Abstract
The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events.
Similar content being viewed by others
References
Al-Dhurafi NA, Masseran N, Zamzuri ZH (2018a) Compositional time series analysis for air pollution index data. Stoch Environ Res Risk Assess 32:2903–2911
Al-Dhurafi NA, Masseran N, Zamzuri ZH, Safari MAM (2018b) Modeling the air pollution index based on its structure and descriptive status. Air Qual Atmos Health 11(2):171–179
Al-Dhurafi NA, Masseran N, Zamzuri ZH, Razali AM (2018c) Modeling unhealthy air pollution index using a peaks-over-threshold method. Environ Eng Sci 35(2):101–110
Alyousifi Y, Masseran N, Ibrahim K (2018) Modeling the stochastic dependence of air pollution index data. Stoch Environ Res Risk Assess 32(6):1603–1611
Azmi SZ, Latif MT, Ismail AS, Juneng L, Jemain AA (2010) Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia. Air Qual Atmos Health 3:53–64
Begueria S (2005) Uncertainties in partial duration series modeling of extremes related to the choice of threshold value. J Hydrol 303:215–230
Ben-Zvi A (2009) Rainfall intensity–duration–frequency relationships derived from large partial duration series. J Hydrol 367:104–114
Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London
Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Hanson PJ, Irland LC, Lugo AE, Peterson CJ, Simberloff D, Swanson FJ, Stocks BJ, Wotton BM (2001) Climate change and forest disturbances: climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. Bioscience 51(9):723–734
Davison A, Smith R (1990) Models for exceedances over high thresholds. J R Stat Soc Ser B 52:393–442
Department of Environment (1997) A guide to air pollutant index in Malaysia (API). Ministry of Science, Technology and the Environment, Kuala Lumpur, Malaysia. https://aqicn.org/images/aqi-scales/malaysia-api-guide.pdf
Douglas EM, Vogel RM, Kroll CN (2000) Trends in floods and low flows in the United States: impact of spatial correlation. J Hydrol 240:90–105
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall/CRC, Boca Raton
Ghosh S, Resnick SA (2010) A discussion on mean excess plots. Stoch Process Appl 120:1492–1517
Google (2019) Source: https://maps.googleapis.com/maps/api/geocode/json?address=Klang%2CSelangor&key=xxx
Gulia S, Nagendra SMS, Khare M, Khanna I (2015) Urban air quality management—a review. Atmos Pollut Res 6:286–304
Gyarmati-Szabo J, Bogachev LV, Chen H (2017) Nonstationary POT modelling of air pollution concentrations: statistical analysis of the traffic and meteorological impact. Environmetrics 28(5):e2449-1–e2449-15
Husler J, Li D, Raschke M (2011) Estimation for the generalized Pareto distribution using maximum likelihood and goodness of fit. Commun Stat Theory Methods 40:2500–2510
Jayasooriya VM, Ng AWM, Muthukumaran S, Perera BJC (2017) Green infrastructure practices for improvement of urban air quality. Urban For Urban Green 21:34–47
Karim F, Hasan M, Marvanek S (2017) Evaluating annual maximum and partial duration series for estimating frequency of small magnitude floods. Water 9:481
Khaliq MN, Ouarda TBMJ, Ondo J-C, Gachon P, Bobee B (2006) Frequency analysis of sequence of dependent and/or non-stationary hydro-meteorological observations: a review. J Hydrol 329:534–552
Koutsoyiannis D, Kozonis D, Manetas A (1998) A mathematical framework for studying rainfall intensity–duration–frequency relationships. J Hydrol 206:118–135
Kumar P, Jain S, Gurjar BR, Sharma P, Khare M, Morawska L, Britter R (2013) New directions: can a “blue sky” return to Indian megacities? Atmos Environ 71:198–201
Kumar P, Morawska L, Martani C, Biskos G, Neophytou M, Di Sabatino S, Bell M, Norford N, Britter R (2015) The rise of low-cost sensing for managing air pollution in cities. Environ Int 75:199–205
Lang M, Ouarda TBMJ, Bobee B (1999) Towards operational guidelines for over-threshold modeling. J Hydrol 225:103–117
Li Z, Li C, Xu Z, Zhou X (2014) Frequency analysis of precipitation extremes in Heihe River basin based on generalized Pareto distribution. Stoch Environ Res Risk Assess 28(7):1709–1721
Lui JC, Mickley LJ, Sulprizio MP, Dominici F, Yue X, Ebisu K, Anderson GB, Khan RFA, Bravo MA, Bell ML (2016) Particulate air pollution from wildfires in the Western US under climate change. Clim Change 138:655–666
Masseran N (2017) Modeling fluctuation of PM10 Data with existence of volatility effect. Environ Eng Sci 34(11):816–827
Masseran N, Razali AM, Ibrahim K, Zaharim A, Sopian K (2013) Application of the single imputation method to estimate missing wind speed data in Malaysia. Res J Appl Sci Eng Technol 6(10):1780–1784
Masseran N, Razali AM, Ibrahim K, Latif MT (2016) Modeling air quality in main cities of Peninsular Malaysia by using a generalized Pareto model. Environ Monit Assess 188(1):65-1–65-12
Mohymont B, Demarée GR, Faka DN (2004) Establishment of IDF-curves for precipitation in the tropical area of central Africa—comparison of techniques and results. Nat Hazards Earth Syst Sci 4:375–387
Pickands J (1975) Statistical inference using extreme order statistics. Ann Stat 3:119–131
Reiss R-D, Thomas M (2007) Statistical analysis of extreme values: with application to insurance, finance, hydrology and other fields. Die Deutsche Bibliothek, Berlin
Ribatet M (2007) POT: modelling peak over a threshold. R News 7:33–36
Rizzo ML (2008) Statistical computing with R. Chapman and Hall/CRC, Boca Raton
Sahani M, Zainon NA, Wan Mahiyuddin WR, Latif MT, Hod R, Khan MF, Tahir NM, Chan C-C (2014) A case-crossover analysis of forest fire haze events and mortality in Malaysia. Atmos Environ 96:257–265
Scarrott C, MacDonald A (2012) A review of extreme value threshold estimation and uncertainty quantification. REVSTAT Stat J 10:33–60
Smith RL (1984) Threshold methods for sample extremes. Stat Extrem Appl NATO ASI Ser 131:621–638
Southworth H, Heffernan JE (2014) texmex: statistical modelling of extreme values. R package version 2.1
Van de Vyver V (2015) Bayesian estimation of rainfall intensity–duration–frequency relationships. J Hydrol 529:1451–1463
Vrban S, Wang Y, McBean EA, Binns A, Gharabaghi B (2018) Evaluation of stormwater infrastructure design storms developed using partial duration and annual maximum series models. J Hydrol Eng 23(12):04018051
Willems P (2000) Compound intensity/duration/frequency-relationships of extreme precipitation for two seasons and two storm types. J Hydrol 233:189–205
Xia J, Du H, Zeng S, She D, Zhang Y, Yan Z, Ye Y (2012) Temporal and spatial variations and statistical models of extreme runoff in Huaihe River Basin during 1956–2010. J Geogr Sci 22(6):1045–1060
Xu Q, Li X, Wang S, Wang C, Huang F, Gao Q, Wu L, Tao L, Guo J, Wang W, Guo X (2016) Fine particulate air pollution and hospital emergency room visits for respiratory disease in urban areas in Beijing, China, in 2013. PLoS ONE 11(4):e0153099
Yang T, Shao QX, Hao Z-C, Chen X, Zhang Z, Xu C-Y, Sun L (2010) Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River Basin, China. J Hydrol 380:386–405
Yoo J-M, Lee YR, Kim D, Jeong MJ, Stockwell WR, Kundu PK, Oh SM, Shin DB, Lee SJ (2014) New indices for wet scavenging of air pollutants (O3, CO, NO2, SO2, and PM10) by summertime rain. Atmos Environ 82:226–237
Zhang H, Wang S, Hao J, Wang X, Wang S, Chai F, Li M (2016) Air pollution and control action in Beijing. J Clean Prod 112(2):1519–1527
Zhou S-M, Deng Q-H, Lui W-W (2012) Extreme air pollution events: modeling and prediction. J Cent South Univ Technol 19:1668–1672
Zidek JV, Shaddick G, White R, Meloche J, Chatfield C (2005) Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution. Environmetrics 16:481–493
Acknowledgements
The author is indebted Malaysian Department of Environment for providing air pollution data. This research would not be possible without the sponsorship from the Universiti Kebangsaan Malaysia (Grant Number DIP-2018-038).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Masseran, N., Safari, M.A.M. Risk assessment of extreme air pollution based on partial duration series: IDF approach. Stoch Environ Res Risk Assess 34, 545–559 (2020). https://doi.org/10.1007/s00477-020-01784-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-020-01784-2