Atmospheric Metal Constituent, Concentration and Health Impacts in the Urban Environment of Faisalabad (Pakistan)
Bibliographic record
Abstract
Currently rapidly growing heavy metal pollution is a burning issue due to deteriorating air quality and health status of the inhabitants of the world. In the present study heavy metals in the solid aerosols as well as in the human blood samples from randomly selected sites related to Faisalabad environment were estimated using Atomic absorption spectrophotometry (AAS). The order of concentration of these toxic metals were found to be Zn>Cu>Cr>Ni>Pb>Cd for solid aerosols while Cu>Zn>Pb>Ni>Cr>Cd for blood samples. Overall effect of all these elements present in solid aerosols and blood samples is 22.5 % and 23.11% respectively. The contribution of different kinds of industries is the major source of emission of these elements in the environment. From where these enter in the food web and food chain when deposited on land or inhaled by the inhabitants of the area concerned. Concentration factor and Pollution load index (PLI) were also calculated which showed the shifting of Cd, Cu, Zn, Cr and Ni from solid aerosols to human blood causing severe health hazards while Pb was the only element found in blood which has its origin other than solid aerosols. This state of affairs shows that atmosphere of Faisalabad has worsened to an extent that protective measures are urgently required to overcome the health hazards generated by these potentially toxic pollutants.
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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 | 0.000 | 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) | 0.002 | 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".