Identification of Organic Compounds in Solid Aerosols Related to Faisalabad Environment Using XRD Technique
Bibliographic record
Abstract
Faisalabad is a big industrial city with huge air quality problems, being one of the most polluted cities in the world. Although public policies have developed to minimize atmospheric aerosol pollution, there is a lack of adequate knowledge and poor characterization of these aerosols. In this study we sampled 100 aerosol samples from different pools covering almost all the aspects of Faisalabad environment. The results obtained from an investigation of solid aerosols in the Industrial city of Faisalabad (Pakistan) are reported and analysed in this paper. X-ray diffraction studies of the various solid aerosols pools (residential, industrial, transportational, commercial and mix pools) showed that non-clay organic compounds such as GB-Naphthylbismuth dioxide,Sodium hippurate, Sodium-GA-naphthylamine-4-sulfonate tetrahydrate, Potassium phenoxide, Bismuth salicylate, Cadmium salicylate hydrate, Barium phenolsulfonate are contained in most of the samples in almost comparable amounts.The results of Solid aerosols collected from various pools show that the sources of GB-Naphthylbismuth dioxide,Sodium hippurate, Sodium-GA-naphthylamine-4-sulfonate tetrahydrate, Potassium phenoxide, Bismuth salicylate, Cadmium salicylate hydrate, Barium phenolsulfonatein the Solid aerosols are both local and remote. No doubt the main objective of this study was not to investigate the human health hazards however; an attempt has been made to correlate health hazards on behalf of their size distribution.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".