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Fibrous air filters are common devices used to remove airborne particles. Their performance is typically measured through their resistance to airflow and captured particle mass. Models describing the evolution of filter performance have been heavily researched; however, the need for improvement remains. Experimental work is irreplaceable in the development of high-fidelity models, yet the estimation of necessary variables is not trivial and may be influenced by selected measurement instruments and analysis methodologies. Therefore, the purpose of this work is to propose a framework to investigate the response of common aerosol measurement instruments, their corresponding analysis methodologies, and the application of their data. A Scanning Mobility Particle Sizer (SMPS) and Laser Aerosol Spectrometer (LAS) were selected for consideration, and their recorded data were compared against baseline measurements. The results of the experiments indicated that the SMPS and LAS yielded a ratio of estimated mass concentrations to the baseline mass concentrations of approximately 1.175 and 0.749, respectively. Regarding the SMPS, it was suggested that the measurable size range, application of a coverage factor, dynamic shape factor, and association between the curve fits and histograms were influential in the final estimates. For the LAS, the application of a curve fit, its association to the histograms, and the selection of the sampling periods were influential. Considering the results, the impact of these factors may not be considered negligible and may skew reproducibility between studies and fossilize confounding factors. Therefore, the proposed methodologies are useful in addressing potential errors in data collection and analysis. [ABSTRACT FROM AUTHOR]

Particulate matter air pollution and volatile organic compounds released into the air from the incomplete combustion of fossil fuels and wildfires creates significant damage to human health and the environment. Advances in air filtration and purification technology are needed to mitigate aerosol hazards. This article details an effort to explore the potential benefits of new materials and methods for the production of nonwoven air filtration media through electrospinning and stabilizing polyacrylonitrile fibers. The investigated production methods include electrospinning fibrous matting onto a stainless steel wire mesh and stabilizing the nonwoven media in a chamber furnace. The media is then tested for air filtration penetration and airflow resistance, and the fiber size distribution is measured using scanning electron microscopy. The experimental results show that the electrospun media approaches the performance criteria for airflow resistance and particle capture efficiency of high-efficiency particulate air (HEPA) filter media. Furthermore, performance estimations for electrospun media of increased thickness and for a decreased filtration velocity show potential to exceed the HEPA media resistance and efficiency criteria. Thus, it is suggested that electrospun and stabilized nonwoven fibrous media are candidates as alternatives to traditionally manufactured HEPA media and may potentially benefit modern air filtration technology and reduce hazards associated with particulate matter. Additionally, the authors recommend future exploration into the carbonization and activation of electrospun filter media for the adsorption and mitigation of volatile organic compounds as a secondary benefit, while maintaining high efficiency and low airflow resistance in the removal of particulate matter from aerosol streams. [ABSTRACT FROM AUTHOR]

In the absence of experimental air filtration test data, it is difficult to predict the air filtration efficiency and airflow resistance of filter media. In this novel work, an effort is undertaken to predict the air filtration efficiency and airflow resistance of filter media based solely on scanning electron microscopy (SEM) images of the filter fabric. The analytical single fiber efficiency (SFE) model is incorporated into a machine-learning model designed to analyze digital imagery. A convolutional neural network (CNN) is developed and optimized using synthetic training data produced from digital replication of air filter media. Simulated air filter media were created to replicate the physical characteristics of actual nonwoven nanofibrous air filter media. Digital grayscale images of the simulated media provided the input data for the CNN. Analytical calculations of efficiency and resistance based on the SFE model provided target data for the CNN. The regression model included seven convolution layers and two hidden fully connected layers along with width reduction and depth expansion methods. Twelve model parameters were optimized using training and validation data of 2100 iterations of simulated media. The model was then employed to predict the air filtration behavior of actual air filter media based on an SEM image of the media. The resulting predictions of air filtration efficiency and airflow resistance based solely on an SEM image suggests the potential viability of using synthetic data derived from simulated media to train machine learning models for real-world applications. To the knowledge of the authors, this is the first such application of a CNN to predict air filtration efficiency and airflow resistance using an SEM image, synthetic data created from simulated media, and the SFE analytical model. • Convolutional neural networks enable prediction of air filtration efficiency. • Analytical single fiber efficiency models are applied to simulated media. • Computer vision with CNNs enables prediction of airflow pressure resistance. • Machine learning modeling with CNNs will advance the science of air filtration. [ABSTRACT FROM AUTHOR]

Fibrous filters are commonly utilized to remove aerosols from the air to capture contaminants and pollutants prior to atmospheric release, or to clean atmospheric air itself. An ideal filter achieves the highest capture efficiency possible while providing the least airflow resistance possible. From this, the ability to predict how a filter operates based upon its functional parameters, such as filtration velocity and particle size and type, and characteristic parameters, such as its fiber size and media porosity, are integral to not only properly applying and estimating filter performance, but also facilitating the improvement and development of filtration technology. The purpose of this work is to review both classical and contemporary particle loading models to provide a comprehensive discussion built upon a foundation of first principles. The different relevant particle capture mechanisms are presented and discussed in terms of fibrous air filtration, along with the separate loading regimes. Specific loading models are then introduced considering these concepts and are separated into three categories: 1) analytical models, 2) computational models, and 3) experimental models. In short summary, analytical models have been well-covered and benefit from their ease of use but suffer from a difficulty in describing the stochastic nature of realistic particle loading. Computational models benefit from their inherent incorporation of first principles thereby addressing the dynamic nature of particle loading, however they may be difficult to apply outside of research environments due to their complexity and computationally demanding nature. Experimental models benefit from the avoidance of simplifying assumptions and the ability to correlate data describing phenomena that are difficult to quantify but are limited to applications covered by their experimental parameters and have difficulty addressing anomalies that present themselves. Suggested future work from identified research gaps focus on the lack of available data and reliable methods to measure media characteristics as well as developing models that predict the full spectrum of the loading process based upon media and particle characteristics. • Particle loading models predict filter performance from factors such as loaded mass. • Prevalent work is categorized as analytical, computational, or experimental. • Included models are presented in terms of foundational particle capture mechanisms. • Strengths and weaknesses of the model categories are presented and discussed. • Future work is identified through current research gaps. [ABSTRACT FROM AUTHOR]

COVID-19 forced the human population to rethink its way of living. The threat posed by the potential spread of the virus via an airborne transmission mode through ventilation systems in buildings and enclosed spaces has been recognized as a major concern. To mitigate this threat, researchers have explored different technologies and methods that can remove or decrease the concentration of the virus in ventilation systems and enclosed spaces. Although many technologies and methods have already been researched, some are currently available on the market, but their effectiveness and safety concerns have not been fully investigated. To acquire a broader view and collective perspective of the current research and development status, this paper discusses a comprehensive review of various workable technologies and methods to combat airborne viruses, e.g., COVID-19, in ventilation systems and enclosed spaces. These technologies and methods include an increase in ventilation, high-efficiency air filtration, ionization of the air, environmental condition control, ultraviolet germicidal irradiation, non-thermal plasma and reactive oxygen species, filter coatings, chemical disinfectants, and heat inactivation. Research gaps have been identified and discussed, and recommendations for applying such technologies and methods have also been provided in this article. • Comprehensive review of COVID-19 mitigation methods focusing on HVAC systems. • Investigated methods include ventilation, filtration, and air ionization. • Other featured methods include environmental conditioning and ultraviolet light. • Developing technologies include heat, nanoparticles, chemical, and plasma methods. • Identified research gaps provide paths for future work and development. [ABSTRACT FROM AUTHOR]