Human exposure against airborne pathogens in an office environment

Airborne exposure has been highlighted during the COVID-19 pandemic as a probable infection route. This experimental study investigates different protection methods at an office workstation, where the concentration characteristics are studied under the mixing ventilation conditions. The protection methods were the room air purifier, personal air purifier, face mask, and workstation partition panels. In experiments, the breathing machine, nebulizer, and syringe pump was used to generate an aerosol distribution of paraffin oil into the room. The breathing thermal manikin and the thermal dummy simulated the exposed and infected person, respectively. The concentration characteristics were measured from the manikin breathing zone. The temporal concentration characteristics were measured from zero concentration to steady-state conditions. The study provides insights into the effects of different protection methods for occupational health and safety decision-making for office indoor environments. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

COVID-19 airborne transmission risk calculation using CO2 concentrations in a 3D office environment

The ongoing COVID-19 pandemic has caused millions of deaths worldwide along with detrimental socioeconomic consequences. Existing evidence suggests that the rate of indoor transmission is directly linked with the Indoor Air Quality (IAQ) conditions. Most of the existing methodologies for virus transmissibility risk estimation are based on the well-known Wells-Riley equation and assume well-mixed, uniform conditions;so spatiotemporal variations within the indoor space are not captured. In this work, a novel fine-grained methodology for real-time virus transmission risk estimation is developed using a 3D model of a real office room with 31 occupants. CONTAM-CFD0 software is used to compute the airflow vectors and the resulting 3D CO2 concentration map (attributed to the exhalations from the occupants). Simulation results are also provided that demonstrate the efficacy of using CO2 sensors for estimating the infection risk in real-time in the 3D office environment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Template for Extended Abstract contributions to INDOOR AIR 2022

This investigation presents results of Computational Fluid Dynamics (CFD) modelling of aerosol behaviour within an arbitrary 'realistic' 100m2 office environment, with dynamic and variable respiratory droplet release profile applied based on published findings (Morawska et al., 2009). A multitude of ventilation strategies and configurations have been applied to the base model to compare the effectiveness of reducing the concentration of suspended aerosols over time. A key finding of the investigation indicates a relatively low sensitivity to increasing outside air percentage, and that the benefit from this strategy is heavily dependent on the in-duct droplet decay factor. The application of local recirculating air filtration systems with MERV-13 filters mounted on occupant desks proved significantly more effectiveness than increasing outside air concentration from 25% to 100% in reducing the quantity of suspended aerosols. This highlights that the ventilation industry should perhaps focus on opportunities to integrate filtration systems into furniture, partitions, cabinetry etc., and that an appliance-based solution may be more beneficial for reducing COVID-19 transmission in buildings (and likely more straightforward) than modifications to central ventilation systems, particularly in the application of refurbishments and retrofits. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

A Surveillance System Controlling Covid-19 in Office Environments

COVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office e nvironment a n i dentification system that uses a deep learning model that prioritizes object recognition for the identification o f e mployees w ho w ear a f ace m ask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. L oss o f Smell after the next component. One person can use one disposable card to check the smell of sniffing. E ach d isposable c ard has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence a nd o ffers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person. © 2022 IEEE.

Deep Learning with Statistical Analysis for Stress Prediction of Remote Working IT Employees in COVID-19 Pandemic

The COVID-19 pandemic has transformed the working environment of employees in information technology (IT) sector from traditional office environment into remote working environment. The changes in working environment, lack of physical activities, and food intake result in direct impact on physical and mental well-being. The stress among IT employees in remote working gets increased owing to the absence of proper physical workstation, and extended inactive behaviour results in high discomfort and pain. So, the advent of deep learning (DL) models assists the stress predictive procedure in understanding the pattern proficiently and delivers efficient perceptions about probable forthcoming interventions. In this view, this study develops a novel deep learning based knowledge management for stress prediction (DLKM-SP) technique among IT employees working from remote places in COVID-19 pandemic. The proposed DLKM-SP model aims to predict the stress level of the IT employees by the selection of features and optimal classification process. In addition, the DLKM-SP technique involves correlation based feature selection and principal component analysis (PCA) based feature reduction technique to choose an optimal subset of features. Moreover, attention based bidirectional long short term memory (ABiLS TM) technique was employed for the classification process for determining the proper class labels. Furthermore, arithmetic optimization algorithm is applied to improve the training process of the ABiLS TM approach. The effectiveness of the proposed model is examined using its own stress prediction dataset with numerous samples collected from IT employees. A detailed comparison study is implemented to highlight the enhanced predictive performance of the DLKM-SP approach in terms of different evaluation measures. © 2022 IEEE.