An Analytical and Predictive Approach of Statistical Technique for Air Pollutants

  • Abstract
  • Keywords
  • References
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  • Abstract

    Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The prediction of concentration of air pollutants in urban areas has become a major focus area of air quality research today due to their health effects. In the present study statistical model based on neural network (NN) has been developed to predict the pollutants such as NOX, NO2 and particulate matters (PM2.5 and PM10 ) for Delhi city at different locations such as ITO (Income tax office), and DTU (Delhi technological university) . Error estimation is also done in this study.



  • Keywords

    Error estimation, Meteorology, Neural Network, Prediction, Statistical technique.

  • References

      [1] Boznar, M., Lesjak, M., and Mlakar, P., “A neural network based method for short term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain”, Atmos. Environ.,1993, 27B:221–230.

      [2] Chaloulakou A, Grivas G, Spyrellis N., "Neural network and multiple regression models for PM10 prediction in Athens A comparative assessment”, J Air Waste Manage Assoc, 2003b; 53: 1183–90.

      [3] Gardner, M. W., Dorling, S. R., “Artificial Neural Networks (The Multilayer Perceptron) - a Review of Applications in the Atmospheric Sciences”, Atmospheric Environment 32, 1998, 2627-2636.

      [4] Gardner, M. W., Dorling, S. R, “Neural network modeling and prediction of hourly NOx and NO2 concentration in urban air in London”, Atmospheric Environment 33, 1999,709 – 719.

      [5] Gardner, M. W., Dorling, S. R, “Statistical surface ozone models: an improved methodology to account for non-linear behavior”, Atmospheric Environment 34, 2000, 21-34.

      [6] Goyal P., Kumar A., Yadav Kr. Vijay., “Forecasting of air pollutants in Delhi using different statistical techniques”, Indian Journal of air Pollution Control, Vol. xii, no.2, 2012, p57-66.

      [7] Wassermann R.,Tao T.Y., Whitesides M., “Structure and reactivity of alkyalsiloxane monolayers formed by reaction of alkyltrichlorosilanes on silicon subtracts”, Langmuir, 1989, pp1074-1087.

      [8] Yi J., Prybutok V.R., “A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area”, Environmental pollution 92,1996, 349-357.




Article ID: 23826
DOI: 10.14419/ijet.v7i4.39.23826

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