Modeling Fluctuation of PM10 Data with Existence of Volatility Effect

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Modeling time series data of particulate matter (PM) will provide a good understanding about the dynamic behavior of this pollution variable. In fact, a suitable model can be used as a practical tool for planning purposes and controlling adverse effects of air pollution. This article utilized an autoregressive integrated moving average (ARMA) with the combination of generalized autoregressive conditional heteroscedastic (ARCH/GARCH) to provide a suitable model that can overcome the problematic volatility effect that exists in the PM10 data. Hourly PM10 data for the city of Kuala Lumpur have been analyzed. Based on several statistical approaches, such as the autocorrelation function, R2 coefficient, and Akaike's Information Criterion, an ARMA(1,0)-GARCH(1,1) has been determined to be the best model to describe the data. In fact, incorporation of GARCH(1,1) is able to improve forecasting performance of PM10 data, instead of relying on only a single ARMA(1,0) model.

Original languageEnglish
Pages (from-to)816-827
Number of pages12
JournalEnvironmental Engineering Science
Volume34
Issue number11
DOIs
Publication statusPublished - 1 Nov 2017

Fingerprint

modeling
Particulate Matter
Air pollution
Autocorrelation
Akaike information criterion
Time series
Pollution
autocorrelation
particulate matter
Planning
atmospheric pollution
volatility
effect
time series
pollution
incorporation
planning
city

Keywords

  • air-pollution modeling
  • ARMA-GARCH model
  • volatility effect

ASJC Scopus subject areas

  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

Cite this

Modeling Fluctuation of PM10 Data with Existence of Volatility Effect. / Masseran, Nurulkamal.

In: Environmental Engineering Science, Vol. 34, No. 11, 01.11.2017, p. 816-827.

Research output: Contribution to journalArticle

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