A New Air Quality Index for Cities
Bibliographic record
Abstract
Global population growth has led to increased populations living in urban areas. Often, this enhances stresses on space, ecosystems, infrastructures, facilities and personal lifestyles. Problems related to quality of life in cities are increasingly relevant, especially with regard to environmental issues. Due to a generalised increase of mobility and road traffic in urban areas, the total emissions from road traffic have risen significantly, assuming the main responsibility for the disregard of air quality standards. In urban environment the typical anthropogenic sources are mainly the road traffic and, when existing, the industrial activity. The quantitative evaluation of traffic air pollution levels is the basis on which air pollution control policies stand. The evaluation of air quality may be occasional or long-term. Occasional evaluation is useful in the context of information and alert systems for the population, working normally in real or almost-real time. Data is acquired through measurements made on an hourly or daily average basis and concentration episodes are evaluated and reported. When long-term data is considered, then we talk about long-term trend analysis. In order to find an air quality index, the pollutant concentrations are combined through a classification scale anchored on the legal limits and, on the other side, on the impacts over human health. Typically these classification models consider only the worse pollutant, i.e. the one which concentration is higher given a certain scale. Two air quality evaluation models are referred, both working in real time: a Canadian and a Portuguese experience. The objective of this chapter is to present a new air quality index, cityAIR, developed for urban contexts. The mathematical formulation of cityAIR stands on two logics: whenever at least one of the pollutants considered overcomes the legal limits for the concentration, this will be the only relevant one for the index calculation, and the value will be the minimum of the scale (zero or red); when there is no limit violation, then all the pollutants are considered for the overall air quality, which is calculated through a multi-criteria combination of the concentrations, where trade-off is allowed. A long-term model is proposed and applied to two mid-sized Portuguese cities. A cityAIR values surface was calculated for Viana do Castelo and Braga considering concentrations of CO, NO2, O3, C6H6 and PM10.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".