Analysis of Approaches to Determining the Atmosphere Pollution Level of Settlements
Bibliographic record
Abstract
The aim of the work is to analyze the methodology for calculating indices are used both in the Russian Federation and in a number of foreign countries, that allow us to draw a conclusion about the level of atmospheric pollution. The article considers approaches to the calculation of such indices as IZA, KIZA (Russia), AQI (USA, Australia), DAQI (Great Britain), CAQI, YACAQI (European Union), AQHI (Canada, Hong Kong), PSI (Singapore). The main calculation formulas of the indices, the parameters on the basis of which they are calculated and how the results can be interpreted are described. The conclusion about the applicability of these methods on the territory of Russia is made. The calculation part was made on the basis of data on the concentrations of pollutants obtained at automatic atmospheric air monitoring stations in Irkutsk for 2019. In addition, the absolute and relative frequency of occurrence of various index values was calculated. It was found that despite the apparent similarity of the results, the analysis should be carried out at the level of sub-indices or pollution indices for each individual substance. In addition, the calculation of the absolute and relative frequencies of the occurrence of indices corresponding to different levels of pollution showed that averaging the results hides the occurrence of dangerous levels of pollution that may be critical for sensitive population groups (people with chronic diseases, children, the elderly).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".