Effect of Occupant Activity on Indoor Particle Concentrations in Korean Residential Buildings

Viral infection transmission and indoor air quality: A systematic review

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data

Exposure to bioaerosols in indoor environments, especially public venues that have a high occupancy and poor ventilation, is a serious public health concern. However, it remains challenging to monitor and determine real-time or predict near-future concentrations of airborne biological matter. In this study, we developed artificial intelligence (AI) models using physical and chemical data from indoor air quality sensors and physical data from ultraviolet light-induced fluorescence observations of bioaerosols. This enabled us to effectively estimate the bioaerosol (bacteria-, fungi- and pollen-like particle) and 2.5-µm and 10-µm particulate matter (PM_2.5 and PM₁₀) on a real-time and near-future (≤60 min) basis. Seven AI models were developed and evaluated using measured data from an occupied commercial office and a shopping mall. A long short-term memory model required a relatively short training time and gave the highest prediction accuracy of ∼ 60 %-80 % for bioaerosols and ∼ 90 % for PM on the testing and time series datasets from the two venues. This work demonstrates how AI-based methods can leverage bioaerosol monitoring into predictive scenarios that building operators can use for improving indoor environmental quality in near real-time.

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies-first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions.

COVID-19 and Lockdown: The Potential Impact of Residential Indoor Air Quality on the Health of Teleworkers

In addition to outdoor atmospheric contamination, indoor exposure to pollutants is a prime contributor to the overall human exposure, and may condition the expressiveness and severity of respiratory, cardiovascular, and allergic diseases. This situation has worsened due to COVID-19, as people have spent more time indoors to comply with social isolation and mandatory telework. The primary purpose of this study was to assess and compare indoor air quality (IAQ) in a significant sample of dwellings of workers from a Higher Education Institution (HEI) in Portugal who were teleworking and their usual workplace. The levels of carbon dioxide, carbon monoxide, and formaldehyde, particles with equivalent diameters of less than 10 μm, 5 μm, 2.5 μm, 1 μm, 0.5 μm, and 0.3 μm, and ultrafine particles, as well as the level of thermal comfort, were measured at both of the sites assessed. It was found that most of the houses studied, as well as the HEI, had good IAQ, although there were places where the concentrations of some pollutants were above the legal standards. On the other hand, a link was identified between the IAQ and the symptoms and diseases observed in the workers who participated in the study. These results offer the opportunity to make corrective interventions, thereby controlling the sources of pollutants and promoting better ventilation in order to reduce the risk for workers.

Assessment and mitigation of toddlers' personal exposure to black carbon before and during the COVID-19 pandemic: A case study in Singapore

Black carbon (BC), an important indicator of traffic-related air pollution (TRAP) in urban environments, is receiving increased attention because of its adverse health effects. Personal exposure (PE) of adults to BC has been widely studied, but little is known about the exposure of young children (toddlers) to BC in cities. We carried out a pilot study to investigate the integrated daily PE of toddlers to BC in a city-state with a high population density (Singapore). We studied the impact of urban traffic on the PE of toddlers to BC by comparing and contrasting on-road traffic flow (i.e., volume and composition) in Singapore in 2019 (before the COVID-19 pandemic) and in 2020 (during the COVID-19 pandemic). Our observations indicate that the daily BC exposure levels and inhaled doses increased by about 25% in 2020 (2.9 ± 0.3 μg m-3 and 35.5 μg day-1) compared to that in 2019 (2.3 ± 0.4 μg m-3 and 28.5 μg day-1 for exposure concentration and inhaled dose, respectively). The increased BC levels were associated with the increased traffic volume on both weekdays and weekends in 2020 compared to the same time period in 2019. Specifically, we observed an increase in the number of trucks as well as cars/taxis and motorcycles (private transport) and a decline in the number of buses (public transport) in 2020. The implementation of lockdown measures in 2020 resulted in significant changes in the time, place and duration of PE of toddlers to BC. The recorded daily time-activity patterns indicated that toddlers spent almost all the time in indoor environments during the measurement period in 2020. When we compared different ventilation options (natural ventilation (NV), air conditioning (AC), and portable air cleaner (PAC)) for mitigation of PE to BC in the home environment, we found a significant decrease (>30%) in daily BC exposure levels while using the PAC compared to the NV scenario. Our case study shows that the PE of toddlers to BC is of health concern in indoor environments in 2020 because of the migration of the increased TRAP into naturally ventilated residential homes and more time spent indoors than outdoors. Since toddlers' immune system is weak, technological intervention is necessary to protect their health against inhalation exposure to air pollutants.