The COVID-19 pandemic was caused by a highly contagious coronavirus that has triggered worldwide control actions such as social distancing and lockdowns. COVID-19 control actions have resulted in improved air quality locally and around the world in the short-term by limiting human activity. We analyzed the impacts of social distancing and transboundary pollutants on air quality changes using open data and examined the corresponding health benefits focusing on two domestic cities (Seoul and Daegu) in Korea where the spread of coronavirus was severe. During the COVID-19 pandemic, PM_2.5, PM₁₀, and NO₂ concentrations decreased significantly by 31%, 61%, and 33%, respectively, compared to the previous three years. In particular, the PM_2.5/PM₁₀ ratio fell 24.5% after the implementation of social distancing, suggesting a decrease in anthropogenic emissions. Moreover, we found that the air quality index (AQI) also improved significantly, with a focus on reducing exposure to sensitive groups. In Seoul and Daegu, improved air quality prevented 250 and 78 premature deaths, and health costs were USD 884 million and USD 278 million, respectively. On the other hand, health loss due to COVID-19 deaths was in sharp contrast to USD 7.1 million and USD 543.6 million. Our findings indicate a significant association between COVID-19 prevalence patterns and health outcomes.

IoT-based monitoring devices can transmit real-time and long-term thermal environment data, enabling innovative conversion for the evaluation and management of the indoor thermal environment. However, long-term indoor thermal measurements using IoT-based devices to investigate health effects have rarely been conducted. Using apartments in Seoul as a case study, we conducted long-term monitoring of thermal environmental using IoT-based real-time wireless sensors. We measured the temperature, relative humidity (RH), and CO₂ in the kitchen, living room, and bedrooms of each household over one year. In addition, in one of the houses, velocity and globe temperatures were measured for multiple summer and autumn seasons. Results of our present study indicated that outdoor temperature is an important influencing factor of indoor thermal environment and indoor RH is a good indicator of residents’ lifestyle. Our findings highlighted the need for temperature management in summer, RH management in winter, and kitchen thermal environment management during summer and tropical nights. This study suggested that IoT devices are a potential approach for evaluating personal exposure to indoor thermal environmental risks. In addition, long-term monitoring and analysis is an efficient approach for analyzing complex indoor thermal environments and is a viable method for application in healthcare.

A common form of particulate air filters is the pleated filter. The pressure drop and particle removal efficiency are the most important performance indicators of this filter. A computational fluid dynamics (CFD) tool was utilized to predict the pressure drop of a two-stage filter cartridge. In the CFD analysis, the flow through the porous medium was used as an indicator of the pressure drop in the filter. The pressure drop of the two-stage filter was predicted through an experiment involving a single-stage filter at 1 m/s. The pleated filter cartridge size was 361 × 277 × 25 (L × W × H) mm³ and the pitch of the pleats was 3.8 mm. Four classes of filters (E10, E11, E12, H13) were used in the experiments. The filter resistances of the four grades were estimated through the single-stage filter experiment, and the pressure drop of the two-stage filter was predicted at 0.5–1 m/s. To verify the predicted CFD analysis values, two-stage filters were fabricated and tested at 0.75 m/s. For the E12 + H13 combination filter, the analytical value was 111 Pa, which agreed with the experimental value of 109.9 Pa. The other filters exhibited an error of <2%.