Evaluation Of Nutrients And Heavy Metals In The Waters Of Seman Basin
Bibliographic record
Abstract
The purpose of this study is to evaluate the water pollution of the Seman River based on the concentration of heavy metals and nutrients, as well as to investigate the possible sources of these pollutants.This study was conducted during the period April -June 2019 for analysis of nutrients and during the period April -May 2019 for analysis of heavy metals.Water samples were sampled at seven points along the river impacted by human settlements, human activity and agricultural activities.The samples were analyzed for Nickel, Lead, Cobalt and Copper using an Atomic Absorption Spectrophotometer (AAS).Calorimetric methods were used to determine levels of phosphates, total phosphorous, nitrites, and ammoniums.All analyzes were performed using standard analytical methods (APHA, DIN, ISO).Interpretation of results was conducted using descriptive statistics method and compared with international water quality standards.The average concentration of heavy metals in the analyzed water samples, in ascending order, was Co < Cu < Ni < Pb.This trend was applied to all water samples in both expeditions.The average concentration of lead was the highest at 0.345mg/L, followed by nickel at 0.073mg/L, and by copper at 0.028mg/L.Cobalt was not identified in any of the water samples analyzed.Regarding the average concentration of heavy metals, the waters of this basin are classified as highly polluted waters.heavy -class V of environmental quality.Referring to the measured average values of nitrites, ammoniums, orthophosphates, and total phosphorus, the waters of this basin have a very bad environmental quality and are not suitable for growing fish.The major sources of both nitrogen and phosphorus in water include municipal wastewater discharges, sewage, urban and agricultural runoff, animal feed lots and industrial wastes.Detergents and other laundry materials are the major contributors of phosphorus in water.Therefore, there is a need to formulate and adopt strict rules for managing and minimizing the causes of pollutants, thus managing and minimizing environmental pollution and the health risks associated with it.The assessment of agricultural activities is also very important, since an increase in the concentration of metals during the rainy seasons due to runoff from these sources has been observed.
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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 | 0.001 |
| 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".