The air pollution index (API) is an important figure used for measuring the quality of air in the environment. The API is determined based on the highest average value of individual indices for all the variables which include sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and suspended particulate matter (PM10) at a particular hour. API values that exceed the limit of 100 units indicate an unhealthy status for the exposed environment. This study investigates the risk of occurrences of API values greater than 100 units for eight urban areas in Peninsular Malaysia for the period of January 2004 to December 2014. An extreme value model, known as the generalized Pareto distribution (GPD), has been fitted to the API values found. Based on the fitted model, return period for describing the occurrences of API exceeding 100 in the different cities has been computed as the indicator of risk. The results obtained indicated that most of the urban areas considered have a very small risk of occurrence of the unhealthy events, except for Kuala Lumpur, Malacca, and Klang. However, among these three cities, it is found that Klang has the highest risk. Based on all the results obtained, the air quality standard in urban areas of Peninsular Malaysia falls within healthy limits to human beings.
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Afroz, A., Hassan, M. N., & Ibrahim, N. A. (2003). Review of air pollution and health impact in Malaysia. Environmental Research, 92, 71–77.
Ahmat, H., Yahaya, A. S., & Ramli, N. A. (2015). PM10 analysis for three industrial areas using extreme value. Sains Malaysiana, 44, 175–185.
Benktander, G., & Segerdahl, C. (1960). On the analytical representation of claim distributions with special reference to excess of loss reinsurance. Brussels: XVIth International Congress of Actuaries.
Chen, B., & Kan, H. (2004). Particulate air pollution in urban areas of Shanghai, China: healt-based economic assessment. Science of the Total Environment, 322, 71–79.
Coles, S. (2001). An introduction to statistical modeling of extreme values. London: Springer.
Davison, A. C. (1984). Modelling extremes over high thresholds with an application. Statistical Extremes and Applications, NATO ASI Series, 131, 461–482.
Davison, A., & Smith, R. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society: Series B, 52, 393–442.
Department of Environment. (1997). A guide to air pollutant index in Malaysia (API). Kuala Lumpur, Malaysia: Ministry of Science, Technology and the Environment.
Ee-Ling, O., Mustaffa, N. I. H., Amil, N., Khan, M. F., & Latif, M. T. (2015). Source contribution of PM2.5 at different locations on the Malaysian peninsula. Bulletin of Environmental Contamination and Toxicology, 94, 537–542.
Ercelebi, S. G., & Toros, H. (2009). Extreme value analysis of Istanbul air pollution data. Clean, 37, 122–131.
Fujii, Y., Tohno, S., Amil, N., Latif, M. T., Oda, M., Matsumoto, J., & Mizohata, A. (2015). Annual variations of carbonaceous PM2.5 in Malaysia: influence by Indonesian peatland fires. Atmospheric Chemistry and Physics Discussions, 15, 22419–22449.
Ghosh, S., & Resnick, S. A. (2010). A discussion on mean excess plots. Stochastic Processes and their Applications, 120, 1492–1517.
Gin, O. K. (2009). Historical dictionary of Malaysia (pp. 157–158). Malaysia: Scarecrow Press.
Grimshaw, S. D. (1993). Computing maximum likelihood estimates for the generalized Pareto distribution. Technometrics, 35, 185–191.
Hall, W., & Wellner, J. (1981). Mean residual life. In Statistics and Related Topics (pp. 169–184).
Hosking, J. R. M., & Wallis, J. R. (1987). Parameter and quantile estimation for the generalized Pareto distribution. Technometrics, 29, 339–349.
Husler, J., Li, D., & Raschke, M. (2011). Estimation for the generalized Pareto distribution using Maximum likelihood and goodness of fit. Communications in Statistics-Theory and Methods, 40, 2500–2510.
Juneng, L., Latif, M. T., Tangang, F. T., & Mansor, H. (2009). Spatio-temporal characteristics of PM10 concentration across Malaysia. Atmospheric Environment, 23, 4584–4594.
Khan, M., Latif, M., Saw, W., Amil, N., Nadzir, M., Sahani, M., Tahir, N., & Chung, J. (2015). Fine particulate matter associated with monsoonal effect and the responses of biomass fire hotspots in the tropical environment. Atmospheric Chemistry and Physics Discussions, 15, 22215–22261.
Masseran, N., Razali, A. M., Ibrahim, K., Zaharim, A., & Sopian, K. (2013). Application of the single imputation method to estimate missing wind speed data in Malaysia. Research Journal of Applied Sciences, Engineering and Technology, 6, 1780–1784.
Md Hashim, N., & Ahmad, S. (2006). Kebakaran hutan dan isu pencemaran udara di Malaysia: Kes jerebu pada Ogos 2005. Jurnal e-Bangi, 1, 19.
Melaka and George Town, Historic Cities of the Straits of Malacca (2008). Available from: http://whc.unesco.org/en/list/1223/.
Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3, 119–131.
Reiss, R.-D., & Thomas, M. (2007). Statistical analysis of extreme values: with application to insurance, finance, hydrology and other fields. Berlin: Die Deutsche Bibliothek.
Ribatet, M. (2007). POT: Modelling Peak Over a Threshold. R News, 7, 33–36.
Sahani, M., Zainon, N. A., Wan Mahiyuddin, W. R., Latif, M. T., Hod, R., Khan, M. F., Tahir, N. M., & Chan, C.-C. (2014). A case-crossover analysis of forest fire haze events and mortality in Malaysia. Atmospheric Environment, 96, 257–265.
Scarrott, C., & MacDonald, A. (2012). A review of extreme value threshold estimation and uncertainty quantification. REVSTAT- Statistical Journal, 10, 33–60.
Singh, V. P., & Guo, H. (1995). Parameter estimation for 3-parameter generalized Pareto distribution by the principle of maximum entropy (POME). Hydrological Sciences-Journal-des Sciences Hydrologiques, 40, 165–181.
Smith, R. L. (1984). Threshold methods for sample extremes. Statistical Extremes and Applications, NATO ASI Series, 131, 621–638.
Smith, R. L. (1985). Maximum likelihood estimation in a class of nonregular cases. Biometrika, 72, 67–90.
Smith, R. L. (1989). Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. Statistical Science, 4, 367–377.
Southworth, H., & Heffernan, J. E. (2014). texmex: Statistical modelling of extreme values. R package version 2.1.
The World According to GaWC 2008. (2009). Globalization and World Cities Study Group and Network (GaWC). Loughborough: Loughborough University.
Varkkey, H. (2013). Patronage politics, plantation fires and transboundary haze. Environmental Hazards, 12, 200–217.
Wan Mahiyuddin, W. R., Sahani, M., Aripin, R., Latif, M. T., Thach, T.-Q., & Wong, C.-M. (2013). Short-term effects of daily air pollution on mortality. Atmospheric Environment, 65, 69–79.
Yong, D. L., & Peh, K. S. H. (2014). South-east Asia’s forest fires: blazing the policy trail. ORYX.
Zhou, S.-M., Deng, Q.-H., & Lui, W.-W. (2012). Extreme air pollution events: Modeling and prediction. Journal of Central South University of Technology, 19, 1668–1672.
The authors are indebted to the staff of the Department of Environment Malaysia for providing the wind speed data that made this paper possible. This research would not be possible without the sponsorship from Universiti Kebangsaan Malaysia and Ministry of Higher Education in Malaysia (grant number FRGS/1/2014/SG04/UKM/03/1 and DPP-2015-FST).
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Masseran, N., Razali, A.M., Ibrahim, K. et al. Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model. Environ Monit Assess 188, 65 (2016). https://doi.org/10.1007/s10661-015-5070-9
- Air pollution assessment
- Unhealthy events
- Generalized Pareto model
- Return period