Physiological equivalent temperature (PET) is one of most used indices for outdoor human well-being evaluation; its determination is particularly helpful for adaptation strategies in built-up areas affected by the urban heat island (UHI) phenomenon. In this work, we presented a methodology to compute spatially and temporally resolved PET values during a heatwave at the city level, based on a combination of satellite products, in situ measurements and Envi-met model runs upscaled from specific test areas to the broader city. The method exploits the ECOSTRESS sensor to detect surface thermal patterns at different diurnal times by developing an hourly based index called hUHTI (hourly urban heatwave thermal index) that serves as a proxy. A case study on Prato (Italy) municipality during the 2021 summer heatwave events is presented. Based on the available satellite products, a set of six hourly diurnal PET maps at 10 m spatial resolution were derived and daytime outdoor thermal patterns and trends were investigated according to land cover. hUHTI index resulted a more suitable tool as PET proxy compared to the sole ECOSTRESS land surface temperature (LST) product, especially for morning and evening times. Hourly PET maps were summarized by the use of an average exceedance map providing public administrations and stakeholders a synthetic tool for urban regeneration purposes at city scale.

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM_2.5 and PM₁₀ daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM_2.5 and 20 for PM₁₀ concentrations were compared vs. regulatory-grade stations during a full “heating season” (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM₁₀ than PM_2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R². AirQino stations under-estimated both PM_2.5 and PM₁₀ reference concentrations (MB = −4.8 and −2.9 μg/m³, respectively), while PurpleAir stations over-estimated PM_2.5 concentrations (MB = +5.4 μg/m³) and slightly under-estimated PM₁₀ concentrations (MB = −0.4 μg/m³). PurpleAir stations were finer than AirQino at capturing the time variation of both PM_2.5 and PM₁₀ daily concentrations (R² = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM_2.5/PM₁₀ ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM_2.5 vs. PM₁₀ outputs.

Urban environments are contaminated by a multitude of air pollutants. Tropospheric ozone (O₃), nitrogen dioxide (NO₂) as well as coarse particulate matter (PM₁₀) and fine particulate matter (PM_2.5) are the most dangerous for human health. However, urban greenery, in particular trees, offer a variety of ecosystem services, including the ability to improve air quality. We planted 170 young trees in the city of Florence using five species with proven capabilities to remove air pollutants, and open-field research was conducted to assess their pollution removal potential. Multi-sensor monitoring devices were used to monitor air pollutant concentrations and meteorological parameters from the first three years after planting. The devices were installed inside/outside the plantation and above/below the canopies. The experiment showed that the selection of suitable species effectively led to an improvement in air quality, with a reduction in monitored air pollutants below the canopy. In detail, a reduction in O₃ and NO₂ was detected for the second (2023) and third (2024) growing seasons, while a reduction in PM₁₀ was only observed in 2024. The highest average reduction percentage was found for O₃ (−9.1%) and PM₁₀ (−24.5%) during 2023 and 2024, respectively. These findings highlight that nature-based solutions are really effective in air pollution mitigation, suggesting their implementation to further expand urban reforestation programmes and preserve human health.