Daily, seasonal, and spatial patterns of PM10 in Seoul, Korea

Kyu Jong Lee, Seoung Bum Kim, Sun Kyoung Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Various analyses of the complex behavior in ambient air pollutants have been conducted to extract their implicit patterns and meaningful information. In the present study, we conducted some statistical analyses to identify daily, seasonal, and spatial patterns of particulate matters (PM10) in Seoul, Korea. We used the daily PM10 mass concentration data observed at 25 different monitoring sites in Seoul, Korea from 2005 to 2009. Analysis of variance and a k-means clustering algorithm were used to investigate seasonal and spatial patterns of PM10 concentrations. Moreover, we used a bootstrap method to calculate the probabilities that PM10 concentrations exceeded the environment limit or the comprehensive air quality index in different months.

Original languageEnglish
Title of host publicationProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Pages278-283
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China
Duration: 2011 Jul 102011 Jul 12

Publication series

NameProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

Other

Other2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
CountryChina
CityBeijing
Period11/7/1011/7/12

Keywords

  • air pollution
  • bootstrapping
  • k-menas clustering
  • particulate matter

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Fingerprint Dive into the research topics of 'Daily, seasonal, and spatial patterns of PM<sub>10</sub> in Seoul, Korea'. Together they form a unique fingerprint.

Cite this