ABSTRACT
A dimensionless number Nr for the effective design of facial masks was derived and compared with other dimensionless numbers of fluid mechanics. Nr is found to closely resemble Euler's number (Eu). Nr is equal to the logarithmic function of the ratio of inertial force (Fi) of the air to the pressure force (Fp) of the air through the porous membrane of the mask. Nr is then introduced as a novel dimensionless number (Habib number) Ha in which the air flow through a facial mask is derived with parameters for an effective barrier from the COVID-19 virus (SARS COV 2). The introduction of Ha was not only for a comparison reason with other dimensionless numbers of fluid mechanics but also the number Ha is an essential extension of an early work on "Fluid mechanics of facial masks as personal protection equipment (PPE) of COVID-19 virus"[Rev. Sci. Instrum. 92, 074101 (2021)], in which the air flow through the mask is in its optimum design conditions to shield against the COVID-19 virus. As a result, an optimum Nr of expressing the flow of the O2 and N2 gases through the porous membrane was determined (Nr = NO2 = NN2 = Ha = -4.4). This was obtained when the N95 mask with specifications of a = 20 nm, l = 30 μm, and ϵ = 30% was used, with respect to the pressure gradient of the human lungs during exhaled and inhaled conditions, PAverage = 20 mm Hg (g cm-1 S-2), and to the size of the COVID-19 virus of about 125 nm (0.125 μm). In addition, a range of values of Nr was analyzed with respect to the optimum (Ha) value of Nr. On the one hand, when the range value of Nr falls between 0 ≥ Nr ≥ Ha, the mask has less resistance than Nr < -4.4, but not necessarily its optimum design conditions. On the other hand, when Nr = 0, the flow through the mask has no resistance at all, as if the mask does not exist. © 2023 Author(s).
ABSTRACT
The COVID-19 pandemic had a significant impact on travel mode choices in cities across the world. Driven by perceptions of risk and the fear of infection, the pandemic resulted in an increased preference for private vehicles and active modes and a reduced preference for public transit and ride-sourcing. As travel behavior and modal preferences evolve, a key question is whether the pandemic will result in long-term changes to travel mode choices. This study uses data from a web-based survey to examine the factors influencing mode choices for non-commuting trips in the post-pandemic era. Specifically, it uses stated preference data to develop a random parameter mixed logit model, which is used to compare the elasticity of key variables across different income and age groups. The results of the study highlight the influence of sociodemographic attributes and pre-pandemic travel habits on anticipated post-pandemic mode choices. Additionally, the results suggest that frequent users of private vehicles, public transit, and active modes are likely to continue to use these modes post-pandemic. Furthermore, the results highlight the potential for the perception of shared modes to influence post-pandemic mode choice decisions. The results of the study offer insights into policy measures that could be applied to address the increased use of private vehicles and reduced use of transit during the pandemic, while also emphasizing the need to ensure that certain segments of the population can maintain a sufficient level of mobility and access to transport.
ABSTRACT
Introduction: Healthcare workers (HCWs) are at an increased risk of exposure to SARS-CoV-2 infection. There is a need for urgent intervention to incease knowledge and practices related to infection prevention and control (IPC) during the current pandemic. Objectives: The objectives of the current study are to assess knowledge, attitude and practices among HCWs regarding IPC practices related to COVID-19 and whether training session can be used as an effective educational tool to improve knowledge. Methods: This cross sectional study was conducted among HCWs from Sindh. After assessment of baseline knowledge, attitude and practices via pre test, a virtual session on COVID-19 IPC practices was conducted, which was followed by post test. Results: Among 240 participant, 141 (59%) were frontline workers dealing with patients with COVID-19. Only 76 (31%) had previous training on IPC before pandemic and even during pandemic, few (n=95, 40%) had attended a training workshop. Majority (70.4%) of participants were working in a facility with an established IPC department. There was an overall statistically significant improvement in knowledge before and after the education workshop (p value < 0.01). The majority of HCWs believed that poor compliance with personal protective equipment (PPE) was due to hot climate, interference with daily work, increased workload and long working hours. The knowledge and reported compliance of hand hygiene were good among majority of participants ( > 90% ). A large number of HCWs (88%) carry hand sanitizers all the time and frequently clean their belongings during current pandemic. Although 75% of HCWs believed that PPE can Aprotect them from contracting infections, a poor compliance of wearing PPE was reported while dealing with patients with COVID-19. Conclusion: Frequent awareness sessions can help in improving knowledge related to infection control and practices among HCWs.
ABSTRACT
A fluid mechanics model of inhaled air gases, nitrogen (N2) and oxygen (O2) gases, and exhaled gas components (CO2 and water vapor particles) through a facial mask (membrane) to shield the COVID-19 virus is established. The model was developed based on several gas flux contributions that normally take place through membranes. Semiempirical solutions of the mathematical model were predicted for the N95 facial mask accounting on several parameters, such as a range of porosity size (i.e., 1-30 nm), void fraction (i.e., 10-3%-0.3%), and thickness of the membrane (i.e., 10-40 µm) in comparison to the size of the COVID-19 virus. A unitless number (Nr) was introduced for the first time to describe semiempirical solutions of O2, N2, and CO2 gases through the porous membrane. An optimum Nr of expressing the flow of the inhaled air gases, O2 and N2, through the porous membrane was determined (NO2 = NN2 = -4.4) when an N95 facial mask of specifications of a = 20 nm, l = 30 µm, and ε = 30% was used as a personal protection equipment (PPE). The concept of the optimum number Nr can be standardized not only for testing commercially available facial masks as PPEs but also for designing new masks for protecting humans from the COVID-19 virus.