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Particulate Matter Exposure of Passengers at Bus Stations: A Review

Author

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  • Le Thi Nhu Ngoc

    (Department of BioNano Technology, Gachon University, 1342 Seongnam-Daero, Sujeong-Gu, Seongnam-Si, Gyeonggi-do 13120, Korea)

  • Minjeong Kim

    (Korea Railroad Research Institute (KRRI), 176 Cheoldobakmulkwan-ro, Uiwang-si, Gyeonggi-do 16105, Korea)

  • Vu Khac Hoang Bui

    (Department of BioNano Technology, Gachon University, 1342 Seongnam-Daero, Sujeong-Gu, Seongnam-Si, Gyeonggi-do 13120, Korea)

  • Duckshin Park

    (Korea Railroad Research Institute (KRRI), 176 Cheoldobakmulkwan-ro, Uiwang-si, Gyeonggi-do 16105, Korea)

  • Young-Chul Lee

    (Department of BioNano Technology, Gachon University, 1342 Seongnam-Daero, Sujeong-Gu, Seongnam-Si, Gyeonggi-do 13120, Korea)

Abstract

This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passengers waiting at bus stations. The usual factors affecting the characteristics and composition of PM include industrial emissions and meteorological factors (temperature, humidity, wind speed, rain volume) as well as bus-station-related factors such as fuel combustion in vehicles, wear of vehicle components, cigarette smoking, and vehicle flow. Several studies have proven that bus stops can accumulate high PM levels, thereby elevating passengers’ exposure to PM while waiting at bus stations, and leading to dire health outcomes such as cardiovascular disease (CVD), respiratory effects, and diabetes. In order to accurately predict PM pollution, an artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) have been developed. ANN is a data modeling method of proven effectiveness in solving complex problems in the fields of alignment, prediction, and classification, while the ANFIS model has several advantages including non-requirement of a mathematical model, simulation of human thinking, and simple interpretation of results compared with other predictive methods.

Suggested Citation

  • Le Thi Nhu Ngoc & Minjeong Kim & Vu Khac Hoang Bui & Duckshin Park & Young-Chul Lee, 2018. "Particulate Matter Exposure of Passengers at Bus Stations: A Review," IJERPH, MDPI, vol. 15(12), pages 1-20, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:12:p:2886-:d:191072
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    References listed on IDEAS

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    1. Lu Bai & Jianzhou Wang & Xuejiao Ma & Haiyan Lu, 2018. "Air Pollution Forecasts: An Overview," IJERPH, MDPI, vol. 15(4), pages 1-44, April.
    2. Jinna Lu & Hongping Hu & Yanping Bai, 2014. "Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-9, July.
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    Cited by:

    1. Obuks A. Ejohwomu & Majeed Oladokun & Olalekan S. Oshodi & Oyegoke Teslim Bukoye & David John Edwards & Nwabueze Emekwuru & Olumide Adenuga & Adegboyega Sotunbo & Ola Uduku & Mobolanle Balogun & Rose , 2022. "The Exposure of Workers at a Busy Road Node to PM 2.5 : Occupational Risk Characterisation and Mitigation Measures," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    2. Jaeseok Heo & Yelim Jang & Michael Versoza & Gihwan Kim & Duckshin Park, 2021. "A New Method of Removing Fine Particulates Using an Electrostatic Force," IJERPH, MDPI, vol. 18(12), pages 1-10, June.
    3. Ying Zhang & Zhengdong Huang & Jiacheng Huang, 2022. "A Comparison of Particulate Exposure Levels during Taxi, Bus, and Metro Commuting among Four Chinese Megacities," IJERPH, MDPI, vol. 19(10), pages 1-20, May.

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