An Integral Approach to Quantifying the Clean Air Delivery Rate (CADR) of Indoor Air Cleaning Devices

SAMAN HARATIAN, P. S. Ganesh Subramanian, Vishal Verma, Mohammad Heidarinejad, Brent Stephens, Max Sherman, Illinois Institute of Technology

     Abstract Number: 41
     Working Group: Reducing Aerosol Exposure with Control Technologies and Interventions

Abstract
Evaluations of indoor air cleaner performance commonly involve pulse injection and decay tests conducted in a controlled chamber with and without an air cleaning device operating. Analysis of concentration decay data to generate an estimate of the clean air delivery rate (CADR), also called the equivalent clean air (ECA) flow rate, most commonly involves comparing first-order loss rate constants between the two test conditions and multiplying the difference by the volume of the room. Although the first-order exponential decay assumption may be held for some types of pollutants and tests, sometimes pollutant decay tests exhibit behavior that is not consistent with first-order kinetics, which results in error, biases, and/or high uncertainty in estimates of CADR. This is particularly evident in many bioaerosol decay tests. To address these issues, we introduce a novel integral approach to quantify loss rates and CADR from time-resolved contaminant concentration data resulting from pulse injection and decay tests of air cleaning devices, which does not rely on conventional first-order assumptions. We explore the utility of the developed approach using several examples from literature and investigate the magnitudes of uncertainty associated with both the integral technique and traditional first-order approaches. We find that the integral approach can replicate first-order kinetics (when applicable) and can also be used to estimate CADR for other decay forms, such as 2-stage decay or when there is no specific model form. The integral method is particularly sensitive to measurement uncertainty and requires some assumptions to approximate the area under the decay curve, especially when time-steps are large. Both estimation approaches reveal the importance of incorporating robust measures of uncertainty in estimating EAC to guide selection and specification of air cleaning technologies.