Optimizing air purification for household particulate matters using sensor-based and time-based intervention strategies--颗粒学报PARTICUOLOGY

Wang, M., Lu, T., & Li, Y. (2023). Optimizing air purification for household particulate matters using sensor-based and time-based intervention strategies. Particuology, 79, 78-84. https://doi.org/10.1016/j.partic.2022.11.008

Optimizing air purification for household particulate matters using sensor-based and time-based intervention strategies

Meng Wang ^a *, Tianjun Lu ^b, Yang Li ^c

a Department of Epidemiology and Environmental Health, School of Public and Health Professions, University at Buffalo, Buffalo, 14214, USA
b Department of Earth Science and Geography, California State University, Dominguez Hills, Carson, 90747, USA
c Department of Environmental Science, Baylor University, Waco, 76798, USA

10.1016/j.partic.2022.11.008

Volume 79, August 2023, Pages 78-84

Received 29 September 2022, Revised 15 November 2022, Accepted 16 November 2022, Available online 25 November 2022, Version of Record 24 January 2023.

E-mail: mwang54@buffalo.edu

Highlights

Abstract

Filtration efficiency of portable air cleaner (PAC) is affected by resident perceptions and adherences to when and how to operate the PAC. Incorporating PAC with smart control and sensor technology holds the promise to effectively reduce indoor air pollutants. This study aims to evaluate the efficiency of a PAC at removing indoor fine particulate matters (PM_2.5) exposure under two automated operation settings: (1) a time-based mode in which the operation time is determined based on perceived time periods of indoor pollution by residents; (2) a sensor-based mode in which an air sensor monitor is used to determine the PAC based on the actual PM_2.5 level against the indoor air quality guideline. The study was conducted in a residential room for 55 days with a rolling setting on PAC (no filtration, sensor-based, time-based filtrations) and a continuous measurement of PM_2.5. We found that the PAC operated with sensor-based mode removed PM2.5 concentrations by 47% and prolonged clean air (<35 μg/m³) period by 23% compared to the purifications with time-based mode which reduced PM2.5 by 29% and increased clean air period by 13%. The sensor-based filtration identified indoor pollution episodes that are hardly detected by personal perceptions. Our study findings support an automated sensor-based approach to optimize the use of PAC for effectively reducing indoor PM2.5 exposure.

Graphical abstract

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