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Abstract

Re-delimitation of rainfall regions plays an important role in determining the rainfall pattern of an area. This study aims to reconstruct the delimitation of rainfall regions for the western region of Peninsular Malaysia. This study involved only the collection of rainfall data at 133 stations from 1960 to 2010. These data were obtained from the Department of Irrigation and Drainage, Malaysia. The analysis methods applied include kriging, contouring and topology using a geographical information system. The results showed that the new delimitation of the western region has been formed with an area reduction of 10% compared to the original western region found by Dale. This is due to some areas in the western region have not received rainfall between 2540 and 2794 mm. The area that getting the rainfall between 2540 and 2794 mm is 46,413.6 km2, in contrast to the sized of Dale’s western region of 51,596.2 km2. The area that frequently getting rainfall of between 2540 and 2794 during 1960s to 2010 are Parit Buntar, Taiping, Kuala Kangsar, Ipoh, Teluk Intan, Tanjong Malim, Batang Kali, Cameron Highlands, Subang, Petaling Jaya, Klang, Kajang and Bangi. The new delimitation formed through this study can be used as a guide by the agencies that manage water resources in Perak, Selangor and Negeri Sembilan in planning a more efficient water supply system.
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Bibliography

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ANAND B., KARUNANIDHI D. 2020. Long term spatial and temporal rainfall trend analysis using GIS and statistical methods in Lower Bhavani basin, Tamil Nadu, India. Indian Journal of Geo-Marine Sciences. No. 49(3) p. 419–427.
BARI J.A., VENNILA G. 2020. Spatial analysis of rainfall in northern part of Erode district, Tamil Nadu, India using GIS. Indian Journal of Geo Marine Sciences. No. 49(6) p. 1108–1113.
CHAN N.W. 1985. The variability in Northwest Peninsular Malaysia. Malaysian Journal of Tropical Geography. No. 12 p. 9–19.
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CHIA L.S. 1974. A study of the rainfall patterns in West Malaysia. PhD Thesis. Singapore. University of Singapore.
DALE W.L. 1959. The rainfall of Malaya. Part 1. Journal of Tropical Geography. No. 13 p. 23–37.
GOOVAERTS P. 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology. Vol. 228(1) p. 113–129. DOI 10.1016/S0022-1694(00)00144-X.
ISLAM T., RICO-RAMIREZ M.A., HAN D., SRIVASTAVA P.K. 2012. A Joss- Waldvogeldisdrometer derived rainfall estimation study by collocated tipping bucket and rapid response rain gauges. Atmospheric Science Letters. Vol. 13(2) p. 139–150. DOI 10.1002/asl.376.
KAIWART M. P., MISHRA P. K., SINHA J. 2020. Rainfall trend analysis for the Mahanadi Main Canal Command, Chhattisgarh, India [online]. Roorkee, India. Indian Institute of Technology Roorkee and National Institute of Hydrology. [Access 15.02.2021]. Available at: https://www.iitr.ac.in/rwc2020/pdf/papers/RWC _123_Manoj_Prabhakar_Kaiwart.pdf
KHALIL A. 2020. Rainfall trend analysis in the Mae Klong River Basin, Thailand. Songklanakarin Journal of Science & Technology. Vol. 42(4) p. 879–888. DOI 10.14456/sjst-psu.2020.113.
KORTE G. 1997. The GIS book: Understanding the value and implementation of geographic information system. 4th ed. Boston. Cengage Learning. ISBN 978-1-56-690127-7 pp. 414.
LI M., SHAO Q., RENZULLO L. 2010. Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation. Stochastic Environmental Research and Risk Assessment. Vol. 24(1) p. 117–130. DOI 10.1007/s00477-009-0305-3.
LIM J.T. 1976. Rainfall minimum in Peninsular Malaysia during the northwest monsoon. Monthly Weather Review. Vol. 104(1) p. 96–99.
LOCKWOOD J.G. 1967. Probable maximum 24-hour precipitation over Malaya by statistical methods. Meteorological Magazine. No. 96 p. 11–19.
NASIR N. 2007. Persekitaran sistem maklumat geografi [Introduction of geographical information systems]. Tanjong Malim. Universiti Pendidikan Sultan Idris Press. ISBN 978-9-83-375914-9 pp. 147.
PRAVEEN B., TALUKDAR S., MAHATO S., MONDAL J., SHARMA P., ISLAM A. R. M. T., RAHMAN A. 2020. Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Scientific Reports. Vol. 10(1) p. 1–21. DOI 10.1038/s41598-020-67228-7.
SHAHARUDDIN A., NOORAZUAN M.H. 2006. Analysing rain patterns and trend in Negeri Sembilan using the GIS Polygon Thiessen and Isohyet Contours methods. Geografia Malaysian Journal of Society & Space. Vol. 3(2) p. 1–12.
TANGANG F.T., LIEW J.N., MOHD. SALMI N., MOHD. IDRIS J., SHAHARUDDIN A., ALUI B. 2004. Interannual evolution of Indian Ocean sea surface temperature anomaly and its relationship with precipita-tion variability in Malaysia. In: Marine science into the new millennium: New perspectives & challenges. Proceedings of the Asia-Pacific Conference on Marine Science & Technology. Ed. S.M. Phang, V.C. Chong, S.C. Ho, M. Noraieni, O.L.S. Jillian. 12–16 May 2002, Kuala Lumpur, Malaysia. UMMReC p. 537– 551.
WAN RUSLAN I. 1994. Pengantar hidrologi [Introduction of hydrology]. Kuala Lumpur. Dewan Bahasa dan Pustaka. ISBN 978-9-83-624434-5 pp. 159.
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Authors and Affiliations

Mohmadisa Hashim
1
ORCID: ORCID
Nasir Nayan
1
ORCID: ORCID
Zahid Mat Said
1
ORCID: ORCID
Dewi Liesnoor Setyowati
2
ORCID: ORCID
Yazid Saleh
1
ORCID: ORCID
Hanifah Mahat
1
ORCID: ORCID
See L. Koh
1

  1. Universiti Pendidikan Sultan Idris, Faculty of Human Sciences, Department of Geography and Environment, 35900, Tanjong Malim, Malaysia
  2. Universitas Negeri Semarang, Semarang City, Indonesia
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Abstract

This paper aims to explore the relationship between the Air Quality Index (AQI), COVID-19 incidence rates, and population density within Malaysia’s ten most populous cities from January 2018 to December 2021. Data were sourced from the Department of Statistics Malaysia, the World Air Quality Index Project, and Our World in Statistics. The methodology integrated population-based city classification and AQI assessment, cluster analysis through SPSS, and Generalized Additive Mixed Model (GAMM) analysis using R Studio despite encountering a data gap in AQI for five months in 2019. Cities were organized into three clusters based on their AQI: Cluster One included Ipoh, Penang, Kuala Lumpur, and Melaka, Cluster Two comprised Kuantan, Seremban, Johor Bahru, and Kota Bharu, Cluster Three featured Kota Kinabalu and Kuching. GAMM analysis revealed prediction accuracies for AQI variations of 58%, 60%, and 41% for the respective clusters, indicating a notable impact of population density on air quality. AQI variations remained unaffected by COVID-19, with a forecasted improvement in air quality across all clusters. The paper presents novel insights into the negligible impact of COVID-19 on AQI variations and underscores the predictive power of population dynamics on urban air quality, offering valuable perspectives for environmental and urban planning.
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Authors and Affiliations

Wong Ming Wong
1
ORCID: ORCID
Shian-Yang Tzeng
2
ORCID: ORCID
Hao-Fan Mo
3
ORCID: ORCID
Wunhong Su
4
ORCID: ORCID

  1. International College, Krirk University, Thailand
  2. School of Economics and Management, Quanzhou University of Information Engineering, China
  3. JinWen University of Science and Technology, Taiwan
  4. 4School of Accounting, Hangzhou Dianzi University, China

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