Multidisciplinary environmental assessment of oil refinery activities in Erbil, Iraq: Implications for water, soil, air, and human health
Bibliographic record
Abstract
This study presents an extensive environmental impact assessment of the Erbil Oil Refinery, located in the Kurdistan Region of Iraq. The evaluation included surface water, groundwater, soil, and air quality analyses to identify the ecological and public health implications of refinery operations. Surface water samples from the Greater Zab River revealed elevated biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and copper concentrations downstream from the refinery, suggesting localized organic and heavy metal contamination. Groundwater analysis from six wells detected widespread exceedance of Total Petroleum Hydrocarbons (TPH), arsenic, and lead beyond Iraqi permissible limits, indicating serious risks to potable water safety. Air quality monitoring showed high concentrations of PM₂.₅ exceeding USEPA standards, particularly near the refinery, while PM₁₀ remained within safe limits in most seasons. Soil samples collected from eight sites demonstrated significant petroleum hydrocarbon presence and elevated levels of trace metals such as lead and copper near the refinery. Using the Canadian Council of Ministers of the Environment (CCME) indices, surface water and groundwater were classified as "fair" to "good", while soil quality ranged from "medium" to "low". The findings underscore the urgent need for regulatory enforcement, remediation strategies, and long-term monitoring to protect environmental and human health in Erbil.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".