Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decades thanks to a constant growth in technology. An example is given by the Meteonetwork association, born in 2002, and mainly composed by atmospheric science enthusiasts, who built up in time a huge weather station network in Italy. In recent years, they have enlarged their horizons over Europe, displaying real time observations and daily maps coming from both personal weather stations and official networks. This study described how Meteonetwork has set up an open crowdsourced weather data system, how data are validated, and which products are generated and freely accessible for scientists and stakeholders for their own purposes. Two concrete use cases were described as examples: the Weatherness Project, which selects a subnet of Meteonetwork data for biometeorological and health purposes and the data assimilation process implemented to improve the initial conditions into the WRF meteorological model for daily weather forecasts.

Shift work can lead to circadian desynchronization due to temporary misalignment between working hours and physiological and behavioral functioning, resulting in compromised health, insomnia, worsening of sleep quality, reduced ability to work during waking hours, and increased cardiovascular risk. We evaluated the effects of shift work on the rest-activity circadian rhythm (RAR) and health status of Italian orthopaedic nurses. The study population was 59 nurses: 44 worked the night shift and 15 worked the day shift. All carried out continuous 5-day actigraphic monitoring to assess RAR, including both the working and the rest period. The rhythmometric analysis showed that, during the working period, the night shift nurses had a significantly lower amplitude than the day shift nurses (p < 0.001), and the acrophase was significantly different between the two groups (p < 0.01). When we stratified the two groups by median body mass index (<25 kg/m² normal weight and ≥25 kg/m² overweight), during the working period, we noted a significantly lower amplitude for both the normal weight and the overweight nurses who worked the night shift (p < 0.01 and p < 0.001, normal weight and overweight respectively). The current findings suggest the need for further study of the relationship between activity levels and shift work.

Acute otitis media (AOM) is a leading cause of pediatric Emergency Department visits, particularly among children under five years of age. Although its seasonal pattern is well established, the role of air pollution and meteorological factors remains unclear. This study aims to investigate their impact on daily AOM visits by integrating machine learning and epidemiological approaches. We conducted a retrospective analysis of pediatric AOM diagnoses (2007–2015) at S. Croce and Carle Hospital (Cuneo, Italy). Predictors included PM10, NO₂, O₃, and eleven meteorological variables. Ensemble machine learning models (Random Forest, XGBoost, and AdaBoost) were trained and validated using 10-fold cross-validation. Model interpretability was assessed through SHAP values. Distributed Lag Nonlinear Models (DLNM) were applied to estimate delayed exposure–response relationships over lag periods of 0–1, 0–3, 0–5, and 0–10 days, with results expressed as Relative Risks (RRs) and 95% Confidence Intervals (CIs). AdaBoost showed the best performance (R² = 0.974; MAE = 0.019 cases/day; cross-validated R² = 0.987). SHAP analysis identified mean temperature as the most influential predictor (44%), while PM10, NO₂, and O₃ each contributed approximately 10%. DLNM analysis confirmed a strong and consistent effect of temperature across all lag periods (RR > 1.20, CI > 1). Moderate associations were observed for NO₂ and PM10 (RR: 1.02–1.04). O₃ exhibited smaller but significant effects at shorter lags (RR = 1.01 at 0–1 days; RR = 1.02 at 0–3 days; CI > 1). Environmental factors, particularly temperature, play a significant role in pediatric AOM incidence. The integration of machine learning and DLNM enhances predictive accuracy and improves the understanding of exposure timing. These findings support the development of early warning systems and targeted preventive strategies under adverse environmental conditions. Further validation in larger urban settings is needed.