Analysis of PM Concentrations in Turin: Annual Trend and Monthly and Daily Mean Concentration

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 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.