Assessment of Tigris River Water Quality for Drinking and Domestic Use in the post-war city of Mosul by using the Canadian Environment Water Quality Index CCME-WQI and Heavy Metal Pollution Index HPI
Bibliographic record
Abstract
River water quality is highly sensitive to human activities such as urbanization, industrialization, and agriculture. It is even more susceptible to significant and prolonged changes, such as those caused by wars, which degrade surface waters and impair their use for drinking, industrial, agricultural, recreational, and other purposes. To assess the impact of the war in Mosul on the quality of the Tigris River’s water, the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was used. A total of 120 water samples were collected along the river in four series during 2022 and 2023. The study revealed that in most riparian sites the Tigris River had higher levels of heavy metals (Cd, Pb, Cr, Ni) than permitted by WHO standards (2004), with exception of Zn. Zone 3 exhibited the highest level of pollution. Significant seasonal and annual variations were observed in most parameters, except pH and sulfate (SO₄²⁻), due to geological and debris-related factors. Based on CCME WQI, water quality dropped from 85.8 in 2014 (pre-war) to 68.8 in 2022, then slightly improved to 73.25 in 2023, classifying the river as “Fair.” HPI values indicated good quality at sites S1–S3, while sites S4–S10 were unsuitable. The pollution ranking by metal was: Cd > Pb > Ni > Cr > Zn. Cd and Pb pose a very high ecological risk due to their high bio accumulative potential in the Tigris river.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".