DOI: 10.1016/j.atmosenv.2014.08.060
Scopus记录号: 2-s2.0-84906761463
论文题名: Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil
作者: Luna A ; S ; , Paredes M ; L ; L ; , de Oliveira G ; C ; G ; , Corrêa S ; M
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 98 起始页码: 98
结束页码: 104
语种: 英语
英文关键词: Air pollution
; Artificial neural networks
; Ozone
; Support vector machine
; Troposphere
Scopus关键词: Air pollution
; Air quality
; Carbon monoxide
; Data handling
; Forecasting
; Mean square error
; Neural networks
; Nitrogen oxides
; Ozone
; Pollution
; Regression analysis
; Statistical mechanics
; Sun
; Troposphere
; Complex networks
; Data flow analysis
; Nitrogen
; Quality control
; Wind
; Automatic monitoring stations
; Coefficient of determination
; Exploratory data analysis
; Global solar radiation (GSR)
; Meteorological factors
; Non-linear regression method
; Nonlinear regression technique
; Root mean square errors
; Support vector machines
; carbon monoxide
; nitric oxide
; nitrogen dioxide
; nitrogen oxide
; ozone
; air quality
; artificial neural network
; concentration (composition)
; ozone
; pollution monitoring
; statistical analysis
; troposphere
; atmospheric pollution
; data set
; emission
; machinery
; meteorology
; nonlinearity
; oxide
; prediction
; solar radiation
; topography
; air pollutant
; air quality
; ambient air
; article
; artificial neural network
; Brazil
; chemometric analysis
; environmental parameters
; meteorological phenomena
; meteorology
; moisture
; priority journal
; scalar wind speed
; solar radiation
; support vector machine
; temperature
; troposphere
; validation process
; Article
; controlled study
; human
; monitoring
; prediction
; topography
; Brazil
; Rio de Janeiro [Brazil]
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: It is well known that air quality is a complex function of emissions, meteorology and topography, and statistical tools provide a sound framework for relating these variables. The observed data were contents of nitrogen dioxide (NO2), nitrogen monoxide (NO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), scalar wind speed (SWS), global solar radiation (GSR), temperature (TEM), moisture content in the air (HUM), collected by a mobile automatic monitoring station at Rio de Janeiro City in two places of the metropolitan area during 2011 and 2012. The aims of this study were: (1) to analyze the behavior of the variables, using the method of PCA for exploratory data analysis; (2) to propose forecasts of O3 levels from primary pollutants and meteorological factors, using nonlinear regression methods like ANN and SVM, from primary pollutants and meteorological factors. The PCA technique showed that for first dataset, variables NO, NOx and SWS have a greater impact on the concentration of O3 and the other data set had the TEM and GSR as the most influential variables. The obtained results from the nonlinear regression techniques ANN and SVM were remarkably closely and acceptable to one dataset presenting coefficient of determination for validation respectively 0.9122 and 0.9152, and root mean square error of 7.66 and 7.85, respectively. For these datasets, the PCA, SVM and ANN had demonstrated their robustness as useful tools for evaluation, and forecast scenarios for air quality. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80829
Appears in Collections: 气候变化事实与影响
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作者单位: Rio de Janeiro State University, Institute of Chemistry, Rua São Francisco Xavier, 524, Maracanã, Rio de Janeiro 20550-013, Brazil; Rio de Janeiro State University, Faculty of Technology, Rodovia Presidente Dutra Km 298, Pólo Industrial, Resende, Rio de Janeiro 27537-000, Brazil
Recommended Citation:
Luna A,S,, Paredes M,et al. Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil[J]. Atmospheric Environment,2014-01-01,98