water quality index variation along AL-Abbasia River ranged between (64.31) " Poor quality" at the upstream near station (A1) and (64.1) "poor" at the downstream near station (A6) which indicates that the ratio is very close between the river upstream and downstream. The water quality assessment was recorded as “poor” because of the high values of Total Hardness (T.H), sulfate and Total Dissolved Solid (T.D.S) where it exceeded the limits. If we excluded (T.H, sulfate and T.D.S) values from water assessment, the result will be changed from (64.5) to (48.86) and the quality of water will be changed from “Poor” to “Good”. According to CWQI, the result calculated by Canadian Water Quality Index method showed that the water quality was fair along the main stream of AL-Abbasia River. The annual average water quality index recorded during the two years was fair in the upstream at station A1 with a value of 74.57 and downstream at station A6 with a value of 61.6. The water quality index during 2019 ranged between fair at A1, A2, and A6 stations and good at A3, A4, and A5 stations with a value ranged between 61.04 at A2 and 82.83 at A3. The water quality index during 2020 year ranged between fair at A1, A2, A3, A4, and A6 stations and marginal at station A5 with values between 77.63 at station A1 and 58.23 at station A5. The Nemerow’s Pollution Index is another method used to calculate WQI. This method gives a statistical analysis to show the state of the river water quality for each parameter and each station along the river. When the value of NPI is greater than 1, then this means that there is a high concentration of this parameter, thus increasing the water pollution. The results obtained from this method indicate that high sulfate, T.H, and turbidity concentrations had a significant impact on water quality during the study period. In 2019, The high NPI was recorded for SO4 and T.H for all stations during the study period, while in 2020, the highest NPI was recorded for T.S.S, SO4, T.H, T.D.S, Mg, and turbidity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".