In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NO_x), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM_2.5) concentration below the apartment building height. Atmospheric NO_x dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NO_x concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

中文翻译：

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使用无人机监控和WRF-CFD建模评估信号交叉口附近道路空气污染的3-D空间范围

在这项研究中，我们使用固定测量，无人机监控和大气弥散模型的协作方法，评估了人口稠密地区信号交叉口附近的近路空气污染的三维（3-D）空间范围。在路边公寓楼中收集的固定测量数据显示，排放污染物（例如氮氧化物（NO _x），黑碳（BC）和超细颗粒物（UFPs））的影响很大，尤其是在高峰时段。道路交叉口附近的垂直无人机监控显示，随着海拔高度的升高，BC浓度而不是公寓楼高度以下的细颗粒物（PM _2.5）浓度呈下降趋势。大气NO _x使用气象研究和预报（WRF）和计算流体动力学（CFD）模型在无人机测量期间对弥散进行了模拟。基于之间所测量的BC和模拟NO协议_X的浓度，我们的结论是，周围的道路交叉点处的空气污染对至少120米范围内的3-d的空间范围内活水平居民的健康半的不利影响高峰时间垂直放置建筑物高度。无人机监控和WRF-CFD建模之间的可比性可以进一步确保使用这些方法识别空气污染热点。