The use of wireless sensor networks (WSN) to address and improve the environmental quality of the built environment is gaining more and more prominence in modern cities. In this scope, our work aims to assess the spatial variability of local climate in relation to the urban morphology and the distribution of materials and vegetation. Furthermore, on-site measured data have been exploited to run and benchmark numerical models for the simulation and visualization of multiple climate parameters.

To study the long-term micro-scale relation between built environment characteristics and environmental parameters, a LoRaWAN based WSN has been deployed in Bolzano (Italy). 17 autonomous sensors of temperature and relative humidity have been installed in a star topology at pedestrian level in different locations of an urban district.

The analysis of the results highlightings the distribution in terms of air temperature and relative humidity and its correlation with the characteristics of the urban fabric. Collected data (time series) have been used to calibrate the numerical models to extend the investigation to a wider spatial context, including the surroundings of the monitored area, as well as to evaluate local human thermal comfort conditions. Moreover, by means of long-lasting experimental campaign, the reliability and potential hw/sf drawbacks of the low-cost WSN have been assessed.

The work carried out so far opens up to many further developments. The data management flow has been designed according to interoperability principles, which make the collected information possibly available to any decision-support systems for the benefit of planners and policymakers. In addition, the open-source nature of the network allows for a sustainable scaling-up, along with the chance to integrate it with co-creation and citizen science initiatives. Finally, the simulation of the microclimatic conditions can be exploited to address sheat island intensity reduction strategies in extensive urban areas.