Flachsbart P G
Department of Urban and Regional Planning, University of Hawaii at Manoa, Honolulu 96822, USA.
J Expo Anal Environ Epidemiol. 1999 May-Jun;9(3):245-60. doi: 10.1038/sj.jea.7500038.
This paper presents statistical models of passenger exposure to carbon monoxide (CO) inside a motor vehicle as it traveled a coastal highway in Honolulu, Hawaii during morning periods between November, 1981 and May, 1982. The 3.85-mile study site was divided into three links. The models predict the average CO concentration inside the vehicle's passenger cabin on the third link as a function of several variables: the average CO concentrations inside the cabin on previous links; traffic, temporal, and meteorological variables; motor vehicle CO emission factors; and ambient CO concentrations. Based on data for 80 trips, the three most powerful models (adjusted R2 = 0.69) were nonlinear combinations of four variables: the average CO concentration inside the cabin for the second link; wind speed and direction; and either the travel time, vehicle speed or CO emission factor for the third link. Several nonlinear models were based on data for 62 trips for which nonzero, ambient CO concentrations were available. For this database, the most practical models (adjusted R2 = 0.67) combined three variables: the ambient CO concentration; the second-link travel time; and either the travel time, vehicle speed or CO emission factor for the third link. Two factors of third-link CO exposure varied seasonally. Relatively lighter traffic flows and stronger winds lowered cabin exposures during late fall, while heavier traffic flows and calmer winds elevated cabin exposures during winter and spring. This study confirms the importance of seasonal effects on cabin exposure, as observed by a California study, and adds new insights about their effects.
本文介绍了1981年11月至1982年5月早晨时段,一辆机动车在夏威夷檀香山沿海高速公路行驶时乘客接触一氧化碳(CO)的统计模型。3.85英里的研究地点被分为三个路段。这些模型根据几个变量预测第三个路段车内乘客舱内的平均CO浓度:前几个路段车内的平均CO浓度;交通、时间和气象变量;机动车CO排放因子;以及环境CO浓度。基于80次行程的数据,三个最有效的模型(调整后的R2 = 0.69)是四个变量的非线性组合:第二个路段车内的平均CO浓度;风速和风向;以及第三个路段的行驶时间、车速或CO排放因子。几个非线性模型基于62次行程的数据,这些行程有非零的环境CO浓度数据。对于这个数据库,最实用的模型(调整后的R2 = 0.67)组合了三个变量:环境CO浓度;第二个路段的行驶时间;以及第三个路段的行驶时间、车速或CO排放因子。第三个路段CO暴露的两个因素随季节变化。相对较轻的交通流量和较强的风在深秋降低了车内暴露水平,而较重的交通流量和较平静的风在冬季和春季提高了车内暴露水平。本研究证实了加利福尼亚一项研究所观察到的季节效应对车内暴露的重要性,并增加了关于其影响的新见解。