1. 上海交通大学 船舶海洋与建筑工程学院， 上海 200240; 2. 中山大学 广东省智能交通系统重点实验室， 广州 510006
基金资助:国家重点研发计划资助项目（2016YFC0200500）, 上海市环保局重大专项资助项目（HHk2014-8)Estimation of Vertical Concentrations of Fine Particulates Alongside an Elevated Expressway
GAO Ya,WANG Zhanyong,LU Qingchang,PENG Zhongren1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Guangdong Provincial Key Laboratory of Intelligent Transportation System, Sun Yat-sen University, Guangzhou 510006, China
Contact:高雅（1991-），女，山东省邹城市人，博士生，主要从事交通环境研究.E-mail:gaoya_sandy@sjtu.edu.cn.通信作者：彭仲仁，男，教授，博士生导师，E-mail:zrpeng@sjtu.edu.cn. 摘要/Abstract
摘要： 以上海市高架路为对象，通过移动设备开展数据采集，建立广义加性模型，对高架路细颗粒物（PM2.5）浓度的垂直分布及其与微观尺度下的交通、气象、位置等因素之间的关系进行了系统研究，并将原始影响因素的主成分分析结果作为输入变量，提出基于主成分分析法（PCA）的高架路交通污染物浓度垂直变化的神经网络预测模型(PCA-BPNN).结果表明：高度、相对湿度和交通流量对PM2.5浓度垂直变化有着显著影响；PCA-BPNN模型能够较好地处理污染物扩散的非线性问题，消除变量间多重共线性，有效弥补污染物垂直扩散模型在道路微观尺度上预测的不足.
关键词: 城市高架路, 垂直分布, 广义加性模型, 主成分分析, 神经网络模型
Abstract: A study on vertical variation of PM2.5 concentrations was carried out in this paper. Field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai, China. A back propagation neural network based on principal component analysis (PCA-BPNN), was applied to predict the vertical PM2.5 concentration and examined with the field measurement dataset. Experimental results indicated that the PCA-BPNN model provides reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity. Furthermore, this paper investigated the vertical distribution of PM2.5 and their relationship with traffic volume, weather and height by generalized additive model (GAM). These findings reveal the vertical distribution of PM2.5 concentration and the potential of the proposed model that will be applicable to predict the vertical trends of air pollution in similar situations.
Key words: urban elevated expressway, vertical variations, generalized additive model (GAM), principal component analysis (PCA), back propagation neural network (BPNN)
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