PROSPECTIVE ON LANDFILLS IMPACT ON SOIL CHARACTERISTIC AND GROUNDWATER QUALITY – CASE STUDY, RABIGH CITY IN WESTERN REGION OF SAUDI ARABIA
Bibliographic record
Abstract
Landfills are used in Saudi Arabia to dispose industrial and domestic wastes.The absence of effective management leads to pollution of the groundwater sources.This research focuses on the impact of landfills on soil and groundwater pollutions in Rabigh city in Saudi Arabia.The analysis involved 14 groundwater and 17 soil samples.The permissible limits were based on Saudi General Authority for Meteorology and Environmental Protection (GAMEP) while the Canadian Council of Ministers of the Environment (CCME) standards for the soil.The cations exceeding the standard were Na+, Ca2+, and Mg2 + while the anions were SO42-, Cl-, HCO3-, and NO3-respectively.Total dissolve solids (TDS) distribution indicated high salinity near landfills.Two hydrogeochemical type facies were identified NaCl and CaCl2.The highest concentrations of heavy metals, As, Cu, Co, Cr, Mn, Al, and Fe, were estimated as 0.059, 0.261, 0.640, 2.96, 3.30, 4.75, and 1170 mg/l respectively.They were below the permissible limits.The pH of the soil ranged from 7 to 9.6 suggesting strongly alkaline soil, due to the occurrence of Sodium Carbonate or Sodium Bicarbonate.The distribution of soil pH indicates the highest value is far upstream landfills.The Chromium and Nickel in the soil exceeds the permitted limit.
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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".