VOLUNTEERED GEOGRAPHIC INFORMATION FOR MAPPING URBAN CLIMATE AND AIR QUALITY: TESTING AND ASSESSING ‘SNIFFER BIKES’ WITH LOW-COST SENSORS
Keywords: Urban climate, Volunteered Geography, Citizen Science, Low-cost sensor, Mobile Mapping, Air quality
Abstract. Impacts of climate change and air pollutants are a growing concern. Reliable and accessible monitoring systems to assess air quality and climate extremes are essential to inform decision-makers and increase awareness of citizens. Approaches from Volunteered Geography play a pivotal role both in research and empowerment by new low-cost technologies. Recently, the spread of GeoICT and micro-sensors are offering opportunities for mobile environmental mapping. In general, official stations acquire data with high accuracy and reliability; in contrast low-cost mobile devices increase the spatio-temporal resolution of air sampling but with lower accuracy. Aims of study are i) assessing accuracy of temperature values from Sodaq Air and MeteoTracker devices; ii) assessing accuracy on PM 2.5 acquisition for Sodaq Air; iii) geovisualizing three months of environmental monitoring in the city of Padua. Accuracy assessment for air temperature was performed by using a calibrated thermometer; PM 2.5 from Sodaq Air were compared with an official air quality station. Preliminary results on dynamic mobile mapping indicate that temperature values from MeteoTracker present good accuracy, while those from Sodaq Air showed bias of approximately +2.5 °C. Air quality data from the latter seems to present, in this phase of development, some limitations, since comparative analysis with official air quality station indicates 93% of overestimation, on average. On the other hand, the environmental campaign with mobile mapping devices at urban scale highlights the capability of geovisualizing hotspots and densifying georeferenced data acquisition over space and time. Further software/hardware implementation and applied research are required with various devices in different environmental conditions to improve data quality and reliability.