Interpretation of air quality data using an air quality index for the city of Kanpur, India
Bibliographic record
Abstract
An air quality index (AQI) is proposed for the City of Kanpur, India, for simplified public information and data interpretation. A maximum operator concept is used to determine the overall AQI; maximum value of sub-indices (of each pollutant) is taken as the overall AQI. The mathematical functions for calculating sub-indices are proposed based on health criteria of the USEPA and Indian air quality standards. The pollutants included in the AQI are: SO2, SPM (suspended particulate matter), O3, NO2, PM10 (particulate matter with a diameter of 10 μm or less), and CO. The investigations into data interpretation using the AQI for Kanpur city have shown that air quality worsens (very poor to severe) in winter months and also during the early summer months (March, April, and part of May). These months are characterized by dusty winds resulting in high SPM. The air quality generally improves in monsoon and post-monsoon period (good to moderate) as rain washes out the pollutants. Over 95% of the time, sub-index values corresponding to SPM levels were responsible for overall AQI. Key words: air quality index, air quality data interpretation, Kanpur, India.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".