In the scenario of massive urbanization and global climate change, the acquisition of microclimatic data in urban areas plays a key role for responsive adaptation and mitigation strategies. The enrichment of kinematic sensor data with precise, high-frequency and robust positioning directly relates to the possibility of creating added-value services devoted to improving life-quality of urban communities. This work presents a low-cost cloud-connected mobile monitoring platform for multiple environmental parameters and their spatial variation in the urban context.
The hardware consists of a U-blox C099-F9P GNSS receiver, coupled with a Raspberry Pi 4 through the serial port. This way, we expanded the connection buses, connectivity protocols and onboard computation capabilities of the prototype. Furthermore, the open-source suite RTKLIB has been installed on NOOBS to enable U-blox proprietary data format to RINEX v3.13 conversion. The GNSS processing chain evaluates three different positional solutions: single point, static point with differential corrections (DCs), and kinematic with DCs. DCs have been achieved by means of a certified base station (downloading the RINEX files from a public service). In addition, GFZRNX toolbox is encompassed within the routine to improve the GNSS data quality.
To compare the configurations under test, the variance of northing, easting, and height parameters has been evaluated for every single positional solution. The kinematics one has proven to be the best method for mobile applications, especially when data quality advancement is applied through GFZRNX (supported by Levene tests). The performance of GFZRNX in reducing the number of floating solutions has been investigated, as well.
The achieved results confirm the approach is suitable for urban monitoring mobile networks, where several nodes can be installed, for instance, on public transport vehicles (commercial average speed below 40 km/h) to expand the spatio-temporal resolution (centimetre-level accuracy in seconds) of environmental data, while keeping low the number of sensors to deploy. The outlook is the assessment of statistic correlations between local microclimatic conditions, urban surface usage and urban morphology to aid the development of sustainable energy and climate action plans at the municipality level.
