Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric
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| internalnotes | Abdullah, A.M., Samah, M.A.A. and Tham, Y.J. (2012). An Overview of the Air Pollution Trend in Klang Valley, Malaysia. Open Environ. Sci. 6: 13–19. 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. Ecol. Modell. 221: 1119– 1130. Afroz, R., Hassan, M.N. and Ibrahim, N.A. (2003). Review of Air Pollution and health impacts in Malaysia. Environ. Res. 92: 71–77, doi: 10.1016/S0013-9351(02)00059-2. Almeida, J.A.S., Barbosa, L.M.S., Pais, A.A.C.C. and Formosinbo, S.J. (2007). Improving Hierarchical Cluster Analysis: A New Method with Outlier Detection and Automatic Clustering. Chemom. Intell. Lab. Syst. 87: 208–217. 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. J. Environ. Prot. 4: 1– 10, doi: 10.4236/jep.2013.412A001. 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. and Yamin, M. (2014a). 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, doi: 10.1007/s11270-014-2063-1. 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. (2014b). 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, p. 307, doi: 10.1007/978-981-4560-70-2_56. 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: 83–88. doi: 10.11113/jt.v72.2934 Azmi, S.Z., Latif, M.T., Ismail, A.S., Juneng, L. and Jemain, A.A. (2010). Trend and Status of Air quality at Three Different Monitoring Stations in the Klang Valley, Malaysia. Air Qual. Atmos. Health 3: 53–64, doi: 10.1007/s11869-009-0051-1. Bock, H.H. (1996). Probabilistic Models in Cluster Analysis. Comput. Stat. Data Anal. 23: 5–28, doi: 10.1016/0167- 9473(96)88919-5. 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 Biol. 12: 1151–1173. 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 Midlatitude Site. Atmos. Chem. Phys. 9: 2695–2714. Department of Environment (DOE) (2013). Air Pollutant Index (API). Available from: http://www.doe.gov.my/w ebportal/en/info-umum/english-air-pollutant-index-api/. 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. Atmos. Environ. 60: 172–181. Elminir, H. (2005). Dependence of Urban Air Pollutants on Meteorology. Sci. Total Environ. 350: 225–237. Giorgi, F. and Meleux, F. (2007). Modelling the Regional Effects of Climate Change on Air Quality. C.R. Geosci. 339: 721–733, doi: 10.1016/j.crte.2007.08.006. Henry, R.C., Lewis, C.W., Hopke, P.K. and Williamson, H.J. (1984). Review of Receptor Model Fundamentals. Atmos. Environ. 18: 1507–1515. Hopke, P.K. (1985). Receptor Modelling in Environmental Chemistry. Wiley, New York. Johnson, R.A. and Wichern, D.W. (1992). Applied Multivariate Statistical Analysis. 3rd ed. Prentice-Hall Int., New Jersey. 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 Chemometric Techniques. Environ. Monit. Assess. 173: 625–641, doi: 10.1007/s10661-010- 1411-x. Kamal, M.M., Jailani, R. and Shauri, R.L.A. (2006). Prediction of Ambient Air Quality Based on Neural Network Technique. 4 th Student Conference on Research and Development, p. 115–119. doi: 10.1109/SCORED.2 006.4339321. Kannel, P.R., Lee, S., Kanel, S.R. and Khan, S.P. (2007). Chemometric Application in Classification and Assessment of Monitoring Locations of an Urban River System. Anal. Chim. Acta 582: 390–399. Koppmann, R. (2007). Volatile Organic Compounds in the Atmosphere. Blackwell Publishing Ltd., Singapore. Lioy, P.J., Zelenka, M.P., Cheng, M.D. and Reiss, N.M. (1989). The effect of Sampling Duration on the Ability to Resolve Source Types Using Factor Analysis. Atmos. Environ. 23: 239–254. 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. Sci. Total Environ. 313: 77–89, doi: 10.1016/S0048-9697(02)00683-6. 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. Build. Environ. 46: 577–583, doi: 10.1016/j.buildenv.20 10.09.004. Manjunath, B.G., Frick, M. and Reiss, R.D. (2012). Some Notes on Extremal Discriminant Analysis. J. Multivar. Anal. 103: 107–115, doi: 10.1016/j.jmva.2011.06.012. 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 Pollut. 209: 29–43. 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. Ecol. Econ. 55: 235–250, doi: 10.1016/j.eco lecon.2004.09.022. 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 Environmetric Techniques: A Case Study in Malaysia. Environ. Sci. Processes Impacts 15: 1717–1728, doi: 10.1039/c3em00161j. Norusis, M.J. (1990). SPSS Base System User’s Guide. USA: SPSS, Chicago, IL. 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. Int. J. Appl. Sci. Eng. 7: 127–132. Romieu, I. and Hernandez, M. (1999). Air Pollution and Health in Developing Countries: Review of Epidemiological Evidence. In: Mc Granahan, G. and Murray, F. (Eds.), Health and Air Pollution in Rapidly Developing Countries. Stockholm Environment Institute, Sweden, p. 43– 6. Satheeshkumar, P. and Khan, A.B. (2011). Identification of Mangrove Water Quality by Multivariate Statistical Analysis Methods in Pondicherry Coast, India. Environ. Monit. Assess. 184: 3761–3774, doi: 10.1007/s10661- 011-2222-4. Shrestha, S. and Kazama, F. (2007). Assessment of Surface Water Quality Using Multivariate Statistical Techniques: A Case Study of the Fuji River Basin, Japan. Environ. Modell. Softw. 22: 464–475, doi: 10.1016/j.envsoft.2006. 02.001. Simeonov, V., Einax, J.W., Stanimirova, I. and Kraft, J. (2002). Envirometric Modeling and Interpretation of River Water Monitoring Data. Anal. Bioanal. Chem. 374: 898–905, doi: 10.1007/s00216-002-1559-5. 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? Atmos. Environ. 39: 2513–2517. 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 Res. 38: 3980–3992, doi: 10.1016/j.watres.2004.06.011. 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. Anal. Chim. Acta 35: 3581– 3592. Soares, P.K., Bruns, R.E. and Scarminio, I.S. (2008). Statistical Mixture Design - Varimax Factor Optimization for selective Compound Extraction from Plant Material. Anal. Chim. Acta 613: 48–55. doi: 10.1016/j.aca.2008. 02.051. 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. Int. J. Appl. Sci. Technol. 1: 42–49. Ward, J.H. (1963). Hierarchical Grouping to Optimize an Objective Function. J. Am. Stat. Assoc. 58: 236–244. Yu, T.Y. and Chang, L.F.W (2000). Selection of the Scenarios of Ozone Pollution at Southern Taiwan Area Utilizing Principal Component Analysis. Atmos. Environ. 34: 4499–4509. |
| originalfilename | 6003-01-FH02-ESERI-15-03259.pdf |
| person | DELL dell Dell |
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| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15226 |
| spelling | 15226 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15226 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 16 1.6 DELL dell Dell 2024-08-30 11:15:32 6003-01-FH02-ESERI-15-03259.pdf UniSZA Private Access Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric Aerosol and Air Quality Research This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric 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 novel design of air quality monitoring network for better management of air pollution can be achieved. 15 4 1545-1558 Abdullah, A.M., Samah, M.A.A. and Tham, Y.J. (2012). An Overview of the Air Pollution Trend in Klang Valley, Malaysia. Open Environ. Sci. 6: 13–19. 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. Ecol. Modell. 221: 1119– 1130. Afroz, R., Hassan, M.N. and Ibrahim, N.A. (2003). Review of Air Pollution and health impacts in Malaysia. Environ. Res. 92: 71–77, doi: 10.1016/S0013-9351(02)00059-2. Almeida, J.A.S., Barbosa, L.M.S., Pais, A.A.C.C. and Formosinbo, S.J. (2007). Improving Hierarchical Cluster Analysis: A New Method with Outlier Detection and Automatic Clustering. Chemom. Intell. Lab. Syst. 87: 208–217. 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. J. Environ. Prot. 4: 1– 10, doi: 10.4236/jep.2013.412A001. 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. and Yamin, M. (2014a). 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, doi: 10.1007/s11270-014-2063-1. 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. (2014b). 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, p. 307, doi: 10.1007/978-981-4560-70-2_56. 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: 83–88. doi: 10.11113/jt.v72.2934 Azmi, S.Z., Latif, M.T., Ismail, A.S., Juneng, L. and Jemain, A.A. (2010). Trend and Status of Air quality at Three Different Monitoring Stations in the Klang Valley, Malaysia. Air Qual. Atmos. Health 3: 53–64, doi: 10.1007/s11869-009-0051-1. Bock, H.H. (1996). Probabilistic Models in Cluster Analysis. Comput. Stat. Data Anal. 23: 5–28, doi: 10.1016/0167- 9473(96)88919-5. 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 Biol. 12: 1151–1173. 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 Midlatitude Site. Atmos. Chem. Phys. 9: 2695–2714. Department of Environment (DOE) (2013). Air Pollutant Index (API). Available from: http://www.doe.gov.my/w ebportal/en/info-umum/english-air-pollutant-index-api/. 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. Atmos. Environ. 60: 172–181. Elminir, H. (2005). Dependence of Urban Air Pollutants on Meteorology. Sci. Total Environ. 350: 225–237. Giorgi, F. and Meleux, F. (2007). Modelling the Regional Effects of Climate Change on Air Quality. C.R. Geosci. 339: 721–733, doi: 10.1016/j.crte.2007.08.006. Henry, R.C., Lewis, C.W., Hopke, P.K. and Williamson, H.J. (1984). Review of Receptor Model Fundamentals. Atmos. Environ. 18: 1507–1515. Hopke, P.K. (1985). Receptor Modelling in Environmental Chemistry. Wiley, New York. Johnson, R.A. and Wichern, D.W. (1992). Applied Multivariate Statistical Analysis. 3rd ed. Prentice-Hall Int., New Jersey. 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 Chemometric Techniques. Environ. Monit. Assess. 173: 625–641, doi: 10.1007/s10661-010- 1411-x. Kamal, M.M., Jailani, R. and Shauri, R.L.A. (2006). Prediction of Ambient Air Quality Based on Neural Network Technique. 4 th Student Conference on Research and Development, p. 115–119. doi: 10.1109/SCORED.2 006.4339321. Kannel, P.R., Lee, S., Kanel, S.R. and Khan, S.P. (2007). Chemometric Application in Classification and Assessment of Monitoring Locations of an Urban River System. Anal. Chim. Acta 582: 390–399. Koppmann, R. (2007). Volatile Organic Compounds in the Atmosphere. Blackwell Publishing Ltd., Singapore. Lioy, P.J., Zelenka, M.P., Cheng, M.D. and Reiss, N.M. (1989). The effect of Sampling Duration on the Ability to Resolve Source Types Using Factor Analysis. Atmos. Environ. 23: 239–254. 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. Sci. Total Environ. 313: 77–89, doi: 10.1016/S0048-9697(02)00683-6. 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. Build. Environ. 46: 577–583, doi: 10.1016/j.buildenv.20 10.09.004. Manjunath, B.G., Frick, M. and Reiss, R.D. (2012). Some Notes on Extremal Discriminant Analysis. J. Multivar. Anal. 103: 107–115, doi: 10.1016/j.jmva.2011.06.012. 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 Pollut. 209: 29–43. 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. Ecol. Econ. 55: 235–250, doi: 10.1016/j.eco lecon.2004.09.022. 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 Environmetric Techniques: A Case Study in Malaysia. Environ. Sci. Processes Impacts 15: 1717–1728, doi: 10.1039/c3em00161j. Norusis, M.J. (1990). SPSS Base System User’s Guide. USA: SPSS, Chicago, IL. 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. Int. J. Appl. Sci. Eng. 7: 127–132. Romieu, I. and Hernandez, M. (1999). Air Pollution and Health in Developing Countries: Review of Epidemiological Evidence. In: Mc Granahan, G. and Murray, F. (Eds.), Health and Air Pollution in Rapidly Developing Countries. Stockholm Environment Institute, Sweden, p. 43– 6. Satheeshkumar, P. and Khan, A.B. (2011). Identification of Mangrove Water Quality by Multivariate Statistical Analysis Methods in Pondicherry Coast, India. Environ. Monit. Assess. 184: 3761–3774, doi: 10.1007/s10661- 011-2222-4. Shrestha, S. and Kazama, F. (2007). Assessment of Surface Water Quality Using Multivariate Statistical Techniques: A Case Study of the Fuji River Basin, Japan. Environ. Modell. Softw. 22: 464–475, doi: 10.1016/j.envsoft.2006. 02.001. Simeonov, V., Einax, J.W., Stanimirova, I. and Kraft, J. (2002). Envirometric Modeling and Interpretation of River Water Monitoring Data. Anal. Bioanal. Chem. 374: 898–905, doi: 10.1007/s00216-002-1559-5. 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? Atmos. Environ. 39: 2513–2517. 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 Res. 38: 3980–3992, doi: 10.1016/j.watres.2004.06.011. 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. Anal. Chim. Acta 35: 3581– 3592. Soares, P.K., Bruns, R.E. and Scarminio, I.S. (2008). Statistical Mixture Design - Varimax Factor Optimization for selective Compound Extraction from Plant Material. Anal. Chim. Acta 613: 48–55. doi: 10.1016/j.aca.2008. 02.051. 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. Int. J. Appl. Sci. Technol. 1: 42–49. Ward, J.H. (1963). Hierarchical Grouping to Optimize an Objective Function. J. Am. Stat. Assoc. 58: 236–244. Yu, T.Y. and Chang, L.F.W (2000). Selection of the Scenarios of Ozone Pollution at Southern Taiwan Area Utilizing Principal Component Analysis. Atmos. Environ. 34: 4499–4509. |
| spellingShingle | Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric |
| summary | This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric 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 novel design of air quality monitoring network for better management of air pollution can be achieved. |
| title | Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric |
| title_full | Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric |
| title_fullStr | Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric |
| title_full_unstemmed | Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric |
| title_short | Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric |
| title_sort | identification source of variation on regional impact of air quality pattern using chemometric |