The air we breathe: Numerical investigation of ventilation strategies to mitigate airborne dispersion of MERS-CoV in inpatient wards.

Ventilation strategies for infection control in hospitals has been predominantly directed towards isolation rooms and operating theatres, with relatively less emphasis on perceived low risk spaces, such as general wards. Typically, the ventilation systems in general wards are intended to optimize patient thermal comfort and energy conservation. The emission of pathogens from exhalation activity, such as sneezing, by an undiagnosed infectious patient admitted to general wards, is a significant concern for infection outbreaks. However, the ventilation guidelines for general wards with respect to infection control are vague. This research article presents a numerical study on the effect of varying air change rates (3 h-1, 6 h-1, 9 h-1, 13 h-1) and exhaust flow rates (10%, 50% of supply air quantity) on the concentration of airborne pathogens in a mechanically ventilated general inpatient ward. The findings imply that the breathing zone directly above the source patient has the highest level of pathogen exposure, followed by the breathing zones at the bedside and adjacent patients close to the source patient. The dispersion of pathogens throughout the ward over time is also apparent. However, a key difference while adopting a lower ACH (3 h-1) and a higher ACH (13 h-1) in this study was that the latter had a significantly lower number of suspended pathogens in the breathing zone than the former. Thus, this research suggests high ventilation rates for general wards, contrary to current ventilation standards. In addition, combining a higher air change rate (13 h-1) with a high exhaust flow rate (50% of supply air) through a local exhaust grille dramatically reduced suspended pathogens within the breathing zone, further mitigating the risk of pathogen exposure for ward users. Therefore, this study presents an effective ventilation technique to dilute and eliminate airborne infectious pathogens, minimizing their concentration and the risk of infection.

A Novel IoT-Enabled Wireless Sensor Grid for Spatial and Temporal Evaluation of Tracer Gas Dispersion.

Current IoT applications in indoor air focus mainly on general monitoring. This study proposed a novel IoT application to evaluate airflow patterns and ventilation performance using tracer gas. The tracer gas is a surrogate for small-size particles and bioaerosols and is used in dispersion and ventilation studies. Prevalent commercial tracer-gas-measuring instruments, although highly accurate, are relatively expensive, have a long sampling cycle, and are limited in the number of sampling points. To enhance the spatial and temporal understanding of tracer gas dispersion under the influence of ventilation, a novel application of an IoT-enabled, wireless R134a sensing network using commercially available small sensors was proposed. The system has a detection range of 5-100 ppm and a sampling cycle of 10 s. Using Wi-Fi communication, the measurement data are transmitted to and stored in a cloud database for remote, real-time analysis. The novel system provides a quick response, detailed spatial and temporal profiles of the tracer gas level, and a comparable air change rate analysis. With multiple units deployed as a wireless sensing network, the system can be applied as an affordable alternative to traditional tracer gas systems to identify the dispersion pathway of the tracer gas and the general airflow direction.

Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models.

Indoor air quality (IAQ) standards have been evolving to improve the overall IAQ situation. To enhance the performances of IAQ screening models using surrogate parameters in identifying unsatisfactory IAQ, and to update the screening models such that they can apply to a new standard, a novel framework for the updating of screening levels, using machine learning methods, is proposed in this study. The classification models employed are Support Vector Machine (SVM) algorithm with different kernel functions (linear, polynomial, radial basis function (RBF) and sigmoid), k-Nearest Neighbors (kNN), Logistic Regression, Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron Artificial Neural Network (MLP-ANN). With carefully selected model hyperparameters, the IAQ assessment made by the models achieved a mean test accuracy of 0.536-0.805 and a maximum test accuracy of 0.807-0.820, indicating that machine learning models are suitable for screening the unsatisfactory IAQ. Further to that, using the updated IAQ standard in Hong Kong as an example, the update of an IAQ screening model against a new IAQ standard was conducted by determining the relative impact ratio of the updated standard to the old standard. Relative impact ratios of 1.1-1.5 were estimated and the corresponding likelihood ratios in the updated scheme were found to be higher than expected due to the tightening of exposure levels in the updated scheme. The presented framework shows the feasibility of updating a machine learning IAQ model when a new standard is being adopted, which shall provide an ultimate method for IAQ assessment prediction that is compatible with all IAQ standards and exposure criteria.

Evaluation of Indoor Air Quality Screening Strategies: A Step-Wise Approach for IAQ Screening.

Conducting a full indoor air quality (IAQ) assessment in air-conditioned offices requires large-scale material and manpower resources. However, an IAQ index can be adopted as a handy screening tool to identify any premises (with poor IAQ) that need more comprehensive IAQ assessments to prioritize IAQ improvements. This study proposes a step-wise IAQ screening protocol to facilitate its cost-effective management among building owners and managers. The effectiveness of three IAQ indices, namely θ1 (with one parameter: CO2), θ2 (with two parameters: CO2 and respirable suspended particulates, RSP) and θ3 (with three parameters: CO2, RSP, and total volatile organic compounds, TVOC) are evaluated. Compared in a pairwise manner with respect to the minimum satisfaction levels as stated in the IAQ Certification Scheme by the Hong Kong Environmental Protection Department, the results show that a screening test with more surrogate IAQ parameters is good at identifying both lower and higher risk groups for unsatisfactory IAQ, and thus offers higher resolution. Through the sensitivity and specificity for identifying IAQ problems, the effectiveness of alternative IAQ screening methods with different monitoring parameters is also reported.