The composition of the bacterial communities collected from the PM10 samples inside the Seoul subway and railway station.

Health implications of indoor air quality (IAQ) have drawn more attention since the COVID epidemic. There are many different kinds of studies done on how IAQ affects people's well-being. There hasn't been much research that looks at the microbiological composition of the aerosol in subway transit systems. In this work, for the first time, we examined the aerosol bacterial abundance, diversity, and composition in the microbiome of the Seoul subway and train stations using DNA isolated from the PM10 samples from each station (three subway and two KTX stations). The average PM10 mass concentration collected on the respective platform was 41.862 µg/m3, with the highest average value of 45.95 µg/m3 and the lowest of 39.25 µg/m3. The bacterial microbiomes mainly constituted bacterial species of soil and environmental origin (e.g., Acinetobacter, Brevundimonas, Lysinibacillus, Clostridiodes) with fewer from human sources (Flaviflexus, Staphylococcus). This study highlights the relationship between microbiome diversity and PM10 mass concentration contributed by outdoor air and commuters in South Korea's subway and train stations. This study gives insights into the microbiome diversity, the source, and the susceptibility of public transports in disease spreading.

Characterization of bacterial species and antibiotic resistance observed in Seoul, South Korea's popular Gangnam-gu area.

Public transportation facilities, especially road crossings, which raise the pathogenic potential of urban environments, are the most conducive places for the transfer of germs between people and the environment. It is necessary to study the variety of the microbiome and describe its unique characteristics to comprehend these relationships. In this investigation, we used 16 S rRNA gene sample sequencing to examine the biological constituents and inhalable, thoracic, and alveolar particles in aerosol samples collected from busy areas in the Gangnam-gu district of the Seoul metropolitan area using a mobile vehicle. We also conducted a comparison analysis of these findings with the previously published data and tested for antibiotic resistance to determine the distribution of bacteria related to the human microbiome and the environment. Actinobacteria, Cyanobacteria, Bacteriodetes, Proteobacteria, and Firmicutes were the top five phyla in the bacterial 16 S rRNA libraries, accounting for >90 % of all readings across all examined locations. The most prevalent classes among the 12 found bacterial classes were Bacilli (45.812 %), Gammaproteobacteria (25.238 %), Tissierellia (13.078 %), Clostridia (5.697 %), and Alphaproteobacteria (5.142 %). The data acquired offer useful information on the variety of bacterial communities and their resistance to antibiotic drugs on the streets of Gangnam-gu, one of the most significant social centers in the Seoul metropolitan area. This work emphasizes the relevance of biological particles and particulate matter in the air, and it suggests more research is needed to perform biological characterization of the ambient particulate matter.

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms.

Particulate matter (PM) of sizes less than 10 µm (PM10) and 2.5 µm (PM2.5) found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future PM10 and PM2.5 on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed PM10 and PM2.5 forecasting framework is demonstrated using comparisons with the different existing deep learning models.