Particulate Matter Exposure of Passengers at Bus Stations: A Review.
Le Thi Nhu NgocMinjeong KimVu Khac Hoang BuiDuckshin ParkPublished in: International journal of environmental research and public health (2018)
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.
Keyphrases
- particulate matter
- air pollution
- neural network
- cardiovascular disease
- mental health
- type diabetes
- endothelial cells
- healthcare
- machine learning
- deep learning
- systematic review
- metabolic syndrome
- single cell
- electronic health record
- coronary artery disease
- risk assessment
- social media
- induced pluripotent stem cells
- cardiovascular risk factors