Existence of Long Memory Phenomenon in Air Pollutant Concentrations using Surrogate Data
Nuryazmin Ahmat Zainuri1, Noorhelyna Razali2, Haliza Othman3, Alias Jedi4, Noraishikin Zulkarnain5
1Nuryazmin Ahmat Zainuri*, Department of Engineering Education (DEEd), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
2Noorhelyna Razali, Department of Engineering Education (DEEd), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
3Haliza Othman, Department of Engineering Education (DEEd), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
4Alias Jedi, Department of Mechanical Engineering and Manufacturing, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
5Noraishikin Zulkarnain, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Manuscript received on November 13, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 752-757 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6868129219/2019©BEIESP | DOI: 10.35940/ijitee.B6868.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study investigated the existence of long memory phenomenon in air pollutant concentrations specifically the ozone concentration obtained at six monitoring stations in Peninsular Malaysia. The main objective was to select the best method in detecting the long memory. Four methods used in this study were Rescaled Range (R/S) method, Aggregated Variance (V/S) method, Aggregated Absolute Value (A/S) method and Peng’s (P/S) method. Surrogate data testing was used to verify the existence of long memory. The average estimated Hurst value obtained by using VS and PS method are found to be near to the actual Hurst value compared to RS and AS method. The biased and MSE value also showed that the VS and PS method is the most appropriate method in estimating the H value. Based on the result obtained, it can be concluded that long memory exists in ozone concentration data used in the study. The VS and PS method are the best method in detecting the long memory phenomenon.
Keywords: Hurst Value, long Memory, Ozone Concentration, Surrogate data.
Scope of the Article: Environmental Engineering