DOI: 10.1016/j.atmosenv.2014.12.058
Scopus记录号: 2-s2.0-84921471149
论文题名: Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm
作者: Wang Z ; , Lu F ; , He H ; -D ; , Lu Q ; -C ; , Wang D ; , Peng Z ; -R
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 104 起始页码: 264
结束页码: 272
语种: 英语
英文关键词: Carbon monoxide
; Fine particulate matter
; Fine-scale estimation
; Genetic algorithm
; Road intersection
; Wavelet neural network
Scopus关键词: Algorithms
; Backpropagation
; Carbon
; Carbon monoxide
; Genetic algorithms
; Neural networks
; Pollution
; Traffic control
; Transportation
; Back-propagation neural networks
; Distribution patterns
; Fine particulate matter
; Fine-scale
; Pollutant concentration
; Road intersections
; Velocity fluctuations
; Wavelet neural networks
; Roads and streets
; carbon monoxide
; air pollutant
; air quality
; Article
; artificial neural network
; back propagation
; chemical reaction kinetics
; clinical evaluation
; concentration process
; controlled study
; data processing
; genetic algorithm
; measurement accuracy
; meteorological phenomena
; neural network wavelet
; particulate matter
; pollution monitoring
; priority journal
; red light
; road intersection
; traffic
; wavelet analysis
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: At road intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly in the green-light period, which generates higher velocity fluctuation and thus higher emission rates. Additionally, the frequent changes of wind direction further add the highly variable dispersion of pollutants at the street scale. It is, therefore, very difficult to estimate the distribution of pollutant concentrations using conventional deterministic causal models.For this reason, a hybrid model combining wavelet neural network and genetic algorithm (GA-WNN) is proposed for predicting 5-min series of carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations in proximity to an intersection. The proposed model is examined based on the measured data under two situations. As the measured pollutant concentrations are found to be dependent on the distance to the intersection, the model is evaluated in three locations respectively, i.e.110m, 330m and 500m. Due to the different variation of pollutant concentrations on varied time, the model is also evaluated in peak and off-peak traffic time periods separately. Additionally, the proposed model, together with the back-propagation neural network (BPNN), is examined with the measured data in these situations. The proposed model is found to perform better in predictability and precision for both CO and PM2.5 than BPNN does, implying that the hybrid model can be an effective tool to improve the accuracy of estimating pollutants' distribution pattern at intersections. The outputs of these findings demonstrate the potential of the proposed model to be applicable to forecast the distribution pattern of air pollution in real-time in proximity to road intersection. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81948
Appears in Collections: 气候变化事实与影响
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作者单位: Center for ITS and UAV Applications Research State Key Laboratory of Ocean Engineering, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Geographic Science, Nantong University, Nantong, China; Logistics Research Center, Shanghai Maritime University, Shanghai, China; Department of Urban and Regional Planning, University of Florida, PO Box 115706, Gainesville, FL, United States
Recommended Citation:
Wang Z,, Lu F,, He H,et al. Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm[J]. Atmospheric Environment,2015-01-01,104