Pattern recognition of Kedah River water quality data by implementation of principal component analysis

This study examines Kedah River Basin, Kedah, Malaysia, to achieve the objective of identifying and recognizing pollutant sources contributing to the water quality using a large dataset extending over a period of eight years, from the year 1997 to 2006. Principal Component Analysis was applied to simplify and provide a better understanding for the complex relationships among water quality parameters such as DO, BOD, COD, SS, pH, NH3-NL, temperature, conductivity, turbidity, salinity, dissolved solids, total solids, NO3, Cl, Ca, PO4, As, Hg, Cd, Cr, Pb, Zn, Ca, Fe, K, Mg, Na, Oil and Grease, MBAS, E.coli and Coliform. Graphical presentation of the data also helps a better view of the overall analysis to appoint sources of pollutant in accordance to their effect. Similar pattern of water quality data reveals nine Principal Components responsible for the data structure and explained 73% of the total variance of the data set. PC score model provided apportionment of various sources contributing to the water quality. Consequently the nine causes of pollutants involved are natural causes in terms of strong river current and geological location of this river, industrial and factories effluent discharge, construction, coal and metal mining, agricultural and sewage plant, human waste and illegal oil dumping

River water quality modeling using combined principle component analysis (PCA) and multiple linear regressions (MLR): a case study at Klang River, Malaysia

A collective set of data over five years (2003 to 2007) in Klang River, Selangor were studied in attempt to assess and determine the contributions of sources affecting the water quality. A precise technique of multiple linear regressions (MLR) were prepare as an advance tool for surface water modeling and forecasting. Likewise, principle component analysis (PCA) was used to simplify and understand the complex relationship among water quality parameters. Nine principle components were found responsible for the data structure provisionally named as soil erosion, anthropogenic input, surface runoff, fecal waste, detergent, urban domestic waste, industrial effluent, fertilizer waste and residential waste explains 72% of the total variance for all the data sets. Meanwhile, urban domestic pollution accounted as the highest pollution contributor to the Klang River. Thus, the advancement of receptor model was applied in order to identify the major sources of pollutant at Klang River. Result showed that the use of PCA as inputs improved the MLR model prediction by reducing their complexity and eliminating data collinearity where R2 value in this study is 0.75 and the model indicates that 75% variability of WQI explained by the five independent variables used in the model. This assessment presents the importance and advantages poses by multivariate statistical analysis of large and complex databases in order to get improved information about the water quality and then helps to reduce the sampling time and cost for reagent used prior to analyses