Evaluation of the Groundwater Quality Affected by Solid Waste Leachate Around Al-Diwaniyah Dumpsite Based on WQI
Bibliographic record
Abstract
Open and unsanitary landfills have served for many years in developing countries such as Iraq as a standard and economically inexpensive method of solid waste disposal. Leachate generated from these dumps' bases seriously affects the surrounding environment, especially groundwater sources. There have been reports of potential environmental hazards associated with leachate in the Al-Diwaniyah open landfill in Iraq. Therefore, in this investigation the quality of groundwater and characteristics of observation wells around the dumpsite was studied. Groundwater samples collected from four hand-excavated wells at a dumpsite were analyzed periodically using standard methods in dry and wet seasons through the period (September 2023–March 2024) in order to evaluate leachate pollutants and their impact on groundwater quality. The main analyzed parameters in leachate and groundwater included pH, Electrical Conductivity, Turbidity, Total Suspended Solid, Total Dissolved Solid, BOD5, COD, Chloride, Sulphate, Nitrate, in addition to heavy metals including Iron, Zinc, Copper, Chromium, Lead, and Cadmium. To illustrate the spatial distribution of pollutants during the dry and rainy seasons, indicators were used to assess groundwater quality. The results of the groundwater quality index (Canadian model) reported poor groundwater and unsuitable for drinking and agriculture in (GW1, GW2) neighboring the dumpsite in the range of (100-500) m from the dumpsite. In contrast, GW3 water quality is often threatened, except for GW4, which was unsuitable for drinking but can be used for agriculture. Extending this research to other regions would enhance the environmental monitoring of groundwater and assess possible threats to human health in the study area. Constructing an engineered landfill that complies with authorized environmental standards would also be beneficial.
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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 | 0.000 |
| 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".