DOI: 10.1016/j.atmosenv.2014.04.051
Scopus记录号: 2-s2.0-84901006073
论文题名: Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering
作者: Elangasinghe M ; A ; , Singhal N ; , Dirks K ; N ; , Salmond J ; A ; , Samarasinghe S
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 94 起始页码: 106
结束页码: 116
语种: 英语
英文关键词: Air quality modelling
; Artificial neural network
; K-means clustering
; Marine aerosols
Scopus关键词: Aerosols
; Air quality
; Sun
; Time series analysis
; Wind
; Air quality modelling
; K-means clustering
; K-means clustering techniques
; Marine aerosols
; Meteorological parameters
; Positive Matrix Factorization
; Root mean squared errors
; Transport characteristics
; Neural networks
; air quality
; artificial neural network
; atmospheric modeling
; atmospheric transport
; cluster analysis
; coastal zone
; concentration (composition)
; marine atmosphere
; particle size
; particulate matter
; time series analysis
; traffic emission
; wind velocity
; aerosol
; article
; artificial neural network
; atmospheric dispersion
; biomass
; cluster analysis
; concentration (parameters)
; correlation coefficient
; environmental temperature
; humidity
; meteorology
; New Zealand
; particle size
; particulate matter
; priority journal
; seashore
; solar radiation
; statistical significance
; temperature
; time series analysis
; velocity
; wind
; New Zealand
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: This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p<0.005) improvement in the performance of the ANN time series model and also showed better performance in picking up high concentrations. For the presented case study, the correlation coefficient between observed and predicted concentrations improved from 0.77 to 0.79 for PM2.5 and from 0.63 to 0.69 for PM10 and reduced the root mean squared error (RMSE) from 5.00 to 4.74 for PM2.5 and from 6.77 to 6.34 for PM10. The techniques presented here enable the user to obtain an understanding of potential sources and their transport characteristics prior to the implementation of costly chemical analysis techniques or advanced air dispersion models. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81240
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Civil and Environmental Engineering, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand; School of Population Health, The University of Auckland, Private Bag 92019, Auckland, New Zealand; School of Environment, The University of Auckland, Private Bag 92019, Auckland, New Zealand; Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
Recommended Citation:
Elangasinghe M,A,, Singhal N,et al. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering[J]. Atmospheric Environment,2014-01-01,94