Basic Research for the Development of a Vegetation Biofilter Operation Algorithm in a Subway Station

Background and objective: We measured the particulate matter (PM) reduction rate when the vegetation biofilter operated for 1 hour every 6 hours. As a result of data analysis, PM concentrations in the subway station showed a high correlation with the train stop duration depending on the service frequency of train operations, and the reduction rate was high in the section where PM concentrations as well as the change rates were high. Therefore, in order to maximize PM reduction using the biofilter, it is necessary to optimize the performance of the biofilter in various environments by designing an intelligent algorithm that takes the frequency of train operations into account. Methods: We collected PM10 and PM2.5 data for 2 weeks in position 5 (POS5) and POS6. The multi-module vegetation biofilter was installed in POS6, whereas it was not in POS5. The average hourly data for PM concentrations were obtained, and the change in concentrations was analyzed every 24 hours. Results: PM in subway stations is known to spread from the tracks to waiting areas as the train moves. Therefore, the change in PM concentrations by time zone has a high correlation of about 0.8 with the sum of the stop duration depending on the frequency of train operations. The vegetation biofilter is operated 4 times a day for 1 hour every 6 hours. The reduction rate reached up to 30% in 6-8 hours, where the PM concentrations increased sharply. Conclusion: We came up with an algorithm to maximize the PM reduction effect by identifying the effect using the vegetation biofilter as a means for air purification in public use facilities such as subway stations. To this end, there is a need for a method that can dynamically select the section where the PM concentrations increase rapidly and set the biofilter operation time.