Assessing Personal Exposure to Airborne Particulate Matter with Wearable Sensors and Ventilation Rate Models

Air pollution is a major contributor to global morbidity and mortality. Accurate assessment of individual's exposure to air pollution is important to quantify the impact of air pollution on human health. Historically, human exposure to air pollution has been quantified using pollutant concentrations from fixed air quality monitoring stations. This approach does not consider the subject's activities and the differences between indoor and outdoor air pollution; however, these limitations can be overcome using wearable sensors. In this work, we propose a new approach to measure personal exposure to airborne Particulate Matter (PM) that consists in using a wearable/portable air quality sensor to measure air quality at the subject's location, a wearable Heart Rate (HR) sensor to collect HR timeseries, and a ventilation rate (VE) model to estimate the volume of inhaled air per minute (L/min) based on HR and other subject's covariates. Finally, VE and PM timeseries are combined to estimate the inhaled pollutant doses over time, as a measure of personal exposure. To model VE as a function of HR, 4 literature models are considered. The estimates obtained with the 4 models are compared in 3 representative subjects. Initial data analysis showed that the 4 models may drive to statistically significant differences in exposure estimates, thus the choice of the model can be a critical aspect of this approach. Regardless of the model used, timeseries of inhaled PM revealed significant daily variations in pollutant exposure, highlighting the importance of methodologies for accurate personal exposure assessment.