Hyper-Localized Pollution Mapping Using Low-Cost Wearable Monitors and Citizen Science in Hong Kong

Abstract

Low-cost sensors have demonstrated their advances in acquiring hyper-localized data compared to traditional, high-maintenance air quality monitoring stations. The study aims to leverage the mobility of participants equipped with low-cost wearable monitors (LWMs) by comparing their exposure to particulate matter (PM) across indoor-home, outdoor-walking, and hybrid-commuting micro-environments. The LWMs would be calibrated first through field co-location and the multiple linear regression models. The coefficient of determination (R2) of PM1.0 and PM2.5 increased to over 0.85 after calibration, along with the reduced root mean square error of 2.25 and 3.46 μg/m3, respectively. The 26-day PM data collection with geographic locations could identify individual exposure patterns, local source contributions, and hotspot maps. Commuting constituted a small fraction of daily time (4–8%) but contributed a disproportionate impact, accounting for 11% of individual PM exposure. Indoor-home PM2.5 exposure varied significantly among the urban districts. Based on the PM2.5 hotspot map, the elevated concentration was mainly concentrated in dense residential areas and historical industrial areas, as well as interchanges of major roads and the highway system. LWMs acting as non-regulatory instruments can complement monitoring stations to provide missing short-term and hyper-localized air pollution data. Future studies should integrate long-term monitoring and citizen science across seasons and geographical regions to address pollutant spatiotemporal variability for building and city sustainability.