Challenges and opportunities of low-cost sensors in capturing the impacts of construction activities on neighborhood air quality

In large metropolitan areas such as Toronto, planners are increasingly relying on urban densification to accommodate population growth sustainably. While infill developments support the city ' s long-term climate goals, on-going construction impacts air quality for local communities. Understanding how neighborhoods are impacted by these localized sources can be achieved by implementing a network of low-cost sensors. In this study, we placed twelve low-cost sensors on balconies in a Toronto neighborhood impacted by various construction projects. The study aims to capture the impact of construction and heavy-duty traffic and provide a better understanding of spatial variability in fine particulate matter (PM 2.5 ). The locations were compared using time series analysis, inverse distance weighing (IDW) for spatial heterogeneity, and spectral analysis to quantify the contribution of local sources. Sensors exhibited inter-sensor variability, which was corrected upon calibration. Sensors located near and far from construction sites showed similar temporal trends, however locations near construction sites measured greater PM 2.5 concentrations, where the hourly average concentration for sensors near construction sites ranged between 6.8 and 8.5 mu g/m 3 and sensors further away ranged between 5.4 and 6.2 mu g/m 3 . Spatial variability was also captured by IDW where sensors near construction sites were more heterogenous and exhibited greater concentrations. Spectral analysis demonstrated that local sources contributed up to 23% of PM 2.5 levels near construction while distant locations had a maximum of 11% local contribution. By using a network of sensors, we explore how construction sites create localized hotspots within a neighborhood.