Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, Ireland.
Sci Total Environ. 2016 Apr 15;550:1065-1074. doi: 10.1016/j.scitotenv.2016.01.096. Epub 2016 Feb 7.
Personal measurement studies and modelling investigations are used to examine pollutant exposure for pedestrians in the urban environment: each presenting various strengths and weaknesses in relation to labour and equipment costs, a sufficient sampling period and the accuracy of results. This modelling exercise considers the potential benefits of modelling results over personal measurement studies and aims to demonstrate how variations in fleet composition affects exposure results (presented as mean concentrations along the centre of both footpaths) in different traffic scenarios. A model of Pearse Street in Dublin, Ireland was developed by combining a computational fluid dynamic (CFD) model and a semi-empirical equation to simulate pollutant dispersion in the street. Using local NOx concentrations, traffic and meteorological data from a two-week period in 2011, the model were validated and a good fit was presented. To explore the long-term variations in personal exposure due to variations in fleet composition, synthesised traffic data was used to compare short-term personal exposure data (over a two-week period) with the results for an extended one-year period. Personal exposure during the two-week period underestimated the one-year results by between 8% and 65% on adjacent footpaths. The findings demonstrate the potential for relative differences in pedestrian exposure to exist between the north and south footpaths due to changing wind conditions in both peak and off-peak traffic scenarios. This modelling approach may help overcome potential under- or over-estimations of concentrations in personal measurement studies on the footpaths. Further research aims to measure pollutant concentrations on adjacent footpaths in different traffic and wind conditions and to develop a simpler modelling system to identify pollutant hotspots on our city footpaths so that urban planners can implement improvement strategies to improve urban air quality.
每种方法在劳动力和设备成本、足够的采样期以及结果的准确性方面都有各自的优势和劣势。本建模研究考虑了模型结果相对于个人测量研究的潜在优势，并旨在展示车队组成的变化如何影响不同交通情景下的暴露结果（表示为两条人行道中心的平均浓度）。通过结合计算流体动力学 (CFD) 模型和半经验方程，对爱尔兰都柏林的皮尔斯街进行了建模，以模拟街道中的污染物扩散。使用 2011 年两周内的当地 NOx 浓度、交通和气象数据，对模型进行了验证，并呈现了良好的拟合度。为了探索由于车队组成变化导致的个人暴露的长期变化，使用综合交通数据将短期个人暴露数据（两周内）与延长一年的结果进行了比较。在两周的时间内，个人暴露量比一年的结果低 8%至 65%，这在相邻的人行道上存在差异。研究结果表明，由于高峰和非高峰交通情景中风况的变化，北行和南行的行人暴露量可能存在相对差异。这种建模方法可以帮助克服在人行道上进行个人测量研究时浓度的潜在低估或高估问题。进一步的研究旨在测量不同交通和风向条件下相邻人行道上的污染物浓度，并开发一个更简单的建模系统来识别城市人行道上的污染物热点，以便城市规划者可以实施改善策略来改善城市空气质量。
