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交通相关空气污染与呼吸道效能。

Traffic-related air pollution and respiratory tract efficiency.

作者信息

Badyda A J, Dąbrowiecki P, Czechowski P O, Majewski G, Doboszyńska A

机构信息

Faculty of Environmental Engineering, Warsaw University of Technology, 20 Nowowiejska St., 00-653, Warsaw, Poland,

出版信息

Adv Exp Med Biol. 2015;834:31-8. doi: 10.1007/5584_2014_13.

Abstract

High concentrations of air pollutants are characteristic of the vicinity of urban busy roads. Numerous studies have shown that these concentrations are significantly higher in comparison with areas located in a certain distance from roads and especially those in rural areas. Inhabitants living in the proximity of roads are, therefore, likely to be more exposed to adverse effects of air pollutants. On the basis of a study realized in 2008-2012 among nearly 5,000 residents of Warsaw and non-urbanized areas, we used generalized linear regression models (GRM) to identify factors that most significantly influence the variability of respiratory function variables. GRMs combine multiple classes of models and estimation methods such as simple, multiple, or factorial regression, ANOVA, ANCOVA, etc. Therefore, they allow receiving results based also on interactions between the independent variables. This paper presents the results of GRM for the forced expiratory volume in 1 s (FEV1) distribution. They indicate that the variation of FEV1 is associated with personal factors such as age, height, weight, BMI, or gender, as well as with factors related to the place of residence: traffic density, duration, and the floor of residence. The results clearly show that living in the proximity of busy roads in the city is linked with a significant decrease in FEV1 values.

摘要

城市繁忙道路附近的空气污染物浓度很高。大量研究表明,与距离道路一定距离的区域,尤其是农村地区相比,这些浓度要高得多。因此,居住在道路附近的居民可能更容易受到空气污染物的不利影响。基于2008年至2012年对近5000名华沙居民和非城市化地区居民进行的一项研究,我们使用广义线性回归模型(GRM)来确定对呼吸功能变量变异性影响最显著的因素。广义线性回归模型结合了多种类型的模型和估计方法,如简单回归、多元回归或因子回归、方差分析、协方差分析等。因此,它们也允许基于自变量之间的相互作用得出结果。本文展示了1秒用力呼气量(FEV1)分布的广义线性回归模型结果。结果表明,FEV1的变化与年龄、身高、体重、BMI或性别等个人因素有关,也与居住地点相关的因素有关:交通密度、时长和居住楼层。结果清楚地表明,居住在城市繁忙道路附近与FEV1值显著下降有关。

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