Mohd Kamil Yusoff

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors.

This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)(1) for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r=0.977, p<0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.

Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques.

This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta Rivers WQ and accentuated the roles of weathering and surface runoff in determining the rivers WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.

Cation Dependence, pH Tolerance, and Dosage Requirement of a Bioflocculant Produced by Bacillus spp. UPMB13: Flocculation Performance Optimization through Kaolin Assays

A bioflocculant-producing bacterial strain with highly mucoid and ropy colony morphological characteristics identified as Bacillus spp. UPMB13 was found to be a potential bioflocculant-producing bacterium. The effect of cation dependency, pH tolerance and dosage requirement on flocculating ability of the strain was determined by flocculation assay with kaolin as the suspended particle. The flocculating activity was measured as optical density and by flocs formation. A synergistic effect was observed with the addition of monovalent and divalent cations, namely, Na+, Ca2+, and Mg2+, while Fe2+ and Al3+ produced inhibiting effects on flocculating activity. Divalent cations were conclusively demonstrated as the best cation source to enhance flocculation. The bioflocculant works in a wide pH range, from 4.0 to 8.0 with significantly different performances (P < 0.05), respectively. It best performs at pH 5.0 and pH 6.0 with flocculating performance of above 90%. A much lower or higher pH would inhibit flocculation. Low dosage requirements were needed for both the cation and bioflocculant, with only an input of 50 mL/L for 0.1% (w/v) CaCl2 and 5 mL/L for culture broth, respectively. These results are comparable to other bioflocculants produced by various microorganisms with higher dosage requirements.

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.

Application of Environmetric Methods to Surface Water Quality Assessment of Langkawi Geopark (Malaysia)

Cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) were applied to evaluate the spatial variation in the river water quality data matrix of Langkawi Geopark. The CA result rendered two groups based on their similar properties. Group 1 comprised the sampling sites LG1, LG2, LG3, LG4, LG5, LG6, LG7, LG9, LG10, LG11, LG13, and LG14; Group 2 was further divided into two groups: Group 2(i) consisted of LG8, LG15, LG17, and LG19 while Group 2(ii) consisted of LG12, LG16, and LG18. DA revealed that COD, Cr and SO4 were the most significant parameters for discrimination between Group 1 and Group 2. The PCA results extracted seven components for Group 1 and six components for Group 2. Agriculture and sand mining were identified as the main latent pollution sources contributing to Group 1, while recreational activities constituted the major pollution source contributing to Group 2. This study illustrates the usefulness of environmetric techniques in the interpretation of complex data, optimizing monitoring networks to a lower cost mentoring program and controlling the degradation of surface water quality in Langkawi Geopark.

Open source geographical resources analysis support system (GRASS) for landslide hazard assessment

Purpose – The primary aim of this research is to investigate the application of open source geographic information system software, geographical resources analysis support system (GRASS) for landslide hazard assessment.Design/methodology/approach – Five parameters affecting landslide occurrence derived from topographical, geological and land use maps of Cameron highland were used for the assessment.Findings – The results showed that about 93 percent of the study area falls under zone II that is of low hazard, with less than 7 percent on zone III with moderate hazard and only less than 1 percent falls under zone IV, which is of high hazard.Research limitations/implications – The accuracy of the landslide hazard map needs to be assessed by cross‐correlation with landslide occurrence in the field.Practical implications – The map produced showed the potential application of GRASS as a tool for producing landslide hazard assessment map.Originality/value – The major outcome of this research is the possible use ...

Using Principal Component Scores and Artificial Neural Networks in Predicting Water Quality Index

The management of river water quality is a major environmental challenge. One of the major challenges is in determining point and non-point sources of pollutants. Industrial and municipal wastewater discharges can be considered as constant polluting sources, unlike surface water runoff which is seasonal and highly affected by climate. According to Aiken et al. (1982), 42 tributaries in Peninsular Malaysia are categorized as very polluted including the Langat River. Until 1999, there were about 13 polluted tributaries and 36 polluted rivers due to human activities such as, industry, construction and agriculture (Department of Environment, Malaysia (DOE), 1999). In 1990, there were 48 clean rivers classified as clean but the number is reduced to 32 rivers in 1999 (Rosnani Ibrahim, 2001).

Phosphorus Migration in an Unconfined Aquifer Using Modflow and Mt3dms

Abstract The rapid rate of urbanization and increasing demand for water in agriculture and industry are the reasons for considering groundwater as a main source of water. This can be a prologue to contamination of groundwater. Phosphorus as a type of nutrient that is derived from fertilizers has adverse effect on surface and subsurface water. The aim of this study was to monitor groundwater quality and the fate of contamination via three dimensional finite-different groundwater flow simulation (i.e. Visual MODFLOW version 4.2.). The study area was the campus of University Putra Malaysia. The monitoring indicated that the concentration of phosphorus is higher than those imposed by the standard of the Malaysian Department of Environment (DOE). Results of contamination transport modelling revealed the different rates of phosphorus transport in layers at the end of simulation period.

Performance of Real-Time Kinematic Global Positioning System and Automatic Level Surveying for Height Determination - A Comparison

The purpose of the research was to determinevertical accuracy of RTK-GPS in comparing with automaticlevel surveying procedure as has been applied in the vicinity of the University Putra Malaysia campus. A comparison of the differences between the two methods indicates 95% confidence accuracy. The results obtained by this experiment indicated an accuracy of 0-10 centimeters in the measurement of elevation by RTK-GPS system. The result of the present work also indicated that the RTK-GPS system might be very useful in surveying work as carried out by topographers, engineers, and surveyors etc.