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Cho, Kyung Hwa
Environmental Monitoring and Modeling Lab (EM2)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

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Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

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Title
Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)
Author
Park, SechanKim, MinjeongKim, MihaeNamgung, Hyeong-GyuKim, Ki-TaeCho, Kyung HwaKwon, Soon-Bark
Keywords
Indoor air quality;  Particulate matter (PM);  Artificial neural network (ANN);  Subway stations
Issue Date
201811
Publisher
ELSEVIER SCIENCE BV
Citation
JOURNAL OF HAZARDOUS MATERIALS, v.341, no., pp.75 - 82
Abstract
The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN’s performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67 ∼ 80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance.
URI
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DOI
http://dx.doi.org/10.1016/j.jhazmat.2017.07.050
ISSN
0304-3894
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