Using low-cost sensors and GPS to assess spatiotemporal variations in personal exposure to PM2.5 in the Washington State Twin Registry

Epidemiological studies typically rely on exposure assessments based on ambient PM_2.5 concentrations at participants' home addresses. However, these approaches neglect personal exposures indoors and across different non-residential microenvironments. To address this problem, our study combined low-cost sensors and GPS to conduct two-week personal PM_2.5 monitoring in 168 adults recruited from the Washington State Twin Registry between 2018 and 2021. PM_2.5 mass concentration, size-resolved particle number concentration, temperature, humidity, and GPS coordinates were recorded at 1-min intervals, providing 5,161,737 data points. We used GPS coordinates and a processing algorithm for automatic classification of microenvironments, including seven land use types and vehicles, and time spent indoors/outdoors. The low-cost sensors were calibrated in-situ, using regulatory monitoring data within 600 m of participants' outdoor measurements (R² = 0.93). A linear mixed model was used to estimate the associations of multiple spatiotemporal factors with personal exposure concentrations. The average PM_2.5 exposure concentration was 8.1 ± 15.8 μg/m³ for all participants. Indoor exposure concentration was higher than outdoor exposure level, and indoor exposure dose contributed 77 % to the total exposure. Exposures in residential and industrial land use had a higher concentration than in other areas, and accounted for 69 % of the total exposure dose. Furthermore, personal exposure concentration was the highest during winter and evening hours, possibly due to cooking and heating-related behaviors. This study demonstrates that personal monitoring can capture spatiotemporal variations in PM_2.5 exposure more accurately than home-based approaches based on ambient air quality, and suggests opportunities for controlling exposures in certain microenvironments.