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Use of Low-cost Air Quality Sensors to Quantify Human Activity Pattern Based PM2.5 Exposures
JIAYU LI, Aliaksei Hauryliuk, Krystal Suero, Shifali Kerudi, Megan Henriksen, Albert A. Presto, Carnegie Mellon University
Abstract Number: 53
Working Group: Indoor Aerosols
Abstract
People spend 87% of their time in buildings. However, epidemiological studies usually interpret the harmful effects of PM2.5 based on outdoor concentrations. This compromise is mainly due to the lack of affordable and accurate measurement techniques to sample air pollutants with a high spatiotemporal resolution. This study focused on characterizing air pollution profiles of indoor and outdoor environments, utilizing multiple real-time multi-pollutant sensors (RAMPs). The measured results were coupled with human activity patterns (HAP), detailing whereabouts of people, to estimate HAP-based personal exposures. The HAP-based exposure was compared with that solely determined based on outdoor measurement.
We sampled over 20 indoor environments and 10 representative outdoor environments with RAMPs for PM2.5, CO, and, O3 concentrations. The 20 indoor environments can be characterized as three major categories (residential buildings, common spaces, and restaurants). The 10 outdoor sites represent urban environments. The diurnal patterns of PM2.5, CO, and O3 concentrations from each category are significantly different. In residential buildings, PM2.5 and CO emissions related to cooking dominate during the mealtimes, especially for families with gas stoves. The common spaces, including offices, classrooms, gyms, and libraries, show a lower concentration due to better ventilation conditions. By coupling these diurnal patterns with HAP, we used the Monte Carlo Simulation to estimate the HAP-based personal exposure. The simulation indicated that personal exposure is highly dependent on residential environments. The HAP-based PM2.5 exposure is 5-10% lower than the exposure solely based on outdoor concentrations, while the HAP-based CO and O3 exposures 150-200 % higher and 30-50 % lower than those estimated solely from outdoor measurements.