Classification of Extreme Ground-Level Ozone Based on Generalized Extreme Value Model for Air Monitoring Station
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Classification of Extreme Ground-Level Ozone Based on Generalized Extreme Value Model for Air Monitoring Station

Authors: Siti Aisyah Zakaria, Nor Azrita Mohd Amin, Noor Fadhilah Ahmad Radi, Nasrul Hamidin

Abstract:

Higher ground-level ozone (GLO) concentration adversely affects human health, vegetations as well as activities in the ecosystem. In Malaysia, most of the analysis on GLO concentration are carried out using the average value of GLO concentration, which refers to the centre of distribution to make a prediction or estimation. However, analysis which focuses on the higher value or extreme value in GLO concentration is rarely explored. Hence, the objective of this study is to classify the tail behaviour of GLO using generalized extreme value (GEV) distribution estimation the return level using the corresponding modelling (Gumbel, Weibull, and Frechet) of GEV distribution. The results show that Weibull distribution which is also known as short tail distribution and considered as having less extreme behaviour is the best-fitted distribution for four selected air monitoring stations in Peninsular Malaysia, namely Larkin, Pelabuhan Kelang, Shah Alam, and Tanjung Malim; while Gumbel distribution which is considered as a medium tail distribution is the best-fitted distribution for Nilai station. The return level of GLO concentration in Shah Alam station is comparatively higher than other stations. Overall, return levels increase with increasing return periods but the increment depends on the type of the tail of GEV distribution’s tail. We conduct this study by using maximum likelihood estimation (MLE) method to estimate the parameters at four selected stations in Peninsular Malaysia. Next, the validation for the fitted block maxima series to GEV distribution is performed using probability plot, quantile plot and likelihood ratio test. Profile likelihood confidence interval is tested to verify the type of GEV distribution. These results are important as a guide for early notification on future extreme ozone events.

Keywords: Extreme value theory, generalized extreme value distribution, ground-level ozone, return level.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 517

References:


[1] M. A. B. A. Tajudin et al., “Risk of concentrations of major air pollutants on the prevalence of cardiovascular and respiratory diseases in urbanized area of Kuala Lumpur, Malaysia,” Ecotoxicol. Environ. Saf., vol. 171, no. December 2018, pp. 290–300, 2019, doi: 10.1016/j.ecoenv.2018.12.057.
[2] M. Department of Environment (DoE), “National Policy on the Environment.” Ministry of Science, Technology and the Environment, Malaysia, pp. 1–24, 2002.
[3] M. Ismail, “Time-series analysis of ground-level ozone in Muda Irrigation Scheme Area (MADA), Kedah,” J. Sustain. Sci. Manag., vol. 6, no. 1, pp. 79–88, 2011.
[4] N. R. Awang, N. A. Ramli, N. I. Mohammed, and A. S. Yahaya, “Time Series Evaluation of Ozone Concentrations in Malaysia Based on Location of Monitoring Stations Time Series Evaluation of Ozone Concentrations in Malaysia Based on Location of Monitoring Stations,” no. June, 2016.
[5] Mohamed Elnour Yassen, Jamaluddin Md. Jahi, and Shaharuddin Ahmad, “Evaluation of long term trends in oxide of nitrogen concentrations in the Klang Valley region, Malaysia,” Malaysian J. Environ. Manag., vol. 6, pp. 59–72, 2005, (Online). Available: http://www.ems-malaysia.org/mjem/index.html%5Cnhttp://journalarticle.ukm.my/2229/%5Cnhttp://journalarticle.ukm.my/2229/1/2005_4_ElNour.pdf.
[6] N. I. Mohammed, N. Azam Ramli, A. Shukri Yahya, and N. Adyani Ghazali, “Aotx Indices in Response to the Crops Yield Under the Malaysia Climate,” Ijrras, vol. 7, no. 3, pp. 304–309, 2011.
[7] J. M. Rajab, M. Z. MatJafri, and H. S. Lim, “Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia,” Atmos. Environ., vol. 71, pp. 36–43, 2013, doi: 10.1016/j.atmosenv.2013.01.019.
[8] K. C. Tan, H. S. Lim, and M. Z. M. Jafri, “Analysis of total column ozone in Peninsular Malaysia retrieved from SCIAMACHY,” Atmos. Pollut. Res., vol. 5, no. 1, pp. 42–51, 2014, doi: 10.5094/APR.2014.006.
[9] M. T. Latif, E. Z. Abidin, and S. M. Praveena, “The Assessment of Ambient Air Pollution Trend in Klang Valley,” World Environ., vol. 5, no. 1, pp. 1–11, 2015, doi: 10.5923/j.env.20150501.01.
[10] A. Z. Mohd Amin, N. A., Adam, M. B., Aris, “Extreme Value Analysis for Modeling High PM10 Level in Johor Bahru,” J. Teknol., vol. 1, no. 76, pp. 171–179, 2015.
[11] H. Hasan, N. Fadhilah, A. Radi, and S. Kassim, “Modeling the Distribution of Extreme Share Return in Malaysia Using Generalized Extreme Value (GEV) Distribution,” vol. 89, pp. 82–89, 2012, doi: 10.1063/1.4724121.
[12] H. Hassan, N. Salam, and M. D. Adam, “Modelling Extreme Temperature in Malaysia Using Generalized Extreme Value Distribution,” Int. J. Math. Comput. Phys. Electr. Comput. Eng., vol. 7, no. 6, pp. 983–989, 2013.
[13] M. Amin, N. Azrita, and Z. S. Aisyah, “Comparison on the Analysis on PM10 Data based on Average and Extreme Series,” vol. 05025, pp. 1–5, 2018.
[14] N. S. Muhammad, A. I. Akashah, and J. Abdullah, “Analysis of extreme rainfall indices in peninsular Malaysia,” J. Teknol., vol. 78, no. 9–4, pp. 15–20, 2016, doi: 10.11113/jt.v78.9677.
[15] S. El Adlouni, T. B. M. J. Ouarda, X. Zhang, R. Roy, and B. Bobe, “Generalized maximum likelihood estimators for the nonstationary generalized extreme value model,” vol. 43, pp. 1–13, 2007, doi: 10.1029/2005WR004545.
[16] Tor H. Oiamo, “Ground-Level Ozone,” in Encyclopedia of Quality of Life and Well-Being Research, Springer Netherlands, 2014, pp. 2619–2619.
[17] J. M. Rajab, H. S. Lim, and M. Z. Matjafri, “Monthly distribution of diurnal total column ozone based on the 2011 satellite data in Peninsular Malaysia,” Egypt. J. Remote Sens. Sp. Sci., vol. 16, no. 1, pp. 103–109, 2013, doi: 10.1016/j.ejrs.2013.04.003.
[18] S. Coles, An Introduction to Statistical Modeling of Extreme Values. Springer-Verlag, London, 2001.
[19] S. Hazarika, P. Borah, and A. Prakash, “The assessment of return probability of maximum ozone concentrations in an urban environment of Delhi: A Generalized Extreme Value analysis approach,” Atmos. Environ., vol. 202, no. August 2018, pp. 53–63, 2019, doi: 10.1016/j.atmosenv.2019.01.021.
[20] F. Calderón-Vega, C. Mösso, A. D. García-Soto, and E. Delgadillo-Ruiz, “Single Site Extreme Wave Analysis in the Pacific Ocean Comparing Stationary and Non-stationary GEV Models,” Curr. J. Appl. Sci. Technol., vol. 32, no. 6, pp. 1–12, 2019, doi: 10.9734/cjast/2019/v32i630038.
[21] H. Hasan, N. F. A. Radi, and S. Kassim, “Modeling the distribution of extreme share return in Malaysia using Generalized Extreme Value (GEV) distribution,” in The 5th International Conference on Research and Education in Mathematics, 2012, vol. 1450, no. June, pp. 82–89, doi: 10.1063/1.4724121.
[22] A. J. Cannon, “Computers & Geosciences GEVcdn: An R package for nonstationary extreme value analysis by generalized extreme value conditional density estimation network $,” Comput. Geosci., vol. 37, no. 9, pp. 1532–1533, 2011, doi: 10.1016/j.cageo.2011.03.005.
[23] F. De Paola, M. Giugni, F. Pugliese, A. Annis, and F. Nardi, “GEV parameter estimation and stationary vs. non-stationary analysis of extreme rainfall in African test cities,” Hydrology, vol. 5, no. 2, pp. 1–22, 2018, doi: 10.3390/hydrology5020028.
[24] S. Jiang and L. Kang, “Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model,” E3S Web Conf., vol. 79, 2019, doi: 10.1051/e3sconf/20197903022.
[25] Anita R., “Identification of Climate Change with Generalized Extreme Value (GEV) Distribution Approach,” J. Phys. Conf. Ser., vol. 423, 2013, doi: 10.1088/1742-6596/423/1/012026.
[26] H. Hasan, N. Fadhilah, B. A. Radi, and S. B. Kassim, “Modeling of Extreme Temperature Using Generalized Extreme Value (GEV) Distribution: A Case Study of Penang,” in Proceedings of the world Congress on Engineering, 2012, vol. I, pp. 4–9.
[27] H. Hasan, N. Salam, and S. Kassim, “Modeling annual extreme temperature using generalized extreme value distribution: A case study in Malaysia,” AIP Conf. Proc., vol. 1522, pp. 1195–1203, 2013, doi: 10.1063/1.4801267.
[28] K. Pearson, “Skew Variation in Homogeneous Material,” Philos. Trans. R. Soc. London. A, vol. 186, no. 1895, pp. 343–414, 1895, (Online). Available: http://www.jstor.org/stable/90649%0Ahttp://www.jstor.org/page/info/about/policies/terms.jsp%0Ahttp://www.jstor.org.