Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure.

Abstract

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. 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 the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. 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 on the simulation increased exposure by up to 3 μg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.