School of Civil & Environmental Engineering, Cornell University, Ithaca, NY, USA.
University of Vermont, Department of Civil and Environmental Engineering, Votey Hall, 33 Colchester Ave., Burlington, VT, 05405, USA.
Environ Res. 2020 Mar;182:108999. doi: 10.1016/j.envres.2019.108999. Epub 2019 Dec 4.
Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.
车辆交通是城市地区有毒空气污染的主要原因之一,与广泛的不良健康后果有关。大多数用于交通规划和健康影响研究的车辆空气质量分析都估计了一个人在其家庭中测量或建模的空气污染物浓度的暴露情况。本研究评估了来自车辆交通的细颗粒物暴露情况,以及在一个庞大而分散的地区,由于没有考虑白天人口流动而导致的暴露分类错误的程度和原因。我们通过整合基于活动的出行需求、车辆排放和空气扩散模型来开发动态暴露模型,以评估在佐治亚州亚特兰大大都市区,车辆暴露分类错误的程度、组成和空间模式。总体而言,我们发现当考虑到人口流动时,人口暴露估计值增加了 51%。在郊区和农村地区,误差要大得多,因为在那里暴露被低估了,而在高交通量道路附近和城市中心,暴露可能被高估。在工作和旅行时的暴露占了大部分误差。我们发现的误差比以前的研究都要大,这些研究都集中在更紧凑的城市地区。由于许多人一天中的大部分时间都不在家,而且车辆排放已知会在道路沿线形成“热点”,因此基于家庭的暴露情况不太可能成为一个人实际暴露情况的可靠估计值。在车辆排放暴露研究中考虑人口流动情况可能会揭示更有效的缓解策略,不同出行模式的人群之间的暴露差异,以及减少健康研究中的暴露分类错误。