城市高架路沿侧细颗粒物的垂直分布特征研究

Ya Gao; Zhanyong Wang; Qingchang Lu; Zhongren Peng

doi:10.16183/j.cnki.jsjtu.2018.06.004

城市高架路沿侧细颗粒物的垂直分布特征研究

Translated title of the contribution: Estimation of Vertical Concentrations of Fine Particulates Alongside an Elevated Expressway

Ya Gao
, Zhanyong Wang
, Qingchang Lu
, Zhongren Peng

Shanghai Jiao Tong University
Sun Yat-Sen University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

A study on vertical variation of PM_2.5 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 PM_2.5 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 PM_2.5 and their relationship with traffic volume, weather and height by generalized additive model (GAM). These findings reveal the vertical distribution of PM_2.5 concentration and the potential of the proposed model that will be applicable to predict the vertical trends of air pollution in similar situations.

Translated title of the contribution	Estimation of Vertical Concentrations of Fine Particulates Alongside an Elevated Expressway
Original language	Chinese (Traditional)
Pages (from-to)	650-657
Number of pages	8
Journal	Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University
Volume	52
Issue number	6
DOIs	https://doi.org/10.16183/j.cnki.jsjtu.2018.06.004
State	Published - 1 Jun 2018
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Back propagation neural network (BPNN)
Generalized additive model (GAM)
Principal component analysis (PCA)
Urban elevated expressway
Vertical variations

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10.16183/j.cnki.jsjtu.2018.06.004

Cite this

@article{f0406377f3aa4c15b8001572c43d3bc1,

title = "城市高架路沿侧细颗粒物的垂直分布特征研究",

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.",

keywords = "Back propagation neural network (BPNN), Generalized additive model (GAM), Principal component analysis (PCA), Urban elevated expressway, Vertical variations",

author = "Ya Gao and Zhanyong Wang and Qingchang Lu and Zhongren Peng",

year = "2018",

month = jun,

day = "1",

doi = "10.16183/j.cnki.jsjtu.2018.06.004",

language = "Chinese (Traditional)",

volume = "52",

pages = "650--657",

journal = "Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University",

issn = "1006-2467",

publisher = "Shanghai Jiaotong University",

number = "6",

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TY - JOUR

T1 - 城市高架路沿侧细颗粒物的垂直分布特征研究

AU - Gao, Ya

AU - Wang, Zhanyong

AU - Lu, Qingchang

AU - Peng, Zhongren

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

KW - Back propagation neural network (BPNN)

KW - Generalized additive model (GAM)

KW - Principal component analysis (PCA)

KW - Urban elevated expressway

KW - Vertical variations

UR - https://www.scopus.com/pages/publications/85053114087

U2 - 10.16183/j.cnki.jsjtu.2018.06.004

DO - 10.16183/j.cnki.jsjtu.2018.06.004

M3 - Article

AN - SCOPUS:85053114087

SN - 1006-2467

VL - 52

SP - 650

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JO - Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University

JF - Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University

IS - 6

ER -

城市高架路沿侧细颗粒物的垂直分布特征研究

Abstract

UN SDGs

Keywords

Access to Document

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