Prediction PM10 Concentration Using VAR Time Series
-
https://doi.org/10.14419/ijet.v7i3.25.17723 -
Concentration, PM10, Prediction, Time Series, VAR -
Abstract
This paper presents a case study from Kangar’s monitoring station using a monthly average data (1999-2015). The objective of this study is to predict the PM10 concentration by using the VAR time series model. This model was adapted to quantify and understand the interaction of PM10 concentration and meteorological parameters for air quality control using (temperature, wind speed, and relative humidity) as independent parameters and particulate matter (PM10) as a dependent parameter. The performance indicator results were (R2 = 0.887), (IA = 0.954), (PA=0.966), and (NAE=0.087) respectively. This study indicates that the VAR time series model is a good model to predict PM10 concentration since the results obtained are close to the performance criteria.
Â
-
References
[1] Shaari MS, Hussain NE, Ismail MS. Relationship between Energy Consumption and Economic Growth : Empirical Evidence for Malaysia. Bus Syst Rev. 2012;2(1).
[2] Shaari MS, Rahim HA, Rashid IMA. Relationship Among Population , Energy Consumption and Economic Growth in Malaysia. Int J Sci. 2013;13(1):39–45.
[3] Sims CA. Macroeconomics and Reality’. Sims Source Econom [Internet]. 1980;48(1):1–48. Available from: http://www.jstor.org/stable/1912017%0Ahttp://about.jstor.org/terms
[4] Zhang C, Zhou K, Yang S, Shao Z. Exploring the transformation and upgrading of China’s economy using electricity consumption data: A VAR–VEC based model. Phys A Stat Mech its Appl [Internet]. Elsevier B.V.; 2017;473:144–55. Available from: http://dx.doi.org/10.1016/j.physa.2017.01.004
[5] Brooks C. Introductory Econometrics for Finance. Second. Cambridge University Press; 2008. 674 p.
[6] Awang NR, Elbayoumi M, Ramli NA, Yahaya AS. Diurnal variations of ground-level ozone in three port cities in Malaysia. Air Qual Atmos Heal. 2016;9(1).
[7] Dominick D, Talib M, Zain SM, Zaharin A. Spatial assessment of air quality patterns in Malaysia using multivariate analysis. 2012;60:172–81.
[8] Latif MT, Dominick D, Ahamad F, Khan MF, Juneng L, Hamzah FM, et al. Long term assessment of air quality from a background station on the Malaysian Peninsula. Sci Total Environ. Elsevier B.V.; 2014;482–483(2):336–48.
[9] Ul-Saufie AZ, Yahaya AS, Ramli NA, Rosaida N, Hamid HA. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmos Environ. 2013;77:621–30.
[10] Rajab JM, MatJafri MZ, Lim HS. Air Surface Temperature Correlation with Greenhouse Gases by Using Airs Data Over Peninsular Malaysia. Pure Appl Geophys [Internet]. 2013;171(8):1993–2011. Available from: http://link.springer.com/10.1007/s00024-013-0762-y
[11] Mohamed Noor N, Yahaya AS, Abdullah M, Bakri. Variation of air pollutant ( particulate matter - PM10 ) in peninsular Malaysia : Study in the southwest coast of peninsular Malaysia Variation of Air Pollutant ( Particulate Matter - PM 10 ) in Peninsular Malaysia Study in the southwest coast of peninsul. 2015;(August 2016).
[12] Nations U, Programme E, Bank W, Indicators WD, Prices IF, Islands M, et al. Air Pollution and Air Climate Change. In: Statistical Yearbook for Asia and the Pasific. 2011. p. 79–84.
[13] Talbi B. CO2 emissions reduction in road transport sector in Tunisia. Renew Sustain Energy Rev [Internet]. Elsevier; 2017;69(June 2015):232–8. Available from: http://dx.doi.org/10.1016/j.rser.2016.11.208
[14] Ul-Saufie AZ, Yahaya AS, Ramli N, Hamid HA. Performance of Multiple Linear Regression Model for Long-term PM10 Concentration Prediction Based on Gaseous and Meteorological Parameters. J Appl Sci [Internet]. 2012 Dec 1;12(14):1488–94. Available from: http://www.scialert.net/abstract/?doi=jas.2012.1488.1494
-
Downloads
-
How to Cite
R, N., Shukri Yahaya, A., & Abdul Hamid, H. (2018). Prediction PM10 Concentration Using VAR Time Series. International Journal of Engineering & Technology, 7(3.25), 420-422. https://doi.org/10.14419/ijet.v7i3.25.17723Received date: 2018-08-17
Accepted date: 2018-08-17