Fernandez-Bremauntz A A, Ashmore M R
Departamento de Farmacologia, Facultad de Medicina, Universidad Nacional Autonoma de Mexico.
J Expo Anal Environ Epidemiol. 1995 Oct-Dec;5(4):497-510.
The objective of this paper is to compare measurements of carbon monoxide taken concurrently inside vehicles and at fixed-site monitoring stations (FSMs), in order to assess if the FSM stations can be used to estimate commuters' exposure to this pollutant. During the study period ambient CO concentrations were very high. Five selected stations reported concentrations in excess of the Mexican (13 ppm) and United States (9 ppm) 8-hour standards for CO. Since, for all modes of transportation, the in-vehicle concentrations were always larger than the concurrent ambient concentrations, the differences between them were always positive and the ratios were always greater than one. Average, in-vehicle/ambient ratios for each mode of transportation were: automobile, 5.2; minivan, 5.2; minibus, 4.3; bus, 3.1; trolleybus 3.0; and metro, 2.2. A series of simple regression models with a moderate predictive power (R2 = 0.47 to 0.71) were developed for metro, bus, minibus, and automobile commuters. The models include the FSM measurements and also, depending on the mode of transportation, other variables, such as vehicular speed, the route of travel, and the wind speed. In the future, the models should be validated in two ways to determine their predictive power. First, they should be verified against additional samples taken under similar conditions; and second, their applications under different conditions should be explored through sampling during a different season of the year or on other commuting routes.
本文的目的是比较在车辆内部和固定站点监测站（FSMs）同时进行的一氧化碳测量结果，以评估FSM站是否可用于估计通勤者对这种污染物的暴露情况。在研究期间，环境一氧化碳浓度非常高。五个选定站点报告的浓度超过了墨西哥（13 ppm）和美国（9 ppm）的一氧化碳8小时标准。由于对于所有交通方式，车内浓度总是高于同时期的环境浓度，它们之间的差异总是正值，比率总是大于1。每种交通方式的车内/环境平均比率分别为：汽车，5.2；小型货车，5.2；小型巴士，4.3；巴士，3.1；无轨电车，3.0；地铁，2.2。针对地铁、巴士、小型巴士和汽车通勤者建立了一系列具有中等预测能力（R2 = 0.47至0.71）的简单回归模型。这些模型包括FSM测量值，并且根据交通方式还包括其他变量，如车速、行驶路线和风速。未来，应通过两种方式对模型进行验证以确定其预测能力。首先，应根据在类似条件下采集的额外样本进行验证；其次，应通过在一年中的不同季节或其他通勤路线上采样来探索其在不同条件下的应用。
