Classification of road traffic and roadside pollution concentrations for assessment of personal exposure

被引:32
作者
Chen, Haibo [1 ]
Namdeo, Anil [1 ]
Bell, Margaret [1 ]
机构
[1] Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, England
基金
英国工程与自然科学研究理事会;
关键词
traffic classifications; roadside pollution concentrations PEFD; k-means algorithm; RPM and AURN;
D O I
10.1016/j.envsoft.2007.04.006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Nowadays urban pollution exposure from road transport has become a great concern in major cities throughout the world. A modelling framework has been developed to simulate Personal Exposure Frequency Distributions (PEFDs) as a function of urban background and roadside pollutant concentrations, under different traffic conditions. In this paper, we present a technique for classifying roads, according to their traffic conditions. using the traffic characteristics and fleet compositions. The pollutant concentrations data for 2001 from 10 Roadside Pollution Monitoring (RPM) units in the city of Leicester were analysed to understand the spatial and temporal variability of the pollutant concentrations pattems. It was found that variability of pollutants during the day can be associated with specific road traffic conditions. Statistical analysis of two urban and two rural Automated Urban and Rural Network (AURN) background sites for particulates suggests that PM2.5 and PM10 are closely related at urban sites but not at rural sites. The ratio of the two pollutants observed at Marylebone was found to be 0.748, which was applied to Leicester PM10 data to obtain PM2.5 profiles. These results are being used as an element in the PEFDs model to estimate the impact of urban traffic on exposure. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:282 / 287
页数:6
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