Spatial Assessment of Air Pollution Index Using Environ Metric Modeling Techniques

Mohd Khairul Amri, Kamarudin and Hafizan, Juahir and Azman, Azid (2014) Spatial Assessment of Air Pollution Index Using Environ Metric Modeling Techniques. Advances in Environmental Biology, 8 (24). pp. 244-256. ISSN 1995-0756

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Abstract

The quest for industrial and urban development over the years has changed the pattern and source of atmospheric air pollution in Malaysia. This study aims to investigate the spatial variation in the source of air pollution; identify the most significant parameterscontributing to the air pollution and to develop the best receptor model for predicting air pollution index (API). Data from five monitoring stations base on five year's observation (2007-2011) were used. Multivariate techniques such as cluster analysis (HACA), discriminate analysis (DA), principal component analysis (PCA), factor analysis (FA) and modeling techniques comprising of artificial neural network (ANN) and multiple linear regression (MLR) were used in this study. HACA was able to group the five monitoring stations into three clusters, indicating that one station in each cluster can provide a reasonable accurate spatial assessment of air quality within the study area. The result for standard mode, forward stepwise and backward stepwise DA gave a correct assignation of 82.37% (p˂ 0.05) which indicate that all the parameters significantly discriminate spatially. PCA and FA for the three clusters account for more than 62%, 56% and 58% of the total variance respectively indicating that the source of air pollution are from anthropogenic induced point source and nonpoint source. ANN gave a better prediction at R2 = 0.93 and RMSE = 4.87 compared with MLR AR2 = 0.70 and RMSE = 9.57. This indicates that ANN can be applied with more precision in modeling and understanding the dynamic nature of API compared with MLR. However, it can be concluded that the atmosphere exhibit a complex reaction which is made more complicated by man through his daily activities and multivariate analysis, modeling techniques will help to understand the dynamic structure of the source of pollution, reduce the cost of monitoring unnecessary sampling site, save time and accurately predict air pollution index. These findings can provide stakeholders a basic for understanding and developing air pollution control strategies in order to reduce their possible health and environmental effect.

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