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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2015-12-30 15:10:12
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Restricted Document
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12622
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UniSZA
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1. Moustris, K.P., Ziomas, I.C. and Paliatsos, A.G. (2010). 3-day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2, and O3 using artificial neural networks in Athens, Greece. Water, Air & Soil Pollution 209(1-4): 29-43. 2. Azid, A., Juahir, H., Toriman, M. E., Endut A., Kamarudin, M. K. A., Rahman, M. N. A., Hasnam, C. N. C., Saudi, A. S. M. and Yunus, K. (2015). Source Apportionment of Air Pollution: A Case Study In Malaysia. Jurnal Teknologi 72(1): 83-88. 3. Lu, W. Z., He, H. D. and Dong, L. Y. (2011). Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Building and Environment 46: 577-583. 4. Simeonov, V., Einax, J.W., Stanimirova, I. and Kraft, J. (2002). Envirometric modeling and interpretation of river water monitoring data. Analytical and Bioanalytical Chemistry 374: 898–905. 5. Mutalib, S. N. S. A., Juahir, H., Azid, A., Sharif, S. M., Latif, M. T., Aris, A.Z., Zain, S. M. and Dominick, D. (2013). Spatial and temporal air quality pattern recognition using chemometrics techniques: a case study in Malaysia. Environmental Sciences: Processes & Impact 15(9): 1717-1728. 6. Kannel, P. R., Lee, S., Kanel, S. R. and Khan, S. P. (2007). Chemometrics application in classification and assessment of monitoring locations of an urban river system. Analytical Chimica Acta 582: 390-399. 7. Satheeshkumar, P. and Khan, A.B. (2011). Identification of mangrove water quality by multivariate statistical analysis methods in Pondicherry coast, India. Environment Monitoring Assessment 184(6): 3761-3774. 8. Singh, K.P., Malik, A. and Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti River (India) using multivariate statistical techniques: A case study. Analytica Chimica Acta 35: 3581–3592. 9. Giri, D., Murthy, V.K., Adhikary, P.R. and Khanal, S.N. (2007). Cluster analysis applied to atmospheric PM10 concentration data for determination of sources and spatial patterns in ambient air-quality of Kathmandu Valley. Current Science. 93(5): 684-688. 10. Kaufman, L and Rousseeuw, P.J. (I990). Finding Groups in Data. Wiley Interscience, New York. 11. Ibarra-Berastegi, G., Sáenz, I., Ezcurra, A., Ganzedo, U., Argendoña, J.D., Errasti, I.,Farnandez – Ferrero, A. and Polanco – Martínez, J. (2009). Assessing spatial variability of SO2 field as detected by an air quality network using self-organizing maps, cluster, and principal component analysis. Atmospheric Environment. 43: 3829–3836. 12. Juahir, H., Zain, S.M., Yusoff, M.K., Hanidza, T.I.T., Armi, A.S.M., Toriman, M.E. and Mokhtar, M. (2011). Spatial water quality assessment of Langat River Basin (Malaysia) using chemometrics techniques. Environment Monitoring Assessment 173: 625-641. 13. Saithanu, K. & Mekpatyup, J. (2014). Air quality assessment in the urban areas with multivariate statistical analysis at the east of Thailand. International Journal of Pure and Applied Mathematics. 9(2): 169-177. 14. Shrestha, S. and Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software 22: 464–475. 15. Manjunath, B.G., Frick, M. and Reiss, R.D. (2012). Some Notes on Extremal Discriminant Analysis. Journal of Multivariate Analysis. 103: 107–115. 16. Johnson, R.A. and Wichern, D.W. (1992). Applied multivariate statistical analysis. 3rd ed. Prentice-Hall Int.: New Jersey. 17. Hopke, P.K. (1985). Receptor modelling in environmental chemistry. New York: Wiley. 18. Singh, K.P., Malik, A., Mohan, D. and Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India): A case study. Water Research 38: 3980–3992. 19. Yu, T.Y. and Chang, L.F.W (2000). Selection of the scenarios of ozone pollution at southern Taiwan area utilizing principal component analysis. Atmospheric Environment 34: 4499-4509. 20. Liu, C.W., Lin, K.H. and Kuo,Y.M. (2003). Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in Taiwan. The Science of the Total Environment 313, 77–89. 21. Azid, A., Juahir, H., Toriman, M.E., Kamarudin, M.K.A., Saudi, A.S.M., Hasnam, C.N.C., Aziz, N.A.A., Azaman, F., Latif, M.T., Zainuddin, S.F.M., Osman, M.R. & Yamin, M. (2014). Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia. Water Air Soil Pollution. 225: 2063. 22. Pai, T.Y., Sung, P.J., Lin, C.Y., Leu, H.G., Shieh, Y.R., Chang, S.C., Chen, S.W. and Jou, J.J. (2009). Predicting hourly ozone concentration in Dali area of Taichung Country based on multiple linear regression method. International Journal of Applied Science and Engineering 7(2): 127-132. 23. Ul-Saufie, A.Z., Ahmad Shukri, Y., Nor Azam, R. and Hazrul, A.H. (2011). Comparison between multiple linear regression and feed forward back propagation neural network models for predicting PM10 concentration level based on gaseous and meteorological parameters. International Journal of Applied Science and Technology 1(4): 42-49. 24. Azid, A., Juahir, H., Ezani, E., Toriman, M.E., Endut, A., Rahman, M.N.A., Yunus, K., Kamarudin, M.K.A., Hasnam, C.N.C., Saudi, A.S.M. and Umar, R. (2015). Identification source of variation on regional impact of air quality pattern using chemometrics. Aerosol and Air Quality Research: 25. Aertsen, W., Kinta, V., Orshovena, J., Özkan, K. and Muysa, B. (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling 221: 1119-1130. 26. Dominick, D., Juahir, H., Latif, M.T., Zain, S.M. and Aris, A.Z. (2012). Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmospheric Environment 60: 172-181. 27. Azid, A., Juahir, H., Latif, M.T., Zain, S.M. and Osman, M.R. (2013). Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia. Journal Environmental Protection 4: 1-10. 28. Mukhopadhyay, K. and Forssell, O. (2005). An empirical investigation of air pollution from fossil fuel combustion and its impact on health in India during 1973–1974 to 1996–1997. Ecological Economics 55: 235 – 250. 29. Koppmann, R. (2007). Volatile organic compounds in the atmosphere. Singapore: Blackwell Publishing Ltd. 30. De-Vries, W., Butterbach B.K., Denier V.D.G.H. and Oenema, O. (2006). The impact of atmospheric nitrogen deposition on the exchange of carbon dioxide, nitrous oxide and methane from European forests. Global Change Biology 12: 1151–1173. 31. Simmonds, P.G., Manning, A.J., Derwent, R.G., Ciais, P., Ramonent, M., Kazan, V. and Ryall, D. (2005). A burning question. Can recent growth rate anomalies in the greenhouse gases by attributed to large-scale biomass burning events? Atmospheric Environment 39: 2513–2517. 32. Demuzere, M., Trigo, R.M., Vila-Guerau, D.A.J. and Van L.N.P.M. (2009). The impact of weather and atmospheric circulation on O3 and PM10 levels at a rural mid-latitude site. Atmospheric Chemistry and Physics 9: 2695-2714. 33. Azid, A., Juahir, H., Aris, A.Z., Toriman, M.E., Latif, M.T., Zain, S.M., Yusof, K.M.K.K. and Saudi, A.S.M. (2014). Spatial analysis of the air pollutant index in the Southern Region of Peninsular Malaysia using Environmetric Techniques. In From Sources to Solution, Proceeding of the International Conference on Environmental Forensics 2013, Aris, A.Z., Ismail, T.H.T., Harun, R., Abdullah, A.M. and Ishak, M.Y. (Eds.), Springer, New York, pp 307. 34. Giorgi, F. and Meleux, F. (2007). Modelling the regional effects of climate change on air quality. C. R. Geoscience 339: 721–733. 35. Romieu, I. and Hernandez, M. (1999). Air pollution and health in developing countries: review of epidemiological evidence. In: Mc Granahan, G., Murray, F. (Eds.), Health and Air Pollution in Rapidly Developing Countries. Stockholm Environment Institute, Sweden, pp 43 – 66. 36. Elminir, H. (2005). Dependence of urban air pollutants on meteorology. Science of Total Environment 350: 225–237.
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norman
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12622 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12622 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 13 13 765 2015-12-30 15:10:12 1066x765 1066 6929-01-FH02-ESERI-15-04687.jpg UniSZA Private Access Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia] Malaysian Journal of Analytical Sciences This study shows the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and multiple linear regressions (MLR) for assessment of air quality data and recognition of air pollution sources. 12 months data (January-December 2007) consisting of 14 stations in Peninsular Malaysia with 14 parameters were applied. Three significant clusters - low pollution source (LPS), moderate pollution source (MPS), and slightly high pollution source (SHPS) were generated via HACA. Forward stepwise of DA managed to discriminate eight variables, whereas backward stepwise of DA managed to discriminate nine variables out of fourteen variables. The PCA and FA results show the main contributor of air pollution in Peninsular Malaysia is the combustion of fossil fuel from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 account as the most and the highest pollution contributor to Malaysian air quality. From the study, it can be stipulated that the application of chemometrics techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novelty design of air quality monitoring network for better management of air pollution can be achieved via these methods. 19 6 Malaysian Society of Analytical Sciences Malaysian Society of Analytical Sciences 1415-1430 1. Moustris, K.P., Ziomas, I.C. and Paliatsos, A.G. (2010). 3-day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2, and O3 using artificial neural networks in Athens, Greece. Water, Air & Soil Pollution 209(1-4): 29-43. 2. Azid, A., Juahir, H., Toriman, M. E., Endut A., Kamarudin, M. K. A., Rahman, M. N. A., Hasnam, C. N. C., Saudi, A. S. M. and Yunus, K. (2015). Source Apportionment of Air Pollution: A Case Study In Malaysia. Jurnal Teknologi 72(1): 83-88. 3. Lu, W. Z., He, H. D. and Dong, L. Y. (2011). Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Building and Environment 46: 577-583. 4. Simeonov, V., Einax, J.W., Stanimirova, I. and Kraft, J. (2002). Envirometric modeling and interpretation of river water monitoring data. Analytical and Bioanalytical Chemistry 374: 898–905. 5. Mutalib, S. N. S. A., Juahir, H., Azid, A., Sharif, S. M., Latif, M. T., Aris, A.Z., Zain, S. M. and Dominick, D. (2013). Spatial and temporal air quality pattern recognition using chemometrics techniques: a case study in Malaysia. Environmental Sciences: Processes & Impact 15(9): 1717-1728. 6. Kannel, P. R., Lee, S., Kanel, S. R. and Khan, S. P. (2007). Chemometrics application in classification and assessment of monitoring locations of an urban river system. Analytical Chimica Acta 582: 390-399. 7. Satheeshkumar, P. and Khan, A.B. (2011). Identification of mangrove water quality by multivariate statistical analysis methods in Pondicherry coast, India. Environment Monitoring Assessment 184(6): 3761-3774. 8. Singh, K.P., Malik, A. and Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti River (India) using multivariate statistical techniques: A case study. Analytica Chimica Acta 35: 3581–3592. 9. Giri, D., Murthy, V.K., Adhikary, P.R. and Khanal, S.N. (2007). Cluster analysis applied to atmospheric PM10 concentration data for determination of sources and spatial patterns in ambient air-quality of Kathmandu Valley. Current Science. 93(5): 684-688. 10. Kaufman, L and Rousseeuw, P.J. (I990). Finding Groups in Data. Wiley Interscience, New York. 11. Ibarra-Berastegi, G., Sáenz, I., Ezcurra, A., Ganzedo, U., Argendoña, J.D., Errasti, I.,Farnandez – Ferrero, A. and Polanco – Martínez, J. (2009). Assessing spatial variability of SO2 field as detected by an air quality network using self-organizing maps, cluster, and principal component analysis. Atmospheric Environment. 43: 3829–3836. 12. Juahir, H., Zain, S.M., Yusoff, M.K., Hanidza, T.I.T., Armi, A.S.M., Toriman, M.E. and Mokhtar, M. (2011). Spatial water quality assessment of Langat River Basin (Malaysia) using chemometrics techniques. Environment Monitoring Assessment 173: 625-641. 13. Saithanu, K. & Mekpatyup, J. (2014). Air quality assessment in the urban areas with multivariate statistical analysis at the east of Thailand. International Journal of Pure and Applied Mathematics. 9(2): 169-177. 14. Shrestha, S. and Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software 22: 464–475. 15. Manjunath, B.G., Frick, M. and Reiss, R.D. (2012). Some Notes on Extremal Discriminant Analysis. Journal of Multivariate Analysis. 103: 107–115. 16. Johnson, R.A. and Wichern, D.W. (1992). Applied multivariate statistical analysis. 3rd ed. Prentice-Hall Int.: New Jersey. 17. Hopke, P.K. (1985). Receptor modelling in environmental chemistry. New York: Wiley. 18. Singh, K.P., Malik, A., Mohan, D. and Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India): A case study. Water Research 38: 3980–3992. 19. Yu, T.Y. and Chang, L.F.W (2000). Selection of the scenarios of ozone pollution at southern Taiwan area utilizing principal component analysis. Atmospheric Environment 34: 4499-4509. 20. Liu, C.W., Lin, K.H. and Kuo,Y.M. (2003). Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in Taiwan. The Science of the Total Environment 313, 77–89. 21. Azid, A., Juahir, H., Toriman, M.E., Kamarudin, M.K.A., Saudi, A.S.M., Hasnam, C.N.C., Aziz, N.A.A., Azaman, F., Latif, M.T., Zainuddin, S.F.M., Osman, M.R. & Yamin, M. (2014). Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia. Water Air Soil Pollution. 225: 2063. 22. Pai, T.Y., Sung, P.J., Lin, C.Y., Leu, H.G., Shieh, Y.R., Chang, S.C., Chen, S.W. and Jou, J.J. (2009). Predicting hourly ozone concentration in Dali area of Taichung Country based on multiple linear regression method. International Journal of Applied Science and Engineering 7(2): 127-132. 23. Ul-Saufie, A.Z., Ahmad Shukri, Y., Nor Azam, R. and Hazrul, A.H. (2011). Comparison between multiple linear regression and feed forward back propagation neural network models for predicting PM10 concentration level based on gaseous and meteorological parameters. International Journal of Applied Science and Technology 1(4): 42-49. 24. Azid, A., Juahir, H., Ezani, E., Toriman, M.E., Endut, A., Rahman, M.N.A., Yunus, K., Kamarudin, M.K.A., Hasnam, C.N.C., Saudi, A.S.M. and Umar, R. (2015). Identification source of variation on regional impact of air quality pattern using chemometrics. Aerosol and Air Quality Research: 25. Aertsen, W., Kinta, V., Orshovena, J., Özkan, K. and Muysa, B. (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling 221: 1119-1130. 26. Dominick, D., Juahir, H., Latif, M.T., Zain, S.M. and Aris, A.Z. (2012). Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmospheric Environment 60: 172-181. 27. Azid, A., Juahir, H., Latif, M.T., Zain, S.M. and Osman, M.R. (2013). Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia. Journal Environmental Protection 4: 1-10. 28. Mukhopadhyay, K. and Forssell, O. (2005). An empirical investigation of air pollution from fossil fuel combustion and its impact on health in India during 1973–1974 to 1996–1997. Ecological Economics 55: 235 – 250. 29. Koppmann, R. (2007). Volatile organic compounds in the atmosphere. Singapore: Blackwell Publishing Ltd. 30. De-Vries, W., Butterbach B.K., Denier V.D.G.H. and Oenema, O. (2006). The impact of atmospheric nitrogen deposition on the exchange of carbon dioxide, nitrous oxide and methane from European forests. Global Change Biology 12: 1151–1173. 31. Simmonds, P.G., Manning, A.J., Derwent, R.G., Ciais, P., Ramonent, M., Kazan, V. and Ryall, D. (2005). A burning question. Can recent growth rate anomalies in the greenhouse gases by attributed to large-scale biomass burning events? Atmospheric Environment 39: 2513–2517. 32. Demuzere, M., Trigo, R.M., Vila-Guerau, D.A.J. and Van L.N.P.M. (2009). The impact of weather and atmospheric circulation on O3 and PM10 levels at a rural mid-latitude site. Atmospheric Chemistry and Physics 9: 2695-2714. 33. Azid, A., Juahir, H., Aris, A.Z., Toriman, M.E., Latif, M.T., Zain, S.M., Yusof, K.M.K.K. and Saudi, A.S.M. (2014). Spatial analysis of the air pollutant index in the Southern Region of Peninsular Malaysia using Environmetric Techniques. In From Sources to Solution, Proceeding of the International Conference on Environmental Forensics 2013, Aris, A.Z., Ismail, T.H.T., Harun, R., Abdullah, A.M. and Ishak, M.Y. (Eds.), Springer, New York, pp 307. 34. Giorgi, F. and Meleux, F. (2007). Modelling the regional effects of climate change on air quality. C. R. Geoscience 339: 721–733. 35. Romieu, I. and Hernandez, M. (1999). Air pollution and health in developing countries: review of epidemiological evidence. In: Mc Granahan, G., Murray, F. (Eds.), Health and Air Pollution in Rapidly Developing Countries. Stockholm Environment Institute, Sweden, pp 43 – 66. 36. Elminir, H. (2005). Dependence of urban air pollutants on meteorology. Science of Total Environment 350: 225–237.
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| spellingShingle |
Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia]
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| summary |
This study shows the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and multiple linear regressions (MLR) for assessment of air quality data and recognition of air pollution sources. 12 months data (January-December 2007) consisting of 14 stations in Peninsular Malaysia with 14 parameters were applied. Three significant clusters - low pollution source (LPS), moderate pollution source (MPS), and slightly high pollution source (SHPS) were generated via HACA. Forward stepwise of DA managed to discriminate eight variables, whereas backward stepwise of DA managed to discriminate nine variables out of fourteen variables. The PCA and FA results show the main contributor of air pollution in Peninsular Malaysia is the combustion of fossil fuel from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 account as the most and the highest pollution contributor to Malaysian air quality. From the study, it can be stipulated that the application of chemometrics techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novelty design of air quality monitoring network for better management of air pollution can be achieved via these methods.
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| title |
Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia]
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| title_full |
Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia]
|
| title_fullStr |
Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia]
|
| title_full_unstemmed |
Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia]
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| title_short |
Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia [Pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: Satu kajian kes di semenanjung Malaysia]
|
| title_sort |
spatial air quality modelling using chemometrics techniques: a case study in peninsular malaysia [pemodelan ruang kualiti udara menggunakan teknik-teknik kemometrik: satu kajian kes di semenanjung malaysia]
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