Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure
SHAHAR TSAMERET, Jiayu Li, Daniel Furuta, Albert Presto, Provat Saha,
University of Miami Abstract Number: 512
Working Group: Indoor Aerosols
AbstractIndoor air quality is critical to human health, as individuals spend 90% of their time indoors. Specifically, particulate matter (PM) is associated with several adverse health outcomes. However, indoor PM networks are not studied as often as outdoor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The first network contains 21 real-time, affordable, multi-pollutant sensors (RAMPS) outfitted with PurpleAir PM sensors. The second network includes 16 PurpleAir sensors reporting PM concentrations on the PurpleAir map. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares correction factor effects on reported concentrations from the PurpleAir sensors. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 µg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors increased exposure by up to 3 µg/m3. This analysis explores how individual schedules affect PM2.5 exposure and explores the effect of correction factors on a low-cost indoor sensor network.