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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. [ABSTRACT FROM AUTHOR]

This study investigates window opening and closing behaviors in residential bedrooms in Hong Kong by examining occupants' habits regarding window operation, window opening and closing behavior, and the influence of environmental and contextual factors. Using self-reported survey data on window opening and closing behavior and outdoor environmental data from an open-source database, univariable and multivariable logistic mixed-effect models were developed to evaluate the impact of various parameters. The findings indicate that outdoor environmental factors have a limited influence on window opening and closing probabilities. Conversely, time of the day was identified as a significant factor, emphasizing the role of occupant habits and preferences in determining these probabilities. Importantly, the study results agreed with the observations that the drivers between window opening and window closing differed, further emphasizing the need for separate modeling. The study highlights the importance of considering environmental and contextual factors when predicting window operation behaviors. This study contributes to our understanding of building occupant behavior and provides valuable insights for building designers and engineers to enhance energy simulations and optimize building performance. • The time of the day affects window usage. • Occupant habits and preferences influence window opening and closing behaviors. • Drivers for window opening and closing differ. • Separate modeling is needed for window opening and closing. [ABSTRACT FROM AUTHOR]