City Scanner

City Scanner proposes a drive-by solution to capture the spatiotemporal variation in environmental indicators in urban areas, such as air quality or the thermal flux of the build environment. Instead of deploying a dedicated fleet, we have deployed various types of environmental sensors on public works trucks in Cambridge, MA, New York City and Canada. Future deployments are being scheduled. 

Project website

Towards Large-scale Drive-by Sensing with Multi-purpose City Scanner Nodes

Drive-by sensing has the potential to provide hyper-local data to study a number of urban phenomena and deliver actionable insights for public good. Yet drive-by deployments are often characterized by small fleets, huge costs and lack of flexibility to adapt to a city's multiple sensing needs. The City Scanner project aims to accelerate the development of drive-by sensing by turning everyday vehicles into sensing nodes. In this paper we present Greta II, a solar-powered sensing platform that allows for low-cost continuous data collection and streaming of multiple phenomena. Thanks to a modular design it can be customized with off-the-shelf sensors and re-used across different deployments. Greta II can be easily built and deployed without disrupting a vehicle's normal operation. The platform has been validated during test deployments in two large American cities to collect air quality data. Drawing on our experience building and deploying Greta II we discuss challenges to be solved for enabling large-scale drive-by sensing.

Air quality monitoring using mobile low-cost sensors mounted on trashtrucks: Methods development and lessons learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.