Malaysian Journal of Analytical Sciences Vol 19 No 5 (2015): 966 - 978

 

 

 

AIR QUALITY PATTERN ASSESSMENT IN MALAYSIA USING MULTIVARIATE TECHNIQUES

 

(Penilaian Corak Kualiti Udara di Malaysia Menggunakan Teknik Multivariat)

 

Hamza Ahmad Isiyaka and  Azman Azid*

 

East Coast Environmental Research Institute (ESERI),

Universiti Sultan Zainal Abidin,

Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, Malaysia

 

*Corresponding author: azmanazid@unisza.edu.my

 

 

Received: 14 April 2015; Accepted: 9 July 2015

 

 

Abstract

This study aims to investigate the spatial characteristics in the pattern of air quality monitoring sites, identify the most discriminating parameters contributing to air pollution, and predict the level of air pollution index (API) in Malaysia using multivariate techniques. Five parameters observed for five years (2000-2004) were used. Hierarchical agglomerative cluster analysis classified the five air quality monitoring sites into two independent groups based on the characteristics of activities in the monitoring stations. Discriminate analysis for standard, backward stepwise and forward stepwise mode gave a correct assignation of more than 87% in the confusion matrix. This result indicates that only three parameters (PM₁₀, SO₂ and NO₂) with a p<0.0001 discriminate best in polluting the air. The major possible sources of air pollution were identified using principal component analysis that account for more than 58% and 60% in the total variance. Based on the findings, anthropogenic activities (vehicular emission, industrial activities, construction sites, bush burning) have a strong influence in the source of air pollution. Furthermore, artificial neural network (ANN) was used to predict the level of air pollution index at R² = 0.8493 and RMSE = 5.9184. This indicates that ANN can predict more than 84% of the API.

 

Keywords: multivariate techniques, principal component analysis, artificial neural network, air pollution index

 

Abstrak

Kajian ini adalah bertujuan untuk menyiasat ciri-ciri spatial dalam pemantauan corak kualiti udara, mengenal pasti parameter yang menjadi penyumbang kepada pencemaran udara, dan meramalkan tahap indeks pencemaran udara (IPU) di Malaysia menggunakan teknik multivariat. Lima parameter udara bagi lima tahun (2000-2004) telah digunakan. Hirarki algorithma analisa kelompok telah mengkelaskan lima tapak pemantauan kualiti udara kepada dua kumpulan berdasarkan ciri-ciri aktiviti di stesen pemantauan. Analisis pembezalayan bagi kaedah standard, langkah demi langkah ke belakang dan langkah demi langkah ke hadapan memberikan peratusan yang dibenar lebih daripada 87% dalam matriks kekeliruan. Keputusan ini menunjukkan bahawa hanya tiga parameter (PM₁₀, SO₂ dan NO₂) dengan p<0,0001 memberikan pembezalayan yang baik dalam pencemaran di udara. Sumber utama kemungkinan pencemaran udara telah dikenal pasti menggunakan analisis komponen utama yang menyumbang lebih daripada 58% dan 60% dalam jumlah varians. Berdasarkan hasil kajian, aktiviti antropogenik (pelepasan kenderaan, aktiviti perindustrian, tapak pembinaan, pembakaran belukar) mempunyai pengaruh yang kuat dalam sumber pencemaran udara. Tambahan pula, rangkaian neural buatan (RNB) telah digunakan untuk meramal tahap indeks pencemaran udara dengan nilai R² = 0.8493 dan RMSE = 5.9184. Ini menunjukkan bahawa RNB boleh meramalkan IPU lebih daripada 84%.

 

Kata kunci: teknik multivariate, analisis komponen utama, rangkaian neural buatan, indeks pencemaran udara

 

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