International Journal of
Physical Sciences

  • Abbreviation: Int. J. Phys. Sci.
  • Language: English
  • ISSN: 1992-1950
  • DOI: 10.5897/IJPS
  • Start Year: 2006
  • Published Articles: 2569

Full Length Research Paper

Prediction of the rate of dust fall in Quetta city, Pakistan using seasonal ARIMA (SARIMA) modeling

Muhammad Sami1, Amir Waseem2, Yasmin Zahra Jafri3, Syed Haider Shah3, Muzafar Ahmed Khan4, Sher Akbar4, Masood Ahmed Siddiqui4 and Ghulam Murtaza5*    
1Department of Chemistry, Government Degree College, Khuzdar, Pakistan. 2Department of Chemistry, COMSATS Institute of Information Technology, Abbottabad-22060, Pakistan. 3Department of Statistics, University of Baluchistan, Quetta-87300, Pakistan. 4Department of Chemistry, University of Balochistan, Quetta-87300, Pakistan. 5Department of Pharmaceutical Sciences, COMSATS Institute of Information Technology, Abbottabad-22060, Pakistan.  
Email: [email protected]

  •  Accepted: 23 November 2011
  •  Published: 02 March 2012


The components and quantities of atmospheric dust fallout have been reported to be the pollution indicator of large urban areas. The multiplicity and complexity of sources of atmospheric dusts in urban regions has put forward the need for source apportionment of these sources in order to indicate) their contribution to a specific environmental receptor. The study presented here is focused on the investigation of the rate of dust fall in Quetta valley. Having used seasonal autoregressive integrated moving average ARIMA (SARIMA) modeling, the prediction equations were developed to forecast the seasonal rate of dust fall at three different locations out of 10 selected sites in Quetta from 2004 to 2008. In terms of deduced statistical equations, the findings could help to predict, abate, minimize or even control the pollutants, predominantly the heavy and toxic metals present in the dust particulates that are studied by this sort of research work. Seasonal ARIMA (SARIMA) model was found to be a better forecaster of the rate of dust fall having vitally analyzed the entire stochastic models on diverse climatic parameters.

Key words: Particulate matter, rate of dust fall, heavy metals, autoregressive integrated moving average (ARIMA), stochastic modeling, Markov transition matrix (MTM).