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Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia

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Abstract

This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH4, NmHC, THC, O3, and PM10 are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R 2 and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.

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Acknowledgments

The authors acknowledge the Air Quality Division of the Department of Environment (DOE) under the Ministry of Natural Resource and Environment, Malaysia, for giving us permission to utilize air quality data, advice, guidance, and support for this study.

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Authors and Affiliations

  1. East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300, Kuala Terengganu, Terengganu, Malaysia

    Azman Azid, Hafizan Juahir, Mohd Ekhwan Toriman, Mohd Khairul Amri Kamarudin, Ahmad Shakir Mohd Saudi, Che Noraini Che Hasnam, Nor Azlina Abdul Aziz, Fazureen Azaman & Mohammad Yamin

  2. School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Mohd Talib Latif

  3. Kulliyyah of Science, International Islamic University Malaysia, 25200, Kuantan, Pahang, Malaysia

    Syahrir Farihan Mohamed Zainuddin & Mohamad Romizan Osman

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Azid, A., Juahir, H., Toriman, M.E. et al. Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia. Water Air Soil Pollut 225, 2063 (2014). https://doi.org/10.1007/s11270-014-2063-1

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