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1. Evaluation of Filtration Efficiency of Various Filter Media in Addressing Wildfire Smoke in Indoor Environments: Importance of Particle Size and Composition

   https://doi.org/10.3390/atmos14121729  

   Shirman, Tanya; Shirman, Elijah; Liu, Sissi (December 2023, Atmosphere)

   Sahar Zahiri (Ed.)

   Sub-micron particles are ubiquitous in the indoor environment, especially during wildfire smoke episodes, and have a higher impact on human health than larger particles. Conventional fibrous air filters installed in heating, ventilation, and air conditioning (HVAC) systems play an important role in controlling indoor air quality by removing various air pollutants, including particulate matter (PM). However, it is evident that the removal efficiency of wildfire smoke PM and its effect on filter performance is significantly under-studied. This study delves into the size-specific removal efficiency of pine needle smoke, a representative of wildfire smoke and emissions. We test an array of filter media with minimum efficiency reporting values (MERV) spanning 11–15. Both size-resolved particle number concentrations and mass concentrations were measured using an Optical Particle Sizer (OPS, TSI, Inc.) and a Scanning Mobility Particle Sizer (SMPS, TSI, Inc.). Furthermore, we characterize the filter media morphology and smoke particles deposited on filter fibers using Scanning Electron Microscopy (SEM) to gain insights into the interaction dynamics of these particles. Our findings add to the comprehension of the relationship between MERV designations and smoke removal efficiency. Such insight can inform standards and guidelines and equip decision-makers with the knowledge needed to initiate measures for mitigating the impact of air pollution, specifically on the indoor environment. 

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2. On the design of particle filters inspired by animal noses

   https://doi.org/10.1098/rsif.2021.0849  

   Yuk, Jisoo; Chakraborty, Aneek; Cheng, Shyuan; Chung, Chun-I; Jorgensen, Ashley; Basu, Saikat; Chamorro, Leonardo P.; Jung, Sunghwan (March 2022, Journal of The Royal Society Interface)

   Passive filtering is a common strategy to reduce airborne disease transmission and particulate contaminants across scales spanning orders of magnitude. The engineering of high-performance filters with relatively low flow resistance but high virus- or particle-blocking efficiency is a non-trivial problem of paramount relevance, as evidenced in the variety of industrial filtration systems and face masks. Next-generation industrial filters and masks should retain sufficiently small droplets and aerosols while having low resistance. We introduce a novel 3D-printable particle filter inspired by animals’ complex nasal anatomy. Unlike standard random-media-based filters, the proposed concept relies on equally spaced channels with tortuous airflow paths. These two strategies induce distinct effects: a reduced resistance and a high likelihood of particle trapping by altering their trajectories with tortuous paths and induced local flow instability. The structures are tested for pressure drop and particle filtering efficiency over different airflow rates. We have also cross-validated the observed efficiency through numerical simulations. We found that the designed filters exhibit a lower pressure drop, compared to commercial masks and filters, while capturing particles bigger than approximately 10 μm. Our findings could facilitate a novel and scalable filter concept inspired by animal noses. 

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3. On Neural Network Training from Noisy Data using a Novel Filtering Framework

   https://doi.org/10.2514/6.2020-1869  

   Deshpande, Vedang; Das, Niladri; Tadiparthi, Vaishnav; Bhattacharya, Raktim (January 2020, SciTech 2020 Forum)

   null (Ed.)

   We discuss a novel method to train a neural network from noisy data, using Optimal Transport based filtering. We show a comparative study of this methodology with three other filters: the Extended Kalman filter, the Ensemble Kalman filter, and the Unscented Kalman filter, that can also be used for the purpose of training a neural network. We empirically establish that Optimal Transport based filter performs better than the other three filters with respect to root mean square error measure, for non-Gaussian noise in the output. We demonstrate the efficacy of utilizing the Optimal Transport based filtering for neural network training in the context of predicting Mackey-Glass chaotic time series data. 

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4. Carbon dot incorporated multi-walled carbon nanotube coated filters for bacterial removal and inactivation

   https://doi.org/10.1039/c8ra00333e  

   Dong, Xiuli; Al Awak, Mohamad; Wang, Ping; Sun, Ya-Ping; Yang, Liju (January 2018, RSC Advances)

   Multi-walled carbon nanotube (MWCNT) filters incorporated with carbon quantum dots (CDots) or single-walled carbon nanotubes (SWCNTs) were produced for bacteria removal from aqueous solutions and also for inactivating the captured bacteria. TMTP Millipore membranes were used as the base of these filters. The results showed that filters with higher MWCNT loading had higher bacterial removal efficiencies. Filters with a MWCNT loading of 4.5 mg were highly effective at removing bacteria from aqueous solution, resulting in a log reduction of 6.41, 6.41, and 5.41 of E. coli cell numbers in filtrates compared to MWCNT filters without coating, MWCNTs filters with 0.15 mg CDot coating, and MWCNTs filters with 0.15 mg SWCNT coating, respectively. Ionic strength played an important role in bacteria removal. A higher NaCl concentration resulted in higher bacteria removal efficiencies of the filters. Both CDot coatings and SWCNT coatings did not significantly affect the MWCNT filter effects ( P > 0.05). The coatings, especially CDot coatings, significantly inhibited the activities of bacteria retained on the filter surfaces ( P < 0.05). The inhibitory rates were 94.21% or 73.17% on the MWCNT filter surfaces coated with 0.2 mg CDots or SWCNTs, respectively. These results demonstrated that MWCNT filters with CDot coatings were highly effective to remove bacteria from water and to inhibit the activities of the captured bacteria on filter surfaces. 

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5. A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter

   https://doi.org/10.5194/npg-29-241-2022  

   Popov, Andrey A.; Subrahmanya, Amit N.; Sandu, Adrian (January 2022, Nonlinear Processes in Geophysics)

   Abstract. Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to properly sample the high-probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of random noise that widens the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. A localized variant of the proposed method is developed.Numerical experiments with the Lorenz '63 model show that modified filters significantly improve the analyses for low dynamical ensemble sizes. Furthermore, localization experiments with the Lorenz '96 model show that the proposed methodology is extendable to larger systems. 

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