Investigating the randomness and duration of PM10 pollution using functional data analysis

N. Shaadan, S. M. Deni, Abdul Aziz Jemain

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Information on situation of air pollution is critically needed as input in four disciplines of research including risk management, risk evaluation, environmental epidemiology, as well as for status and trend analysis. Two normal practices were identified to evaluate daily air pollution situation; first, pollution magnitude has been treated as the common indicator, and second, the analysis was often conducted based on hourly average data. However, the information on the magnitude level alone to represent the pollution condition based on a rigid point data such as the average was seen as insufficient. Thus, to fill the gap, this study was conducted based on continuously measured data in the form of curves, which is also known as functional data, whereby pollution duration is emphasised. A statistical method based on curve ranking was used in the investigation. The application of the method at Klang, Petaling Jaya and Shah Alam air quality monitoring stations located in the Klang Valley, Malaysia, has shown that pollution duration decreases as the magnitude increases. Shah Alam has the longest pollution duration at low and medium magnitude levels. Meanwhile, all the three stations experienced quite a similar length of average pollution duration for the high magnitude level, that is, about 2.5 days. It was also shown that the occurrence of PM10 pollution at the area is significantly not random.

Original languageEnglish
Pages (from-to)299-308
Number of pages10
JournalPertanika Journal of Science and Technology
Volume26
Issue number1
Publication statusPublished - 1 Jan 2018

Fingerprint

Air Pollution
data analysis
Pollution
pollution
duration
Malaysia
Risk Management
Epidemiology
Air
air pollution
Air pollution
Research
atmospheric pollution
air quality
trend analysis
risk management
epidemiology
Risk management
Air quality
application methods

Keywords

  • Air pollution
  • Curve ranking
  • Functional data analysis
  • Malaysia
  • PM10

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemical Engineering(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

Investigating the randomness and duration of PM10 pollution using functional data analysis. / Shaadan, N.; Deni, S. M.; Jemain, Abdul Aziz.

In: Pertanika Journal of Science and Technology, Vol. 26, No. 1, 01.01.2018, p. 299-308.

Research output: Contribution to journalArticle

Shaadan, N. ; Deni, S. M. ; Jemain, Abdul Aziz. / Investigating the randomness and duration of PM10 pollution using functional data analysis. In: Pertanika Journal of Science and Technology. 2018 ; Vol. 26, No. 1. pp. 299-308.
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