Sains Malaysiana 44(2)(2015): 175–186

PM10 Analysis for Three Industrialized Areas using Extreme Value

(Analisis PM10 bagi Tiga Kawasan Industri menggunakan Nilai Melampau)

HASFAZILAH AHMAT^1,2*, AHMAD SHUKRI YAHAYA¹ & NOR AZAM RAMLI¹

¹Clean Air Research Group, School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Pulau Pinang, Malaysia

²Hasfazilah Ahmat*

Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA

13500 Permatang Pauh, Pulau Pinang, Malaysia

Received: 26 March 2014/Accepted: 3 August 2014

ABSTRACT

One of the concerns of the air pollution studies is to compute the concentrations of one or more pollutants' species in space and time in relation to the independent variables, for instance emissions into the atmosphere, meteorological factors and parameters. One of the most significant statistical disciplines developed for the applied sciences and many other disciplines for the last few decades is the extreme value theory (EVT). This study assesses the use of extreme value distributions of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor; Bukit Rambai, Melaka; and Nilai, Negeri Sembilan. Parameters for all distributions are estimated using the Method of Moments (MOM) and Maximum Likelihood Estimator (MLE). Six performance indicators namely; the accuracy measures which include predictive accuracy (PA), Coefficient of Determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE) are used to find the goodness-of-fit of the distribution. The best distribution is selected based on the highest accuracy measures and the smallest error measures. The results showed that the GEV is the best fit for daily maximum concentration for PM10 for all monitoring stations. The analysis also demonstrates that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 mg/m³ are between ½ and 1½ days.

Keywords: Air pollution; extreme value theory (EVT); PM10; prediction

ABSTRAK

Salah satu kebimbangan di dalam kajian pencemaran udara adalah untuk menyukat kepekatan satu atau lebih zarah pencemar di dalam ruang dan masa berhubung dengan pemboleh ubah bebas, sebagai contoh untuk pelepasan ke atmosfera, faktor dan parameter cuaca. Salah satu disiplin statistik yang paling penting untuk sains gunaan dan pelbagai bidang lain untuk beberapa dekad yang lalu adalah teori nilai melampau (EVT). Kajian ini menilai penggunaan taburan nilai melampau dua parameter Gumbel, dua dan tiga parameter Weibull, Nilai Ekstrim Teritlak (GEV) dan dua dan tiga parameter Taburan Pareto Teritlak (GPD) pada kepekatan maksimum data harian PM10 yang dicatatkan dalam tahun 2010 - 2012 di Pasir Gudang, Johor; Bukit Rambai, Melaka dan Nilai, Negeri Sembilan. Parameter untuk semua taburan dianggarkan menggunakan kaedah momen (MOM) dan Penganggar Kebolehjadian Maksimum (MLE). Enam petunjuk prestasi iaitu; pengukuran kejituan termasuk Ketepatan Peramalan (PA), Pekali Penentuan (R₂), Indeks Persetujuan (IA) dan pengukuran ralat yang terdiri daripada Ralat Min Punca Kuasa Dua (RMSE), Min Ralat Mutlak (MAE) dan Ralat Mutlak Ternormal (NAE) digunakan untuk mencari kebaikan penyesuaian taburan. Taburan terbaik dipilih berdasarkan pengukuran kejituan tertinggi dan pengukuran ralat yang terkecil. Hasil kajian menunjukkan bahawa GEV adalah taburan terbaik untuk kepekatan maksimum harian bagi PM10 di kesemua stesen pemantauan. Analisis juga menunjukkan bahawa anggaran bilangan hari kepekatan PM10 melebihi Garis Panduan Kualiti Udara Ambien Malaysia (MAAQG) bagi kepekatan harian PM10 iaitu 150 μg/m³ adalah antara ½ dan 1½ hari.

Kata kunci: Nilai melampau (EVT); pencemaran udara; peramalan teori; PM10

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*Corresponding author; email: hasfazilah.ahmat@gmail.com