TEMPORAL ANALYSIS AND PREDICTIVE MODELING OF AMBIENT AIR QUALITY IN HULU LANGAT DISTRICT, SELANGOR, MALAYSIA: A CHEMOMETRIC APPROACH

Authors

  • Aiman Abdullah Environmental Health Section, Institute of Medical Science and Technology, UNIVERSITI KUALA LUMPUR, MALAYSIA
  • Ahmad Shakir Mohd Saudi Center for Water Engineering Technology, Malaysia France Institute, UNIVERSITI KUALA LUMPUR, MALAYSIA
  • Nur Zahidah Shafii Center for Water Engineering Technology, Malaysia France Institute, UNIVERSITI KUALA LUMPUR, MALAYSIA
  • Mohd Khairul Amri Kamarudin Faculty of Applied Social Sciences, UNIVERSITI SULTAN ZAINAL ABIDIN, MALAYSIA
  • Firdaus Muhammad-Sukki School of Computing, Engineering and the Built Environment, EDINBURGH NAPIER UNIVERSITY, UNITED KINGDOM

DOI:

https://doi.org/10.21837/pm.v22i30.1448

Keywords:

Air quality, Artificial neural network, Chemometrics, Correlation, Principal component analysis

Abstract

One of the most important environmental problems facing the globe today is air pollution. The centre area for the local populace is the Hulu Langat district, which borders Kuala Lumpur, the capital. The purpose of this study is to look at how the ambient air quality varies in Hulu Langat, Selangor. The Air Quality Division of the Malaysian Department of Environment provided five years' worth of secondary data on the air quality at Hulu Langat. The database included five primary air pollutant characteristics sulphur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter with a diameter of 10 microns or less (PM10), in addition to data from the Air Pollutant Index (API). Chemometric analysis was used to examine the results. According to the results, SO2, NO2 and PM10 had the greatest correlations with API readings. A statistical process known as statistical control (SPC) showed that certain PM10 values were over national recommendations and control limits. The artificial neural network method's air quality prediction model demonstrated good accuracy with real data (R2 = 0.9). The results of this investigation indicated a strong correlation between the Hulu Langat air quality data. In order to achieve sustainable environmental practices in the future, it is imperative to engage in extensive collaboration across environmental departments and relevant authorities and engage in continuous monitoring of air quality.

Downloads

Download data is not yet available.

References

Abdullah A. M., Abu Samah M. A., Jun T. Y. (2012). An overview of the air pollution trend in Klang Valley, Malaysia. Open Environ. Sci. 6, 13–19. doi: 10.2174/1876325101206010013. DOI: https://doi.org/10.2174/1876325101206010013

ASEAN (Association of Southeast Asian Nations), (2014). ASEAN Peatland Management Strategy 2006-2020. ASEAN Secretariat, Jakarta, April 2014.

ASEAN (Association of Southeast Asian Nations), (2016). ASEAN Agreement on Transboundary Haze Pollution. ASEAN Secretariat, Jakarta, July 2016.

Azhari A. Z., Mohamed A. F., Latif M. T. (2016). Carbon emission from vehicular source in selected industrial areas in Malaysia. International Journal of the Malay World and Civilisation. 4(1), 89-93.

Azid A., Juahir H., Toriman M. E., Endut A., Kamarudin M. K. A., Rahman M. N. A… (2015). Source apportionment of air pollution: A case study in Malaysia. J. Teknol.,72, 83–88. doi: 10.11113/jt.v72.2934. DOI: https://doi.org/10.11113/jt.v72.2934

Azmi S. Z, Latif M. T, Ismail A. S., Juneng L, Jemain A. A. (2010). Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Qual., Atmos. Health. 3, 53–64. doi: 10.1007/s11869-009-0051-1. DOI: https://doi.org/10.1007/s11869-009-0051-1

Banan N., Latif M. T., Juneng L., Ahamad F. (2013). Characteristics of surface ozone concentrations at stations with different backgrounds in the Malaysian Peninsula. Aerosol Air Qual. Res. 13, 1090–1106. doi: 10.4209/aaqr.2012.09.0259. DOI: https://doi.org/10.4209/aaqr.2012.09.0259

Binyehmed F. M., Abdullah A. M., Zainal Z. (2016). Trend and Status of SO2 Pollution as a Corrosive Agent at Four Different Monitoring Stations in the Klang Valley. Int. J. Adv. Sci. Res. 3, 302–317.

Bloomenthal A. (2020). [Internet] Coefficient of Determination: Overview. Investopedia, July 2020. Available from: https://www.investopedia.com/terms/c/coefficient-of-determination.asp

Cochrane J. (2018). Rain in Indonesia Dampens Forest Fires That Spread Toxic Haze. The New York Times, New York, October 2018. Available from: https://www.nytimes.com/2015/10/29/world/asia/indonesia-forest-fire-toxic-haze.html

DOE (Department of Environment), (2015). [Internet]. KENYATAAN MEDIA. Available from: http://apims.doe.gov.my/v2/

DOSM (Department of Statistics Malaysia), (2017). [Internet]. State Socioeconomic Report 2017. Department of Statistics Malaysia Official Portal, Malaysia, 2017. Available from: https://www.dosm.gov.my/v1/

Haan J. (2017). [Internet]. Indonesia: Fire Count at Record Low for 2017. Future Directions International, Australia, November 2017. Available from: https://www.futuredirections.org.au/publication/indonesia-fire-count-record-low-2017/

How C. Y, Ling Y. E. (2016). The influence of PM2.5 and PM10 on Air Pollution Index (API). Environmental Engineering, Hydraulics and Hydrology: Proceeding of Civil Engineering, Universiti Teknologi Malaysia, Johor, Malaysia. 2016 Jun;3:132.

Hua K. (2018). Applied Chemometric Approach in Identification Sources of Air Quality Pattern in Selangor, Malaysia. Sains Malays. 47, 471–479. doi: 10.17576/jsm-2018-4703-06. DOI: https://doi.org/10.17576/jsm-2018-4703-06

Kamaruddin A. S., Jalaludin J., Choo C. P. (2015). Indoor air quality and its association with respiratory health among Malay preschool children in Shah Alam and Hulu Langat, Selangor. Adv. Environ. Biol. 9, 17-26. DOI: https://doi.org/10.1155/2015/248178

Lee S, Jung S, Lee J. (2019). Prediction model based on an artificial neural network for user-based building energy consumption in South Korea. Energies, 12, 608. doi: 10.3390/en12040608. DOI: https://doi.org/10.3390/en12040608

Lim S Y. (2014). [Internet]. Forest fires contributing to haze in Selangor. Malaymail. Available from: https://www.malaymail.com/news/malaysia/2014/03/13/forest-fires-contributing-to-haze-in-selangor/633995

Ling O. H. L, Musthafa S. N., Mohamed N. (2014). Air Quality and Land Use in Urban Region of Petaling Jaya, Shah Alam and Klang, Malaysia. EnvironmentAsia, 7(1), 1-12.

Ling O. H. L, Tin, K. H, Shaharuddin A, Kadaruddin A, Yaakob M. J. (2010). Urban Growth and Air Quality in Kuala Lumpur City, Malaysia. Environmentasia, 3, 123-128.

Mahidin M. U. (2018). Malaysia Statistical Handbook. 53, 1689–1699.

Ministry of Education (MOE). 2014. [Internet]. Kenyataan media: Kenyataan Akhbar oleh KPPM berkaitan tindakan pihak sekolah menangani fenomena jerebu yang melanda negara 2014. Ministry of Education, Malaysia, Mac 2014. Available from: https://www.moe.gov.my/pemberitahuan/kenyataan-media/kenyataan-akhbar-oleh-kppm-berkaitan-tindakan-pihak-sekolah-menangani-fenomena-jerebu-yang-melanda-negara-2014

Mustaffa, H., Kamarudin, M. K. A., Toriman, M. E., Rosli, M. H., Sunardi, S. (2023). Impact of Suspended Sediment on Pahang River Development using Geographic Information System. Planning Malaysia, 21(1), 116 – 133. DOI: https://doi.org/10.21837/pm.v21i25.1228

Nazeer N, Furuoka F. (2017). Overview of ASEAN environment, transboundary haze pollution agreement and public health. International Journal of Asia-Pacific Studies. 13, 73–94. doi: 10.21315/ijaps2017.13.1.4. DOI: https://doi.org/10.21315/ijaps2017.13.1.4

Saad, M. H. M., Kamarudin, M. K. A., Toriman, M. E., Wahab, N.A, Ata, F. M., Samah, M. A. A., Manoktong, S. N. (2023). Analysis of the Flash Flood Event and Rainfall Distribution Pattern on Relau River Basin Development, Penang, Malaysia. Planning Malaysia, 21(1), 58-71. DOI: https://doi.org/10.21837/pm.v21i25.1224

Sarwat E., El-shanshoury G. I. (2018). Estimation of Air Quality Index by Merging Neural Network with Principal Component Analysis. Int. J. Comput. Appl. 1, 1-12. doi: 10.26808/rs.ca.i8v1.01. DOI: https://doi.org/10.26808/rs.ca.v8n1.01

Saudi A. S. M, Juahir H., Azid A., Toriman M. E., Kamarudin M. K. A., Saudi M. M. (2015). Flood risk pattern recognition by using environmetric technique: a case study in Langat river basin. J. Teknol. 77, 145–152. doi: 10.11113/jt.v77.4142. DOI: https://doi.org/10.11113/jt.v77.4142

Shafii N. Z., Saudi A. S. M., Pang J. C., Abu I. F., Sapawe N, Kamarudin M. K. A… (2019). Application of chemometrics techniques to solve environmental issues in Malaysia. Heliyon, 5:e02534. doi: 10.1016/j.heliyon.2019.e02534. DOI: https://doi.org/10.1016/j.heliyon.2019.e02534

Wei J, Guo X, Marinova D, Fan J. (2014). Industrial SO2 pollution and agricultural losses in China: Evidence from heavy air polluters. J. Cleaner Prod. 64, 404–413. doi: 10.1016/j.jclepro.2013.10.027. DOI: https://doi.org/10.1016/j.jclepro.2013.10.027

WHO (World Health Organization). (2016). Ambient Air Pollution: A global assessment of exposure and burden of disease. World Health Organization: Geneva, Switzerland, 1-131. Available from: https://apps.who.int/iris/handle/10665/250141 DOI: https://doi.org/10.17159/2410-972X/2016/v26n2a4

Zabel G. (2009). Peak People: The Interrelationship between Population Growth and Energy Resources - Resilience. Energy Bulletin. Available from: https://www.resilience.org/stories/2009-04-20/peak-people-interrelationship-between-population-growth-and-energy-resources/

Downloads

Published

2024-02-22

How to Cite

Abdullah, A., Mohd Saudi, A. S., Shafii, N. Z., Kamarudin, M. K. A., & Muhammad-Sukki, F. (2024). TEMPORAL ANALYSIS AND PREDICTIVE MODELING OF AMBIENT AIR QUALITY IN HULU LANGAT DISTRICT, SELANGOR, MALAYSIA: A CHEMOMETRIC APPROACH. PLANNING MALAYSIA, 22(30). https://doi.org/10.21837/pm.v22i30.1448

Most read articles by the same author(s)

1 2 > >>