Leachate Characterization and Evaluation of Ground Water Quality Around Landfill Area Using the Canadian Council Ministers of the Environment Water Quality Index
Bibliographic record
Abstract
Municipal solid waste generation, management, and dumping are economic and ecological concerns that metropolitan areas, particularly those in developing nations, must address. This study intended to ascertain the impacts of solid waste on the quality of groundwater around trash dumps located inside and surrounding landfill sites in the city of Erbil. Samples of groundwater, as well as two samples of leachate, were collected from eight wells situated near landfills during the dry and rainy seasons of August 2021 and February 2022. Several physico-chemical parameters, including pH, EC, NO2, NO3, alkalinity, HCO3, Na, Ca, Mg, Cl, SAR, total hardness, and heavy metals, were evaluated in the samples. During fieldwork, water quality index (z) measurements for summer and winter were combined with longitude (x) and latitude (y) information gathered by GPS. Using the inverse distance weighting approach, integrated xyz data was interpolated in ArcMap GIS software to measure the groundwater quality of the research region. According the CCME Water Quality Index, wells 4 and 8 had WQIs ranging from fair to marginal in both the winter and summer seasons (WQI). According to the data, cadmium contents in summertime were much greater (0.430–2.066 mg. l-) than the WHO standard (0.003 mg. l-1), deeming them unfit for human use. And that the high lead level in the summer (0.843-2.600 mg. l-1) is caused by too many Pb batteries being thrown away.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".