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

• Published in: Sustainable cities and society 2020-09, Vol.60, p.102239, Article 102239
• Main Authors: deSouza, Priyanka, Anjomshoaa, Amin, Duarte, Fabio, Kahn, Ralph, Kumar, Prashant, Ratti, Carlo
• Format: Article
• Language: English
• Subjects: Drive-by sensing; Low-cost sensors; Particulate matter; Routine sampling
• ISSN: 2210-6707
• DOI: 10.1016/j.scs.2020.102239

•From measurements made by mobile low-cost air quality monitors, we develop three techniques to:•Identify and characterize PM2.5 hotspots in cities.•Derive qualitative insights about the relative importance of local versus regional sources of PM2.5.•Estimate PM2.5 source signatures in different parts of cities.•We test these methods using two low-cost optical particle countersdeployed on trash-trucks in the city of Cambridge, MA.•We highlight the lessons we learned during this monitoring experiment. 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.