Environment and Air Quality Division, Texas A&M Transportation Institute, 1111 RELLIS Parkway, Suite 3401, Bryan, TX 77807, USA.
Zachry Department of Civil and Environmental Engineering, Texas A&M University, 201 Dwight Look Engineering Building, College Station, TX 77843, USA.
Int J Environ Res Public Health. 2019 Jul 9;16(13):2433. doi: 10.3390/ijerph16132433.
Population groups vulnerable to adverse effects of traffic-related air pollution correspond to children, pregnant women and elderly. Despite these effects, literature is limited in terms of studies focusing on these groups and a reason often cited is the limited information on their mobility important for exposure assessment. The current study presents a method for assessing individual-level exposure to traffic-related air pollution by integrating mobility patterns tracked by global positioning system (GPS) devices with dynamics of air pollutant concentrations. The study is based on a pool of 17 pregnant women residing in Hidalgo County, Texas. The traffic-related particulate matter with diameter of less than 2.5 micrometer (PM) emissions and air pollutant concentrations are predicted using MOVES and AERMOD models, respectively. The daily average traffic-related PM concentration was found to be 0.32 µg/m, with the highest concentration observed in transit (0.56 µg/m), followed by indoors (0.29 µg/m), and outdoor (0.26 µg/m) microenvironment. The obtained exposure levels exhibited considerable variation between time periods, with higher levels during peak commuting periods, close to the US-Mexico border region and lower levels observed during midday periods. The study also assessed if there is any difference between traffic-related dynamic exposure, based on time-varying mobility patterns, and static exposure, based solely on residential locations, and found a difference of 9%, which could be attributed to the participants' activity patterns being focused mostly indoors.
受交通相关空气污染不利影响的人群包括儿童、孕妇和老年人。尽管存在这些影响,但文献在针对这些人群的研究方面有限,一个常见的原因是关于他们出行模式的信息有限,这对于暴露评估很重要。本研究提出了一种方法,通过将全球定位系统 (GPS) 设备跟踪的移动模式与空气污染物浓度动态相结合,来评估个体水平的交通相关空气污染暴露。该研究基于德克萨斯州伊达尔戈县的 17 名孕妇的数据集。分别使用 MOVES 和 AERMOD 模型来预测交通相关的直径小于 2.5 微米的颗粒物 (PM) 排放量和空气污染物浓度。发现每日平均交通相关 PM 浓度为 0.32 µg/m,其中在过境时浓度最高(0.56 µg/m),其次是室内(0.29 µg/m)和室外(0.26 µg/m)微环境。获得的暴露水平在不同时间段内表现出相当大的差异,在高峰通勤期间水平较高,靠近美墨边境地区,而在中午时段水平较低。该研究还评估了基于时变移动模式的交通相关动态暴露与仅基于居住地点的静态暴露之间是否存在差异,发现存在 9%的差异,这可能归因于参与者的活动模式主要集中在室内。