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首页> 外文期刊>International Journal of Environmental Research and Public Health >Particulate Matter Exposure of Passengers at Bus Stations: A Review
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Particulate Matter Exposure of Passengers at Bus Stations: A Review

机译：公交车站乘客的特定物质暴露：回顾

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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.
机译：这项审查阐明了颗粒物（PM）污染，包括其水平，影响其分布的因素以及其对在公交车站候车的乘客的健康影响。影响PM特性和成分的常见因素包括工业排放和气象因素（温度，湿度，风速，雨量）以及与公交车站相关的因素，例如车辆中的燃料燃烧，车辆部件的磨损，吸烟和车辆流量。多项研究证明，公交车站会积聚高水平的PM，从而使乘客在公交车站等车时接触PM的机会增加，并导致严重的健康后果，如心血管疾病（CVD），呼吸作用和糖尿病。为了准确预测PM污染，已经开发了人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）。人工神经网络是一种数据建模方法，在解决对齐，预测和分类领域中的复杂问题方面被证明是行之有效的，而人工神经网络模型具有许多优势，包括不需要数学模型，模拟人类思维以及简单地解释结果与其他预测方法相比。

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