Forecasting Carbon Monoxide (CO) Pollutant in Putrajaya using the Local Mean Approximation Method

Peramalan Bahan Pencemar Karbon Monoksida (CO) di Putrajaya Menggunakan Kaedah Penghampiran Purata Setempat

  • Shahidatul Idayu Shahizam Jabatan Matematik, Fakulti Sains dan Matematik, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
  • Nor Zila Abd Hamid Jabatan Matematik, Fakulti Sains dan Matematik, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
  • Syafruddin Side Jabatan Matematik, Universitas Negeri Makassar, Makassar, Indonesia
Keywords: Carbon Monoxide Time Series, Local Mean Approximation Method, Chaotic Approach, Phase Space Plot, Cao Method

Abstract

Air polution is a great concern to all parties because it is not only affects human health but also affects the environment and the development of the country. This study examines the forecast of carbon monoxide (CO) in metropolitan areas in Malaysia using the local mean approximation method. This study aims to predict the concentration of CO pollutant that has been observed hourly at the selected area which is Putrajaya. The delay time parameter  was obtained from the determination  method while embedding dimension  was obtained from the Cao method. The presence of chaotic dynamics is successfully detected by using the Cao method and the phase space plot. The one-hour forward forecasting process of CO time series using  and  was carried out using the Local Mean Approximation Method. The result showed that correlation coefficient value is 0.7674 which is close to 1. This forecasting is expected to assist those stakeholders in managing CO pollution effectively especially in metropolitan areas in Malaysia.

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References

Abarbanel, H. D. I. (1996). Analysis of observed chaotic data. New York: Springer-Verlag.

Bahari, M. & Hamid, N. Z. A. (2019). Analysis and prediction of temperature time series using chaotic approach. IOP Conferences Series: Earth and Environmental Science, 286.

Cai, M., Yin, Y., & Xie, M. (2009). Prediction of hourly air pollutant concentrations near urban arterials neural network approach. Transportation Research Part D: Transport and Environment, 14(1), 32-41.

Cao, L. (1997). Practical method of determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110(1-2), 43-50.

Cunningham, B., Cunningham, M. A. & Saigo, B. W. (2014). Environmental Science: A global concern 13th edition. Boston, United States: McGraw Hill.

Farmer, J. D., & Sidorowich, J. J. (1987). Predicting chaotic time Series. The American Physical Society, 59(8), 845-848.

Fraser, A. M. & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2).

Hamid, N. Z. A. & Noorani, M. S. M. (2012). On prediction of Subang, Selangor Daily Rainfall Data: An application of local approximation method. Jurnal Sains dan Matematik. 4(2), 49-57.

Hanafi, N. H., Hassim, M. H. & Noor, Z. Z. (2018). Overview of health impacts due to haze pollution in Johor, Malaysia. Journal Engineering Technology Science. 50(6), 818-831.

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

Jabatan Alam Sekitar Malaysia (2015). Air pollutant index. Diakses pada September 18, 2020 dari http://apims.doe.gov.my/apims/hourly2.php.

Jusoh, K. C. & Hamid, N. Z. A. (2020). Meramal bacaan maksimum harian nitrogen dioksida menerusi pendekatan kalut. Journal of Quality Measurement and Analysis 16(1), 79-89.

Kermani, M. Z. & Kisi. O. (2015). Time series analysis on marine wind-wave characteristics using chaotic theory. Ocean Eng 100(15), 46-53.

Kinoshita, H., Turkan, H., Vucinic, S., Naqvi, S., Bedair, R., Rezaee, R. & Tsatsakis, A. (2020). Carbon monoxide poisoning. Toxicology Reports 7, 169-173.

Mashuri, A., Adenan, N.H. & Hamid, N. Z. A. (2019). Determining the chaotic dynamics of hydrological data in flood-prone area. Civil Engineering amd Architecture 7(6A), 71-76.

Mazlan, S. M., Hamzah, A. & Mahmud, M. (2015). Kualiti udara dalam bangunan di bangunan Sains Biologi, Fakulti Sains dan Teknologi, Universiti Kebangsaan Malaysia. GEOGRAFIA-Malaysian Journal of Society and Space 11(1), 87-96.

Ruslan, A. B., Hamid, N. Z. A. & Jusoh, K. C. (2020). Peramalan aplikasi pendekatan kalut bahan pencemar siri masa CO menggunakan kaedah penambahbaikan dalam penentuan parameter bilangan k-jiran terdekat. Borneo International Journal, 2(4), 11-16.

Schuster, H. G. (1988). Deterministic Chaos: An Introduction. Weinheim: VCH Publishers.

Sivakumar, B., Liong, S. Y., Liaw, C. Y. & Phoon, K. K. (1999). Singapore rainfall behaviour: chaotic. Journal of Hydrologic Engineering, 4(1), 38-48.

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

Shafii, H., Miskam, N, Yassin, A. M., Tawee, S. & Musa, S. M. S. (2018). Status kualiti udara di beberapa kawasan luar Bandar terpilih di negeri Johor. International Conference of Tourism, Business & Technology.

Takens, F. (1981). Detecting strange attractors in turbulence. Dynamical Systems and Turbelence, Warwick 1980, 898, 366-381.

Zaim, W. N. A. B. W. M., & Hamid, N. Z. A. (2017). Peramalan bahan pencemar ozon (O3) di Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak, Malaysia Mengikut Monsun dengan Menggunakan Pendekatan Kalut. Sains Malaysiana, 46(12), 2523-2528.

Published
2021-02-20
How to Cite
Shahizam, S. I., Abd Hamid, N. Z., & Side, S. (2021). Forecasting Carbon Monoxide (CO) Pollutant in Putrajaya using the Local Mean Approximation Method. Journal of Science and Mathematics Letters, 9, 63-71. https://doi.org/10.37134/jsml.vol9.sp.8.2021