Abstract

The accurate measurement of resuspended road dust from driving vehicles is key to assessing its role in air pollution. In this study, we used numerical analysis to investigate the behavior of resuspended road dust and selected the optimum sampling positions for use in a mobile laboratory (ML) to measure resuspended road dust emissions. Transient simulation of vehicle driving was performed by depicting wheel rim shape and tire tread pattern in detail. The resuspended road dust distributions generated around the vehicle wheels and the flow around the vehicle were analyzed according to the vehicle speed. Based on the analysis, four sampling positions with concentrated distributions of resuspended road dust were selected. To verify the simulation results, sampling inlets were installed on a ML in the selected positions, and resuspended road dust was collected at each position using a Teflon filter at vehicle speeds of 30, 40, and 50 km/h. Mass measurements of the resuspended road dust collected at the four locations showed that the most dust was collected at the position at the bottom of the vehicle immediately behind the wheel, and the overall trend was similar to the simulation results. Therefore, when measuring resuspended road dust using a ML, sampling inlets installed at the locations derived in this study are expected to yield more effective measurements for representing the pollution level of the road.

Copyright © 2021 American Association for Aerosol Research

摘要

准确测量来自驾驶车辆的重新悬浮的道路灰尘是评估其在空气污染中的作用的关键。在这项研究中，我们使用数值分析来研究重悬道路粉尘的行为，并选择了用于移动实验室 (ML) 的最佳采样位置，以测量重悬道路粉尘排放。通过详细描绘轮辋形状和轮胎胎面花纹来进行车辆行驶的瞬态模拟。根据车速分析车轮周围产生的重悬道路粉尘分布和车辆周围的流动。根据分析，选取了4个道路扬尘重悬分布集中的采样点。为了验证模拟结果，在 ML 上的选定位置安装了采样入口，在车速为 30、40 和 50 公里/小时的情况下，使用特氟龙过滤器在每个位置收集重新悬浮的道路灰尘。在四个位置收集的重悬道路灰尘的质量测量表明，在车辆底部紧靠车轮后面的位置收集的灰尘最多，总体趋势与模拟结果相似。因此，当使用 ML 测量重新悬浮的道路灰尘时，安装在本研究中得出的位置的采样入口有望产生更有效的测量值来表示道路的污染水平。在四个位置收集的重悬道路灰尘的质量测量表明，在车辆底部紧靠车轮后面的位置收集的灰尘最多，总体趋势与模拟结果相似。因此，当使用 ML 测量重新悬浮的道路灰尘时，安装在本研究中得出的位置的采样入口有望产生更有效的测量值来表示道路的污染水平。在四个位置收集的重悬道路灰尘的质量测量表明，在车辆底部紧靠车轮后面的位置收集的灰尘最多，总体趋势与模拟结果相似。因此，当使用 ML 测量重新悬浮的道路灰尘时，安装在本研究中得出的位置的采样入口有望产生更有效的测量值来表示道路的污染水平。

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