Particulate Matter Exposure of Passengers at Bus Stations: A Review

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.

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

“…All neurons in a layer are linked to all neurons in neighbor layers by synaptic weights playing the role of signaling coefficients to the corresponding connections [95][96][97]. These connecting signals are real numbers, and the output of each of the artificial neurons is analyzed by many linear or non-linear statistical techniques [95,98]. The multilayer Perceptron (MLP) neural network and the radial basis function (RBF) network are two of the most popular neural networks [98].…”

“…These connecting signals are real numbers, and the output of each of the artificial neurons is analyzed by many linear or non-linear statistical techniques [95,98]. The multilayer Perceptron (MLP) neural network and the radial basis function (RBF) network are two of the most popular neural networks [98].…”

“…Atmosphere 2019, 10, x FOR PEER REVIEW 19 of 28 network and the radial basis function (RBF) network are two of the most popular neural networks [98].…”

Effects of Microwave-Assisted Opuntia humifusa Extract in Inhibiting the Impacts of Particulate Matter on Human Keratinocyte Skin Cell

“…Based on the diameter that can cause harmful effects to humans, PM is primarily classified into two group: PM 10 (≤10 µm) and PM 2.5 (≤2.5 µm) [1][2][3]. At such small sizes, they can easily penetrate and deposit in the respiratory tract, including the bronchi, trachea, and deep lungs [4,5], causing many health problems such as cardiovascular diseases (CVD), respiratory health effects, diabetes, and especially, skin diseases [5,6]. According to a WHO report (2013), PM pollution is associated with about 4.2 million premature deaths each year, including from lung cancer (16%), ischemic heart and stroke (17%), chronic obstructive pulmonary disease (COPD) (25%), and respiratory infection (26%), and is ranked the 14th risk of death worldwide [7].…”

Poluição Atmosférica E Possíveis Efeitos À População De Recife: Um Estudo Baseado Nos Dados Do Departamento De Informática Do Sus (Datasus)

“…As atividades industriais, os veículos automotores e as queimadas são, dentre as atividades antrópicas, as maiores produtoras de substâncias poluentes. Um dos principais poluentes atmosféricos é o material particulado (MP), uma mistura de substâncias granulares suspensas na atmosfera, gerada principalmente pela queima de combustíveis fósseis em veículos automotores (OLMO et al, 2011;XIA, 2016;NGOC et al, 2018…”