Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, CDMX 04510, Mexico.
EarthSense Systems Ltd, The Dock, Leicester, UK.
Sci Total Environ. 2019 Oct 15;687:720-731. doi: 10.1016/j.scitotenv.2019.05.422. Epub 2019 Jun 6.
A computational system consisting of an urban mobility simulator, validated fluid dynamics and an integral exposure model, is proposed to obtain cyclist and pedestrian exposure to PM and NO. Pedestrian activities in the urban anthroposphere include walking and running. The computational experiments take place in a computer-generated urban canyon, subject to emissions from diesel and gasoline Euro 5 and Euro 6 vehicular technologies, in continuous and stop-and-go traffic scenarios, and three wind directions at two speeds. The exposure time in the computational domain of slow and fast pedestrians were obtained. Slow pedestrians had exposure times around 17% more than fast pedestrians due to their higher sidewalk residence time. Runners and cyclists decreased their exposures by 57% and 73% respectively compared with walkers. Two traffic scenarios are implemented: one due the presence of a hump and another without a hump. The presence of the hump, increased exposure and fuel consumption by 60% per heavy duty vehicle, about 44-48% per light duty vehicle and about 54-71% per passenger car. Vehicular technology had a large influence on exposure: Heavy duty-Euro 6 vehicle decreased 86% the exposure to PM and 66% to NO with respect to Euro 5. The proposed computational system provides information on how wind velocity influenced the inhomogeneous pollutant distribution in the street-canyon, causing exposure to be dependent on pedestrian route location. Microscale sidewalk areas in the order of meters containing higher concentrations were thus located. The cleanest routes in the urban canyon were identified. When the wind intensity doubled from 2 to 4 m s, exposure concentration decreased around 45%. The proposed system provides a computational platform to study urban atmospheric fluids, scenarios such as pedestrian routes, vehicular technologies, traffic velocities, meteorological conditions and urban morphology affecting pollution exposure.
提出了一个由城市交通模拟器、验证的流体动力学和综合暴露模型组成的计算系统，以获取骑自行车者和行人的 PM 和 NO 暴露量。行人在城市大气圈中的活动包括步行和跑步。计算实验在计算机生成的城市峡谷中进行，考虑了柴油和汽油 Euro 5 和 Euro 6 车辆技术的排放，在连续和停停走走的交通场景下，以及两种风速下进行。得到了慢步行人和快走行人在计算域中的暴露时间。由于慢行人在人行道上停留的时间较长，因此暴露时间比快走行人长约 17%。与步行者相比，跑步者和骑自行车者的暴露量分别减少了 57%和 73%。实施了两种交通场景：一种是由于存在驼峰，另一种是没有驼峰。驼峰的存在使重型车辆的暴露量和燃料消耗增加了 60%，轻型车辆增加了约 44-48%，乘用车增加了约 54-71%。车辆技术对暴露有很大影响：与 Euro 5 相比，重型 Euro 6 车辆将 PM 暴露量降低了 86%，将 NO 暴露量降低了 66%。所提出的计算系统提供了有关风速如何影响街道峡谷中不均匀污染物分布的信息，导致暴露取决于行人路线位置。因此，找到了含有更高浓度的米级微观人行道区域。确定了城市峡谷中最清洁的路线。当风速从 2 米/秒增加到 4 米/秒时，暴露浓度降低了约 45%。该系统提供了一个计算平台，用于研究影响污染暴露的城市大气流体、行人路线、车辆技术、交通速度、气象条件和城市形态等场景。
