SPATIAL ANALYSIS OF THE CERTAIN AIR POLLUTANTS USING ENVIRONMETRIC TECHNIQUES

Mohammad Azizi Amran, Azman Azid, Hafizan Juahir, Mohd Ekhwan Toriman, Ahmad Dasuki Mustafa, Che Noraini Che Hasnam, Fazureen Azaman, Mohd Khairul Amri Kamarudin, Ahmad Shakir Mohd Saudi, Kamaruzzaman Yunus

Abstract


This study aims to identify the spatial variation of air pollutant and its pattern in the northern part of Peninsular Malaysia for four years monitoring observation (2008-2011) based on the seven air monitoring stations. Air pollutant variables that used in this study were Nitrogen Dioxide (NO2), Ozone (O3), Carbon Monoxide (CO), and Particulate Matter (PM10) data and had been supplied by Department Of Environment Malaysia (DOE). ANOVA, environmetric techniques (HACA and Descriptive Analysis) and Artificial Neural Network (ANN) approach were used in data analysed. According to ANOVA single test, significance p-value of PM10 (p= 2.5E-268) is smaller than significance alpha level (p=0.05) and it suitable parameter for further analysis in construct the prevention actions compared to O3, NO2 and CO. HACA categorized seven air monitoring station into three cluster group of station such as High Concentrated Site (HCS), Moderate Concentrated Site (MCS), and Low Concentrated Site (LCS). Descriptive statistics show the 25th percentile, median, and 75th percentile boxplot and identified the greater (>500 µg/m3) and smaller (<0.05ppm) outliers, and comparing distributions between each air pollutant. The findings from ANN have verified that the R2 and RMSE value (0.7981 and 5.734, respectively) were categorized as a significant value for the future prediction. In contrast, PM10 levels in Air Pollutant Index equal to 43.59 were 67.91 ug/m3, O3 (0.038 ppm), NO2 (0.019 ppm), and then CO (1.27 ppm) concentration values. This proved that the PM10 concentration was categorized as a main contributor to the air pollutant measurement of statistical method compared with other pollutants.


Keywords


ANOVA, environmetric techniques, descriptive analysis, artificial neural network, air pollutant index

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DOI: http://dx.doi.org/10.11113/jt.v75.3977

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