• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.410-424
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• 2021

Background: Subway stations have the characteristics of being located underground and are a representative public-use facility used by an unspecified number of people. As concerns about indoor air quality (IAQ) increase, various management measures are being implemented. However, there are few systematic studies and cases of long-term continuous measurement of underground station air quality. Objectives: The purpose of this study is to analyze changes and factors influencing IAQ in subway stations through real-time continuous long-term measurement using IoT-based IAQ sensing equipment, and to evaluate the IAQ improvement effect of a bio-filter system. Methods: The IAQ of a subway station in Seoul was measured using IoT-based sensing equipment. A bio-filter system was installed after collecting the background concentrations for about five months. Based on the data collected over about 21 months, changes in indoor air quality and influencing factors were analyzed and the reduction effect of the bio-filter system was evaluated. Results: As a result of the analysis, PM₁₀, PM_2.5, and CO₂ increased during rush hour according to the change in the number of passengers, and PM₁₀ and PM_2.5 concentrations were high when a PM warning/watch was issued. There was an effect of improving IAQ with the installation of the bio-filter system. The reduction rate of a new-bio-filter system with improved efficiency was higher than that of the existing bio-filter system. Factors affecting PM_2.5 in the subway station were the outdoor PM_2.5, platform PM_2.5, and the number of passengers. Conclusions: The IAQ in a subway station is affected by passengers, ventilation through the air supply and exhaust, and the spread of particulate matter generated by train operation. Based on these results, it is expected that IAQ can be efficiently improved if a bio-filter system with improved efficiency is developed in consideration of the factors affecting IAQ and proper placement.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.