Environmental Monitoring and Assessment

, Volume 87, Issue 3, pp 235–254 | Cite as

Using Improved Neural Network Model to Analyze RSP, NOx and NO2 Levels in Urban Air in Mong Kok, Hong Kong

  • W. Z. LuEmail author
  • W. J. Wang
  • X. K. Wang
  • Z. B. Xu
  • A. Y. T. Leung


As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NOx and NO2 are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NOx and NO2 concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.

environmental pollution neural networks nitrogen dioxide nitrogen oxides principal component analysis radial basis function respirable suspended particulate 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • W. Z. Lu
    • 1
    Email author
  • W. J. Wang
    • 2
    • 3
  • X. K. Wang
    • 4
  • Z. B. Xu
    • 2
  • A. Y. T. Leung
    • 1
  1. 1.Department of Building and ConstructionCity University of Hong KongHong Kong
  2. 2.Xi'an Jiao Tong UniversityXi'anChina
  3. 3.BC DepartmentCity University of Hong KongChina
  4. 4.State Key Laboratory Hydraulics of High Speed FlowsSichuan UniversityChengduP.R. China

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