Spatio-Temporal Analysis of PM10 in Southern Peninsular Malaysia

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In this study, the particulate matter with diameter less than 10 micrometers (PM10) is being observed. Other factors that influenced the pollutant dispersion are also being studied prior to identification of their relationship. The aim of this study is to identify the trend of PM10 concentrations in the Southern Peninsular of Malaysia during the period 2005 to 2015 by using spatio-temporal analysis in regards to air pollution. The inverse distance weighted (IDW) is used for the spatio interpolation data and mapping. The trends of the PM10 concentration are illustrated via map which indicates the affected and vulnerable area of Southern Peninsular Malaysia especially during Haze episode.

     

     


  • Keywords


    PM10; haze; spatio-temporal; trend analysis

  • References


      [1] Brereton, F., Moro, M., Ningal, T., Ferreira, S., 2011. Technical report on GIS analysis, mapping and linking of contextual data to the European Social Survey 1–33.

      [2] DOE, 2013. New Malaysian Ambient Air Quality Standard.

      [3] Fujii, Y. et al., 2016. A case study of PM2.5 characterization in Bangi, Selangor, Malaysia during the southwest monsoon season. Aerosol and Air Quality Research, 16(11), pp.2685–2691.

      [4] Ibrahim, M.Z., Ismail, M. and Hwang, Y.K., 2012. Mapping the Spatial Distribution of Criteria Air Pollutants in Peninsular Malaysia Using Geographical Information System (GIS). INTECH Open Access Publisher.

      [5] Jabatan Alam Sekitar, 2014. Environmental Quality ( Clean Air ) Regulations 2014 2014, 90.

      [6] Jamil, A. et al., 2011. PM10 monitoring using MODIS AOT and GIS in Kuala Lumpur, Malaysia. Research Journal of Chemistry and Environment, 15(2), pp.982–985.

      [7] Sansuddin, N., Ramli, N.A., Yahaya, A.S. et al. Environ Monit Assess (2011) 180: 573. https://doi.org/10.1007/s10661-010-1806-8.

      [8] Shareef, M.M., Husain, T. and Alharbi, B., 2016. Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques. Journal of Environmental Protection, 7(6), pp.895–911.

      [9] Wang, Q., Jiang, N., Yin, S., Li, X., Yu, F., Guo, Y. and Zhang, R., 2017. Carbonaceous species in PM 2.5 and PM 10 in urban area of Zhengzhou in China: Seasonal variations and source apportionment. Atmospheric Research, 191, pp.1-11.

      [10] World Health Organization, 2016. World Health Statistics 2016: Monitoring Health for the SDGs Sustainable Development Goals. World Health Organization.


 

View

Download

Article ID: 15267
 
DOI: 10.14419/ijet.v7i3.9.15267




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.