Particulate Matter Exposure of Passengers at Bus Stations: A Review

Ngoc, Le Thi Nhu; Kim, Minjeong; Bui, Vu Khac Hoang; Park, Duckshin; Lee, Young-Chul

doi:10.3390/ijerph15122886

Open AccessReview

Particulate Matter Exposure of Passengers at Bus Stations: A Review

by

Le Thi Nhu Ngoc

¹,

Minjeong Kim

²,

Vu Khac Hoang Bui

¹,

Duckshin Park

^2,*

and

Young-Chul Lee

^1,*

¹

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

²

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

^*

Authors to whom correspondence should be addressed.

Int. J. Environ. Res. Public Health 2018, 15(12), 2886; https://doi.org/10.3390/ijerph15122886

Submission received: 3 November 2018 / Revised: 3 December 2018 / Accepted: 14 December 2018 / Published: 17 December 2018

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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.

Keywords: particulate matter; bus station; personal exposure; ANN model; ANFIS model

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MDPI and ACS Style

Ngoc, L.T.N.; Kim, M.; Bui, V.K.H.; Park, D.; Lee, Y.-C. Particulate Matter Exposure of Passengers at Bus Stations: A Review. Int. J. Environ. Res. Public Health 2018, 15, 2886. https://doi.org/10.3390/ijerph15122886

AMA Style

Ngoc LTN, Kim M, Bui VKH, Park D, Lee Y-C. Particulate Matter Exposure of Passengers at Bus Stations: A Review. International Journal of Environmental Research and Public Health. 2018; 15(12):2886. https://doi.org/10.3390/ijerph15122886

Chicago/Turabian Style

Ngoc, Le Thi Nhu, Minjeong Kim, Vu Khac Hoang Bui, Duckshin Park, and Young-Chul Lee. 2018. "Particulate Matter Exposure of Passengers at Bus Stations: A Review" International Journal of Environmental Research and Public Health 15, no. 12: 2886. https://doi.org/10.3390/ijerph15122886

APA Style

Ngoc, L. T. N., Kim, M., Bui, V. K. H., Park, D., & Lee, Y.-C. (2018). Particulate Matter Exposure of Passengers at Bus Stations: A Review. International Journal of Environmental Research and Public Health, 15(12), 2886. https://doi.org/10.3390/ijerph15122886

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Particulate Matter Exposure of Passengers at Bus Stations: A Review

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