Indoor Air Quality Information to Optimize Ventilation Parameters
CHETHANI ATHUKORALA, Suresh Dhaniyala, Clarkson University
Abstract Number: 539
Working Group: Indoor Air Purification Technologies, Best Practices, and their Health Impacts
Abstract
Ventilation systems help maintain thermal comfort and healthy indoor air quality by controlling outdoor air rate, using in-line filters and additionally incorporating air cleaning methods. Often, the ventilation systems are not sophisticated enough to satisfy both the necessary comfort and indoor air quality levels. Often, indoor air quality is not even measured, so our inability to maintain indoor air is not even captured. In this study, we monitored air quality, energy, thermal parameters in a heavily-used lecture room in Clarkson University over a period of two years to understand the interplay between these parameters and determine the optimal pathway to achieve necessary indoor conditions while minimizing energy use. The monitoring suite included research-grade instruments such as Aerodynamic Particle Sizer (APS), Condensation Particle Counter (CPC), and Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) and a network of low-cost TelosAir sensors that measured particulate matter, CO2, VOCs, temperature, and relative humidity. Four sensors were located around the room and four sensors were placed in the ventilation system to monitor outdoor air line, return air line, and supply air line. We analyzed the data to determine the relation between ventilation system operation, outdoor air conditions, indoor space usage, and resultant indoor air quality. Using a data-driven Machine learning model to drive the ventilation system, we demonstrate that we can achieve both indoor air and thermal comfort goals while reducing energy use relative to the baseline, manual system. We will present our air quality monitoring approach and the obtained results and discuss the optimization pathway for ventilation system operation that we determined.