Time series analysis model for particulate matter of air pollution data in Dhaka city

Md Arafat Rahman*, M. Amir Hossain

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Time series analysis and forecasting has become an important tool in many applications in the field of air pollution and environmental management. ARIMA (Autoregressive Integrated Moving Average) models form an important part of the Box-Jenkins approach to time series data modelling. In this study Box-Jenkins method was used to construct ARIMA model for monthly particulate matter of air pollution data with a total of 108 readings from Dhaka meteorological station for the period 2002-2010. An attempt has been made to construct an ARIMA (0, 0, 2) (2, 1, 0)12 model in a systematic and scientific manner. Based on the fitted ARIMA model, monthly particulate matter of air pollution for further two years has been predicted. It will help to make better decision for controlling air pollution in Dhaka city.

Original languageEnglish
Pages (from-to)63-69
Number of pages7
JournalAsian Journal of Water, Environment and Pollution
Volume9
Issue number4
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • air pollution
  • ARIMA model
  • forecasting
  • particulate matter
  • Time series analysis

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