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Quantifying Temporal Ventilation Trends in Indoor Air Quality in University Buildings Using Professional Grade Low-Cost Sensors
SABRINA WESTGATE, Daniel Alvarado-Velez, Eben Cross, David Hagan, Nga Lee Ng, Georgia Institute of Technology
Abstract Number: 368
Working Group: Indoor Aerosols
Abstract
In recent years, low-cost air quality sensors have become increasingly accessible, expanding opportunities to examine indoor air pollution over extended time periods and with high time resolution. Although we spend the majority of our time indoors, we have much to learn about how various factors, such as occupancy, influence indoor air quality. The rise of aerosol transmissible SARS-COVID19 has further highlighted the need to empirically evaluate - in real time - current ventilation systems and indoor air pollution dynamics in public spaces. This is of especial interest since in-situ carbon dioxide (CO2) is often used as a proxy for understanding aerosol dynamics and potential SARS-COVID19 transmission. To assess indoor air quality and aerosol dynamics in a university setting, professional grade, low-cost sensors (QuantAQ MODULAIR and MODULAIR-PM) with the capability to detect particulate matter (PM1, PM2.5, PM10), CO2, and various gas-phase species were installed in rooms across the Georgia Institute of Technology campus beginning in Fall 2020. The rooms examined vary in size, ventilation type (fan coil units vs. heating, ventilation, and air conditioning units (HVAC)), percent outdoor air introduced into the room, and occupancy. In this study, data collected beginning in Fall 2020 was used to understand the variations in indoor air quality as a function of time, occupancy, and pollutant type. Room ventilation rates were estimated from the decays of in-situ CO2 levels after class times and PM decays after cleaning or human activities. The calculated ventilation rates were compared to each other and to facilities ventilation records. Results show a clear impact of mechanical ventilation schedules on CO2 and PM decay rates and indicate the applicability of using CO2 data from room occupancy to estimate room and aerosol ventilation. Moreover, results indicate a relationship between PM size and decay rate, providing insight to indoor aerosol dynamics.