Sains Malaysiana 46(12)(2017): 2523–2528

http://dx.doi.org/10.17576/jsm-2017-4612-30

 

Peramalan Bahan Pencemar Ozon (O3) di Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak, MalaysiaMengikut Monsun dengan Menggunakan Pendekatan Kalut

(Forecasting Ozone Pollutant (O3) in Universiti Pendidikan Sultan Idris, Tanjung Malim,
Perak, Malaysia, based on Monsoon using Chaotic Approach)

 

WAN NUR AFATEEN BINTI WAN MOHD ZAIM* & NOR ZILA ABD HAMID

 

Jabatan Matematik, Fakulti Sains dan Matematik, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak Darul Ridzuan, Malaysia

 

Diserahkan: 14 Februari 2017/Diterima: 7 Jun 2017

 

ABSTRAK

Peramalan bahan kepekatan ozon (O₃) adalah sangat penting kerana udara yang mengandungi O₃ boleh menyebabkan pelbagai penyakit kronik seperti kanser dan asma. Kajian ini merupakan kajian rintis dengan menggunakan pendekaan kalut bagi meramal kepekatan O₃ di kawasan pendidikan di Malaysia. Data yang dikaji merupakan siri masa O₃ yang dicerap mengikut jam di stesen yang terletak di Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak. Sebelum model peramalan dibina, siri masa diuji terlebih dahulu untuk mengenal pasti sama ada dinamik kalut hadir ataupun tidak. Pendekatan kalut mempunyai dua langkah iaitu pembinaan semula ruang fasa dan proses peramalan. Pembinaan semula ruang fasa memerlukan penetapan dua parameter terlebih dahulu iaitu masa tunda τ dan matra pembenaman m. Kedua-dua parameter tersebut masing-masing diperoleh daripada kaedah purata maklumat bersama dan kaedah Cao. Melalui plot ruang fasa dan kaedah Cao, sifat kalut didapati hadir dalam siri masa O₃. Oleh itu, model peramalan melalui pendekatan kalut menggunakan kaedah penghampiran purata setempat dibina. Pendekatan kalut ini merupakan salah satu kaedah alternatif yang boleh digunakan untuk meramal siri masa O₃. Pekali korelasi adalah dipilih sebagai petunjuk prestasi bagi memberi gambaran tentang kekuatan hubungan antara nilai sebenar dengan nilai peramalan. Nilai pekali korelasi bagi siri masa O₃ ketika Monsun Timur Laut adalah 0.8921. Manakala, nilai pekali korelasi ketika Monsun Barat Daya adalah 0.9002. Diharap dengan pendekatan kalut ini dapat membantu pihak bertanggungjawab untuk mengawal pencemaran O₃ di kawasan pendidikan di Malaysia.

 

Kata kunci: Kaedah penghampiran purata setempat; Monsun Barat Daya; Monsun Timur Laut; pendekatan kalut; ozon;

 

ABSTRACT

Forecasting concentration of ozone (O₃) is very important because the air containing O₃ can cause chronic diseases such as cancer and asthma. This study is a pilot study using chaotic approach to forecast the concentration of _O3 in Malaysian education area. The studied data were the hourly O₃ observed at the station located at Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak. Before the forecasting model can be built, the time series are tested in advance to determine the existence of chaotic dynamics. Chaotic approach has two steps, namely the reconstruction of phase space and forecasting process. Before the phase space can be reconstructed, there are two parameters that need to be determined namely the delay time τ and embedding dimension m. Both of these parameters were obtained from the average mutual information method and Cao method, respectively. Through phase space plot and Cao method, chaotic dynamic are present in the studied O₃ time series. Therefore, the forecasting model through chaotic approach using local mean approximation method is built. This chaotic approach is one of the alternative methods that can be used to forecast the O₃ time series. Correlation coefficient is chosen to present the relationship between the observed value and forecasted value. The correlation coefficient for the O₃ time series during Northeast Monsoon is 0.8921. Meanwhile, the value of the correlation coefficient during Southwest Monsoon is 0.9002. It is hoped that the chaotic approach can help responsible agency to manage O₃ pollution in Malaysian education area.

 

Keywords: Chaotic approach; local average approximation method; Northeast Monsoon; ozone; Southwest Monsoon

RUJUKAN

Abarbanel, H.D.I. 1996. Analysis of Observed Chaaotic Data. New York: Springer-Verlag.

Adenan, N.H. & Noorani, M.S.M. 2015. Peramalan data siri masa aliran sungai di dataran banjir dengan menggunakan pendekatan kalut. Sains Malaysiana 44(3): 463-471.

Awang, N.R., Elbayoumi, M., Ramli, N.A. & Yahaya, A.S. 2016. The Influence of spatial variability of critical conversion point (CCP) in production of ground level ozone in the context of tropical climate. Aerosol and Air Quality Research 16(1): 153-165.

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 and Air Quality Research 13(3): 1090-1106.

Cakmak, S., Hebbern, C., Vanos, J., Crouse, D.L. & Burnett, R. 2016. Ozone exposure and cardiovascular-related mortality in the Canadian Census Health and Environment Cohort (CANCHEC) by spatial synoptic classi fi cation zone. Environmental Pollution 214(2): 589-599.

Cao, L. 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110(1): 43-50.

Ghazali, N.A., Ramli, N.A., Yahaya, A.S., Yusof, N.F.F.M.D., Sansuddin, N. & Al Madhoun, W.A. 2010. Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques. Environmental Monitoring and Assessment 165(1-4): 475- 489.

Hamid, N.Z.A. & Noorani, M.S.M. 2013. An improved prediction model of ozone concentration time series based on chaotic approach. Wold Academy of Science, Engineering and Technology 7(11): 206-211.

Hamid, N.Z.A. & Noorani, M.S.M. 2014. Suatu kajian perintis menggunakan pendekatan kalut bagi pengesanan sifat dan peramalan siri masa kepekatan PM10. Sains Malaysiana 43(3): 475-481.

Hamid, N.Z.A., Noorani, M.S.M., Juneng, L. & Latif, M.T. 2013. Prediction of ozone concentrations using nonlinear prediction method. AIP Conference Proceedings 1522: 125-131.

Hardy, K. 2005. Linear Algebra for Engineers and Scientists using Matlab. Upper Saddle River, New Jersey: Pearson Education, Inc.

Indira, P., Inbanathan, S.S.R., Selvaraj, R.S. & Suresh, A.A. 2016. Chaotic analysis on surface ozone measurements at tropical urban coastal station Chennai, India. IOSRD International Journal of Earth Science 2(1): 1-8.

Ismail, M., Abdullah, S., Yuen, F.S. & Nurul Adyani, G. 2016. A ten-year investigation on ozone and it precursors at Kemaman, Terengganu, Malaysia. EnvironmentAsia 9(1): 1-8.

Kocak, K., Saylan, L. & Sen, O. 2000. Nonlinear time series prediction of O3 concentration in Istanbul. Atmospheric Environment 34(8): 1267-1271.

Madaniyazi, L., Nagashima, T., Guo, Y., Pan, X. & Tong, S. 2016. Projecting ozone-related mortality in East China. Environment International 92-93: 165-172.

Mudway, I.S. & Kelly, F.J. 2000. Ozone and the lung: A sensitive issue. Mol. Aspects Med. 21(1-2): 1-48.

Muhamad, M., Ul-Saufie, A.Z. & Deni, S.M. 2015. Three days ahead prediction of daily 12 hour ozone (O3) concentrations for urban area in Malaysia. Journal of Environmental Science and Technology 8(3): 102-112.

Ramli, N.A., Ghazali, N.D. & Shukri Yahaya, A. 2010. Diurnal fluctuations of ozone concentrations and its precursors and prediction of ozone using multiple linear regressions. Malaysian Journal of Environmental Management 11(2): 57-69.

Sivakumar, B. 2002. A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. Journal of Hydrology 258(1-4): 149-162.

Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M. & Pereira, M.C. 2007. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software 22(1): 97-103.

Sprott, J.C. 2003. Chaos and Time-Series Analysis. Oxford: Oxford University Press.

Tan, K.C., Lim, H.S. & Mohd Zubir Mat Jafri. 2016. Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in Peninsular Malaysia. Atmospheric Pollution Research 7(3): 533-546.

Velickov, S. 2004. Nonlinear dynamics and chaos with application to hydrodynamics and hydrological modelling. PhD Dissertation. Delft University of Technology, Netherlands (Unpublished).

 

 

*Pengarang untuk surat-menyurat; email: ateenzaim@gmail.com