For traffic-related pollutants like ultrafine particles (UFP, Dp < 100 nm), a significant fraction of overall exposure occurs within or close to the transit microenvironment. Therefore, understanding exposure to these pollutants in such microenvironments is crucial to accurately assessing overall UFP exposure. The aim of this study was to develop models for predicting in-cabin UFP concentrations if roadway concentrations are known, taking into account vehicle characteristics, ventilation settings, driving conditions and air exchange rates (AER). Particle concentrations and AER were measured in 43 and 73 vehicles, respectively, under various ventilation settings and driving speeds. Multiple linear regression (MLR) and generalized estimating equation (GEE) regression models were used to identify and quantify the factors that determine inside-to-outside (I/O) UFP ratios and AERs across a full range of vehicle types and ages. AER was the most significant determinant of UFP I/O ratios, and was strongly influenced by ventilation setting (recirculation or outside air intake). Inclusion of ventilation fan speed, vehicle age or mileage, and driving speed explained greater than 79% of the variability in measured UFP I/O ratios.
展开▼
机译:对于与超细颗粒(UFP,DP <100nm)这样的流量相关的污染物,总体暴露的大部分发生在过渡微环境内或靠近过渡微环境。因此,在这种微环境中理解这些污染物的接触至关重要,可以准确评估整体UFP暴露。本研究的目的是开发用于预测机舱UFP浓度的模型,如果公路浓度,考虑到车辆特性,通风设置,驾驶条件和空气汇率(AER)。在各种通风设置和驱动速度下,分别在43和73辆上测量颗粒浓度和AER。多元线性回归(MLR)和广义估计方程(GEE)回归模型用于识别和量化确定内部到外部(I / O)UFP比率和AERS跨越全范围的车辆类型和年龄的因素。 AER是UFP I / O比率最显着的决定因素,受通气环境(再循环或外部进气)的强烈影响。包含通风风扇速度,车辆时效或里程,驱动速度在测量的UFP I / O比率中解释了大于79%的变异性。
展开▼