Indoor air quality improvement with filtration and UV-C on mitigation of particulate matter and airborne bacteria: Monitoring and modeling
Li P. Koziel J.A. Paris R.V. Macedo N. Zimmerman J.J. Wrzesinski D. Sobotka E. Balderas M. Walz W.B. Liu D. Yedilbayev B. Ramirez B.C. Jenks W.S.
February 2024Academic Press
Journal of Environmental Management
2024#351
Indoor air, especially with suspended particulate matter (PM), can be a carrier of airborne infectious pathogens. Without sufficient ventilation, airborne infectious diseases can be transmitted from one person to another. Indoor air quality (IAQ) significantly impacts peoples daily lives as people spend 90% of their time indoors. An industrial-grade air cleaner prototype (filtration + ultraviolet light) was previously upgraded to clean indoor air to improve IAQ on two metrics: particulate matter (PM) and viable airborne bacteria. Previous experiments were conducted to test its removal efficiency on PM and airborne bacteria between the inlet and treated air. However, the longer-term improvement on IAQ would be more informative. Therefore, this research focused on quantifying longer-term improvement in a testing environment (poultry facility) loaded with high and variable PM and airborne bacteria concentrations. A 25-day experiment was conducted to treat indoor air using an air cleaner prototype with intermittent ON and OFF days in which PM and viable airborne bacteria were measured to quantify the treatment effect. The results showed an average of 55% reduction of total suspended particulate (TSP) concentration between OFF days (110 μg/m3) and ON days (49 μg/m3). An average of 47% reduction of total airborne viable bacteria concentrations was achieved between OFF days (∼3200 CFU/m3) and ON days (∼2000 CFU/m3). A cross-validation (CV) model was established to predict PM concentrations with five input variables, including the status of the air cleaner, time (h), ambient temperature, indoor relative humidity, and day of the week to help simulate the air-cleaning effect of this prototype. The model can approximately predict the air quality trend, and future improvements may be made to improve its accuracy.
Air pollution control , Disease control , Infectious disease , Occupational health , Poultry facility , Ultraviolet light , UV disinfection
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Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States
Livestock Nutrient Management Research Unit, USDA-ARS Conservation & Production Research Laboratory, Bushland, TX, United States
Department of Statistics, Iowa State University, Ames, IA, United States
Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United States
Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
Department of Biomedical Sciences, Iowa State University, Ames, IA, United States
Department of Geography and Environmental Sciences, al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of Chemistry, Iowa State University, Ames, IA, United States
Department of Agricultural and Biosystems Engineering
Livestock Nutrient Management Research Unit
Department of Statistics
Department of Veterinary Diagnostic and Production Animal Medicine
Department of Mechanical Engineering
Department of Biomedical Sciences
Department of Geography and Environmental Sciences
Department of Chemistry
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