Experimental Evaluations of the Impact of an Additive Oxidizing Electronic Air Cleaner on Particles and Gases

Abstract

Electronic air cleaning (EAC) technologies have garnered significant attention for use in buildings. Many EAC technologies rely on the addition of reactive constituents to indoor air to react with gas-phase compounds, enhance particle deposition, and/or inactivate microorganisms. However, limited data are available on the efficacy of many EAC technologies and their potential to form chemical byproducts during operation. Here we experimentally evaluate the indoor air quality impacts, specifically targeting particles and gases but not microbial constituents, of a commercially available additive oxidizing EAC that generates positive and negative ions and hydrogen peroxide (H2O2). Tests were conducted in a large unoccupied test chamber in Chicago, IL and an unoccupied laboratory in Portland, OR under a combination of natural conditions (i.e., without pollutant injection) and perturbation conditions (i.e., with pollutant injection and decay). A combination of integrated and time-resolved measurements was used across both test locations. Chamber tests at lower airflow rates demonstrated that operation of the EAC: (i) had no discernible impact on particle concentrations or particle loss rates, with estimated clean air delivery rates (CADRs) for various particle measures less than ±10 m3/h, (ii) was associated with apparent decreases in some volatile organic compounds (VOCs) and increases in other VOCs and aldehydes, especially acetaldehyde, although a combination of high propagated uncertainty, limitations in test methods (e.g., lack of replicates), and variability between repeated tests limit what quantitative conclusions can be drawn regarding gas-phase organics; (iii) did generate H2O2, assessed using a crude measure, and (iv) did not generate ozone (O3). Laboratory tests at higher airflow rates, which involved injection and decay of particles and a single VOC (limonene), both simultaneously and separately, demonstrated that: (i) pollutant loss rates for both particles and limonene were slightly lower with the EAC on compared to off, yielding slightly negative pollutant removal efficiencies (albeit largely within propagated uncertainty) and (ii) there was a change in observed concentrations of one potential limonene degradation product, m/z 59 (putatively identified as acetone), with steady-state levels increasing from 10 ppb (air cleaner off) to 15 ppb (air cleaner on). No increases or decreases beyond measurement uncertainty were observed for other analyzed gaseous limonene degradation products. Overall, both chamber and laboratory tests demonstrated negligible effectiveness of this device at the test conditions described herein for removing particles and mixed results for VOCs, including decreases in some VOCs, no discernible differences in other VOCs, and apparent increases in other compounds, especially lower molecular weight aldehydes including acetaldehyde.

Impact of do‐it‐yourself air cleaner design on the reduction of simulated wildfire smoke in a controlled chamber environment

“…CADR increased with increasing fan speed, 18 , 19 , 20 increasing filter thickness, 20 increasing filter rating (i.e., minimum effective reporting value – MERV), 20 increasing number of filters used 18 , 20 and increasing particle size. 21 …”

“… 22 Similarly, a large range of CADRs (168–825 CFM) have been observed for the 5‐filter CR box. 18 , 19 , 20 , 21 The range in reported CADRs may be impacted by specific design features used in each study (e.g., filter or fan manufacturer), but also by the evaluation method. For example, Pistochini and McMurry 18 estimated CADR by measuring flow through the fan and applying the rated efficiency of the filter, Srikrishna 20 estimated the single pass efficiency by measuring the particle number concentration with the fan on and off, and others measured decay rates with salt 19 or incense.…”

“…For example, Pistochini and McMurry 18 estimated CADR by measuring flow through the fan and applying the rated efficiency of the filter, Srikrishna 20 estimated the single pass efficiency by measuring the particle number concentration with the fan on and off, and others measured decay rates with salt 19 or incense. 21 These differences across studies make it difficult to compare DIY air cleaner performance across designs and in relation to commercial air cleaners, which are often characterized by a certified CADR following the measurement protocol developed by the Association of Home Appliance Manufacturers (AHAM). 23…”

Can 10x cheaper, lower-efficiency particulate air filters and box fans complement High-Efficiency Particulate Air (HEPA) purifiers to help control the COVID-19 pandemic?

“…The first study is by UC-Davis 78 measured 600 cfm at lowest fan speed to 850 cfm at highest fan speed. The second study is by Illinois Institute of Technology 79 (IIT), measured below 300 cfm in the range of 0.5 to 3 μm (dust) at the highest fan speed but above 450 cfm in the range of 5-11 μm (pollen). Both used five 2” MERV-13 filters (instead of four in our study) of similar dimensions so it is unclear why the studies by UC-Davis and IIT differ by a factor of two.…”

“…Traditional methods that measure the effect of the air purifier (and its CADR) using the decay rate of aerosols generated or injected into an isolated, unoccupied room require greater investment of time, effort, risk of inhalation of the generated aerosols, and training/expertise compared to the much simpler DIY test methods described here. 78 79 We use ambient aerosols to rapidly compare different filters, fans, and configurations with low-cost, measurement tools available online (e.g. Amazon) also making these results easy to replicate and verify with limited resources.…”