Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model
详细信息    查看全文
  • 作者:Nurulkamal Masseran ; Ahmad Mahir Razali…
  • 关键词:Air pollution assessment ; Unhealthy events ; Generalized Pareto model ; Return period
  • 刊名:Environmental Monitoring and Assessment
  • 出版年:2016
  • 出版时间:January 2016
  • 年:2016
  • 卷:188
  • 期:1
  • 全文大小:1,442 KB
  • 参考文献:Afroz, A., Hassan, M. N., & Ibrahim, N. A. (2003). Review of air pollution and health impact in Malaysia. Environmental Research, 92, 71–77.CrossRef
    Ahmat, H., Yahaya, A. S., & Ramli, N. A. (2015). PM10 analysis for three industrial areas using extreme value. Sains Malaysiana, 44, 175–185.CrossRef
    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.CrossRef
    Coles, S. (2001). An introduction to statistical modeling of extreme values. London: Springer.CrossRef
    Davison, A. C. (1984). Modelling extremes over high thresholds with an application. Statistical Extremes and Applications, NATO ASI Series, 131, 461–482.CrossRef
    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.CrossRef
    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.CrossRef
    Ghosh, S., & Resnick, S. A. (2010). A discussion on mean excess plots. Stochastic Processes and their Applications, 120, 1492–1517.CrossRef
    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.CrossRef
    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.CrossRef
    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.CrossRef
    Juneng, L., Latif, M. T., Tangang, F. T., & Mansor, H. (2009). Spatio-temporal characteristics of PM10 concentration across Malaysia. Atmospheric Environment, 23, 4584–4594.CrossRef
    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.CrossRef
    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.CrossRef
    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.CrossRef
    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.CrossRef
    Smith, R. L. (1984). Threshold methods for sample extremes. Statistical Extremes and Applications, NATO ASI Series, 131, 621–638.CrossRef
    Smith, R. L. (1985). Maximum likelihood estimation in a class of nonregular cases. Biometrika, 72, 67–90.CrossRef
    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.CrossRef
    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.CrossRef
    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.CrossRef
    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.CrossRef
  • 作者单位:Nurulkamal Masseran (1) (2)
    Ahmad Mahir Razali (1) (2)
    Kamarulzaman Ibrahim (1) (2)
    Mohd Talib Latif (3) (4)

    1. School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
    2. Centre for Modeling and Data Analysis (DELTA), Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
    3. School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
    4. Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
  • 刊物类别:Earth and Environmental Science
  • 刊物主题:Environment
    Monitoring, Environmental Analysis and Environmental Ecotoxicology
    Ecology
    Atmospheric Protection, Air Quality Control and Air Pollution
    Environmental Management
  • 出版者:Springer Netherlands
  • ISSN:1573-2959
文摘
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.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700