Analysis of PM Concentrations in Turin: Annual Trend and Monthly and Daily Mean Concentration
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)(2021)(2022)(2023)(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 aims to visualize an entire dataset, which is then useful as the first step in future studies that will correlate the data shown with epidemiological data, providing a comprehensive understanding of the effects of air pollution on human health.With this study, therefore, we want to visualize the importance of having and making public a dataset of many consecutive years in order to better characterize the urban environment in question (city of Turin) thus giving the scientific community the possibility to visualize trends over the years. .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".