Temporal Analysis of PM Concentrations in Turin: Assessment and Data Elaboration for Air Pollution Monitoring
Bibliographic record
Abstract
As urban populations continue to grow, understanding the dynamics of particulate matter (PM) concentrations in these areas is increasingly important.This study investigated the temporal variations of PM1, PM2.5, and PM10 in the urban area of Turin, located in the Po Valley, Italy, utilizing high-resolution data from a monitoring campaign over a four-year period (2020-2024), focusing on identifying seasonal and weekly variations.The results revealed significant differences in PM concentrations between different seasons.The findings reveal a strong seasonality, with higher PM levels in winter due to domestic heating, traffic emissions, and adverse meteorological conditions, while summer months show lower concentrations.Winter concentrations often exceed WHO air quality guidelines, with PM10 levels surpassing EU annual thresholds, emphasizing the need for stricter emission control policies during colder months when pollution poses significant health risks.Weekly fluctuations in PM concentrations were also observed, with peaks mid-week and at the end of each week.These fluctuations are likely influenced by human activities and meteorological factors, suggesting that interventions targeting specific periods could help reduce pollution levels.The use of the Palas Fidas 200S instrument, with its high temporal resolution, allowed for detailed examination of daily and hourly trends, offering insights into the dynamics of PM concentrations.These findings are critical for epidemiological studies examining the link between air pollution and public health outcomes, particularly cardiovascular diseases.This study is part of a future research effort that integrates both concentration and epidemiological data, offering a comprehensive understanding of the effects of air pollution on human health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".