SPATIAL ANALYSIS OF THE CERTAIN AIR POLLUTANTS USING ENVIRONMETRIC TECHNIQUES

Amran, Mohammad Azizi and Azid, Azman and Juahir, Hafizan and Toriman, Mohd Ekhwan and Mustafa, Ahmad Dasuki and Che Hasnam, Che Noraini and Azaman, Fazureen and Kamarudin, Mohd Khairul Amri and Mohd Saudi, Ahmad Shakir and Yunus, Kamaruzzaman (2015) SPATIAL ANALYSIS OF THE CERTAIN AIR POLLUTANTS USING ENVIRONMETRIC TECHNIQUES. Jurnal Teknologi, 75 (1). pp. 241-249. ISSN 0127–9696

[img] Text
SPATIAL ANALYSIS OF THE CERTAIN AIR POLLUTANTS USING ENVIRONMETRIC TECHNIQUES.pdf
Restricted to Registered users only

Download (881Kb) | Request a copy

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.

Item Type: Article
Keywords: ANOVA, environmetric techniques, descriptive analysis, artificial neural network, air pollutant index
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HA Statistics
Q Science > Q Science (General)
Q Science > QA Mathematics
Faculty / Institute: East Coast Environmental Research Institute (ESERI)
Depositing User: Dr Azman Azid
Date Deposited: 26 Jul 2015 07:44
Last Modified: 26 Jul 2015 07:44
URI: http://erep.unisza.edu.my/id/eprint/3514

Actions (login required)

View Item View Item