A novel methodology for using light-scattering sensors to measure indoor particulate matter in a multi-site clinical trial

Measuring airborne fine particulate matter (PM2.5) in the context of health-based field research introduces many logistical challenges including remote equipment setup, data access, and maintenance by both researchers and participants. In preparation for a 17-site, 228-household clinical trial, we developed methods to validate low-cost, light-scattering PM2.5 sensors (model PAII-SD, PurpleAir, Inc, USA) against a Federal Equivalent Method instrument (model BAM 1020, Met One Instruments, USA) for remote measurement of indoor PM2.5 concentrations among participating households over 6 months. To allow real-time, remote data access by the research team, we developed procedures for pairing the light-scattering PM2.5 sensors for each household with a portable Wi-Fi hotspot device (model Solis Lite, Skyroam, Inc, USA) prior to shipping them to participants. Armed with this remote data access, we created a process for daily automated data extractions to ensure sensors remained online and to assess data quality. Our methods support the feasibility of obtaining long-term, continuous PM2.5 measurement reliably and accurately in the context of a research trial using low-cost, light-scattering sensors. The framework we describe in this methods paper can serve as a model for future environmental health research studies.