Assessment of Water Quality Impacts from Tharthar and Habbaniyah Lakes Canals on the Euphrates River Using HWQI and HMPI, Western Iraq
Bibliographic record
Abstract
In recent years, Iraqi lands have been suffering from a severe water crisis due to extreme climate changes, including high temperatures and low rainfall, as well as poor water quality resulting from high concentrations of pollutants.Therefore, there is a need to preserve and sustain water resources, the current study aimed to influence the waters of Tharthar and Habbaniyah lakes on the quality of the Euphrates River water near the city of Al-Khalidiyah (Anbar Governorate) by employing a group of physical and chemical factors and heavy metals through mathematical indicators to determine the quality of water, namely the Horton's Water Quality Index (HWQI) and the heavy metal pollution index (HMPI).Four sites were selected in the study area: Site 1 was at the Euphrates River (before the mixing zone), Site 2 was selected at the Habbaniyah Canal, Site 3 was selected at the Tharthar Canal, and Site 4 was selected at the Euphrates River after the mixing zone, for the period from July 2023 to March 2024.14 environmental factors were examined, including: pH, Alkalinity, EC, Turbidity, TDS, TSS, Hardness, Ca, Mg, DO, BOD5, NO3, PO4, SO4, most of which exceeded the Iraqi standard limits except for the following factors: pH, EC, TSS, DO, NO3, while four heavy elements were examined: Pb, Ni, Cd, Cu, all of whose concentrations were within the Iraqi standard limits.The results of the concentrations of physical and chemical factors and heavy metals were consistent with the results of the HWQI and the HMPI, where the lowest rate of the HWQI was recorded at 74.5 in the Tharthar Canal, where the water quality was described as good, while the highest rate was recorded at 142 in the Euphrates River (site 1), where the water quality was described as poor, compared to Site 4 (Mixing Area), which scored 73.5 (Good).The highest HMPI was recorded at 6.323 in Habbaniyah Canal, while the lowest was 0.921 in the Euphrates River (site 1), compared to Site 4 (Mixing Area) which scored 2.872.The results of the current study showed that the quality of the Tharthar Canal water, which is affected by the dilution of its water from the Al-Halwa Canal, which flows into it, has a positive effect on improving the quality of the Euphrates River water.Conversely, the Habbaniyah Canal water harms the quality of the Euphrates River water in the current study area.Due to the lack of application of HWQI and HMPI water quality indices in the current study area, these indices were applied, as water quality indices are the backbone of integrated water quality management, through which abstract concepts of quality are transformed into understandable data that can be measured and analyzed, which enables decision makers to identify problems and evaluate and protect aquatic ecosystems effectively and sustainable.
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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 | 0.001 |
| 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".